Metabolomics: New Insights into Medicine

*Metabolomics - New Insights into Biology and Medicine*

[41] Lukasova M, Hanson J, Tunaru S, Offermanns S. Nicotinic acid (niacin): New lipid-independent mechanism of action and therapeutic potentials. Trends in Pharmacological Sciences. 2011;**32**:700-707. DOI: 10.1016/j.

[42] Zhang Z, Lv J, Pan L, Zhang Y. Roles

and applications of probiotic lactobacillus strains. Applied Microbiology and Biotechnology. 2018;**102**:8135-8143. DOI: 10.1007/

tips.2011.08.002

s00253-018-9217-9

**36**

**39**

**Chapter 3**

**Abstract**

Metabolomic Discovery of

*Natalia V. Beloborodova, Andrey V. Grechko*

Cause of Pathology

*and Andrey Yu Olenin*

of microbiota dysfunction.

organ with many functions.

**1. Introduction**

Microbiota Dysfunction as the

In the twenty-first century, metabolomics allowed evaluating the profile of metabolites of various classes of compounds in the human body. The most important achievement of the metabolic approach is to obtain evidence of the intersection of human biochemical pathways and its microbiota. The effect of certain microbial metabolites on the work of key enzymes involved in the biotransformation of amino acids and other substances becomes more important in patients at risk of developing neurological and mental disorders and also contributes to the development of life-threatening conditions up to multiple organ failure after operations, injuries, and serious diseases. The authors of this chapter call the microbiota an "invisible organ," emphasizing its functional significance, and not just taxonomy, as previously thought. This chapter will discuss the mutually beneficial integration of the metabolome/microbiome in the body of healthy people and will focus on the effects

**Keywords:** homeostasis, microbiota, "invisible organ," bacterial metabolites

Homeostasis is key for the normal performance of a human body. Many parameters are constantly maintained in fairly narrow vital ranges, such as temperature, acidity in the intracellular and intercellular spaces, the electrolyte concentrations, hormones, vitamins, etc. The traditional view is that the body itself is able to maintain the constancy of its internal environment due to a complex system of feedback (**Figure 1**). Each organ helps to maintain homeostasis, ensuring its specific function. It acts as a backward force that returns the system to equilibrium in the event of deviations from the normal state. Along with other organs, the microbiota plays an important role in maintaining homeostasis, despite being an "invisible organ." By the way, in terms of weight, the microbiota should be attributed to the largest

organ that can be compared only with the brain or liver: this can be easily ascertained using simple calculations based on known facts about the weight of human organs relative to the body weight of an adult (**Figure 2**). The human microbiota, which is a community of gut microorganisms, can be considered as an independent

#### **Chapter 3**

## Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology

*Natalia V. Beloborodova, Andrey V. Grechko and Andrey Yu Olenin*

#### **Abstract**

In the twenty-first century, metabolomics allowed evaluating the profile of metabolites of various classes of compounds in the human body. The most important achievement of the metabolic approach is to obtain evidence of the intersection of human biochemical pathways and its microbiota. The effect of certain microbial metabolites on the work of key enzymes involved in the biotransformation of amino acids and other substances becomes more important in patients at risk of developing neurological and mental disorders and also contributes to the development of life-threatening conditions up to multiple organ failure after operations, injuries, and serious diseases. The authors of this chapter call the microbiota an "invisible organ," emphasizing its functional significance, and not just taxonomy, as previously thought. This chapter will discuss the mutually beneficial integration of the metabolome/microbiome in the body of healthy people and will focus on the effects of microbiota dysfunction.

**Keywords:** homeostasis, microbiota, "invisible organ," bacterial metabolites

#### **1. Introduction**

Homeostasis is key for the normal performance of a human body. Many parameters are constantly maintained in fairly narrow vital ranges, such as temperature, acidity in the intracellular and intercellular spaces, the electrolyte concentrations, hormones, vitamins, etc. The traditional view is that the body itself is able to maintain the constancy of its internal environment due to a complex system of feedback (**Figure 1**). Each organ helps to maintain homeostasis, ensuring its specific function. It acts as a backward force that returns the system to equilibrium in the event of deviations from the normal state. Along with other organs, the microbiota plays an important role in maintaining homeostasis, despite being an "invisible organ."

By the way, in terms of weight, the microbiota should be attributed to the largest organ that can be compared only with the brain or liver: this can be easily ascertained using simple calculations based on known facts about the weight of human organs relative to the body weight of an adult (**Figure 2**). The human microbiota, which is a community of gut microorganisms, can be considered as an independent organ with many functions.

**Figure 1.** *Diagram of the interaction of organs that support the state of homeostasis.*


**Figure 2.**

*Microbiota as a big but "invisible organ," % of body mass compared to other vital organs in an adult weighing 70 kg.*

In the twenty-first century, a new insight on the processes occurring in the human body in health and disease on the basis of the new knowledge of the microbiota is formed. Detection and identification of the trillions of bacteria that form the microbiota of healthy and sick people are made possible by the use of modern technologies, for example, sequencing of the 16S rRNA gene.

*Metabolite-based approaches (or metabolomics)* to the study of the human microbiota are more significant progress in biology and medicine, searching for answers to the question "What are the chemical and pathophysiological results of the metabolic activity of the microbiota?" Today many research teams are searching for answers to this question [1].

The host organism is a habitat for the microbiota, so maintaining homeostasis is vital for the survival of hundreds of bacterial species. The microbiota seeks to restore homeostasis in the case of minor metabolic disorders that are not systemic in nature, and it has a huge amount of possibilities for this. If changes in the vital functions

**41**

num and ileum, 104

–107

*Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology*

against the host, it is manifested by diseases, even death (sepsis).

which there are many qualities and attributes of the organ.

of the body are serious, a new quality (pathology) is formed, the microbiota is also radically rebuilt: this is manifested not only in changes in the species composition of bacteria (taxonomy) but also in metabolic processes. Other non-normal products of microbial metabolism from the intestines enter the systemic circulation, and they can interfere with the endogenous metabolic pathways. When the microbiota works

The medical community has not yet formed an understanding of the role of the microbiota as a separate organ. A search query ("microbiota as an organ") or ("microbiome as an organ") in specialized databases, such as the Web of Science, Scopus, and Pubmed, gives a negative result. At the same time, a number of review articles are actually present which describe in detail the physiology and biochemistry of the close interaction of the intestinal microbiota with the host organism, in

This chapter formulates ideas about the microbiota as an organ, which has become possible due to the results of studies with metabolomic equipment of recent years. The material presented in this chapter relies primarily on articles published after 2010. Specialists working in both fundamental and clinical medicine are undoubtedly interested in the growing information about the role of microbiota in maintaining homeostasis, as well as the participation of microorganisms of the human body in the metabolic pathways, which are directly related to the develop-

Food intake, its conversion, and excretion of waste products are material sources for the normal functioning of a human body. The aim of nutrition from a biochemical viewpoint is to maintain the body's critical parameters in narrowly defined value rates. The concept of a "living healthy organism" consists precisely in the ability to resist change and maintain the constancy of the composition and properties of its internal environment. The basis of digestion is a fairly universal mechanism, which includes splitting of the main components, such as carbohydrates (including polysaccharides), fats, biopolymers (proteins, macromolecules based on nucleotide sequences), etc., to individual low-molecular substances and then to the synthesis of low- and high-molecular weight compounds, which are the material basis for cells and organs as well as the energy source for biochemical reactions. Interest to low-molecular weight compounds has grown particularly in recent years. The Human Metabolome Database (HMDB) was created and is constantly updated by the international researcher group. Now it contains information on more than 100,000 individual low-molecular compounds

(metabolites), constituting about 25,000 pathways of metabolism [2].

(cells/mL) in different parts of the gastrointestinal tract is duodenum, 101

; cecum, 108

–103

; and large intestine, 1011–1012. A large number

; jeju-

Food digestion is one of the main complex processes that form homeostasis. Transformation of the matter occurs throughout the gastrointestinal tract. Food undergoes ever-deeper processing as you move through it. Enzymes directly involved in this can potentially have endogenous and exogenous origin. The endogenous pathway is carried out with the participation of its own secrets produced by the body with the participation of organs that promote digestion and the excretion of waste products. The complex of biochemical reactions that coincide with the active participation of the microbiota, consisting of hundreds, sometimes reaching up to several thousand species, is presented as an alternative to it. In the literature there is no single point of view about the density of microorganism colonization of the human digestive system. According to [3], the relative content of microorganisms

*DOI: http://dx.doi.org/10.5772/intechopen.87176*

ment of various pathologies.

**2. Microbiota in a healthy body**

#### *Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology DOI: http://dx.doi.org/10.5772/intechopen.87176*

of the body are serious, a new quality (pathology) is formed, the microbiota is also radically rebuilt: this is manifested not only in changes in the species composition of bacteria (taxonomy) but also in metabolic processes. Other non-normal products of microbial metabolism from the intestines enter the systemic circulation, and they can interfere with the endogenous metabolic pathways. When the microbiota works against the host, it is manifested by diseases, even death (sepsis).

The medical community has not yet formed an understanding of the role of the microbiota as a separate organ. A search query ("microbiota as an organ") or ("microbiome as an organ") in specialized databases, such as the Web of Science, Scopus, and Pubmed, gives a negative result. At the same time, a number of review articles are actually present which describe in detail the physiology and biochemistry of the close interaction of the intestinal microbiota with the host organism, in which there are many qualities and attributes of the organ.

This chapter formulates ideas about the microbiota as an organ, which has become possible due to the results of studies with metabolomic equipment of recent years. The material presented in this chapter relies primarily on articles published after 2010. Specialists working in both fundamental and clinical medicine are undoubtedly interested in the growing information about the role of microbiota in maintaining homeostasis, as well as the participation of microorganisms of the human body in the metabolic pathways, which are directly related to the development of various pathologies.

#### **2. Microbiota in a healthy body**

*Metabolomics - New Insights into Biology and Medicine*

In the twenty-first century, a new insight on the processes occurring in the human body in health and disease on the basis of the new knowledge of the microbiota is formed. Detection and identification of the trillions of bacteria that form the microbiota of healthy and sick people are made possible by the use of modern

*Microbiota as a big but "invisible organ," % of body mass compared to other vital organs in an adult weighing* 

*Metabolite-based approaches (or metabolomics)* to the study of the human microbiota are more significant progress in biology and medicine, searching for answers to the question "What are the chemical and pathophysiological results of the metabolic activity of the microbiota?" Today many research teams are searching for

The host organism is a habitat for the microbiota, so maintaining homeostasis is vital for the survival of hundreds of bacterial species. The microbiota seeks to restore homeostasis in the case of minor metabolic disorders that are not systemic in nature, and it has a huge amount of possibilities for this. If changes in the vital functions

technologies, for example, sequencing of the 16S rRNA gene.

*Diagram of the interaction of organs that support the state of homeostasis.*

**40**

**Figure 1.**

**Figure 2.**

*70 kg.*

answers to this question [1].

Food intake, its conversion, and excretion of waste products are material sources for the normal functioning of a human body. The aim of nutrition from a biochemical viewpoint is to maintain the body's critical parameters in narrowly defined value rates. The concept of a "living healthy organism" consists precisely in the ability to resist change and maintain the constancy of the composition and properties of its internal environment. The basis of digestion is a fairly universal mechanism, which includes splitting of the main components, such as carbohydrates (including polysaccharides), fats, biopolymers (proteins, macromolecules based on nucleotide sequences), etc., to individual low-molecular substances and then to the synthesis of low- and high-molecular weight compounds, which are the material basis for cells and organs as well as the energy source for biochemical reactions. Interest to low-molecular weight compounds has grown particularly in recent years. The Human Metabolome Database (HMDB) was created and is constantly updated by the international researcher group. Now it contains information on more than 100,000 individual low-molecular compounds (metabolites), constituting about 25,000 pathways of metabolism [2].

Food digestion is one of the main complex processes that form homeostasis. Transformation of the matter occurs throughout the gastrointestinal tract. Food undergoes ever-deeper processing as you move through it. Enzymes directly involved in this can potentially have endogenous and exogenous origin. The endogenous pathway is carried out with the participation of its own secrets produced by the body with the participation of organs that promote digestion and the excretion of waste products. The complex of biochemical reactions that coincide with the active participation of the microbiota, consisting of hundreds, sometimes reaching up to several thousand species, is presented as an alternative to it. In the literature there is no single point of view about the density of microorganism colonization of the human digestive system. According to [3], the relative content of microorganisms (cells/mL) in different parts of the gastrointestinal tract is duodenum, 101 –103 ; jejunum and ileum, 104 –107 ; cecum, 108 ; and large intestine, 1011–1012. A large number

of publications give the relative content of microorganisms in the range of 102 –1013, while the maximum values are recorded in the cecum and transverse colon.

The specificity of food digestion is due to the variety of enzymes capable of carrying out similar biochemical transformations, if not entirely, then at least of its many components, due to intestinal microbiota. The synthesis of specific proteins, including enzymes, is due to the presence of various nucleotide DNA sequences. The diversity of these sequences in a complex system consisting of hundreds, or even thousands, of individual species of microorganisms is significantly higher than that of human. The lifetime of a particular microorganism, depending on the immune response of the host organism, correlates with the function that promotes or interferes with its vital activity. The production of specific microorganism killer proteins is not observed in the case of symbiosis. Processes of synthesis of interleukins and phagocytosis are immediately activated in the alternative situation [4]. A big array of metagenomic studies of human intestinal microbiota collected in recent years in various information repositories, such as the National Center for Biotechnology Information (NCBI).

The role of microbiota is quite significant already at the stage of primary processing of nutrients. For example, in [5], the fact is given that only bacteroids of the *Bacteroides thetaiotaomicron* contain nucleotide sequences for the synthesis of 260 glycosidic hydrolases, while the entire human genome is capable of producing only 17 such enzymes, and 9 of them are not fully characterized. The author of the review [6] provides several specific metabolic pathways associated with intestinal microbiota. These include (i) cleavage of polysaccharides to monomers, followed by processing into short-chain fatty acids; (ii) depolymerization of proteins to amino acids, with further conversion of some of them (glycine, lysine, arginine, leucine, isoleucine, and valine) to nitrogen-containing heterocyclic compounds, for example, substituted indoles; (iii) neutralization and detoxification of arene-containing components from the external environment; and (iv) biotransformation of fats and bile acids and their inclusion in biochemical processes that promote energy cells, for example, in the Krebs cycle.

The species composition of the microbiota is specific for each person and depends on many factors, such as age, diet, use of antibiotics, etc. We can talk about two components of the microbiota—obligate or transient. A self-organizing ecosystem with the dominance of some species of microorganisms and the oppression of others arises in a normally functioning organism. The classification and systematization of information on the species and genetic diversity of the microbiota of the human body were carried out independently by two scientific communities in the United States and the European Union, which resulted in the appearance of two databases: Human Microbiome Project (HMP) [7] and Metagenomics of the Human Intestinal Tract (MetaHIT) [8].

Extensive information on the composition of the intestinal microbiota of a healthy person is contained in the literature. These studies indicate the dominance of several genera of strict anaerobes, and the main ones are *Bacteroides*, *Prevotella*, *Eubacterium*, *Ruminococcus*, *Clostridium*, *Lactobacillus*, and *Bifidobacterium*. The data on the microbial community of the gastrointestinal tract are summarized in detail in the 2018 review [9] and presented in **Table 1**, which reflects the gradual change in the species composition of microorganisms as food progresses and digests.

A huge number of types of microorganisms perform the biochemical functions which we call the "conveyor" of the microbiota [10]. The diversity of species with different biochemical activity provides coordinated work of the microbiota. The final metabolite formation depends on many factors: the quality and quantity of substrate (food components); the function of the stomach, pancreas, liver, and gallbladder; bowel motility; etc. definitely influence the metabolism of microbiota. The normal biotransformation of any of the substrates in the intestinal lumen takes place sequentially [10]. Biochemistry of deep food transformation is in many respects similar to the metabolic characteristic of microorganisms. The main part of

**43**

*Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology*

the individual amino acids that come from food after the cleavage of polypeptides is further spent on the synthesis of its own proteins, which are necessary for the functioning of the body. Residual amino acids can be transformed into other substances of a non-protein nature, performing a number of important functions not related to

**The dominant species composition of the microbiota**

Throat, esophagus *Streptococcus*, *Prevotella*, *Actinomyces*, *Gemella*, *Rothia*, *Granulicatella*, *Haemophilus*,

Small intestine *Enterococcus*, *Escherichia coli*, *Klebsiella*, *Lactobacillus*, *Staphylococcus*, *Streptococcus*,

Cecum *Lactobacillus*, *Enterococcus*, *Escherichia coli, Bacteroides*, *Clostridium leptum*,

*Differences in the composition of the microbiota throughout the gastrointestinal tract (adapted from [9]).*

*Bacteroides fragilis*, *Clostridium lituseburense*, *Gammaproteobacterium*

Oral cavity *Gemella*, *Granulicatella*, *Streptococcus, Prevotella*, *Veillonella*, *Porphyromonas*, *Neisseria*, *Rothia*, *Lactobacillus*, *Fusobacterium*

This trend is most pronounced for aromatic amino acids such as phenylalanine, tyrosine, and tryptophan. The transformations of the phenylalanine-tyrosine pair occurring in the liver are contained in the human metabolome database (**Figure 3**). Phenylalanine and tyrosine are interchangeable in terms of metabolism. Phenylalanine is transformed into tyrosine under the action of a complex compound of Fe2+ ions with phenylalanine-4-hydroxylase with the participation of L-erythrotetrahydrobiopretin. Then both amino acids are transformed into 4-hydroxyphenylpyruvic acid, and then, by successive transformations, they are transformed into acetoacetic and fumaric acids—components of the Krebs cycle under the action of the same enzymes with the participation of the same substances [11]. There is no direct conversion of phenylpyru-

The pathway of tyrosine processing, namely, its biotransformation in tyramine further into three directions—dopamine, homovanillin, and dopachinone—is important for the normal functioning of human mental activity (**Figure 4**). All biochemical transformations that make up these metabolic pathways occur with the direct action of enzymes. However, enzymes for not all reactions are listed in the HMDB. The label "??" (**Figure 4**) refers to the absence of data on the enzyme. The pathway reactions

The first type is rather trivial transformations, such as the conversion of an aldehyde to the corresponding carboxylic acid, for example, homovanillin to homovanillic acid. Such transformations are well known in classical organic chemistry. These reactions do not require enzymes; it is enough to have an oxidizing agent, such as molecular oxygen, hydrogen peroxide, reactive oxygen species, etc. The situation is different in the case of the formation of nitrogencontaining heterocycles formed from aromatic amino acids. The information about enzyme in HMDB is not available for the key dopachinone conversion reaction to leukodopachrome. A detailed study of the mechanism of this reaction, contained in [12], shows that nitric oxide (I) takes an active part in it. This fact is complicated only by understanding the base of interactions. Many reactions of

vic acid to 4-hydroxyphenylpyruvic acid in this metabolism scheme.

can be divided into two types: "traditional" and "unusual."

*DOI: http://dx.doi.org/10.5772/intechopen.87176*

*Veillonella* Stomach *Helicobacter pylori*, *Veillonella*, *Lactobacillus*

*Clostridium coccoides* Rising gut *Bacteroides*, *Lactobacillus*, *Bifidobacterium*

Colon *Bacteroides*, *Clostridium*, *Desulfomonas*, *Desulfovibrio*

**Part of the gastrointestinal** 

**tract**

**Table 1.**

digestion or the building function.

*Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology DOI: http://dx.doi.org/10.5772/intechopen.87176*


#### **Table 1.**

*Metabolomics - New Insights into Biology and Medicine*

of publications give the relative content of microorganisms in the range of 102

The specificity of food digestion is due to the variety of enzymes capable of carrying out similar biochemical transformations, if not entirely, then at least of its many components, due to intestinal microbiota. The synthesis of specific proteins, including enzymes, is due to the presence of various nucleotide DNA sequences. The diversity of these sequences in a complex system consisting of hundreds, or even thousands, of individual species of microorganisms is significantly higher than that of human. The lifetime of a particular microorganism, depending on the immune response of the host organism, correlates with the function that promotes or interferes with its vital activity. The production of specific microorganism killer proteins is not observed in the case of symbiosis. Processes of synthesis of interleukins and phagocytosis are immediately activated in the alternative situation [4]. A big array of metagenomic studies of human intestinal microbiota collected in recent years in various information repositories, such

The role of microbiota is quite significant already at the stage of primary processing of nutrients. For example, in [5], the fact is given that only bacteroids of the *Bacteroides thetaiotaomicron* contain nucleotide sequences for the synthesis of 260 glycosidic hydrolases, while the entire human genome is capable of producing only 17 such enzymes, and 9 of them are not fully characterized. The author of the review [6] provides several specific metabolic pathways associated with intestinal microbiota. These include (i) cleavage of polysaccharides to monomers, followed by processing into short-chain fatty acids; (ii) depolymerization of proteins to amino acids, with further conversion of some of them (glycine, lysine, arginine, leucine, isoleucine, and valine) to nitrogen-containing heterocyclic compounds, for example, substituted indoles; (iii) neutralization and detoxification of arene-containing components from the external environment; and (iv) biotransformation of fats and bile acids and their inclusion in biochemical processes that promote energy cells, for example, in the Krebs cycle. The species composition of the microbiota is specific for each person and depends on many factors, such as age, diet, use of antibiotics, etc. We can talk about two components of the microbiota—obligate or transient. A self-organizing ecosystem with the dominance of some species of microorganisms and the oppression of others arises in a normally functioning organism. The classification and systematization of information on the species and genetic diversity of the microbiota of the human body were carried out independently by two scientific communities in the United States and the European Union, which resulted in the appearance of two databases: Human Microbiome Project

(HMP) [7] and Metagenomics of the Human Intestinal Tract (MetaHIT) [8]. Extensive information on the composition of the intestinal microbiota of a healthy person is contained in the literature. These studies indicate the dominance of several genera of strict anaerobes, and the main ones are *Bacteroides*, *Prevotella*, *Eubacterium*, *Ruminococcus*, *Clostridium*, *Lactobacillus*, and *Bifidobacterium*. The data on the microbial community of the gastrointestinal tract are summarized in detail in the 2018 review [9] and presented in **Table 1**, which reflects the gradual change in

the species composition of microorganisms as food progresses and digests.

A huge number of types of microorganisms perform the biochemical functions which we call the "conveyor" of the microbiota [10]. The diversity of species with different biochemical activity provides coordinated work of the microbiota. The final metabolite formation depends on many factors: the quality and quantity of substrate (food components); the function of the stomach, pancreas, liver, and gallbladder; bowel motility; etc. definitely influence the metabolism of microbiota. The normal biotransformation of any of the substrates in the intestinal lumen takes place sequentially [10]. Biochemistry of deep food transformation is in many respects similar to the metabolic characteristic of microorganisms. The main part of

while the maximum values are recorded in the cecum and transverse colon.

as the National Center for Biotechnology Information (NCBI).

–1013,

**42**

*Differences in the composition of the microbiota throughout the gastrointestinal tract (adapted from [9]).*

the individual amino acids that come from food after the cleavage of polypeptides is further spent on the synthesis of its own proteins, which are necessary for the functioning of the body. Residual amino acids can be transformed into other substances of a non-protein nature, performing a number of important functions not related to digestion or the building function.

This trend is most pronounced for aromatic amino acids such as phenylalanine, tyrosine, and tryptophan. The transformations of the phenylalanine-tyrosine pair occurring in the liver are contained in the human metabolome database (**Figure 3**). Phenylalanine and tyrosine are interchangeable in terms of metabolism. Phenylalanine is transformed into tyrosine under the action of a complex compound of Fe2+ ions with phenylalanine-4-hydroxylase with the participation of L-erythrotetrahydrobiopretin. Then both amino acids are transformed into 4-hydroxyphenylpyruvic acid, and then, by successive transformations, they are transformed into acetoacetic and fumaric acids—components of the Krebs cycle under the action of the same enzymes with the participation of the same substances [11]. There is no direct conversion of phenylpyruvic acid to 4-hydroxyphenylpyruvic acid in this metabolism scheme.

The pathway of tyrosine processing, namely, its biotransformation in tyramine further into three directions—dopamine, homovanillin, and dopachinone—is important for the normal functioning of human mental activity (**Figure 4**). All biochemical transformations that make up these metabolic pathways occur with the direct action of enzymes. However, enzymes for not all reactions are listed in the HMDB. The label "??" (**Figure 4**) refers to the absence of data on the enzyme. The pathway reactions can be divided into two types: "traditional" and "unusual."

The first type is rather trivial transformations, such as the conversion of an aldehyde to the corresponding carboxylic acid, for example, homovanillin to homovanillic acid. Such transformations are well known in classical organic chemistry. These reactions do not require enzymes; it is enough to have an oxidizing agent, such as molecular oxygen, hydrogen peroxide, reactive oxygen species, etc. The situation is different in the case of the formation of nitrogencontaining heterocycles formed from aromatic amino acids. The information about enzyme in HMDB is not available for the key dopachinone conversion reaction to leukodopachrome. A detailed study of the mechanism of this reaction, contained in [12], shows that nitric oxide (I) takes an active part in it. This fact is complicated only by understanding the base of interactions. Many reactions of

#### **Figure 3.**

*Normal metabolism of phenylalanine and tyrosine in the liver. Enzymes (coenzymes): (1) phenylalanine-4 hydroxylase (Fe2+); (2) aspartate aminotransferase, cytoplasmic tyrosine aminotransferase; (3) L-amino-acid oxidase (FAD); (4) 4-hydroxyphenylpyruvate dioxygenase (Fe3+); (5) homogentisate 1,2-dioxygenase; (6) maleylacetoacetate isomerase; (7) fumarylacetoacetase (according to the HMDB).*

tyrosine metabolism are supported by a complex of copper ions with tyrosinase, well known in the biochemistry of microorganisms and used in biotechnology (see, e.g., [13]).

A significant part of the reactions given in **Figure 4**, with a sufficient degree of confidence, occurs with the participation of enzymes of exogenous (microbiological) origin generated by the microbiota. The formation of metabolites not only with the benzene but also with the indole ring occurs as a result of tyrosine biotransformation, including the participation of microbial enzymes.

Indoles, including those synthesized using human microbiota enzymes, play an important role in metabolism. Such physiologically important substances as serotonin, tryptamine, and derivatives of quinic acid belong to them (**Figure 5**). Many of the compounds involved in the indole metabolism are able to pass through the blood–brain barrier. About 95% of tryptophan enters the brain as a conjugate with kynurenine compounds, whose final metabolic products are kinuric and quinolinic acids, 3-hydroxykynurenine [14].

Reactions associated with the presence of endogenous enzymes and enzymes of microbial origin are in a state of dynamic equilibrium with the normal functioning of biochemical processes in the body. Microbiota metabolism is able to quickly adjust in a direction that helps to maintain homeostasis with moderate deviations (abnormalities with dietary errors, travels with changing time zones, etc.). The dynamic metabolism of the "invisible organ" is provided by the potential of the metabolic pathways, such as catecholamine biosynthesis (**Figure 6**) with participation of numerous species of microorganisms.

Microbiota metabolism can also be seriously affected if the disorders are systemic under the influence of adverse external factors (e.g., massive antimicrobial therapy, severe poisoning, hypoxia, blood loss, etc.). These disorders can manifest themselves clinically by developing a critical state, which often puts the existence of the organism (its life) at risk.

**45**

**Figure 4.**

*HMDB).*

**3. Microbiota dysfunction in pathology**

Disturbances in the normal functioning of the gastrointestinal tract are largely due to changes in the digestion processes associated with the state of the microbiota. As noted above, the microbiota composition depends on the heredity and health

*Tyrosine metabolism. Enzymes (coenzymes): (1) aromatic-L-amino-acid decarboxylase, (Pyridoxal-5*′*-phosphate); (2) tyrosinase (Cu2+); (3) amiloride-sensitive amine oxidase [copper-containing] (Cu2+, Ca2+, topaquinone); (4) dopamine beta-hydroxylase (Cu2+, pyrroloquinoline, quinone); (5) aldehyde dehydrogenase (dimeric NADP-preferring); (6) amine oxidase [flavin-containing] A (FAD); (7) aldehyde dehydrogenase,(dimeric NADP-preferring); (8) catechol O-methyltransferase (Mg2+) (according to the* 

**3.1 Diseases of the digestive tract**

*Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology*

*DOI: http://dx.doi.org/10.5772/intechopen.87176*

*Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology DOI: http://dx.doi.org/10.5772/intechopen.87176*

#### **Figure 4.**

*Metabolomics - New Insights into Biology and Medicine*

tyrosine metabolism are supported by a complex of copper ions with tyrosinase, well known in the biochemistry of microorganisms and used in biotechnology

*Normal metabolism of phenylalanine and tyrosine in the liver. Enzymes (coenzymes): (1) phenylalanine-4 hydroxylase (Fe2+); (2) aspartate aminotransferase, cytoplasmic tyrosine aminotransferase; (3) L-amino-acid oxidase (FAD); (4) 4-hydroxyphenylpyruvate dioxygenase (Fe3+); (5) homogentisate 1,2-dioxygenase; (6)* 

A significant part of the reactions given in **Figure 4**, with a sufficient degree of confidence, occurs with the participation of enzymes of exogenous (microbiological) origin generated by the microbiota. The formation of metabolites not only with the benzene but also with the indole ring occurs as a result of tyrosine biotransfor-

Indoles, including those synthesized using human microbiota enzymes, play an important role in metabolism. Such physiologically important substances as serotonin, tryptamine, and derivatives of quinic acid belong to them (**Figure 5**). Many of the compounds involved in the indole metabolism are able to pass through the blood–brain barrier. About 95% of tryptophan enters the brain as a conjugate with kynurenine compounds, whose final metabolic products are kinuric and quinolinic

Reactions associated with the presence of endogenous enzymes and enzymes of microbial origin are in a state of dynamic equilibrium with the normal functioning of biochemical processes in the body. Microbiota metabolism is able to quickly adjust in a direction that helps to maintain homeostasis with moderate deviations (abnormalities with dietary errors, travels with changing time zones, etc.). The dynamic metabolism of the "invisible organ" is provided by the potential of the metabolic pathways, such as catecholamine biosynthesis (**Figure 6**) with participa-

Microbiota metabolism can also be seriously affected if the disorders are systemic under the influence of adverse external factors (e.g., massive antimicrobial therapy, severe poisoning, hypoxia, blood loss, etc.). These disorders can manifest themselves clinically by developing a critical state, which often puts the existence of

mation, including the participation of microbial enzymes.

*maleylacetoacetate isomerase; (7) fumarylacetoacetase (according to the HMDB).*

**44**

(see, e.g., [13]).

**Figure 3.**

acids, 3-hydroxykynurenine [14].

the organism (its life) at risk.

tion of numerous species of microorganisms.

*Tyrosine metabolism. Enzymes (coenzymes): (1) aromatic-L-amino-acid decarboxylase, (Pyridoxal-5*′*-phosphate); (2) tyrosinase (Cu2+); (3) amiloride-sensitive amine oxidase [copper-containing] (Cu2+, Ca2+, topaquinone); (4) dopamine beta-hydroxylase (Cu2+, pyrroloquinoline, quinone); (5) aldehyde dehydrogenase (dimeric NADP-preferring); (6) amine oxidase [flavin-containing] A (FAD); (7) aldehyde dehydrogenase,(dimeric NADP-preferring); (8) catechol O-methyltransferase (Mg2+) (according to the HMDB).*

#### **3. Microbiota dysfunction in pathology**

#### **3.1 Diseases of the digestive tract**

Disturbances in the normal functioning of the gastrointestinal tract are largely due to changes in the digestion processes associated with the state of the microbiota. As noted above, the microbiota composition depends on the heredity and health

of the host, climate, nutrition, bad habits, etc. A system itself is able to return to a state of homeostasis in the case of mild disorders. The microbiota has mechanisms to adapt to the effects of antibacterial substances. Antibiotics are originally the products of bacteria which they use as competitive advantage in the conditions

#### **Figure 5.**

*Simplified scheme of normal tryptophan metabolism. Enzymes (coenzymes): (1) tryptophan 5-hydroxylase (Fe2+); (2) tryptophan 2,3-dioxygenase (heme); (3) aromatic-L-amino-acid decarboxylase (pyridoxal-5*′*-phosphate); (4) indolethylamine N-methyltransferase; (5) kynurenine formamidase; (6) kynurenine 3-monooxygenase (FAD); (7) aldehyde dehydrogenase, mitochondrial (NAD); (8) aldehyde dehydrogenase, mitochondrial or aldehyde oxidase (FAD, molybdopterin, 2Fe-2S); (9) kynureninase (pyrophosphate); (10) acetylserotonin O-methyltransferase (S-Adenosyl methionine) (according to the HMDB).*

#### **Figure 6.**

*Catecholamine biosynthesis. Enzymes (coenzymes): (1) tyrosine 3-monooxygenase (Fe2+); (2) aromatic-Lamino-acid decarboxylase (pyridoxal-5*′*-phosphate); (3) dopamine beta-hydroxylase (Cu2+, pyrroloquinoline, quinone); (4) phenylethanolamine N-methyltransferase) (according to the HMDB).*

**47**

*Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology*

anti-biological and can disrupt biochemical processes.

of nutrient substrate deficiency in their habitat. However, significant changes in species composition may be developed under the influence of broad-spectrum antibacterial drugs, since the massive use of antibiotics (xenobiotics) is violent and

The microbiota is involved in the transformation of xenobiotics and provides a range of reactions including acetylation, deacylation, decarboxylation, dehydroxylation, demethylation, etc. under the influence of low-quality products and synthetic drugs [15]. Modern possibilities of metabolic methods allow an extensive study of another important function of the microbiota—detoxification of the host organism, which maintains its normal state longer in the conditions of retention

Disorders of the microbial products of short-chain fatty acids SCFA (acetic, propionic, butyric) are most thoroughly studied as a result of the suppression of the normal functioning of anaerobic bacteria. Normally, SCFA requires enterocytes as the main source of energy, respectively; their deficiency contributes to the violation of mucosal trophism, reduction of reparative processes, development of ulcers, and inflammation. Persistent indigestion disorders and chronic gastroenterological diseases are the clinical manifestations of serious changes in the species composi-

Different genera of anaerobic bacteria are called responsible for the production of SCFA. For example, large amount of carbohydrate dissimilation butyrate from dissimilation is associated with some *Clostridia* clusters, other SCFAs, and *Bifidobacterium* spp. In his review, Nyangale et al. rightly noted that several members of the microbiota have been linked with diseases mainly affecting the gut, lake inflammatory bowel disease, such as ulcerative colitis, Crohn's disease, colorectal cancer, and irritable bowel syndrome, although mechanisms involved are still not yet fully understood [16]. The authors consider the possibilities of metabolite analysis to assess the metabolic activity of the microbiota, to measure volatile and nonvolatile metabolite in biological samples, and to give metabolic pathways the contribution of microbiota to which it is most pronounced. These pathways include also the transformation of glucose and amino acids into SCFA, amino acid, microbial degradation of tyrosine to *p*-hydroxyphenylacetic and *p*-hydroxyphenylbenzoic acids (including bypassing tyramine), and degradation of tryptophan to indolepro-

A metabolite composition, determined in the feces, may indicate the composition of microbiota and its changes associated with the use of antibiotics [17]. The use of chemometric approaches in relation to the primary mass spectral data of the samples under study allows one to reliably find the differences between patients with inflamed intestines and the control group. The authors consider that changes in the microbiota phenotype cause this kind of deviations. The ratio of the species composition of microbiota—obligate or transient—significantly affects the metabolite composition that enters the circulatory system from the bowel. Thus, the role of *Bacillus* and *Lactobacillus*, colonizing the epithelium of the gastrointestinal tract, is systematically examined in a review of Ilinskaya et al. [18]. The metabolite composition depends significantly on the activity of their enzymatic systems, even

*DOI: http://dx.doi.org/10.5772/intechopen.87176*

and self-repairing of the microbiota.

tion and dysfunction of the microbiota.

pionate and indoleacetate (including bypassing tryptamine).

with a relatively low content of such microorganisms in the microbiota.

In such acquired endocrinological diseases as obesity, type 2 diabetes (not related to heredity) can be attributed to pathological conditions due to metabolic disorders involving the microbiota. Microbiota can influence the development of diabetes [19]. Changes in the microbiological composition—dysbacteriosis caused, for example, by the use of antibiotics, may contribute to an increase in insulin dysfunction, a long-term consequence of which is the development of type 2 diabetes. Due diet may ensure opportune correction of the microbiota and prevent

#### *Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology DOI: http://dx.doi.org/10.5772/intechopen.87176*

*Metabolomics - New Insights into Biology and Medicine*

of the host, climate, nutrition, bad habits, etc. A system itself is able to return to a state of homeostasis in the case of mild disorders. The microbiota has mechanisms to adapt to the effects of antibacterial substances. Antibiotics are originally the products of bacteria which they use as competitive advantage in the conditions

*Simplified scheme of normal tryptophan metabolism. Enzymes (coenzymes): (1) tryptophan* 

*5-hydroxylase (Fe2+); (2) tryptophan 2,3-dioxygenase (heme); (3) aromatic-L-amino-acid decarboxylase (pyridoxal-5*′*-phosphate); (4) indolethylamine N-methyltransferase; (5) kynurenine formamidase; (6) kynurenine 3-monooxygenase (FAD); (7) aldehyde dehydrogenase, mitochondrial (NAD); (8) aldehyde dehydrogenase, mitochondrial or aldehyde oxidase (FAD, molybdopterin, 2Fe-2S); (9) kynureninase (pyrophosphate); (10) acetylserotonin O-methyltransferase (S-Adenosyl methionine) (according to the* 

*Catecholamine biosynthesis. Enzymes (coenzymes): (1) tyrosine 3-monooxygenase (Fe2+); (2) aromatic-Lamino-acid decarboxylase (pyridoxal-5*′*-phosphate); (3) dopamine beta-hydroxylase (Cu2+, pyrroloquinoline,* 

*quinone); (4) phenylethanolamine N-methyltransferase) (according to the HMDB).*

**46**

**Figure 6.**

**Figure 5.**

*HMDB).*

of nutrient substrate deficiency in their habitat. However, significant changes in species composition may be developed under the influence of broad-spectrum antibacterial drugs, since the massive use of antibiotics (xenobiotics) is violent and anti-biological and can disrupt biochemical processes.

The microbiota is involved in the transformation of xenobiotics and provides a range of reactions including acetylation, deacylation, decarboxylation, dehydroxylation, demethylation, etc. under the influence of low-quality products and synthetic drugs [15]. Modern possibilities of metabolic methods allow an extensive study of another important function of the microbiota—detoxification of the host organism, which maintains its normal state longer in the conditions of retention and self-repairing of the microbiota.

Disorders of the microbial products of short-chain fatty acids SCFA (acetic, propionic, butyric) are most thoroughly studied as a result of the suppression of the normal functioning of anaerobic bacteria. Normally, SCFA requires enterocytes as the main source of energy, respectively; their deficiency contributes to the violation of mucosal trophism, reduction of reparative processes, development of ulcers, and inflammation. Persistent indigestion disorders and chronic gastroenterological diseases are the clinical manifestations of serious changes in the species composition and dysfunction of the microbiota.

Different genera of anaerobic bacteria are called responsible for the production of SCFA. For example, large amount of carbohydrate dissimilation butyrate from dissimilation is associated with some *Clostridia* clusters, other SCFAs, and *Bifidobacterium* spp.

In his review, Nyangale et al. rightly noted that several members of the microbiota have been linked with diseases mainly affecting the gut, lake inflammatory bowel disease, such as ulcerative colitis, Crohn's disease, colorectal cancer, and irritable bowel syndrome, although mechanisms involved are still not yet fully understood [16]. The authors consider the possibilities of metabolite analysis to assess the metabolic activity of the microbiota, to measure volatile and nonvolatile metabolite in biological samples, and to give metabolic pathways the contribution of microbiota to which it is most pronounced. These pathways include also the transformation of glucose and amino acids into SCFA, amino acid, microbial degradation of tyrosine to *p*-hydroxyphenylacetic and *p*-hydroxyphenylbenzoic acids (including bypassing tyramine), and degradation of tryptophan to indolepropionate and indoleacetate (including bypassing tryptamine).

A metabolite composition, determined in the feces, may indicate the composition of microbiota and its changes associated with the use of antibiotics [17]. The use of chemometric approaches in relation to the primary mass spectral data of the samples under study allows one to reliably find the differences between patients with inflamed intestines and the control group. The authors consider that changes in the microbiota phenotype cause this kind of deviations. The ratio of the species composition of microbiota—obligate or transient—significantly affects the metabolite composition that enters the circulatory system from the bowel. Thus, the role of *Bacillus* and *Lactobacillus*, colonizing the epithelium of the gastrointestinal tract, is systematically examined in a review of Ilinskaya et al. [18]. The metabolite composition depends significantly on the activity of their enzymatic systems, even with a relatively low content of such microorganisms in the microbiota.

In such acquired endocrinological diseases as obesity, type 2 diabetes (not related to heredity) can be attributed to pathological conditions due to metabolic disorders involving the microbiota. Microbiota can influence the development of diabetes [19]. Changes in the microbiological composition—dysbacteriosis caused, for example, by the use of antibiotics, may contribute to an increase in insulin dysfunction, a long-term consequence of which is the development of type 2 diabetes. Due diet may ensure opportune correction of the microbiota and prevent

further development of the disease. In a similar study for type 2 diabetes, cited in [20], the authors come to analogous conclusions. The authors agree that function is more important than taxonomy when discussing the role of microbiota in the development of metabolic disorders and diseases of the gastrointestinal tract [21].

In the future, methods of diagnosing gastrointestinal diseases and methods of treatment through the modulation of the microbiota based on information about intermediate metabolites and end products of microbial biodegradation of various compounds can be constructed and developed.

#### **3.2 Microbial metabolites in oncology**

Changes in the human body due to microbiota metabolism can affect cells and tissues and contribute to the development of benign and malignant tumors. The biochemistry and physiology of oncological processes is not completely clear, but certain metabolic shifts can be fixed instrumentally for some types of oncological diseases [22, 23]. The successful search for links between the patterns of normal functioning of the microbiota and the biochemistry of carcinogenesis is detailed in recent reviews [24, 25]. This indicates the prospects of such concept and allows us to call the microbiota "a key orchestrator of cancer therapy."

Most of the data on the correlation between a microbiota and cancer tumors is in the gastroenterology [26–31]. Such intestinal microorganisms as *Fusobacterium nucleatum*, *Streptococcus gallolyticus*, *Bacteroides fragilis*, *Escherichia coli*, and *Enterococcus faecalis* are most often mentioned as potential participants of the process. The inflammatory process in the epithelium or deeper tissues of the intestinal wall leads to increased local blood supply. At the same time, a favorable substrate is created for the massive multiplication of bacteria, the formation of microbial biofilms, which contributes to the activation of the enzymatic systems of bacteria, increasing concentrations of potentially dangerous mutagenic products of microbial metabolism. According to [30], the highest specificity of microorganisms contributing to the occurrence of colorectal cancer is noted in streptococci such as *Streptococcus bovis* and *Streptococcus gallolyticus.* Other authors indicate a violation of homeostasis in the intestine and emphasize the role of *Lactobacillus* deficiency in reducing the protective mechanisms [28].

The analysis of statistical data shows that there is an activation of the biosynthesis of fatty acids against the background of inhibition of the biosynthesis of amino acids and glycan in patients with colorectal cancer compared with the control group [26]. Statistically significant differences in the levels of metabolites of microbial origin, namely, an increase in the relative concentrations of phenylacetic, isobutyric, valeric, isovaleric acids, and hexose-phosphates with a simultaneous decrease in taurine, glutamine, β-alanine, isoleucine, galactose, xylose, glycerol, methanol, ornithine, guanidine, choline acid, and its derivatives, 4-aminohippuric acid, have been identified in a recent paper [32].

Certainty is not currently attainable regarding the use of volatile fatty acids as markers of oncology. Reducing the levels of SCFA (acetic, butyric), secondary bile acids, concomitant increase in amino acids (leucine, valine, proline, serine) valeric, isobutyric, isovaleric acid can be associated with the activity of enzymatic systems of *Ruminococcus* spp., *Fusobacterium*, *Porphyromonas*, *Clostridia*, *Lachnospiraceae*. Changes in the composition of the microbiota in patients with colorectal cancer, noted by the authors of the review [31], can be used as a diagnostic method. Also, the review authors [33] propose to use the following compound profile: short-chain fatty acids (mainly butyric acid), cholium-kilot on deoxycholic acid derivatives, bacterial toxin fragilis, and trimethylamine-N-oxide for the diagnosis of colorectal cancer. Other authors [34] also suggest a bacterial metabolite butyric acid as a marker for colorectal cancer.

**49**

*Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology*

*Roseburia*, promote an increase in the content of butyric acid [35].

An alternative concept is that volatile fatty acids, for example, butyric acid, may have a protective effect, which slows down the development of large intestine malignancies. Butyrate-producing bacteria contained in the microbiota of the gastrointestinal tract, such as *Faecalibacterium prausnitzii*, *Eubacterium rectale*, or

A treatment of large amounts of information on substances of bacterial origin potentially capable of being included in human metabolism allows us to distinguish six groups of compounds, based on the profile of which early diagnosis of colorectal cancer can be built [29]. There are short-chain fatty acids, bile acids, indoles, cresols, phenolic (phenyl-containing fatty) acids, and polyamines. Analysis of literature data [27] shows that under the influence of microbiota, changes in the directions of chemical transformation of glucose, fats, and amino acids are possible. The metabolic profile, largely formed by the microbiota, was used as a diagnostic method for cancer not directly related to the gastrointestinal tract. Statistically significant differences in the content of substances involved in the metabolism of glycerol lipids and retinol and ways of ethylbenzene degradation can be used to diagnose bladder cancer. Such metabolites are actively produced and/or absorbed with the participation of enzymes of *Herbaspirillum*, *Gemella*, *Bacteroides*, *Porphyrobacter*, *Faecalibacterium*, *Aeromonas*, and *Marmoricola* [36].

A change in the metabolic profile of amino acids such as valine, cysteine, tyrosine, and 6-hydroxynicotinic acid can be used as a method for diagnosing oral cancer [37]. Substances of microbial origin and components of the metabolism of *Helicobacter pylori* have a significant impact on the formation and growth of malignant neoplasms of the esophagus, large intestine, pancreas, and lung. A crosssectional statistical analysis shows that the likelihood of oncological complications associated with *Helicobacter pylori* increases in smokers and patients diagnosed with

The metabolites produced by the microbiota of the upper respiratory tract and lungs may influence the development of oncological processes in them. Three types of bacteria, *Granulicatella*, *Streptococcus*, and *Veillonella*, are mentioned most often in this connection. They probably have differences in the metabolism of polyamines, expressed in elevated levels of putrescine and similar products. According to other data, dysbiosis and an increase in *Streptococcus* and *Mycobacterium* are

However, waste products of bacteria can contribute to the development of breast cancer [22, 40–42]. The waste products of bacteria of the gastrointestinal tract can contribute to the development of malignant tumors of any other location: lung cancer [38, 39], bladder [36], pancreas [38], including hormone-dependent forms

Statistically significant correlations between the levels of secondary bile acids and the incidence of breast cancer were found in [22]. The authors believe that lithocholic acid, which is a product of the metabolism of microorganisms, is able to limit the proliferation of breast cancer cells both in vitro and in vivo by activating the TGR5 receptor. Changes in the metabolism of hormones, cysteine, and methionine and the biosynthesis of fatty acids associated with breast cancer were noted in a similar study [41], but there is no definite connection between them. The search for low-molecular markers of breast cancer, carried out in [42], allowed identification of 12 compounds (amino acids, organic acids, and nucleosides) that pretend to this role. These compounds are included in the metabolism of amino acid and nucleoside metabolism. Microbiota metabolites are able to act as accelerants and inhibitors of oncological processes. Now a scientific search in this field of knowledge is in the stage of intensive development and accumulation of a critical amount of information. The use of metabolomic approaches in combination with modern methods of statistical processing of

practically not associated with the development of lung cancer [39].

of breast cancer [22, 38, 40–42], and prostate cancer [43].

*DOI: http://dx.doi.org/10.5772/intechopen.87176*

chronic pancreatitis and diabetes [38].

#### *Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology DOI: http://dx.doi.org/10.5772/intechopen.87176*

*Metabolomics - New Insights into Biology and Medicine*

compounds can be constructed and developed.

call the microbiota "a key orchestrator of cancer therapy."

*Lactobacillus* deficiency in reducing the protective mechanisms [28].

**3.2 Microbial metabolites in oncology**

been identified in a recent paper [32].

further development of the disease. In a similar study for type 2 diabetes, cited in [20], the authors come to analogous conclusions. The authors agree that function is more important than taxonomy when discussing the role of microbiota in the development of metabolic disorders and diseases of the gastrointestinal tract [21]. In the future, methods of diagnosing gastrointestinal diseases and methods of treatment through the modulation of the microbiota based on information about intermediate metabolites and end products of microbial biodegradation of various

Changes in the human body due to microbiota metabolism can affect cells and tissues and contribute to the development of benign and malignant tumors. The biochemistry and physiology of oncological processes is not completely clear, but certain metabolic shifts can be fixed instrumentally for some types of oncological diseases [22, 23]. The successful search for links between the patterns of normal functioning of the microbiota and the biochemistry of carcinogenesis is detailed in recent reviews [24, 25]. This indicates the prospects of such concept and allows us to

Most of the data on the correlation between a microbiota and cancer tumors is in the gastroenterology [26–31]. Such intestinal microorganisms as *Fusobacterium nucleatum*, *Streptococcus gallolyticus*, *Bacteroides fragilis*, *Escherichia coli*, and *Enterococcus faecalis* are most often mentioned as potential participants of the process. The inflammatory process in the epithelium or deeper tissues of the intestinal wall leads to increased local blood supply. At the same time, a favorable substrate is created for the massive multiplication of bacteria, the formation of microbial biofilms, which contributes to the activation of the enzymatic systems of bacteria, increasing concentrations of potentially dangerous mutagenic products of microbial metabolism. According to [30], the highest specificity of microorganisms contributing to the occurrence of colorectal cancer is noted in streptococci such as *Streptococcus bovis* and *Streptococcus gallolyticus.* Other authors indicate a violation of homeostasis in the intestine and emphasize the role of

The analysis of statistical data shows that there is an activation of the biosynthesis of fatty acids against the background of inhibition of the biosynthesis of amino acids and glycan in patients with colorectal cancer compared with the control group [26]. Statistically significant differences in the levels of metabolites of microbial origin, namely, an increase in the relative concentrations of phenylacetic, isobutyric, valeric, isovaleric acids, and hexose-phosphates with a simultaneous decrease in taurine, glutamine, β-alanine, isoleucine, galactose, xylose, glycerol, methanol, ornithine, guanidine, choline acid, and its derivatives, 4-aminohippuric acid, have

Certainty is not currently attainable regarding the use of volatile fatty acids as markers of oncology. Reducing the levels of SCFA (acetic, butyric), secondary bile acids, concomitant increase in amino acids (leucine, valine, proline, serine) valeric, isobutyric, isovaleric acid can be associated with the activity of enzymatic systems of *Ruminococcus* spp., *Fusobacterium*, *Porphyromonas*, *Clostridia*, *Lachnospiraceae*. Changes in the composition of the microbiota in patients with colorectal cancer, noted by the authors of the review [31], can be used as a diagnostic method. Also, the review authors [33] propose to use the following compound profile: short-chain fatty acids (mainly butyric acid), cholium-kilot on deoxycholic acid derivatives, bacterial toxin fragilis, and trimethylamine-N-oxide for the diagnosis of colorectal cancer. Other authors [34] also suggest a bacterial metabolite butyric acid as a marker for

**48**

colorectal cancer.

An alternative concept is that volatile fatty acids, for example, butyric acid, may have a protective effect, which slows down the development of large intestine malignancies. Butyrate-producing bacteria contained in the microbiota of the gastrointestinal tract, such as *Faecalibacterium prausnitzii*, *Eubacterium rectale*, or *Roseburia*, promote an increase in the content of butyric acid [35].

A treatment of large amounts of information on substances of bacterial origin potentially capable of being included in human metabolism allows us to distinguish six groups of compounds, based on the profile of which early diagnosis of colorectal cancer can be built [29]. There are short-chain fatty acids, bile acids, indoles, cresols, phenolic (phenyl-containing fatty) acids, and polyamines. Analysis of literature data [27] shows that under the influence of microbiota, changes in the directions of chemical transformation of glucose, fats, and amino acids are possible.

The metabolic profile, largely formed by the microbiota, was used as a diagnostic method for cancer not directly related to the gastrointestinal tract. Statistically significant differences in the content of substances involved in the metabolism of glycerol lipids and retinol and ways of ethylbenzene degradation can be used to diagnose bladder cancer. Such metabolites are actively produced and/or absorbed with the participation of enzymes of *Herbaspirillum*, *Gemella*, *Bacteroides*, *Porphyrobacter*, *Faecalibacterium*, *Aeromonas*, and *Marmoricola* [36].

A change in the metabolic profile of amino acids such as valine, cysteine, tyrosine, and 6-hydroxynicotinic acid can be used as a method for diagnosing oral cancer [37]. Substances of microbial origin and components of the metabolism of *Helicobacter pylori* have a significant impact on the formation and growth of malignant neoplasms of the esophagus, large intestine, pancreas, and lung. A crosssectional statistical analysis shows that the likelihood of oncological complications associated with *Helicobacter pylori* increases in smokers and patients diagnosed with chronic pancreatitis and diabetes [38].

The metabolites produced by the microbiota of the upper respiratory tract and lungs may influence the development of oncological processes in them. Three types of bacteria, *Granulicatella*, *Streptococcus*, and *Veillonella*, are mentioned most often in this connection. They probably have differences in the metabolism of polyamines, expressed in elevated levels of putrescine and similar products. According to other data, dysbiosis and an increase in *Streptococcus* and *Mycobacterium* are practically not associated with the development of lung cancer [39].

However, waste products of bacteria can contribute to the development of breast cancer [22, 40–42]. The waste products of bacteria of the gastrointestinal tract can contribute to the development of malignant tumors of any other location: lung cancer [38, 39], bladder [36], pancreas [38], including hormone-dependent forms of breast cancer [22, 38, 40–42], and prostate cancer [43].

Statistically significant correlations between the levels of secondary bile acids and the incidence of breast cancer were found in [22]. The authors believe that lithocholic acid, which is a product of the metabolism of microorganisms, is able to limit the proliferation of breast cancer cells both in vitro and in vivo by activating the TGR5 receptor. Changes in the metabolism of hormones, cysteine, and methionine and the biosynthesis of fatty acids associated with breast cancer were noted in a similar study [41], but there is no definite connection between them. The search for low-molecular markers of breast cancer, carried out in [42], allowed identification of 12 compounds (amino acids, organic acids, and nucleosides) that pretend to this role. These compounds are included in the metabolism of amino acid and nucleoside metabolism.

Microbiota metabolites are able to act as accelerants and inhibitors of oncological processes. Now a scientific search in this field of knowledge is in the stage of intensive development and accumulation of a critical amount of information. The use of metabolomic approaches in combination with modern methods of statistical processing of

large amounts of data undoubtedly contributes to the development of fundamental and applied medicine in the field of diagnosis and treatment of oncological diseases.

#### **3.3 Neurological pathology and mental disorders**

Some substances that form the amino acid metabolism can overcome the hematoencephalic barrier and have a direct effect on the brain (**Figure 7**) [14, 44–46]. A search for such low-molecular compounds, quantitative determination, and their ratios can serve as the basis for the development of methods for early diagnosis, including cognitive and mental disorders [47]. It is important to note that metabolites can directly enter the region of the medulla, with blood through arteria vertebralisarteria spinalis, bypassing the hemato-encephalic barrier, and that critical vital centers of respiration and circulation are located there.

It is unlikely that metabolites of microbial biotransformation of amino acids are the direct cause of mental or neurological diseases. At the same time, numerous experimental studies indicate the existence of a direct "intestine-microbiota-brain" link. Current evidence suggests that multiple mechanisms, including endocrine and neurocrine pathways, may be involved in gut microbiota-to-brain signaling and that the brain can in turn alter microbial composition and behavior via the autonomic nervous system [48].

The authors in literature sources traditionally attend to the aromatic amino acid tryptophan metabolism mainly due to its relationship with the synthesis of serotonin (5-HT) and melatonin [49]. Tryptophan biotransformation in humans can occur in different ways: either with the participation of endogenous enzymes that are synthesized by the intestinal cell wall or with the participation of bacterial enzymes. Accordingly, the ratios of end products of tryptophan metabolism will differ. This is easily seen by comparing the enzymes and metabolic products of tryptophan in **Figures 5** and **8**.

The traditional view is that the amino acid tryptophan is used primarily for protein synthesis or the formation of serotonin and melatonin. However, more than 90% of tryptophan was found to be metabolized into N-formyl-kynurenine followed by kynurenine (**Figure 8**) [50]. The presence of anthranilic and 3-hydroxyanthranilic acids attracts particular attention as tryptophan metabolites. This pathway is not presented in mammalian metabolism. Such reactions of indole compounds are possible only with the participation of microbiota enzymatic systems. This also applies to picolinic and quinolinic acids, the formation of which

**51**

**Figure 8.**

*to [50]).*

*Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology*

is associated with the opening of the indole ring, which can occur exclusively in the

A decrease of tryptophan, xanthurenic, 3-hydroxyanthranilic, and quinolinic acids in the blood was recorded in the case of clinical occurrences of Alzheimer's disease. The same metabolites are given in [51] as potential markers of Alzheimer's disease. It can be assumed that one of the Alzheimer's disease triggers is a chronic

Now there are two alternative hypotheses in the literature regarding products of tryptophan metabolism and their influence on the development of schizophrenia. One of them postulates that a chronic tryptophan deficiency results in failure of catabolism products, such as 3-hydroxykynurenine, quinolinic, picolinic, xanthurenic, kinureric, and anthranilic acids. Some authors maintain that such deficiency stipulates the psychosomatic symptoms of schizophrenia [52]. Other authors come to the opposite conclusion based on the analysis of statistical data [53]. They indicate a direct correlation of clinical manifestations of schizophrenia with an increased content of kynurenic acid in the cerebrospinal fluid relative to the control group. Such conflicting data emphasize once again the peculiarities of the metabolic approach. You should not limit yourself to searching and measuring one or two metabolites during clinical trials; it is important to evaluate the complex metabolic profile, to compare the indicators with positive and negative dynamics. In addition, other mechanisms that are not related to the metabolism of neurotransmitters may

Thus, attempts to search for low-molecular markers of autism [54] and depressive disorder [55] were unsuccessful. But the data indicating the potential role of the metabolism of aromatic amino acids were discovered in such a mental disorder as anorexia nervosa. Levels of tryptophan and phenylalanine were significantly

Changes in the distribution of the tryptophan metabolism products, such as kynurenine, 3-hydroxy kynurenine, kynurenic, and anthranilic acids, are observed

*Tryptophan metabolism. Enzymes: (1) indoleamine deoxygenase or tryptophan deoxygenase; (2) formidase; (3) kynurenine aminotransferase; (4) kynureninase; (5) kynurenine-3-monooxygenase; (6) kynureninase; (7) 3- hydroxyanthranilate 3,4-dioxygenase; (8) 2-amino-3- carboxymuconate-semialdehyde decarboxylase (according* 

*DOI: http://dx.doi.org/10.5772/intechopen.87176*

process of microbial biotransformation.

be the basis of mental and neurologic disorders.

reduced in patients compared with the healthy ones.

deficiency of these substances.

**Figure 7.** *Scheme of amino acid metabolite transport in the brain.*

#### *Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology DOI: http://dx.doi.org/10.5772/intechopen.87176*

is associated with the opening of the indole ring, which can occur exclusively in the process of microbial biotransformation.

A decrease of tryptophan, xanthurenic, 3-hydroxyanthranilic, and quinolinic acids in the blood was recorded in the case of clinical occurrences of Alzheimer's disease. The same metabolites are given in [51] as potential markers of Alzheimer's disease. It can be assumed that one of the Alzheimer's disease triggers is a chronic deficiency of these substances.

Now there are two alternative hypotheses in the literature regarding products of tryptophan metabolism and their influence on the development of schizophrenia. One of them postulates that a chronic tryptophan deficiency results in failure of catabolism products, such as 3-hydroxykynurenine, quinolinic, picolinic, xanthurenic, kinureric, and anthranilic acids. Some authors maintain that such deficiency stipulates the psychosomatic symptoms of schizophrenia [52]. Other authors come to the opposite conclusion based on the analysis of statistical data [53]. They indicate a direct correlation of clinical manifestations of schizophrenia with an increased content of kynurenic acid in the cerebrospinal fluid relative to the control group. Such conflicting data emphasize once again the peculiarities of the metabolic approach. You should not limit yourself to searching and measuring one or two metabolites during clinical trials; it is important to evaluate the complex metabolic profile, to compare the indicators with positive and negative dynamics. In addition, other mechanisms that are not related to the metabolism of neurotransmitters may be the basis of mental and neurologic disorders.

Thus, attempts to search for low-molecular markers of autism [54] and depressive disorder [55] were unsuccessful. But the data indicating the potential role of the metabolism of aromatic amino acids were discovered in such a mental disorder as anorexia nervosa. Levels of tryptophan and phenylalanine were significantly reduced in patients compared with the healthy ones.

Changes in the distribution of the tryptophan metabolism products, such as kynurenine, 3-hydroxy kynurenine, kynurenic, and anthranilic acids, are observed

#### **Figure 8.**

*Metabolomics - New Insights into Biology and Medicine*

**3.3 Neurological pathology and mental disorders**

centers of respiration and circulation are located there.

nervous system [48].

tryptophan in **Figures 5** and **8**.

large amounts of data undoubtedly contributes to the development of fundamental and applied medicine in the field of diagnosis and treatment of oncological diseases.

Some substances that form the amino acid metabolism can overcome the hemato-

It is unlikely that metabolites of microbial biotransformation of amino acids are the direct cause of mental or neurological diseases. At the same time, numerous experimental studies indicate the existence of a direct "intestine-microbiota-brain" link. Current evidence suggests that multiple mechanisms, including endocrine and neurocrine pathways, may be involved in gut microbiota-to-brain signaling and that the brain can in turn alter microbial composition and behavior via the autonomic

The authors in literature sources traditionally attend to the aromatic amino acid tryptophan metabolism mainly due to its relationship with the synthesis of serotonin (5-HT) and melatonin [49]. Tryptophan biotransformation in humans can occur in different ways: either with the participation of endogenous enzymes that are synthesized by the intestinal cell wall or with the participation of bacterial enzymes. Accordingly, the ratios of end products of tryptophan metabolism will differ. This is easily seen by comparing the enzymes and metabolic products of

The traditional view is that the amino acid tryptophan is used primarily for protein synthesis or the formation of serotonin and melatonin. However, more than 90% of tryptophan was found to be metabolized into N-formyl-kynurenine followed by kynurenine (**Figure 8**) [50]. The presence of anthranilic and 3-hydroxyanthranilic acids attracts particular attention as tryptophan metabolites. This pathway is not presented in mammalian metabolism. Such reactions of indole compounds are possible only with the participation of microbiota enzymatic systems. This also applies to picolinic and quinolinic acids, the formation of which

encephalic barrier and have a direct effect on the brain (**Figure 7**) [14, 44–46]. A search for such low-molecular compounds, quantitative determination, and their ratios can serve as the basis for the development of methods for early diagnosis, including cognitive and mental disorders [47]. It is important to note that metabolites can directly enter the region of the medulla, with blood through arteria vertebralisarteria spinalis, bypassing the hemato-encephalic barrier, and that critical vital

**50**

**Figure 7.**

*Scheme of amino acid metabolite transport in the brain.*

*Tryptophan metabolism. Enzymes: (1) indoleamine deoxygenase or tryptophan deoxygenase; (2) formidase; (3) kynurenine aminotransferase; (4) kynureninase; (5) kynurenine-3-monooxygenase; (6) kynureninase; (7) 3- hydroxyanthranilate 3,4-dioxygenase; (8) 2-amino-3- carboxymuconate-semialdehyde decarboxylase (according to [50]).*

in patients with symptoms of Parkinson's disease [56]. Low levels of norepinephrine, dopamine, homovanillic acid, serotonin, and 5-hydroxyindoleacetic acid in the blood are fixed in these patients relative to the control group [57].

The failure of aromatic L-amino acid decarboxylase in combination with reduced levels of important metabolites such as serotonin, dopamine, and catecholamines leads to disruptions in the normal functioning of the whole organism, including brain activity. Crisis of oculomotor function along with muscular hypotonia and dystonia is observed in combination with other neurological syndromes in a similar state [58]. A decrease in the blood concentrations of homovanillic, 5-hydroxyindoleacetic acids, and 3-*o*-methyldopamine—substances included in the metabolism of tyrosine (**Figure4**) was observed in all patients. It can be noted with a high assurance that the deficiency of these metabolites is due to the lack of the transformation enzymes responsible for these reactions of the aromatic amino acids usually found in the microbiota.

#### **3.4 Prospects for neurorehabilitation**

Scientists have used metabolomics to gain new knowledge about the significance of the role that bacteria play in complex regulatory processes of higher nervous activity. Understanding the potential for managing this process cannot leave psychiatrists, neurologists, and neurorehabilitation specialists indifferent [59–61]. This fact is due to the relevance and high frequency of pathology of the nervous system. Prospects for the correction of microbiota metabolism for neurorehabilitation and the demand for this scientific search for new solutions in this area cannot be overestimated.

One of the areas discussed in the literature is the transformation of the species composition of the patient's microbiota to eliminate the deficiency of certain microorganisms. This idea has a scientific ground that many bacteria from the human microbiota in the in vitro study revealed the ability to produce hormones and neurotransmitters, that is, the presence of appropriate enzyme systems. These data are summarized in the reviews [44, 62] and in brief form are presented in **Table 2**.

Certain reports indicate that the treatment with large doses of *Lactobacillus casei* has a positive effect. Patients with chronic fatigue syndrome reported a decreased strain (n = 39). Patients who took a probiotic reported a significant decrease in symptoms of anxiety and had a substantial increase in the number of *Lactobacillus* and *Bifidobacteria* compared with the control group (p = 0.01). [63]. At the same time, treatment with live microorganisms (including fecal microbiota transplantation, FMT) is hardly predictable and can have negative consequences due to the variability of bacterial metabolism depending on the environment. For example, a randomized, double-blind, controlled study on the use of a drug based on lactobacilli in combination with prebiotic gives a negative result in patients with pancreatic necrosis: the mortality rate in the group receiving the biological product was significantly higher than in the control [64].

Neurorehabilitation of patients in modern clinics is considered as a component of acute cerebral therapy and starts from the earliest periods after injuries, strokes, and brain operations, even at the stage of the patient's stay in the intensive care unit. This is a multicomponent and long-term process aimed not only at saving lives but also at restoring motor activity, correcting neuro-endocrine, cognitive impairments, and emotional status. Different methods of monitoring the effectiveness of intensive care and the rehabilitation of the functional state of patients with various brain injuries are used [65].

The authors of this chapter believe that neurorehabilitation can be significantly enriched with a set of targeted measures aimed at correcting disorders in the development of which metabolic products associated with microbiota are actively involved. Our accumulated data on the magnitude of changes in the profile of

**53**

specific substances.

*Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology*

Norepinephrine *Bacillus subtilis*, *Bacillus mycoides*, *Proteus vulgaris*, *Serratia marcescens* Dopamine *B. subtilis*, *B. mycoides*, *Bacillus cereus*, *Staphylococcus aureus*, *P. vulgaris*, *S.* 

Serotonin *S. aureus*, *Enterococcus faecalis*, *Rhodospirillum rubrum*, *B. subtilis*, *E. coli*, *M.* 

Histamine *M. morganii*, *P. vulgaris*, *Proteus mirabilis*, *Klebsiella* sp., *Enterobacter aerogenes*,

γ-Aminobutyric acid *Bifidobacterium adolescentis*, *B. dentium*, *B. infantis*, *B. angulatum*, *Lactobacillus* 

lactis, *Lactococcus plantarum*, *L. helveticus*

Tyramine *Lactobacillus* spp., *Lactococcus* spp., *Enterococcus* spp., *Carnobacterium*

*marcescens*, *Escherichia coli*, *Morganella morganii*, *Klebsiella pneumonia*, *Hafnia alvei*, *Lactobacillus helveticus*, *Lactobacillus delbrueckii* subsp. bulgaricus

*E. coli*, *B. cereus*, *L. helveticus*, *L. casei*, *L. delbrueckii* subsp. bulgaricus, *Toxoplasma* 

*morganii*, *K. pneumonia*, *H. alvei*, *Lactococcus lactis* subsp. cremoris, *L. lactis* subsp.

*E. cloacae*, *Citrobacter freundii*, *Enterobacter amnigenus*, *Vibrio alginolyticus*, *Acinetobacter lowfli*, *Pseudomonas fluorescens*, *P. putida*, *Aeromonas* spp., *Clostridium* spp., *Photobacterium* spp., *Lactobacillus buchneri*, *Streptococcus thermophilus*

*brevis*, *L. plantarum*, *L. paracasei*, *L. buchneri*, *L. helveticus*, *L. delbrueckii*, *L.* 

microbiota metabolites and their connection with the course and outcome of the disease in patients with lesions of the central nervous system indicate the possibility of their use in choosing tactics for managing patients with this pathology. This complex may include several areas: (i) the first is the additional introduction into the body of substances that are associated with a shortage of other clinical manifestations of pathology. This can be achieved by nutritional correction or dietary supplements, including those obtained using industrial microbiology methods, as well as the administration of parenteral preparations containing the necessary metabolites of microbial origin. (ii) The second is the suppression of the metabolic activity of those types of bacteria in the composition of the microbiota, which in excess produce "unwanted" metabolites, through the selective use of antibacterial drugs with an appropriate mechanism of action. (iii) The third is the elimination of excess unwanted metabolites in the systemic circulation through the targeted use of extracorporeal blood purification procedures with filters/sorbents that remove

*Literary data on the ability of many bacteria: representatives of the human microbiota to participate in the* 

Of course, the use of modern metabolic methods for an objective assessment of the dynamics of the profile of metabolites in parallel with the monitoring of the psychosomatic state, functions of the damaged brain, spasticity level, motor skills, etc. is necessary for the successful implementation of the above directions in a particular patient. But above all, reliable data on key microbial metabolites, the level of which must be monitored in patients in the process of neurorehabilitation to on must be obtained. For example, metabolites associated with the development of septic shock (p-HPhAA) [66, 67] and death (PhA, p-HPhLA) [10] were earlier established for patients with sepsis. At the same time, another metabolite—PhPA—was a characteristic for the metabolic profile of a healthy person. The study of metabolome is conducted using the GC–MS method for patients with affection of the central nervous system of various etiologies [68]. Currently, the purpose of this study is to detect microbial metabolites associated with changes in the neurological status of patients in the process of neurorehabilitation. Preliminary results indicate a number of significant features,

*DOI: http://dx.doi.org/10.5772/intechopen.87176*

**Bacteria**

*gondii*

*reuteri*, *L. zymae*

*production of hormones and neurotransmitters (adapted from [44, 62]).*

**Hormone, neurotransmitter**

(DOPA)

**Table 2.**

Dopamine precursor

*Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology DOI: http://dx.doi.org/10.5772/intechopen.87176*


#### **Table 2.**

*Metabolomics - New Insights into Biology and Medicine*

**3.4 Prospects for neurorehabilitation**

significantly higher than in the control [64].

brain injuries are used [65].

in patients with symptoms of Parkinson's disease [56]. Low levels of norepinephrine, dopamine, homovanillic acid, serotonin, and 5-hydroxyindoleacetic acid in the

The failure of aromatic L-amino acid decarboxylase in combination with reduced levels of important metabolites such as serotonin, dopamine, and catecholamines leads to disruptions in the normal functioning of the whole organism, including brain activity. Crisis of oculomotor function along with muscular hypotonia and dystonia is observed in combination with other neurological syndromes in a similar state [58]. A decrease in the blood concentrations of homovanillic, 5-hydroxyindoleacetic acids, and 3-*o*-methyldopamine—substances included in the metabolism of tyrosine (**Figure4**) was observed in all patients. It can be noted with a high assurance that the deficiency of these metabolites is due to the lack of the transformation enzymes responsible for these

Scientists have used metabolomics to gain new knowledge about the significance of the role that bacteria play in complex regulatory processes of higher nervous activity. Understanding the potential for managing this process cannot leave psychiatrists, neurologists, and neurorehabilitation specialists indifferent [59–61]. This fact is due to the relevance and high frequency of pathology of the nervous system. Prospects for the correction of microbiota metabolism for neurorehabilitation and the demand for

One of the areas discussed in the literature is the transformation of the species composition of the patient's microbiota to eliminate the deficiency of certain microorganisms. This idea has a scientific ground that many bacteria from the human microbiota in the in vitro study revealed the ability to produce hormones and neurotransmitters, that is, the presence of appropriate enzyme systems. These data are summarized in the reviews [44, 62] and in brief form are presented in **Table 2**. Certain reports indicate that the treatment with large doses of *Lactobacillus casei* has a positive effect. Patients with chronic fatigue syndrome reported a decreased strain (n = 39). Patients who took a probiotic reported a significant decrease in symptoms of anxiety and had a substantial increase in the number of *Lactobacillus* and *Bifidobacteria* compared with the control group (p = 0.01). [63]. At the same time, treatment with live microorganisms (including fecal microbiota transplantation, FMT) is hardly predictable and can have negative consequences due to the variability of bacterial metabolism depending on the environment. For example, a randomized, double-blind, controlled study on the use of a drug based on lactobacilli in combination with prebiotic gives a negative result in patients with pancreatic necrosis: the mortality rate in the group receiving the biological product was

Neurorehabilitation of patients in modern clinics is considered as a component of acute cerebral therapy and starts from the earliest periods after injuries, strokes, and brain operations, even at the stage of the patient's stay in the intensive care unit. This is a multicomponent and long-term process aimed not only at saving lives but also at restoring motor activity, correcting neuro-endocrine, cognitive impairments, and emotional status. Different methods of monitoring the effectiveness of intensive care and the rehabilitation of the functional state of patients with various

The authors of this chapter believe that neurorehabilitation can be significantly

enriched with a set of targeted measures aimed at correcting disorders in the development of which metabolic products associated with microbiota are actively involved. Our accumulated data on the magnitude of changes in the profile of

blood are fixed in these patients relative to the control group [57].

reactions of the aromatic amino acids usually found in the microbiota.

this scientific search for new solutions in this area cannot be overestimated.

**52**

*Literary data on the ability of many bacteria: representatives of the human microbiota to participate in the production of hormones and neurotransmitters (adapted from [44, 62]).*

microbiota metabolites and their connection with the course and outcome of the disease in patients with lesions of the central nervous system indicate the possibility of their use in choosing tactics for managing patients with this pathology. This complex may include several areas: (i) the first is the additional introduction into the body of substances that are associated with a shortage of other clinical manifestations of pathology. This can be achieved by nutritional correction or dietary supplements, including those obtained using industrial microbiology methods, as well as the administration of parenteral preparations containing the necessary metabolites of microbial origin. (ii) The second is the suppression of the metabolic activity of those types of bacteria in the composition of the microbiota, which in excess produce "unwanted" metabolites, through the selective use of antibacterial drugs with an appropriate mechanism of action. (iii) The third is the elimination of excess unwanted metabolites in the systemic circulation through the targeted use of extracorporeal blood purification procedures with filters/sorbents that remove specific substances.

Of course, the use of modern metabolic methods for an objective assessment of the dynamics of the profile of metabolites in parallel with the monitoring of the psychosomatic state, functions of the damaged brain, spasticity level, motor skills, etc. is necessary for the successful implementation of the above directions in a particular patient. But above all, reliable data on key microbial metabolites, the level of which must be monitored in patients in the process of neurorehabilitation to on must be obtained. For example, metabolites associated with the development of septic shock (p-HPhAA) [66, 67] and death (PhA, p-HPhLA) [10] were earlier established for patients with sepsis. At the same time, another metabolite—PhPA—was a characteristic for the metabolic profile of a healthy person. The study of metabolome is conducted using the GC–MS method for patients with affection of the central nervous system of various etiologies [68]. Currently, the purpose of this study is to detect microbial metabolites associated with changes in the neurological status of patients in the process of neurorehabilitation. Preliminary results indicate a number of significant features,

for example, positive neurological and psychosomatic dynamics is associated with the appearance and accumulation of the metabolite p-HBA in the intestine and the patient's blood, which is not observed in other groups of patients. The composition of the microbiota in patients with severe neurosomatic pathology using the method of metagenomic sequencing of the 16S pRNA is under study. Correlations with microbial blood metabolites are also being studied. Preliminary data demonstrate significant differences when comparing various patient groups [69]. The results of the multicenter study will serve as the basis for the development and objective evaluation of the effectiveness of the above technologies in the process of neurorehabilitation.

#### **4. Conclusion**

A new level of knowledge about the role of the microbiota in the human body was made possible by metabolomics. In the coming years, this will lead to new solutions in the diagnosis of many "difficult" diseases. Methods of active control of metabolic processes that will subordinate the dysfunction of the "invisible organ" to the benefit of the host will be found. It will lead to the increase in the effectiveness of treatment and successful rehabilitation of patients. In particular, in the field of neurorehabilitation, clinical studies are currently aimed at finding such methods for correcting the metabolism of microbiota that will achieve a balance of low-molecular metabolites as signaling molecules of microbiota to restore brain function.

#### **Acknowledgements**

This work was supported by the Russian Science Foundation Grant № 15**–**15-00110-P.

#### **Author details**

Natalia V. Beloborodova1 \*, Andrey V. Grechko1 and Andrey Yu Olenin2

1 Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russia

2 Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russia

\*Address all correspondence to: nvbeloborodova@yandex.ru

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**55**

*Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology*

Archives of Microbiology. 2018;**200**:203.

DOI: 10.1007/s00203-017-1459-x

10.5772/68046

ymgme.2015.01.005

[10] Beloborodova NV. Interaction of host-microbial metabolism in sepsis. In: Kumar V, editor. Sepsis. InTechOpen; 2017. pp. 3-19. DOI:

[11] Gertsman I, Gangoiti JA, Nyhan WL, Barshop BA. Perturbations of tyrosine metabolism promote the indolepyruvate pathway via tryptophan in host and microbiome. Molecular Genetics and Metabolism. Elsevier. 2015;**114**:431. DOI: 10.1016/j.

[12] Land EJ, Ramsden CA, Riley PA. Pulse radiolysis studies of orthoquinone chemistry relevant to melanogenesis. Journal of Photochemistry and Photobiology. B. 2001;**64**:123. DOI: 10.1016/ S1011-1344(01)00220-2

[13] Faccio G, Kruus K, Saloheimo M, Thöny-Meyer L. Bacterial tyrosinases and their applications. Process Biochemistry. 2012;**47**:1749. DOI: 10.1016/j.procbio.2012.08.018

[14] van den Brink WJ, Palic S, Köhler I, de Lange ECM. Access to the CNS: Biomarker strategies for dopaminergic

Research. 2018;**35**:64. DOI: 10.1007/

[15] Wilson ID, Nicholson JK. Gut microbiome interactions with drug metabolism, efficacy, and toxicity. Translational Research. 2017;**179**:204. DOI: 10.1016/j.trsl.2016.08.002

[16] Nyangale EP, Mottram DS, Gibson GR. Gut microbial activity, implications for health and disease: The potential role of metabolite analysis. Journal of Proteome Research. 2012;**11**:5573. DOI:

treatments. Pharmaceutical

s11095-017-2333-x

10.1021/pr300637d

*DOI: http://dx.doi.org/10.5772/intechopen.87176*

[1] Chernevskaya E, Beloborodova N.

[2] Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vázquez-Fresno R,

metabolome database for 2018. Nucleic Acids Research. 2018;**46**:D608. DOI:

[4] Hooper LV, Macpherson AJ. Immune adaptations that maintain homeostasis with the intestinal microbiota. Nature Reviews. Immunology. 2010;**10**:159.

[5] Cantarel BL, Lombard V, Henrissat B. Complex carbohydrate utilization by the healthy human microbiome. PLoS One. 2012;**7**:e28742. DOI: 10.1371/

[6] Kim CH. Immune regulation by microbiome metabolites. Immunology. 2018;**154**:220. DOI: 10.1111/imm.12930

[7] HMP Consortium. Structure, function and diversity of the

[8] Gill SR, Pop M, DeBoy RT, Eckburg PB, Turnbaugh PJ, Samuel BS, et al. Metagenomic analysis of the human distal gut microbiome. Science. 2006;**312**:1355. DOI: 10.1126/

science.1124234

healthy human microbiome. Nature. 2012;**486**:207. DOI: 10.1038/nature11234

[9] Yadav M, Verma MK, Chauhan NS. A review of metabolic potential of human gut microbiome in human nutrition.

[3] Hornung B, dos Santos VAPM, Smidt H, Schaap PJ. Studying microbial functionality within the gut ecosystem

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*Metabolomics - New Insights into Biology and Medicine*

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A new level of knowledge about the role of the microbiota in the human body was made possible by metabolomics. In the coming years, this will lead to new solutions in the diagnosis of many "difficult" diseases. Methods of active control of metabolic processes that will subordinate the dysfunction of the "invisible organ" to the benefit of the host will be found. It will lead to the increase in the effectiveness of treatment and successful rehabilitation of patients. In particular, in the field of neurorehabilitation, clinical studies are currently aimed at finding such methods for correcting the metabolism of microbiota that will achieve a balance of low-molecular metabolites as signaling molecules of microbiota to restore brain function.

**54**

Russia

**Author details**

15**–**15-00110-P.

**4. Conclusion**

Natalia V. Beloborodova1

**Acknowledgements**

Rehabilitology, Moscow, Russia

provided the original work is properly cited.

\*, Andrey V. Grechko1

This work was supported by the Russian Science Foundation Grant №

1 Federal Research and Clinical Center of Intensive Care Medicine and

\*Address all correspondence to: nvbeloborodova@yandex.ru

2 Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow,

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

and Andrey Yu Olenin2

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[65] Kiryachkov YY, Grechko AV, Kolesov DL, Loginov AA, Petrova MV, Rubanes M, et al. Monitoring of the effectiveness of intensive care and rehabilitation by evaluating the functional activity of the

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[66] Beloborodova NV, Olenin AY, Pautova AK. Metabolomic findings in

[63] Rao V, Bested AC, Beaulne TM, Katzman MA, Iorio C, Berardi JM, et al. A randomized, double-blind, placebocontrolled pilot study of a probiotic in emotional symptoms of chronic fatigue syndrome. Gut Pathogens. 2009;**1**:6.

[62] Lucas P, Landete J, Coton M, Coton E, Lonvaud-Funel A. The tyrosine decarboxylase operon of *Lactobacillus brevis* IOEB 9809: Characterization and conservation in tyramine-producing bacteria. FEMS Microbiology Letters. 2003;**229**:65. DOI: 10.1016/

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[60] Carabotti M, Scirocco A, Maselli MA, Severi C. The gut-brain axis: Interactions between enteric microbiota, central and enteric nervous systems. Annals of Gastroenterology. 2015;**28**:203

*Metabolomics - New Insights into Biology and Medicine*

schizophrenia. Physiology and Behavior. 2007;**92**:203. DOI: 10.1016/j.

10.1016/j.jchromb.2013.10.026

[54] Kałużna-Czaplińska J, Żurawicz E, Jóźwik J. Chromatographic techniques coupled with mass spectrometry for the determination of organic acids in the study of autism. Journal of Chromatography B. 2014;**964**:128. DOI:

[55] Zheng P, Wang Y, Chen L, Yang D, Meng H, Zhou D, et al. Identification and validation of urinary metabolite biomarkers for major depressive disorder. Molecular and Cellular Proteomics. 2013;**12**:207. DOI: 10.1074/

[56] Havelund JF, Andersen AD, Binzer M, Blaabjerg M, Heegaard NHH,

Stenager E, et al. Changes in kynurenine pathway metabolism in Parkinson patients with L-DOPA-induced

dyskinesia. Journal of Neurochemistry. 2017;**142**:756. DOI: 10.1111/jnc.14104

[58] Manegold C, Hoffmann GF, Degen I, Ikonomidou H, Knust A, Laaß MW, et al. Aromatic L-amino acid decarboxylase deficiency: Clinical features, drug therapy and follow-up. Journal of Inherited Metabolic Disease. 2009;**32**:371. DOI: 10.1007/

[59] Dovrolis N, Kolios G, Spyrou GM, Maroulakou I. Computational profiling of the gut-brain axis: *Microflora dysbiosis* insights to neurological disorders. Briefings in Bioinformatics. 2017. bbx154. DOI: 10.1093/bib/bbx154

[57] Sitte HH, Pifl C, Rajput AH, Hörtnagl H, Tong J, Lloyd GK, et al. Dopamine and noradrenaline, but not serotonin, in the human claustrum are greatly reduced in patients with Parkinson's disease: Possible functional implications. The European Journal of Neuroscience. 2017;**45**:192. DOI:

physbeh.2007.05.025

mcp.M112.021816

10.1111/ejn.13435

s10545-009-1076-1

biosynthesis. Brain. 2010;**133**:1810. DOI:

[47] Sadok I, Gamian A, Staniszewska MM. Chromatographic analysis of tryptophan metabolites. Journal of Separation Science. 2017;**40**:3020. DOI:

10.1093/brain/awq087

10.1002/jssc.201700184

[48] Mayer EA, Tillisch K, Gupta A. Gut/brain axis and the microbiota. The Journal of Clinical Investigation. 2015;**125**:926. DOI: 10.1172/JCI76304

[49] Kałużna-Czaplińska J, Gątarek P, Chirumbolo S, Chartrand MS, Bjørklund G. How important is tryptophan in human health? Critical Reviews in Food Science and Nutrition. 2019;**59**:72. DOI: 10.1080/10408398.2017

[50] Chatterjee P, Goozee K, Lim CK, James I, Shen K, Jacobs KR, et al. Alterations in serum kynurenine pathway metabolites in individuals with high neocortical amyloid-β load: A pilot study. Scientific Reports. 2018;**8**:8008. DOI: 10.1038/s41598-018-25968-7

[51] Lv C, Li Q, Liu X, He B, Sui Z, Xu H, et al. Determination of

10.1002/jms.3536

catecholamines and their metabolites in rat urine by ultra-performance liquid chromatography–tandem mass spectrometry for the study of identifying potential markers for Alzheimer's disease. Journal of Mass Spectrometry. 2015;**50**:354. DOI:

[52] Kanchanatawan B, Sirivichayakul S, Thika S, Ruxrungtham K, Carvalho

somatic symptoms in schizophrenia: Association with depression, anxiety, neurocognitive deficits and the tryptophan catabolite pathway.

Metabolic Brain Disease. 2017;**32**:1003. DOI: 10.1007/s11011-017-9982-7

[53] Erhardt S, Schwieler L, Nilsson L, Linderholm K, Engberg G. The kynurenic acid hypothesis of

AF, Geffard M, et al. Physio-

**58**

[61] Singh V, Roth S, Llovera G, Sadler R, Garzetti D, Stecher B, et al. Microbiota dysbiosis controls the neuroinflammatory response after stroke. The Journal of Neuroscience. 2016;**36**:7428. DOI: 10.1523/ jneurosci.1114-16.2016

[62] Lucas P, Landete J, Coton M, Coton E, Lonvaud-Funel A. The tyrosine decarboxylase operon of *Lactobacillus brevis* IOEB 9809: Characterization and conservation in tyramine-producing bacteria. FEMS Microbiology Letters. 2003;**229**:65. DOI: 10.1016/ S0378-1097(03)00787-0

[63] Rao V, Bested AC, Beaulne TM, Katzman MA, Iorio C, Berardi JM, et al. A randomized, double-blind, placebocontrolled pilot study of a probiotic in emotional symptoms of chronic fatigue syndrome. Gut Pathogens. 2009;**1**:6. DOI: 10.1186/1757-4749-1-6

[64] Besselink MG, van Santvoort HC, Buskens E, Boermeester MA, van Goor H, Timmerman HM, et al. Probiotic prophylaxis in predicted severe acute pancreatitis: A randomized, double-blind, placebo-controlled trial. Lancet. 2008;**371**:651. DOI: 10.1016/ s0140-6736(08)60207-x

[65] Kiryachkov YY, Grechko AV, Kolesov DL, Loginov AA, Petrova MV, Rubanes M, et al. Monitoring of the effectiveness of intensive care and rehabilitation by evaluating the functional activity of the autonomic nervous system in patients with brain damage. Obshchaya Reanimatologiya. 2018;**14**(4):21. DOI: 10.15360/1813-9779-2018-4-21-34

[66] Beloborodova NV, Olenin AY, Pautova AK. Metabolomic findings in sepsis as a damage of host-microbial metabolism integration. Journal of Critical Care. 2018;**43**:246. DOI: 10.1016/j.jcrc.2017.09.014

[67] Beloborodova NV, Sarshor YN, Bedova AY, Chernevskaya EA, Pautova AK. Involvement of aromatic metabolites in the pathogenesis of septic shock. Shock. 2018;**50**:273. DOI: 10.1097/shk.0000000000001064

[68] Pautova AK, Bedova AY, Sarshor YN, Beloborodova NV. Determination of aromatic microbial metabolites in blood serum by gas chromatography– mass spectrometry. Journal of Analytical Chemistry. 2018;**73**:160. DOI: 10.1134/S1061934818020089

[69] Beloborodova NV, Chernevskaya EA, Pautova AK, Bedova AY, Sergeev AA. Altered serum profile of aromatic metabolites reflects the biodiversity reduction of gut microbiota in critically ill patients. Critical Care. 2018;**22**(Suppl 1):82. DOI: 10.1186/ s13054-018-1973-5

**61**

**Chapter 4**

**Abstract**

Conditions

*Elizabeth R. Lusczek*

patient care to the ICU.

**1. Introduction**

combat casualty, mass spectrometry

Serum Metabolomics as a

Powerful Tool in Distinguishing

Trauma from Other Critical Illness

Critical illness is highly variable, complicating patient care and recovery. We have previously used metabolomics to investigate several causes of intensive care unit admission, seeking to assess changes in metabolism occurring with each condition. We present a meta-analysis of these serum metabolomes, exploring how the metabolomes differ with each condition. We also present how mass spectrometry-based metabolomics could be used for predictive monitoring. Serum metabolites were previously quantified using nuclear magnetic resonance spectroscopy in patients with traumatic injury, respiratory failure, pancreatitis, and combat trauma. Healthy controls are also included. Spectral features were analyzed with principal component analysis (PCA) to explore patterns in patients' underlying conditions. PCA suggests trauma metabolic profiles, particularly combat casualties, differ from other conditions. Principal components 2 and 3, accounting for 16% of the variation in the model, distinguish samples obtained from trauma patients. Metabolomics is a powerful tool for quantifying variability in critical illness, highlighting trauma as separate from other conditions. This observation is in line with the -omics literature, which has described a massive global "genomic storm" in response to severe injury. Mass spectrometry highlights this extreme variability, which occurs in ICU patients but not healthy controls. With new technology, metabolomics could be used to bring faster, individualized

**Keywords:** metabolomics, NMR, ICU, critical illness, biomarker, traumatic injury,

Critical illness encompasses a wide variety of life-threatening conditions, often requiring intensive monitoring and sophisticated life support, such as dialysis, mechanical ventilation, and nutritional support. Patients are cared for in intensive care units (ICUs), staffed by specialists. Because patients' conditions can change quickly over time, ICU staff are highly trained and nurses regularly care for only one or two patients at a time. Because of these factors, critical illness carries a high cost burden. It has been estimated that anywhere from 17 to 39% of hospital costs in

#### **Chapter 4**

## Serum Metabolomics as a Powerful Tool in Distinguishing Trauma from Other Critical Illness Conditions

*Elizabeth R. Lusczek*

#### **Abstract**

Critical illness is highly variable, complicating patient care and recovery. We have previously used metabolomics to investigate several causes of intensive care unit admission, seeking to assess changes in metabolism occurring with each condition. We present a meta-analysis of these serum metabolomes, exploring how the metabolomes differ with each condition. We also present how mass spectrometry-based metabolomics could be used for predictive monitoring. Serum metabolites were previously quantified using nuclear magnetic resonance spectroscopy in patients with traumatic injury, respiratory failure, pancreatitis, and combat trauma. Healthy controls are also included. Spectral features were analyzed with principal component analysis (PCA) to explore patterns in patients' underlying conditions. PCA suggests trauma metabolic profiles, particularly combat casualties, differ from other conditions. Principal components 2 and 3, accounting for 16% of the variation in the model, distinguish samples obtained from trauma patients. Metabolomics is a powerful tool for quantifying variability in critical illness, highlighting trauma as separate from other conditions. This observation is in line with the -omics literature, which has described a massive global "genomic storm" in response to severe injury. Mass spectrometry highlights this extreme variability, which occurs in ICU patients but not healthy controls. With new technology, metabolomics could be used to bring faster, individualized patient care to the ICU.

**Keywords:** metabolomics, NMR, ICU, critical illness, biomarker, traumatic injury, combat casualty, mass spectrometry

#### **1. Introduction**

Critical illness encompasses a wide variety of life-threatening conditions, often requiring intensive monitoring and sophisticated life support, such as dialysis, mechanical ventilation, and nutritional support. Patients are cared for in intensive care units (ICUs), staffed by specialists. Because patients' conditions can change quickly over time, ICU staff are highly trained and nurses regularly care for only one or two patients at a time. Because of these factors, critical illness carries a high cost burden. It has been estimated that anywhere from 17 to 39% of hospital costs in the United States are due to critical illness. Total costs, including 1 year of care after discharge are estimated at \$121–263 billion, or 5–11% of United States health care expenditures [1]. The cost burden is difficult to estimate, due in part to the complicated recovery process.

Recently, post-intensive care syndrome (PICS) has been identified as a constellation of cognitive, psychological, and physical impairments that result from critical illness [2], occurring with increased prevalence due to the increased survivability of critical illness [3]. ICU-acquired delirium and mechanical ventilation are among the risk factors for PICS, and the effects can be long-lasting. An estimated 90% of patients report ICU-acquired weakness lasting 2–5 years from ICU discharge, and 74% of patients with acute respiratory distress syndrome report cognitive impairments at discharge. Approximately a quarter of these patients report effects lasting as long as 6 years [4].

While survivability from critical illness has increased, it has been difficult to make further advances in patient care and outcomes due to the heterogeneity of the patient population. Respiratory disorders requiring mechanical ventilation, acute myocardial infarction, intracranial hemorrhage, percutaneous cardiovascular procedure with drug-eluting stent, and septicemia are the leading causes of ICU admission, but gastrointestinal disorders, renal disorders, and trauma are also frequent causes of ICU admission [5]. To further complicate matters, as many as 1/3 of ICU patients have multiple co-morbidities. Homogenous patient populations can be difficult to identify, let alone study, in the ICU. As such, a "one-size-fits-all" approach to patient care can lead to unpredictable results. To cope with these hallmarks of critical illness, modern ICU clinicians argue for precision medicine approaches to critical care as a way to improve patient care [6–8].

Metabolomics, which reflects the phenome more closely than other -omics disciplines, may be a key to this endeavor. This terrain has been largely unexplored, save for a few studies. Targeted metabolomics has been used to discriminate noninfectious systemic inflammatory response syndrome (SIRS) from infections SIRS [9]. Untargeted approaches have identified significant, severe metabolic derangements that are associated with mortality [10, 11].

This chapter presents efforts to use metabolomics to explore this difficult-to-study space. Namely, critical illness is highly variable and affects diffuse organ systems in a heterogeneous patient population that may have multiple co-morbidities. Since the metabolome is closest to the phenome, it is more likely to reflect the individual patient's state at any given time than other -omes. As others have pointed out, issues of heterogeneity and variability make biomarker studies problematic [6, 10]. A first step is to examine how metabolic profiles differ with different underlying diseases and with illness severity to get a better sense of this variability. This chapter touches on current efforts in this direction.

#### **2. Metabolomics methodology and previous work**

The NMR-based metabolomics studies we performed were pilot studies seeking to characterize metabolic profiles in combat injury [12], civilian traumatic injury [13], acute pancreatitis [14], and respiratory failure [15]. Healthy controls were also profiled [12, 13].

The use of the same protocol to process serum samples, collect NMR spectra, and quantify metabolites allows for a meta-analysis comparing the metabolic profiles from each study.

**63**

**Table 1.**

*Serum Metabolomics as a Powerful Tool in Distinguishing Trauma from Other Critical Illness…*

Briefly, samples were filtered using a 3 kDa ultracentrifuge filter to remove large molecules such as proteins that bind to the internal standard. Filtrate is mixed in equal parts with 200 mM sodium phosphate buffer and with 50 microliters of the internal standard 3-(trimethylsilyl)propionic acid. A 1D Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence was used to collect spectra, and metabolites were identified and quantified using Chenomx software [16]. Full experimental details can be found in the original research articles

Metabolic profiles were limited to the 41 metabolites identified and quantified in all four studies. Metabolite concentrations (millimoles per liter or mM) were log-transformed and auto-scaled before principal component analysis (PCA) was performed with R software [17]. PCA scores were colored by underlying diagnosis/ patient group (combat trauma, civilian trauma age 21–40, civilian trauma age 65 and older, acute pancreatitis, respiratory failure, healthy controls age 21–40, and

For the purposes of visualizing the diagnosis groups in this meta-analysis, some

In total, 291 serum samples were analyzed with principal component analysis. Most of these were from trauma patients. The number of samples studied in each

Interestingly, the most meaningful pattern in the PCA scores is observed in **Figure 2**, the plot of component 2 vs. component 3. A clear line can be drawn along PC2 and PC3, demarcating the samples from trauma patients (red,

Principal component analysis scores (**Figures 1** and **2**) and loadings (**Figures 3** and **4**) are shown for the first three components. Each dot in the scores plot represents a serum sample, which is colored according to the diagnosis or condition. The loadings plots show how the metabolites profiled contribute to the model. The first three components account for 51% of the variability in the data. Component 1 accounts for 35% of the variation; components 2 and 3 account for 9.8 and 6.6% of the variation, respectively. A three-dimensional

simplifications were made based on the previously published results. Because no clear difference was seen between patients in respiratory failure regardless of underlying cause, patients with chronic obstructive pulmonary disease (COPD) exacerbation, heart failure, and pneumonia were combined into the "respiratory failure" group [15]. Non-hospitalized patients who did not develop pancreatitis [14] and non-hospitalized patients with stable COPD [15] were excluded from this

*DOI: http://dx.doi.org/10.5772/intechopen.87145*

healthy controls age 65 and older).

analysis to facilitate visualization.

diagnosis group is presented in **Table 1**.

biplot (**Figure 5**) helps visualize all the information.

*Number of samples profiled with NMR-based metabolomics per condition studied.*

**3. Meta-analysis results**

[12–15].

*Serum Metabolomics as a Powerful Tool in Distinguishing Trauma from Other Critical Illness… DOI: http://dx.doi.org/10.5772/intechopen.87145*

Briefly, samples were filtered using a 3 kDa ultracentrifuge filter to remove large molecules such as proteins that bind to the internal standard. Filtrate is mixed in equal parts with 200 mM sodium phosphate buffer and with 50 microliters of the internal standard 3-(trimethylsilyl)propionic acid. A 1D Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence was used to collect spectra, and metabolites were identified and quantified using Chenomx software [16]. Full experimental details can be found in the original research articles [12–15].

Metabolic profiles were limited to the 41 metabolites identified and quantified in all four studies. Metabolite concentrations (millimoles per liter or mM) were log-transformed and auto-scaled before principal component analysis (PCA) was performed with R software [17]. PCA scores were colored by underlying diagnosis/ patient group (combat trauma, civilian trauma age 21–40, civilian trauma age 65 and older, acute pancreatitis, respiratory failure, healthy controls age 21–40, and healthy controls age 65 and older).

For the purposes of visualizing the diagnosis groups in this meta-analysis, some simplifications were made based on the previously published results. Because no clear difference was seen between patients in respiratory failure regardless of underlying cause, patients with chronic obstructive pulmonary disease (COPD) exacerbation, heart failure, and pneumonia were combined into the "respiratory failure" group [15]. Non-hospitalized patients who did not develop pancreatitis [14] and non-hospitalized patients with stable COPD [15] were excluded from this analysis to facilitate visualization.

#### **3. Meta-analysis results**

*Metabolomics - New Insights into Biology and Medicine*

cated recovery process.

as long as 6 years [4].

to improve patient care [6–8].

on current efforts in this direction.

ments that are associated with mortality [10, 11].

**2. Metabolomics methodology and previous work**

the United States are due to critical illness. Total costs, including 1 year of care after discharge are estimated at \$121–263 billion, or 5–11% of United States health care expenditures [1]. The cost burden is difficult to estimate, due in part to the compli-

Recently, post-intensive care syndrome (PICS) has been identified as a constellation of cognitive, psychological, and physical impairments that result from critical illness [2], occurring with increased prevalence due to the increased survivability of critical illness [3]. ICU-acquired delirium and mechanical ventilation are among the risk factors for PICS, and the effects can be long-lasting. An estimated 90% of patients report ICU-acquired weakness lasting 2–5 years from ICU discharge, and 74% of patients with acute respiratory distress syndrome report cognitive impairments at discharge. Approximately a quarter of these patients report effects lasting

While survivability from critical illness has increased, it has been difficult to make further advances in patient care and outcomes due to the heterogeneity of the patient population. Respiratory disorders requiring mechanical ventilation, acute myocardial infarction, intracranial hemorrhage, percutaneous cardiovascular procedure with drug-eluting stent, and septicemia are the leading causes of ICU admission, but gastrointestinal disorders, renal disorders, and trauma are also frequent causes of ICU admission [5]. To further complicate matters, as many as 1/3 of ICU patients have multiple co-morbidities. Homogenous patient populations can be difficult to identify, let alone study, in the ICU. As such, a "one-size-fits-all" approach to patient care can lead to unpredictable results. To cope with these hallmarks of critical illness, modern ICU clinicians argue for precision medicine approaches to critical care as a way

Metabolomics, which reflects the phenome more closely than other -omics disciplines, may be a key to this endeavor. This terrain has been largely unexplored, save for a few studies. Targeted metabolomics has been used to discriminate noninfectious systemic inflammatory response syndrome (SIRS) from infections SIRS [9]. Untargeted approaches have identified significant, severe metabolic derange-

This chapter presents efforts to use metabolomics to explore this difficult-to-study space. Namely, critical illness is highly variable and affects diffuse organ systems in a heterogeneous patient population that may have multiple co-morbidities. Since the metabolome is closest to the phenome, it is more likely to reflect the individual patient's state at any given time than other -omes. As others have pointed out, issues of heterogeneity and variability make biomarker studies problematic [6, 10]. A first step is to examine how metabolic profiles differ with different underlying diseases and with illness severity to get a better sense of this variability. This chapter touches

The NMR-based metabolomics studies we performed were pilot studies seeking to characterize metabolic profiles in combat injury [12], civilian traumatic injury [13], acute pancreatitis [14], and respiratory failure [15]. Healthy controls were also

The use of the same protocol to process serum samples, collect NMR spectra, and quantify metabolites allows for a meta-analysis comparing the metabolic

**62**

profiled [12, 13].

profiles from each study.

In total, 291 serum samples were analyzed with principal component analysis. Most of these were from trauma patients. The number of samples studied in each diagnosis group is presented in **Table 1**.

Principal component analysis scores (**Figures 1** and **2**) and loadings (**Figures 3** and **4**) are shown for the first three components. Each dot in the scores plot represents a serum sample, which is colored according to the diagnosis or condition. The loadings plots show how the metabolites profiled contribute to the model. The first three components account for 51% of the variability in the data. Component 1 accounts for 35% of the variation; components 2 and 3 account for 9.8 and 6.6% of the variation, respectively. A three-dimensional biplot (**Figure 5**) helps visualize all the information.

Interestingly, the most meaningful pattern in the PCA scores is observed in **Figure 2**, the plot of component 2 vs. component 3. A clear line can be drawn along PC2 and PC3, demarcating the samples from trauma patients (red,


#### **Table 1.**

*Number of samples profiled with NMR-based metabolomics per condition studied.*

**Figure 1.** *Scores plot of PC1 vs. PC2 for serum samples described in Table 1. Samples are colored by diagnosis.*

#### **Figure 2.**

*Scores plot of PC2 vs. PC3 for serum samples described in Table 1. Samples are colored by diagnosis. These two principal components most clearly distinguish trauma samples from non-trauma samples.*

**65**

**Figure 4.**

*with trauma occupy the lower right quadrant.*

**Figure 3.**

*Serum Metabolomics as a Powerful Tool in Distinguishing Trauma from Other Critical Illness…*

*Loadings plot of PC1 vs. PC2 for serum samples described in Table 1. Loadings values are shown in Table 2.*

*Loadings plot of PC2 vs. PC3 for serum samples described in Table 1. Loadings values are shown in Table 2, and the magnitude of the loadings vector spanned by PC2 and PC3 is calculated. Metabolites most associated* 

*DOI: http://dx.doi.org/10.5772/intechopen.87145*

*Serum Metabolomics as a Powerful Tool in Distinguishing Trauma from Other Critical Illness… DOI: http://dx.doi.org/10.5772/intechopen.87145*

**Figure 3.** *Loadings plot of PC1 vs. PC2 for serum samples described in Table 1. Loadings values are shown in Table 2.*

#### **Figure 4.**

*Metabolomics - New Insights into Biology and Medicine*

*Scores plot of PC1 vs. PC2 for serum samples described in Table 1. Samples are colored by diagnosis.*

*Scores plot of PC2 vs. PC3 for serum samples described in Table 1. Samples are colored by diagnosis. These two* 

*principal components most clearly distinguish trauma samples from non-trauma samples.*

**64**

**Figure 2.**

**Figure 1.**

*Loadings plot of PC2 vs. PC3 for serum samples described in Table 1. Loadings values are shown in Table 2, and the magnitude of the loadings vector spanned by PC2 and PC3 is calculated. Metabolites most associated with trauma occupy the lower right quadrant.*

**Figure 5.**

*Biplot of the first three principal components for serum samples described in Table 1. Samples are colored by diagnosis.*

purple, or magenta) from the healthy controls (gray or black) and the patients with other conditions (blue or light blue).

The loadings vectors for the first three principal components are reported in **Table 2**. Since PC2 and PC3 can be used to discriminate trauma samples from nontrauma samples, we used the loadings of these components to identify the metabolites most associated with trauma. To do this, we calculated the magnitude of the vector spanned by PC2 and PC3, shown in column 4 of **Table 2**. The 10 metabolites with the largest magnitude in the PC loadings are acetoacetate, 3-hydroxybutyrate, trimethylamine N-oxide, 2-hydroxybutyrate, isobutyrate, adipate, lactate, hypoxanthine, glutamate, and alanine. These metabolites reflect disruptions to energy metabolism and oxidative stress.

#### **4. Meta-analysis discussion**

Our metabolomics studies can be united under a common theme: all were done in conditions that are common causes for admission to the ICU. Because sample preparation protocol is the same for all our serum-based NMR metabolomics

**67**

**Table 2.**

disruptions to energy metabolism.

*Serum Metabolomics as a Powerful Tool in Distinguishing Trauma from Other Critical Illness…*

studies, we performed a meta-analysis of the metabolic profiles obtained from these previously published studies [12–15]. Our results suggest that samples from trauma patients are distinguishable from healthy controls and patients with respiratory failure or acute pancreatitis. Principal components 2 and 3 can be used to separate trauma patients' samples from other samples, and highlight oxidative stress and

 *+ (PC3 Loading)2*

*] (1/2)*

*PC2 and PC3 show a clear separation between samples from trauma patients and samples from other research participants. The table is sorted according to the magnitude of the loadings vectors in PC2 and PC3 (column 4).* 

*PC2, principal component 2; PC3, principal component 3; TMAO, trimethylamine N-oxide.*

*Principal component loadings for the first three components for all profiled metabolites.*

*Magnitude of PC2 and PC3 was calculated as follows: [(PC2 Loading)2*

Traumatic injury is known to have a profound effect on molecular processes, impacting more than 80% of cellular functions and pathways, earning the moniker "genomic storm" [18]. In light of this, it is unsurprising that our unsupervised

*DOI: http://dx.doi.org/10.5772/intechopen.87145*

*Serum Metabolomics as a Powerful Tool in Distinguishing Trauma from Other Critical Illness… DOI: http://dx.doi.org/10.5772/intechopen.87145*


*PC2 and PC3 show a clear separation between samples from trauma patients and samples from other research participants. The table is sorted according to the magnitude of the loadings vectors in PC2 and PC3 (column 4). Magnitude of PC2 and PC3 was calculated as follows: [(PC2 Loading)2 + (PC3 Loading)2 ] (1/2) PC2, principal component 2; PC3, principal component 3; TMAO, trimethylamine N-oxide.*

#### **Table 2.**

*Metabolomics - New Insights into Biology and Medicine*

purple, or magenta) from the healthy controls (gray or black) and the patients

*Biplot of the first three principal components for serum samples described in Table 1. Samples are colored by* 

The loadings vectors for the first three principal components are reported in **Table 2**. Since PC2 and PC3 can be used to discriminate trauma samples from nontrauma samples, we used the loadings of these components to identify the metabolites most associated with trauma. To do this, we calculated the magnitude of the vector spanned by PC2 and PC3, shown in column 4 of **Table 2**. The 10 metabolites with the largest magnitude in the PC loadings are acetoacetate, 3-hydroxybutyrate, trimethylamine N-oxide, 2-hydroxybutyrate, isobutyrate, adipate, lactate, hypoxanthine, glutamate, and alanine. These metabolites reflect disruptions to energy

Our metabolomics studies can be united under a common theme: all were done in conditions that are common causes for admission to the ICU. Because sample preparation protocol is the same for all our serum-based NMR metabolomics

with other conditions (blue or light blue).

metabolism and oxidative stress.

**4. Meta-analysis discussion**

**66**

**Figure 5.**

*diagnosis.*

*Principal component loadings for the first three components for all profiled metabolites.*

studies, we performed a meta-analysis of the metabolic profiles obtained from these previously published studies [12–15]. Our results suggest that samples from trauma patients are distinguishable from healthy controls and patients with respiratory failure or acute pancreatitis. Principal components 2 and 3 can be used to separate trauma patients' samples from other samples, and highlight oxidative stress and disruptions to energy metabolism.

Traumatic injury is known to have a profound effect on molecular processes, impacting more than 80% of cellular functions and pathways, earning the moniker "genomic storm" [18]. In light of this, it is unsurprising that our unsupervised

analysis would separate trauma samples from non-trauma samples. In our own work evaluating metabolomes of trauma patients age 21–40 years and trauma patients older than 65 years, we found a clear difference between metabolic profiles of younger healthy controls and older healthy controls. However, the data forced us to reject our hypothesis that metabolomes of older trauma patients would be distinguishable from younger trauma patients [13]. One interpretation of these data is that trauma deals a massive insult to metabolism that completely overtakes any baseline differences in metabolism caused by age.

Trauma from unintentional injury is the most common cause of death for persons age 44 and under [19]. Treatment of traumatic injury remains limited to supportive care such as stopping any bleeding and giving fluids to resuscitate. Lacking specific therapies for traumatic injury, early treatment is a key to improving survival. Metabolomics has already been successfully used to identify succinate, an objective biomarker of mortality, to improve triage [20–22]. However, new technology needs to be developed to bring succinate detection and quantification to the clinic.

#### **5. Improving patient monitoring with metabolomics**

It may be surprising that NMR, with its relatively low resolution, can discriminate metabolic profiles of trauma patients from others. However, this technique does not reflect the extremely variable, highly individualized nature of critical illness. Improving the sensitivity of metabolite detection with mass spectrometry is required to highlight these features of critical illness.

In a preliminary study (manuscript in preparation), we used mass spectrometry to generate metabolic profiles of five ICU patients and five healthy controls. Samples were collected every 4 h for a period of 24 h. A standard methanol/acetone protocol was used to extract metabolites. A Q Exactive™ Quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA) was employed for mass analysis. Analysis was performed in positive mode over a mass range of 70–1050 m/z. Spectra were aligned and processed with Progenesis QI software (Nonlinear Dynamics, Durham, NC).

Spectral intensities of 15,000 features identified by Progenesis QI were logtransformed and principal components analysis was performed using R software. The resulting scores are shown in **Figure 6**. Scores were colored by participant. A single, relatively tight cluster of healthy controls is clearly visible in the upper left quadrant of **Figure 6** (HC01-HC05, colored blue, green, and pink). Strikingly, each ICU patient (ICU01-IC05, red, orange, and purple) is clearly visible, and each patient forms its own unique cluster. Interestingly, the ICU patient colored in red was demonstrably less sick than the other patients, with a lower APACHE II (acute physiology and chronic health evaluation) score and a shorter ICU length of stay. It is likely that the sampling frequency combined with the sensitivity of mass spectrometry allowed us to see such highly individualized patterns in the metabolic profiles.

Based on these data, we posit that mass spectrometry-based metabolomics offers a unique way to characterize the highly individual, highly variable nature of critical illness. The PCA scores in **Figure 6** further offer the tantalizing suggestion that metabolic profiles reflect severity of illness, since the scores of patient with the lower APACHE II score and shorter ICU stay were closest to the scores of the healthy controls. We further hypothesize that, tracked over time, principal component analysis of individual patients' metabolic profiles could offer insight

**69**

dysfunction syndrome [23].

*metabolic profiles in critical illness.*

**Figure 6.**

metabolites that drive the patterns observed here.

*Serum Metabolomics as a Powerful Tool in Distinguishing Trauma from Other Critical Illness…*

into their clinical courses, moving farther away from a "healthy" profile as conditions worsen and closer to a "healthy" profile as conditions improve. Others have used principal component analysis of circulating inflammatory cytokines in a similar way, to identify individual patients who go on to develop multiple organ

*Scores plot of the first two principal components, constructed from metabolic profiles of five ICU patients and five healthy controls. Samples are colored by individual study subject. Subjects HC01 through HC05 (top left quadrant) are healthy controls. Subjects ICU01-ICU05 are ICU patients. Samples were collected in each participant every 4 h for 24 h. PCA clearly illustrates the extreme variability and individuality of* 

This line of inquiry is not without challenges. Collecting samples frequently around the clock from human study participants is a challenge, and a substantial sample bank will have to be obtained to establish a "healthy" metabolic profile. Mass spectrometry results in a large, extremely rich data set of features which are difficult to map to individual metabolites, so it is difficult to identify the set of

Finally, patients will have to be monitored over time and their metabolic profiles

New technology needs to be developed to bring metabolomics to the bedside if it is to be used to track patient trajectories in a clinically useful manner. In the mean-

time, much can be learned about critical illness from metabolomics.

will have to be mapped to their outcomes in order to link their "trajectories" to outcomes or adverse events. This may be a daunting task. However, others have successfully established continuous predictive analytics monitoring from physiologic data in neonatal ICUs [24]. Continuous predictive analytics monitoring allows ICU staff to follow patient trajectories that serve as an early-warning system for sepsis [25], allowing for earlier treatment before inflammation and infection worsen. Since, as with trauma, early intervention is a key to survival from sepsis, bringing predictive monitoring to the ICU is a clear way to improve patient outcomes.

*DOI: http://dx.doi.org/10.5772/intechopen.87145*

*Serum Metabolomics as a Powerful Tool in Distinguishing Trauma from Other Critical Illness… DOI: http://dx.doi.org/10.5772/intechopen.87145*

#### **Figure 6.**

*Metabolomics - New Insights into Biology and Medicine*

baseline differences in metabolism caused by age.

**5. Improving patient monitoring with metabolomics**

required to highlight these features of critical illness.

(Nonlinear Dynamics, Durham, NC).

the clinic.

analysis would separate trauma samples from non-trauma samples. In our own work evaluating metabolomes of trauma patients age 21–40 years and trauma patients older than 65 years, we found a clear difference between metabolic profiles of younger healthy controls and older healthy controls. However, the data forced us to reject our hypothesis that metabolomes of older trauma patients would be distinguishable from younger trauma patients [13]. One interpretation of these data is that trauma deals a massive insult to metabolism that completely overtakes any

Trauma from unintentional injury is the most common cause of death for persons age 44 and under [19]. Treatment of traumatic injury remains limited to supportive care such as stopping any bleeding and giving fluids to resuscitate. Lacking specific therapies for traumatic injury, early treatment is a key to improving survival. Metabolomics has already been successfully used to identify succinate, an objective biomarker of mortality, to improve triage [20–22]. However, new technology needs to be developed to bring succinate detection and quantification to

It may be surprising that NMR, with its relatively low resolution, can discriminate metabolic profiles of trauma patients from others. However, this technique does not reflect the extremely variable, highly individualized nature of critical illness. Improving the sensitivity of metabolite detection with mass spectrometry is

In a preliminary study (manuscript in preparation), we used mass spectrometry to generate metabolic profiles of five ICU patients and five healthy controls. Samples were collected every 4 h for a period of 24 h. A standard methanol/acetone protocol was used to extract metabolites. A Q Exactive™ Quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA) was employed for mass analysis. Analysis was performed in positive mode over a mass range of 70–1050 m/z. Spectra were aligned and processed with Progenesis QI software

Spectral intensities of 15,000 features identified by Progenesis QI were logtransformed and principal components analysis was performed using R software. The resulting scores are shown in **Figure 6**. Scores were colored by participant. A single, relatively tight cluster of healthy controls is clearly visible in the upper left quadrant of **Figure 6** (HC01-HC05, colored blue, green, and pink). Strikingly, each ICU patient (ICU01-IC05, red, orange, and purple) is clearly visible, and each patient forms its own unique cluster. Interestingly, the ICU patient colored in red was demonstrably less sick than the other patients, with a lower APACHE II (acute physiology and chronic health evaluation) score and a shorter ICU length of stay. It is likely that the sampling frequency combined with the sensitivity of mass spectrometry allowed us to see such highly individualized patterns in the metabolic

Based on these data, we posit that mass spectrometry-based metabolomics offers a unique way to characterize the highly individual, highly variable nature of critical illness. The PCA scores in **Figure 6** further offer the tantalizing suggestion that metabolic profiles reflect severity of illness, since the scores of patient with the lower APACHE II score and shorter ICU stay were closest to the scores of the healthy controls. We further hypothesize that, tracked over time, principal component analysis of individual patients' metabolic profiles could offer insight

**68**

profiles.

*Scores plot of the first two principal components, constructed from metabolic profiles of five ICU patients and five healthy controls. Samples are colored by individual study subject. Subjects HC01 through HC05 (top left quadrant) are healthy controls. Subjects ICU01-ICU05 are ICU patients. Samples were collected in each participant every 4 h for 24 h. PCA clearly illustrates the extreme variability and individuality of metabolic profiles in critical illness.*

into their clinical courses, moving farther away from a "healthy" profile as conditions worsen and closer to a "healthy" profile as conditions improve. Others have used principal component analysis of circulating inflammatory cytokines in a similar way, to identify individual patients who go on to develop multiple organ dysfunction syndrome [23].

This line of inquiry is not without challenges. Collecting samples frequently around the clock from human study participants is a challenge, and a substantial sample bank will have to be obtained to establish a "healthy" metabolic profile. Mass spectrometry results in a large, extremely rich data set of features which are difficult to map to individual metabolites, so it is difficult to identify the set of metabolites that drive the patterns observed here.

Finally, patients will have to be monitored over time and their metabolic profiles will have to be mapped to their outcomes in order to link their "trajectories" to outcomes or adverse events. This may be a daunting task. However, others have successfully established continuous predictive analytics monitoring from physiologic data in neonatal ICUs [24]. Continuous predictive analytics monitoring allows ICU staff to follow patient trajectories that serve as an early-warning system for sepsis [25], allowing for earlier treatment before inflammation and infection worsen. Since, as with trauma, early intervention is a key to survival from sepsis, bringing predictive monitoring to the ICU is a clear way to improve patient outcomes.

New technology needs to be developed to bring metabolomics to the bedside if it is to be used to track patient trajectories in a clinically useful manner. In the meantime, much can be learned about critical illness from metabolomics.

### **6. Conclusions**

Critical illness encompasses a variety of life-threatening conditions characterized by the need for frequent, intensive interventions. Patients are heterogeneous and may not respond to treatments in a predictable way; further, their conditions can change quickly over time. Metabolomics, reflective of the phenome, has great potential to impact patient care. NMR-based metabolomics highlights trauma as having a unique impact on the metabolome relative to healthy controls and other conditions. Mass spectrometry, with its increased sensitivity over NMR, highlights an extremely individualized variation in the metabolomes of ICU patients that does not exist in healthy controls. With technological innovations to bring metabolomics to the bedside, it may be used in the future to bring predictive analytics to the ICU, leading to faster and more appropriately individualized interventions, and improving patient care and outcomes.

### **Acknowledgements**

Dr. Lusczek would like to acknowledge Dr. Greg Beilman and Dr. Sayeed Ikramuddin of the University of Minnesota, Department of Surgery. NMR instrumentation was provided by the Minnesota NMR Center. Funding for NMR instrumentation was provided by the Office of the Vice President for Research, the Medical School, the College of Biological Science, NIH, NSF, and the Minnesota Medical Foundation. Mass spectrometry metabolomics data were obtained by the University of Minnesota's Center for Mass Spectrometry and Proteomics.

### **Conflict of interest**

Dr. Lusczek is on the board of directors of the Society for Complex Acute Illness.

#### **Author details**

Elizabeth R. Lusczek Department of Surgery, University of Minnesota, Minneapolis, MN, USA

\*Address all correspondence to: lusc0006@umn.edu

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**71**

*Serum Metabolomics as a Powerful Tool in Distinguishing Trauma from Other Critical Illness…*

Kiehntopf M. Targeted metabolomics

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[10] Antcliffe D, Gordon AC. Metabonomics and intensive care. Critical Care. 2016;**20**(1):68

[11] Rogers AJ, McGeachie M, Baron RM, Gazourian L, Haspel JA, Nakahira K, et al. Metabolomic derangements are associated with mortality in critically ill adult patients. PLoS One.

[12] Lusczek ER, Muratore SL, Dubick MA, Beilman GJ. Assessment of key plasma metabolites in combat casualties. Journal of Trauma and Acute Care Surgery. 2017;**82**(2):309-316

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*DOI: http://dx.doi.org/10.5772/intechopen.87145*

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#### **References**

*Metabolomics - New Insights into Biology and Medicine*

Critical illness encompasses a variety of life-threatening conditions characterized by the need for frequent, intensive interventions. Patients are heterogeneous and may not respond to treatments in a predictable way; further, their conditions can change quickly over time. Metabolomics, reflective of the phenome, has great potential to impact patient care. NMR-based metabolomics highlights trauma as having a unique impact on the metabolome relative to healthy controls and other conditions. Mass spectrometry, with its increased sensitivity over NMR, highlights an extremely individualized variation in the metabolomes of ICU patients that does not exist in healthy controls. With technological innovations to bring metabolomics to the bedside, it may be used in the future to bring predictive analytics to the ICU, leading to faster and more appropriately individualized interventions, and improv-

Dr. Lusczek would like to acknowledge Dr. Greg Beilman and Dr. Sayeed Ikramuddin of the University of Minnesota, Department of Surgery. NMR instrumentation was provided by the Minnesota NMR Center. Funding for NMR instrumentation was provided by the Office of the Vice President for Research, the Medical School, the College of Biological Science, NIH, NSF, and the Minnesota Medical Foundation. Mass spectrometry metabolomics data were obtained by the

University of Minnesota's Center for Mass Spectrometry and Proteomics.

Department of Surgery, University of Minnesota, Minneapolis, MN, USA

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: lusc0006@umn.edu

provided the original work is properly cited.

Dr. Lusczek is on the board of directors of the Society for Complex Acute Illness.

**6. Conclusions**

ing patient care and outcomes.

**Acknowledgements**

**Conflict of interest**

**Author details**

Elizabeth R. Lusczek

**70**

[1] Coopersmith CM, Wunsch H, Fink MP, Linde-Zwirble WT, Olsen KM, Sommers MS, et al. A comparison of critical care research funding and the financial burden of critical illness in the United States. Critical Care Medicine. 2012;**40**(4):1072-1079

[2] Rawal G, Yadav S, Kumar R. Postintensive care syndrome: An overview. Journal of Translational Internal Medicine. 2017;**5**(2):90-92

[3] Elliott D, Davidson JE, Harvey MA, Bemis-Dougherty A, Hopkins RO, Iwashyna TJ, et al. Exploring the scope of post-intensive care syndrome therapy and care: Engagement of non-critical care providers and survivors in a second stakeholders meeting. Critical Care Medicine. 2014;**42**(12):2518-2526

[4] Hoffman LA. Post intensive care syndrome: Risk factors and prevention strategies. Critical Care Alert. 2015;**22**(12):89-93

[5] Society of Critical Care Medicine. Critical Care Statistics [Internet]. Available from: https://www.sccm. org/Communications/Critical-Care-Statistics [Accessed: 27 March 2019]

[6] Sweeney TE, Khatri P. Generalizable biomarkers in critical care: Toward precision medicine. Critical Care Medicine. 2017;**45**(6):934

[7] Maslove DM, Lamontagne F, Marshall JC, Heyland DK. A path to precision in the ICU. Critical Care. 2017;**21**(1):79

[8] Seymour CW, Gomez H, Chang C-CH, Clermont G, Kellum JA, Kennedy J, et al. Precision medicine for all? Challenges and opportunities for a precision medicine approach to critical illness. Critical Care. 2017;**21**(1):257

[9] Schmerler D, Neugebauer S, Ludewig K, Bremer-Streck S, Brunkhorst FM,

Kiehntopf M. Targeted metabolomics for discrimination of systemic inflammatory disorders in critically ill patients. Journal of Lipid Research. 2012;**53**(7):1369-1375

[10] Antcliffe D, Gordon AC. Metabonomics and intensive care. Critical Care. 2016;**20**(1):68

[11] Rogers AJ, McGeachie M, Baron RM, Gazourian L, Haspel JA, Nakahira K, et al. Metabolomic derangements are associated with mortality in critically ill adult patients. PLoS One. 2014;**9**(1):e87538

[12] Lusczek ER, Muratore SL, Dubick MA, Beilman GJ. Assessment of key plasma metabolites in combat casualties. Journal of Trauma and Acute Care Surgery. 2017;**82**(2):309-316

[13] Lusczek ER, Myers C, Popovsky K, Mulier K, Beilman G, Sawyer R. Plasma metabolomics pilot study suggests age and sex-based differences in the metabolic response to traumatic injury. Injury. 2018;**49**(12):2178-2185

[14] Lusczek ER, Colling K, Muratore S, Conwell D, Freeman M, Beilman G. Stereotypical metabolic response to endoscopic retrograde cholangiopancreatography show alterations in pancreatic function regardless of post-procedure pancreatitis. Clinical and Translational Gastroenterology. 2016;**7**(5):e169

[15] Fortis S, Lusczek ER, Weinert CR, Beilman GJ. Metabolomics in COPD acute respiratory failure requiring noninvasive positive pressure ventilation. Canadian Respiratory Journal. 2017;**2017**:9480346. DOI: 10.1155/2017/9480346. 9pp

[16] Weljie AM, Newton J, Mercier P, Carlson E, Slupsky CM. Targeted profiling: Quantitative analysis of 1 H NMR metabolomics data. Analytical Chemistry. 2006;**78**(13):4430-4442

[17] R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. 2018. Available from: http://www.Rproject.org/

[18] Xiao W, Mindrinos MN, Seok J, Cuschieri J, Cuenca AG, Gao H, et al. A genomic storm in critically injured humans. The Journal of Experimental Medicine. 2011;**208**(13):2581-2590

[19] National Center for Injury Prevention and Control (NCIPC). Web-based Injury Statistics Query and Reporting System. Atlanta, GA: Centers for Disease Control and Prevention. Available from: https://webappa.cdc. gov/sasweb/ncipc/leadcause.html

[20] Witowski NE, Lusczek ER, Determan CE, Lexcen DR, Mulier KE, Wolf A, et al. Metabolomic analysis of survival in carbohydrate pre-fed pigs subjected to shock and polytrauma. Molecular BioSystems. 2016;**12**(5):1638-1652

[21] D'alessandro A, Moore HB, Moore EE, Reisz JA, Wither MJ, Ghasasbyan A, et al. Plasma succinate is a predictor of mortality in critically injured patients. Journal of Trauma and Acute Care Surgery. 2017;**83**(3):491-495

[22] Lexcen DR, Lusczek ER, Witowski NE, Mulier KE, Beilman GJ. Metabolomics classifies phase of care and identifies risk for mortality in a porcine model of multiple injuries and hemorrhagic shock. The Journal of Trauma and Acute Care Surgery. 2012;**73**(2):S147-SS55

[23] Namas RA, Almahmoud K, Mi Q, Ghuma A, Namas R, Zaaqoq A, et al. Individual-specific principal component analysis of circulating inflammatory mediators predicts early organ

dysfunction in trauma patients. Journal of Critical Care. 2016;**36**:146-153

[24] Moss TJ, Lake DE, Calland JF, Enfield KB, Delos JB, Fairchild KD, et al. Signatures of subacute potentially catastrophic illness in the intensive care unit: Model development and validation. Critical Care Medicine. 2016;**44**(9):1639

[25] Keim-Malpass J, Kitzmiller RR, Skeeles-Worley A, Lindberg C, Clark MT, Tai R, et al. Advancing continuous predictive analytics monitoring: Moving from implementation to clinical action in a learning health system. Critical Care Nursing Clinics of North America. 2018;**30**(2):273-287

**73**

**Chapter 5**

**Abstract**

treatment.

**1. Introduction**

Perspective

Chinese Medicines for Cancer

*Wei Guo, Hor-Yue Tan, Ning Wang and Yibin Feng*

Treatment from the Metabolomics

Cancer is one of the most prevalent diseases all over the world with poor prognosis and the development of novel therapeutic strategies is still urgently needed. The large amount of successful experiences in fighting against cancer-like diseases with Chinese medicine has suggested it as a great source of alternative treatments to human cancers. Cancer cells have been shown to own a predominantly unique metabolic phenotype to facilitate their rapid proliferation. Metabolic reprogramming is a remarkable hallmark of cancer and therapies targeting cancer metabolism can be highly specific and effective. Based on the sophisticated study of small molecule metabolites, metabolomics can provide us valuable information on dynamically metabolic responses of living systems to certain environmental condition. In this chapter, we systematically reviewed recent studies on metabolism-targeting anticancer therapies based on metabolomics in terms of glucose, lipid, amino acid, and nucleotide metabolisms and other altered metabolisms, with special emphasis on the potential of metabolic treatment with pure compounds, herb extracts, and formulations from Chinese medicines. The trends of future development of metabolism-targeting anticancer therapies were also discussed. Overall, the elucidation of the underlying molecular mechanism of metabolism-targeting pharmacologic therapies will provide us a new insight to develop novel therapeutics for cancer

**Keywords:** metabolomics, cancer metabolism, adjuvant therapies, Chinese medicines

Despite all recent improvements in early detection and pleiotropic therapeutics, cancer is still the leading cause of death all over the world [1]. It is one of the most prevalent diseases with complex risk factors, and the mortality rate is similar to its morbidity, which reflects its poor prognosis. It has been projected that approximately 3.12 million new cases of cancer and a cancer death toll of 2.5 million will occur per year in China, which brings a huge burden on society [2]. To date, there are three conventional cancer therapies for cancer, including surgical resection, chemotherapy, and radiotherapy. However, diverse drawbacks and limitations have been observed in these cancer therapies either alone or in combination. For example, most cancer patients are not suitable to undergo the surgical resection due

#### **Chapter 5**

*Metabolomics - New Insights into Biology and Medicine*

dysfunction in trauma patients. Journal of Critical Care. 2016;**36**:146-153

[24] Moss TJ, Lake DE, Calland JF, Enfield KB, Delos JB, Fairchild KD, et al. Signatures of subacute potentially catastrophic illness in the intensive care unit: Model development and validation. Critical Care Medicine.

[25] Keim-Malpass J, Kitzmiller RR, Skeeles-Worley A, Lindberg C, Clark MT, Tai R, et al. Advancing continuous predictive analytics monitoring: Moving from implementation to clinical action in a learning health system. Critical Care Nursing Clinics of North America.

2016;**44**(9):1639

2018;**30**(2):273-287

NMR metabolomics data. Analytical Chemistry. 2006;**78**(13):4430-4442

[17] R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. 2018. Available from: http://www.R-

[18] Xiao W, Mindrinos MN, Seok J, Cuschieri J, Cuenca AG, Gao H, et al. A genomic storm in critically injured humans. The Journal of Experimental Medicine. 2011;**208**(13):2581-2590

[19] National Center for Injury Prevention and Control (NCIPC). Web-based Injury Statistics Query and Reporting System. Atlanta, GA: Centers for Disease Control and Prevention. Available from: https://webappa.cdc. gov/sasweb/ncipc/leadcause.html

[20] Witowski NE, Lusczek ER, Determan CE, Lexcen DR, Mulier KE, Wolf A, et al. Metabolomic analysis of survival in carbohydrate pre-fed pigs subjected to shock and polytrauma. Molecular BioSystems.

[21] D'alessandro A, Moore HB, Moore EE, Reisz JA, Wither MJ, Ghasasbyan A, et al. Plasma succinate is a predictor of mortality in critically injured patients. Journal of Trauma and Acute Care Surgery. 2017;**83**(3):491-495

[22] Lexcen DR, Lusczek ER, Witowski

Metabolomics classifies phase of care and identifies risk for mortality in a porcine model of multiple injuries and hemorrhagic shock. The Journal of Trauma and Acute Care Surgery.

[23] Namas RA, Almahmoud K, Mi Q, Ghuma A, Namas R, Zaaqoq A, et al. Individual-specific principal component analysis of circulating inflammatory mediators predicts early organ

NE, Mulier KE, Beilman GJ.

2012;**73**(2):S147-SS55

2016;**12**(5):1638-1652

project.org/

**72**

## Chinese Medicines for Cancer Treatment from the Metabolomics Perspective

*Wei Guo, Hor-Yue Tan, Ning Wang and Yibin Feng*

### **Abstract**

Cancer is one of the most prevalent diseases all over the world with poor prognosis and the development of novel therapeutic strategies is still urgently needed. The large amount of successful experiences in fighting against cancer-like diseases with Chinese medicine has suggested it as a great source of alternative treatments to human cancers. Cancer cells have been shown to own a predominantly unique metabolic phenotype to facilitate their rapid proliferation. Metabolic reprogramming is a remarkable hallmark of cancer and therapies targeting cancer metabolism can be highly specific and effective. Based on the sophisticated study of small molecule metabolites, metabolomics can provide us valuable information on dynamically metabolic responses of living systems to certain environmental condition. In this chapter, we systematically reviewed recent studies on metabolism-targeting anticancer therapies based on metabolomics in terms of glucose, lipid, amino acid, and nucleotide metabolisms and other altered metabolisms, with special emphasis on the potential of metabolic treatment with pure compounds, herb extracts, and formulations from Chinese medicines. The trends of future development of metabolism-targeting anticancer therapies were also discussed. Overall, the elucidation of the underlying molecular mechanism of metabolism-targeting pharmacologic therapies will provide us a new insight to develop novel therapeutics for cancer treatment.

**Keywords:** metabolomics, cancer metabolism, adjuvant therapies, Chinese medicines

#### **1. Introduction**

Despite all recent improvements in early detection and pleiotropic therapeutics, cancer is still the leading cause of death all over the world [1]. It is one of the most prevalent diseases with complex risk factors, and the mortality rate is similar to its morbidity, which reflects its poor prognosis. It has been projected that approximately 3.12 million new cases of cancer and a cancer death toll of 2.5 million will occur per year in China, which brings a huge burden on society [2]. To date, there are three conventional cancer therapies for cancer, including surgical resection, chemotherapy, and radiotherapy. However, diverse drawbacks and limitations have been observed in these cancer therapies either alone or in combination. For example, most cancer patients are not suitable to undergo the surgical resection due to the late diagnosis and other factors. As the major therapies for cancer patients in middle and advanced stages, chemotherapy and radiotherapy have been shown to present serious side effects and complications, such as myelosuppression, hematological toxicity, cardiac damage, and liver and kidney dysfunction [1, 3]. Moreover, tumor cells have the ability to develop resistance to evade cell death, and the therapeutic efficacy of the current chemotherapeutic drugs is significantly reduced by the increasingly acquired drug resistance [4]. Therefore, it imminently deserves to develop more effective and less toxic adjuvant therapies for cancer prevention and treatment.

#### **1.1 Cancer metabolism**

It has been reported that cell metabolism has an essential role in the pathological progression of cancer and metabolic reprogramming is a remarkable hallmark of cancer [5]. Cancer cells have been shown to own a predominantly unique metabolic phenotype to facilitate their rapid proliferation, which is dramatically different from normal cells. Cancer cells tend to acquire energy via glycolysis rather than the much more efficient oxidative phosphorylation pathway even in aerobic conditions, which is the famous phenomenon of cancer called the "Warburg effect" [6]. Besides the consumption of glucose, cancer cells have also been reported to favor glutamine as a preferential fuel [7]. Accumulating evidences indicate that mutations in metabolic enzymes can promote the development of cancer. For example, mutations in the tricarboxylic acid (TCA) cycle enzyme isocitrate dehydrogenase, succinate dehydrogenase, and fumarate hydratase can affect the corresponding metabolites a-ketoglutarate, succinate, and fumarate. These changes can further affect the 2-oxoglutarate-dependent dioxygenases and then result in some cancers, such as paraganglioma and renal cell cancer [8–10]. What is more, the drug resistance of cancer cells is also shown to be associated with their metabolic alterations [11]. In this perspective, cancer metabolism has become a potentially fertile area, and therapies targeting cancer metabolism can be highly specific and effective. Nowadays metabolism-targeting anticancer therapies are drawing researchers' great attention and becoming a new therapeutics for cancer treatment [12].

#### **1.2 Metabolomics and cancer**

As a valuable complement to emerging "omics" science including genomics, transcriptomics, and proteomics, metabolomics utilizes leading-edge analytical chemistry technologies and advanced computational approaches to characterize the small endogenous and exogenous molecule metabolites in various biochemical metabolisms from complex biochemical mixtures [13]. Metabolomics can provide us a direct readout on dynamically metabolic responses of living systems to certain genetic modifications or pathophysiological stimuli [14], which has been extensively adopted in the field of disease diagnosis, pharmacodynamic evaluation, therapeutical monitoring, and drug discovery [15]. There are three main analytical chemistry platforms in metabolomics research, namely, nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and gas chromatography MS (GC/MS). Each platform has its own strengths and limitations. There are three main methodological approaches to analyze the small metabolites in metabolomics, namely, targeted, untargeted, and stable isotoperesolved metabolomics (SIRM). Numerous systemic reviews have shown in detail how each analytical platform and methodological approach works in metabolomics studies [16–20].

**75**

**Figure 1.**

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective*

As mentioned above, cell metabolism has an essential role in the pathological progression of cancer, and metabolic reprogramming is a remarkable hallmark of cancer. In this context, it would be conducive to employ metabolomics in the field of cancer research for exploration of tumorigenesis mechanisms, diagnosis and monitoring of tumor, as well as discovery of novel anticancer therapies

Due to their various biological activities and low toxicity, natural products derived from Chinese medicines are reported to be an excellent source for anticancer drugs as a complementary and alternative approach [24]. Chinese medicines have evolved with their own unique theoretical system in Asian countries, especially China over thousands of years. Chinese medicines are usually divided into pure compounds, herb extracts, and formulations. Formulations from Chinese medicines are extensively employed in Chinese hospitals for clinical cancer treatment [25]. Numerous Chinese herb extracts have been reported to show inhibitory effects on cancers [26]. An increasing number of pure compounds derived from Chinese medicine herbs have been shown to inhibit the development of cancers through various mechanisms [27–30]. Besides, a large number of studies have revealed that Chinese medicines in combination with conventional chemotherapy and radiotherapy could increase the therapeutic efficacy and decrease the serious side effects and complications of these therapies [31, 32]. It is convinced that Chinese medicines are gaining increasing reputation and credibility as adjuvant therapies for cancer

Although Chinese medicines have been employed in cancer prevention and treatment for a long time, the underlying mechanisms on how they work remain to be fully elucidated because of their unique medical system with multicomponent nature. In accordance with the holistic perspective of Chinese medicines, metabolomics opens up a unique and novel insight into efficacy evaluation and action mechanism exploration of Chinese medicines as adjuvant therapies for cancer prevention

*The typical flowchart of metabolomics studies on antineoplastic Chinese medicines.*

*DOI: http://dx.doi.org/10.5772/intechopen.88924*

**1.3 Chinese medicines and cancer treatment**

prevention and treatment.

and treatment.

[21–23].

As mentioned above, cell metabolism has an essential role in the pathological progression of cancer, and metabolic reprogramming is a remarkable hallmark of cancer. In this context, it would be conducive to employ metabolomics in the field of cancer research for exploration of tumorigenesis mechanisms, diagnosis and monitoring of tumor, as well as discovery of novel anticancer therapies [21–23].

#### **1.3 Chinese medicines and cancer treatment**

*Metabolomics - New Insights into Biology and Medicine*

and treatment.

**1.1 Cancer metabolism**

therapeutics for cancer treatment [12].

**1.2 Metabolomics and cancer**

to the late diagnosis and other factors. As the major therapies for cancer patients in middle and advanced stages, chemotherapy and radiotherapy have been shown to present serious side effects and complications, such as myelosuppression, hematological toxicity, cardiac damage, and liver and kidney dysfunction [1, 3]. Moreover, tumor cells have the ability to develop resistance to evade cell death, and the therapeutic efficacy of the current chemotherapeutic drugs is significantly reduced by the increasingly acquired drug resistance [4]. Therefore, it imminently deserves to develop more effective and less toxic adjuvant therapies for cancer prevention

It has been reported that cell metabolism has an essential role in the pathological progression of cancer and metabolic reprogramming is a remarkable hallmark of cancer [5]. Cancer cells have been shown to own a predominantly unique metabolic phenotype to facilitate their rapid proliferation, which is dramatically different from normal cells. Cancer cells tend to acquire energy via glycolysis rather than the much more efficient oxidative phosphorylation pathway even in aerobic conditions, which is the famous phenomenon of cancer called the "Warburg effect" [6]. Besides the consumption of glucose, cancer cells have also been reported to favor glutamine as a preferential fuel [7]. Accumulating evidences indicate that mutations in metabolic enzymes can promote the development of cancer. For example, mutations in the tricarboxylic acid (TCA) cycle enzyme isocitrate dehydrogenase, succinate dehydrogenase, and fumarate hydratase can affect the corresponding metabolites a-ketoglutarate, succinate, and fumarate. These changes can further affect the 2-oxoglutarate-dependent dioxygenases and then result in some cancers, such as paraganglioma and renal cell cancer [8–10]. What is more, the drug resistance of cancer cells is also shown to be associated with their metabolic alterations [11]. In this perspective, cancer metabolism has become a potentially fertile area, and therapies targeting cancer metabolism can be highly specific and effective. Nowadays metabolism-targeting anticancer therapies are drawing researchers' great attention and becoming a new

As a valuable complement to emerging "omics" science including genomics, transcriptomics, and proteomics, metabolomics utilizes leading-edge analytical chemistry technologies and advanced computational approaches to characterize the small endogenous and exogenous molecule metabolites in various biochemical metabolisms from complex biochemical mixtures [13]. Metabolomics can provide us a direct readout on dynamically metabolic responses of living systems to certain genetic modifications or pathophysiological stimuli [14], which has been extensively adopted in the field of disease diagnosis, pharmacodynamic evaluation, therapeutical monitoring, and drug discovery [15]. There are three main analytical chemistry platforms in metabolomics research, namely, nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and gas chromatography MS (GC/MS). Each platform has its own strengths and limitations. There are three main methodological approaches to analyze the small metabolites in metabolomics, namely, targeted, untargeted, and stable isotoperesolved metabolomics (SIRM). Numerous systemic reviews have shown in detail how each analytical platform and methodological approach works in metabolomics

**74**

studies [16–20].

Due to their various biological activities and low toxicity, natural products derived from Chinese medicines are reported to be an excellent source for anticancer drugs as a complementary and alternative approach [24]. Chinese medicines have evolved with their own unique theoretical system in Asian countries, especially China over thousands of years. Chinese medicines are usually divided into pure compounds, herb extracts, and formulations. Formulations from Chinese medicines are extensively employed in Chinese hospitals for clinical cancer treatment [25]. Numerous Chinese herb extracts have been reported to show inhibitory effects on cancers [26]. An increasing number of pure compounds derived from Chinese medicine herbs have been shown to inhibit the development of cancers through various mechanisms [27–30]. Besides, a large number of studies have revealed that Chinese medicines in combination with conventional chemotherapy and radiotherapy could increase the therapeutic efficacy and decrease the serious side effects and complications of these therapies [31, 32]. It is convinced that Chinese medicines are gaining increasing reputation and credibility as adjuvant therapies for cancer prevention and treatment.

Although Chinese medicines have been employed in cancer prevention and treatment for a long time, the underlying mechanisms on how they work remain to be fully elucidated because of their unique medical system with multicomponent nature. In accordance with the holistic perspective of Chinese medicines, metabolomics opens up a unique and novel insight into efficacy evaluation and action mechanism exploration of Chinese medicines as adjuvant therapies for cancer prevention and treatment.

#### **Figure 1.**

*The typical flowchart of metabolomics studies on antineoplastic Chinese medicines.*


**77**

**References**

28,651,973

1,25-Dihydroxyvitamin D3

Prostate cancer

LNCaP cells in vitro

GC/ MS

26,541,605 28,916,726 28,737,429 28,918,937

Koningic acid

Colorectal cancer

HCT116 cells in vitro

Integrated

pharmacogenomics

and LC-HRMS

metabolomics

Diethylstilbestrol

Prostate cancer

PC3 cells in vitro

1H NMR

β-Lapachone

Pancreatic ductal

MiaPaCa2 cells in vitro

GC/MS and 1H NMR

β-lap treatment was found to

decrease the NAD-sensitive

pathways, such as glycolysis

and TCA cycle

Lactate, phosphocreatine, and

DES upon conjugation

had a more specific effect

and less toxicity

KA efficacy is not

determined by the status

of individual genes but

by the quantitative extent

of the WE, leading to

a therapeutic window

in vivo

GSH were the biomarkers for

DES treatment

Glycolysis was the highest

scoring pathway only in

KA-treated cells

adenocarcinoma

Vitamin C

Colorectal cancer

KRAS and BRAF mutant lines and isogenic wild-

LC-MS/MS

High levels of vitamin C increased uptake of dehydroascorbic acid (DHA) and decreased glutathione

type counterparts in vitro

**Pure compound**

**Cancer**

**Study**

**Method**

**Significantly changed metabolites or pathways**

**Main findings**

1,25(OH)2D3 decreased glucose uptake and increased citrate/isocitrate due to TCA cycle truncation

Re-wiring glucose metabolizing pathways, and induction of a "differentiated" metabolic phenotype by 1,25(OH)2D3, may prove clinically beneficial

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective*

These results provide a mechanistic rationale for exploring the therapeutic use of vitamin C for CRCs with KRAS or BRAF mutations

Targeting NQO1 may

sensitize the treatment

of β-lap

*DOI: http://dx.doi.org/10.5772/intechopen.88924*


#### *Chinese Medicines for Cancer Treatment from the Metabolomics Perspective DOI: http://dx.doi.org/10.5772/intechopen.88924*

*Metabolomics - New Insights into Biology and Medicine*

**76**

**References**

26,700,591 26,160,839 29,589,762 29,802,724 30,391,728

Magnoline

Prostate cancer

22RV1 cells in vitro

UPLC-MS

Nummularic acid (NA)

Prostate cancer

DU-145 and C4-2 cells

ALEX-CIS

The metabolism pathways

related to glycolysis, TCA,

and glutamine metabolisms

were changed after NA

treatment

Magnoline markedly restored

Cancer cells may result

in death because of

insufficient material

basis to favor their rapid

proliferation

the energy metabolism,

amino acid metabolism, and

fatty acid metabolism

GC–TOF-MS

in vitro

(PCa)

(−)-5-Hydroxy-equol

Hepatocellular

SMMC-7721 cells in vitro

1H NMR

(−)-5-Hydroxy-equol

treatment significantly

altered energy and amino acid

metabolism

Carcinoma

Halofuginone

Colorectal cancer

HCT116 cells in vitro

UPLC-MS/MS, GC/

Metabolomics delineated the

slower rates in both glycolytic

flux and glucose-derived

tricarboxylic acid cycle flux

MS and UPLC/LTQ-Orbitrap MS

Geranylgeranoic acid

Hepatoma

HuH-7 cells in vitro

UPLC/TOF/MS

**Pure compound**

**Cancer**

**Study**

**Method**

**Significantly changed** 

**Main findings**

**metabolites or pathways**

GGA induced a timedependent increase in the

GGA may shift

HuH-7 cells from

aerobic glycolysis to

mitochondrial respiration

through the immediate

upregulation of TIGAR

and SCO2 protein levels

HF regulates Akt/

mTORC1 signaling

pathway to dampen

glucose uptake and

glycolysis in CRC cells

Integrated metabolomics

and further verifications

may facilitate the

exploration of the

anti-HCC mechanisms of

(−)-5-hydroxy-equol

NA may induce energy

crisis to inhibit PCa

cellular contents of fructose

6-phosphate and decrease of

fructose 1,6-diphosphate


**79**

**References**

26,859,520 28,296,891 28,948,276 28,496,003

29,321,577

8u

Hepatocellular

HepG2 cells in vitro

UPLC/Q-TOF MS

8u was found to significantly

inhibit the invasion and

metastasis of HepG2 cells and

regulate intracellular lipid

metabolism

carcinoma

Peiminine

Colorectal cancer

UPLC-MS and GC/MS

MS

UPLC-MS and GC/

Peiminine treatment altered

Peiminine exerted the

predominant therapeutic

effect mainly via the

metabolic regulation of

lipids, amino acids, and

carbohydrates

8u could efficiently

suppress the invasion

and metastasis of HepG2

cells by decreasing the

expression of HSP90α

protein and inhibiting

the PI3K/Akt signaling

pathway

several metabolites, including

lignocerate (24:0), oleate

(18:1n9), glutamine, and

glucose

Isoquercitrin

Bladder cancer

T24 cells in vitro

UPLC/Q-TOF MS

Isoquercitrin treatment was found to regulate lipid and anaerobic glycolysis

Englerin A

Clear cell renal carcinoma

A498 cells in vitro

LC-MS/MS

Flexibilide

Colon cancer

HCT-116 cells in vitro

UPLC/Q-TOF MS

Flexibilide exhibited the therapeutic effect on colon cancer mainly via downregulating PC biosynthesis pathway

Flexibilide exhibited the therapeutic effect on colon cancer mainly via down-regulating PC biosynthesis pathway

Englerin A significantly reversed lipid metabolism and increase ceramides levels

Ceramides may be a mediator of some of the actions of englerin A

**Pure compound**

**Cancer**

**Study**

**Method**

**Significantly changed metabolites or pathways**

**Main findings**

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective*

ISO influenced T24 bladder cancer cell metabolism, and this process was mainly involved in activating the AMPK pathway

*DOI: http://dx.doi.org/10.5772/intechopen.88924*


#### *Chinese Medicines for Cancer Treatment from the Metabolomics Perspective DOI: http://dx.doi.org/10.5772/intechopen.88924*

*Metabolomics - New Insights into Biology and Medicine*

**78**

**References**

30,114,709 28,198,625 29,448,205

27,416,811 30,322,263

Naringenin

Lung cancer

Physapubenolide

Hepatocellular

HepG2 cells in vitro and

GC/MS

tumor tissues and plasma

from a mouse-xenograft

model bearing liver

carcinoma H22 cells in vivo

Serum from the urethaneinduced lung cancer rat

1H NMR

The glycolysis was restored to

Co-therapy has the

superiority over alone

treatment to improve the

therapeutic efficacy

normal levels with co-therapy

of Gnb and Nar

model in vivo

carcinoma

6,7-Dimethoxy-1,2,3,4-

Colorectal

Serum from DMHinduced CRC albino

1H NMR

M1 exhibited to reverse

the perturbed metabolism

pathways in CRC condition,

including glycolysis,

TCA cycle, choline,

phosphatidylinositol and

gluconeogenesismetabolisms

PB disturbed the metabolic

PB exhibits anticancer

activities through

suppression of glycolysis

via the Akt-p53 pathway

pattern and significantly

decreased lactate production

Wistar rat model in vivo

carcinoma

tetrahydro-iso- quinoline-3-carboxylic acid

Curcumin

Hepatocarcinoma

Serum from DEN-induced

GC/MS

hepatocarcinogenesis

model

Omega-3 polyunsaturated

Breast cancer

MCF7 cells in vitro

GC/MS

fatty acids

**Pure compound**

**Cancer**

**Study**

**Method**

**Significantly changed** 

**Main findings**

**metabolites or pathways**

Glycolysis and glutamine

ω-3 PUFA could increase

the anti-breast cancer

potential of Rp

metabolism pathways were

markedly reduced when

treated with the combination

of Rp and ω-3 PUFAs

Curcumin attenuated

Curcumin exhibited a

potent liver protective

agent inhibiting

chemically induced

liver injury through

suppressing liver cellular

metabolism in the

prospective application

M1 has the anti-CRC

potential via the blockade

of IL-6/JAK2/STAT3

oncogenic signaling

metabolic disorders via

increasing concentration of

glucose and fructose, and

decreasing levels of glycine

and proline


**81**

**References** 30,068,874

27,754,384 28,674,386 29,202,102

Resveratrol, curcumin

Prostate cancer

Serum from a mouse

LC-MS and GC/

Glutamine metabolism was

regulated by the compound

combinations

allograft model of prostate

MS

cancer in vivo

and ursolic acid

Chlorogenic acid and caffeic acid

Hepatocellular carcinoma

Serum from DEN-induced HCC model in vivo

16 S rRNA and LC-MS, GC/MS-MS,

GC/MS

Melittin

Ovarian cancer

A2780 and A2780CR cell lines in vitro

LC-MS

Celastrol

Colon cancer

HCT116 cells in vitro

UPLC/MS

**Pure compound**

**Cancer**

**Study**

**Method**

**Significantly changed metabolites or pathways**

**Main findings**

Metabolomics analysis found celastrol changed the levels of lipid markers, carnitine and amino acids. Tryptophan was further identified as a special biomarker by targeted metabolite analysis

The suppression of IDO expression and tryptophan catabolism may be part of the mechanisms of celastrol in its cytotoxic effect against HCT116 colon cancer cells

Melittin treatment of cisplatin-sensitive cells decreased glutamine, proline, and arginine pathways

Both CaA and ChA treatment

reverse 28 metabolites

The levels of

ethanolamine,

L-methionine,

L-tyrosine, and bilirubin

were associated

with diminished

Prevotella 9 and

Lachnospiraceae incertae

sedis and elevated

Ruminococcaceae

UCG-004

Compared with the

individual treatment, the

combined treatment has

the greater antineoplastic

property

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective*

Melittin might have some potential as an adjuvant therapy in cancer treatment

*DOI: http://dx.doi.org/10.5772/intechopen.88924*


#### *Chinese Medicines for Cancer Treatment from the Metabolomics Perspective DOI: http://dx.doi.org/10.5772/intechopen.88924*

*Metabolomics - New Insights into Biology and Medicine*

**80**

**References**

28,125,641 27,533,043 26,744,170 30,871,192

Delta-tocotrienol

Non-small cell

A549 and H1299 cells

1H-NMR

Cellular metabolomics

analysis showed significant

inhibition in the uptake of

glutamine, its derivatives

glutamate and glutathione,

and some EAAs in both cell

lines with δT treatment

in vitro

lung cancer

Acyclic retinoid

Hepatocellular

Liver tissues from mouse

CE-TOFMS and

ACR predominantly reversed

lipogenesis but not glucose

metabolism by inhibiting

linoleic acid metabolites

LC-TOFMS

DEN-induced HCC model

in vivo

carcinoma

Silibinin

Prostate cancer

Tumor tissues from 22Rv1

1H-NMR

Xenograft model in vivo

Genistein and calcitriol

Osteosarcoma

MG-63 Cells in vitro

GC/MS

**Pure compound**

**Cancer**

**Study**

**Method**

**Significantly changed** 

**Main findings**

**metabolites or pathways**

Co-therapy of genistein

The promotional effects

of high level of genistein

on osteosarcoma could

be decreased by the

co-treatment of calcitriol

These findings further

support silibinin

usefulness against PCa

through inhibiting

hypoxia-induced

signaling

Lipid metabolic

reprogramming plays

a critical role in the

protective effects of ACR

on HCC

δT treatment could

suppress the glutamine

uptake via suppressing

glutamine transporters

and then resulted in the

induction of apoptosis

and suppression of cell

proliferation

and calcitriol was found to

regulate lipids and amino

acids rather than energy

metabolism

Silibinin treatment did not

greatly affect glucose uptake

of PCa tumor but decreased

the lipid synthesis


**83**

**References**

27,335,141 29,978,476

Galactolipid 1,2-di-O-linolenoyl-

Melanoma

Serum from a syngeneic mouse model implanted with B16 melanoma in vivo

LC-MS/MS

3-O- β-galactopyranosyl-

sn-glycerol

> 30,668,340

**Table 1.**

*Summary of recent metabolomic studies on anticancer therapies of pure compounds from Chinese medicines.*

Deoxyelephantopin

Melanoma

Kidney tissues from murine B16 metastatic

UPLC/ESI-QTOF MS

Co-therapy of DET and

cisplatin could reverse

the changed urea cycle

metabolites and hippuric

acid in renal tissues caused by

cisplatin

allograft model in vivo

Fisetin

Prostate cancer

Tumor tissues from prostate cancer xenografts in vivo

HPLC/ESI–MS

**Pure compound**

**Cancer**

**Study**

**Method**

**Significantly changed metabolites or pathways**

**Main findings**

Fisetin treatment was shown to downregulate secreted and intracellular hyaluronan (HA), which conferred resistance to prostate oncogenesis

Fisetin is an effective, nontoxic, potent HA synthesis inhibitor

dLGG treatment markedly elevated 12/15-LOX catalyzed oxylipin products in serum

This study shows the novel therapeutic effect of phytoagent dLGG and suggests its potential as a therapeutic agent for metastatic melanoma

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective*

The co-therapy of DET

and cisplatin could be

an effective treatment

with low toxicity for

melanoma

*DOI: http://dx.doi.org/10.5772/intechopen.88924*


**Table 1.** *Summary of recent metabolomic studies on anticancer therapies of pure compounds from Chinese medicines.*

#### *Chinese Medicines for Cancer Treatment from the Metabolomics Perspective DOI: http://dx.doi.org/10.5772/intechopen.88924*

*Metabolomics - New Insights into Biology and Medicine*

**82**

**References**

29,651,531 26,851,007 27,374,097 29,787,425

Gamma-tocotrienol

Cancer

Serum from nonhuman

UPLC/Q-TOF MS

GT3 could regulate the

changed fatty acid betaoxidation, amino acid and purine catabolism metabolism

caused by irradiation

primate models in vivo

Celastrol

Acute

HL-60 cells in vitro and

UPLC-MS

tumor tissue from mice

xenograft model in vivo

promyelocytic

leukemia

Taurine

Breast cancer

Plasma from

GC–TOFMS

Taurine treatment regulated

23 differential metabolites,

which were associated with

glucose, energy and amino

acid, as well as nucleic acid

metabolism

Celastrol treatment regulated

The study firstly

reveals that uridine

deficiency contributes to

mitochondrial apoptosis

induced by celastrol in

APL cells

This initial assessment

also highlights the

utility of metabolomics

in determining

underlying physiological

mechanisms

responsible for the

radioprotective efficacy

of gamma-tocotrienol

uridine metabolite, which

further enhances apoptosis

dimethylbenz[a]

anthracene-induced breast

carcinogenesis in rats

in vivo

**Pure compound**

**Cancer** Hepatoma

SMMC7721 cells in vitro

GC/MS and LC/MS

**Study**

**Method**

**Significantly changed** 

**Main findings**

**metabolites or pathways**

GLA treatment diminished

GC/MS- and LC/

MS-based metabolomics

applied to cell culture

enhanced our current

understanding of the

metabolic response to

GLA treatment and its

mechanism

The antitumor activity

of taurine in rats is

mediated through altered

metabolism of breast

cancer cells

amino acid metabolism and

elevated the metabolisms of

sphingolipid, purine, and

pyrimidine

Increasing excellent reviews have been focused on the application of metabolomics in the metabolic changes and the possible underlying mechanisms behind these alterations in the pathogenesis of different kinds of cancer [33–35]. Little reviews have been highlighted on the metabolism-based anticancer therapies. Since Chinese medicine has been suggested to be a great source of alternative treatments to human cancers, in this chapter we systematically reviewed recent studies from 2015 to March 2019 on metabolism-targeting anticancer therapies based on metabolomics in terms of glucose, lipid, amino acid, and nucleotide metabolisms and other altered metabolisms, with special emphasis on the potential of metabolic treatment with pure compounds, herb extracts, and formulations from Chinese medicines. The typical flowchart of metabolomics studies on antineoplastic Chinese medicines is shown in **Figure 1**. **Table 1** summarized the recent metabolomics studies on anticancer therapies of pure compounds from Chinese medicines. At the same time, the trends of future development of metabolism-targeting anticancer therapies were also discussed.

#### **2. Review on metabolism-targeting Chinese medicine treatment on human cancers**

#### **2.1 Glucose metabolism**

As mentioned above, cancer cells tend to acquire energy via glycolysis rather than the much more efficient oxidative phosphorylation pathway even in aerobic conditions. Glucose and energy metabolisms play an important role in the tumorigenesis of cancer and could be the therapeutic targets for cancer treatment. Pure compounds, herb extracts, and formulations from Chinese medicines, which target glucose and energy metabolisms, are attracting increasing attention for the development of anticancer therapies.

Geranylgeranoic acid (GGA), a kind of acyclic diterpenoids, is derived from some medicinal herbs such as turmeric. UPLC/TOF/MS-based metabolomics analysis was used to study the underlying anticancer mechanism of GGA in human hepatoma-derived HuH-7 cells [36]. It was found that GGA may shift the energetic state of HuH-7 cells from aerobic glycolysis to mitochondrial respiration, which was revealed by a time-dependent augment of fructose 6-phosphate and decline of fructose 1,6-diphosphate in HuH-7 cells after GGA treatment. Halofuginone (HF) is an active compound derived from the febrifugine which can be extracted from the Chinese herb *Dichroa febrifuga* Lour. Chen and his colleagues used the combination of UPLC-MS/MS, GC/MS, and UPLC/LTQ-Orbitrap MS metabolomics from HCT116 cells in vitro to study the anti-colorectal cancer (CRC) properties of HF [37]. They found the slower rates in the fluxes of both glycolytic and glucose-derived TCA cycle after HF treatment mainly via Akt/ mTORC1 signaling pathway. (−)-5-Hydroxy-equol, as an isoflavone derived from microbial biotransformation, was shown to exhibit anti-hepatocellular carcinoma (HCC) potential. To explore the underlying mechanism, a <sup>1</sup> H NMR-based metabolomics of SMMC-7721 cells in vitro was conducted [38]. It was found that (−)-5-hydroxy-equol treatment significantly altered energy and amino acid metabolisms, which revealed that integrated metabolomics and further verifications may facilitate the exploration of the anti-HCC mechanisms of (−)-5-hydroxy-equol. Nummularic acid (NA) is a triterpenoid isolated from a medicinal plant *Fraxinus xanthoxyloides*. To explore its anticancer potential, a ALEX-CIS GC–TOF-MS-based metabolomics analysis of DU-145 and C4-2 cells in vitro was performed [39]. It was shown that the metabolism pathways related to

**85**

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective*

glycolysis, TCA, and glutamine metabolisms were changed after NA treatment,

Magnoline is the primary compound derived from *Cortex Phellodendri amurensis*, which exhibits significant therapeutic potential for PCa. Sun et al. conducted a UPLC-MS metabolomics of 22RV1 cells in vitro on PCa [40]. It was found that magnoline markedly restored the energy metabolism, amino acid metabolism, and

1,25-Dihydroxyvitamin D3 (1,25(OH)2D3), also known as calcitriol, is one of the bioactive forms of nutraceutical vitamin D. Recently, its metabolism-modulating effects against PCa have been reported [41]. Based on the metabolomics analysis of LNCaP cells in vitro, 1,25(OH)2D3 inhibited glucose uptake and increased citrate/isocitrate because of TCA cycle truncation. The re-wiring glucose metabolizing pathways by 1,25(OH)2D3 may prove its metabolism-modulating effects against PCa. Yun et al. found that high exposed level of vitamin C could selectively kill CRC cells harboring KRAS or BRAF mutations [42]. In detail, based on the LC-MS/MS metabolomics between KRAS and BRAF mutant lines and isogenic wild-type counterparts in vitro, high level of exposure of vitamin C could increase uptake of dehydroascorbic acid by GLUT1 transporter and then decrease glutathi-

which suggested NA may induce energy crisis to inhibit prostate cancer.

fatty acid metabolism, which revealed that cancer cells may result in death because of insufficient material basis to favor their rapid proliferation.

one, which could inactivate glyceraldehyde 3-phosphate dehydrogenase

ated the metabolic disorders of diethylnitrosamine (DEN)-induced

(GAPDH). β-Lapachone (β-lap), as a quinone-containing compound derived from the *lapacho* tree located in South America, is bioactivated by NAD(P)H: quinone oxidoreductase 1 (NQO1). Recently, its effects on energy metabolism due to NAD<sup>+</sup> depletion on pancreatic ductal adenocarcinoma (PDA) have been shown [43].

cells in vitro, β-lap treatment was found to decrease the NAD-sensitive pathways, such as glycolysis and TCA cycle, which revealed that targeting NQO1 may sensitize the treatment of β-lap. Diethylstilbestrol (DES), as a nonsteroidal estrogen, is the pharmacological inhibitor to HIF-1a. Arminan et al. employed NMR-based metabolomics of PC3 cells in vitro to explore the metabolic responses of PCa cells to hypoxia and the treatment of DES or its polyacetal conjugate tert-DES [44]. It was shown that lactate, phosphocreatine, and glutathione were the biomarkers for DES treatment. What is more, compared with tert-DES, the cell metabolome had a more extensive impact in the free DES treatment, which revealed that DES upon conjugation had a more specific effect and less toxicity. Koningic acid (KA), as an active natural product derived from the *Trichoderma fungus*, is a selective inhibitor of GAPDH. Recently Liberti et al. employed integrated pharmacogenomics and LC-HRMS metabolomics of HCT116 cells to explore the response of KA to CRC [45]. As a result, they found that partial GAPDH suppression is more selective for highly glycolytic tumors, underscoring the potential of targeting glucose metabolism therapy could be an integral part of precision medicine. Rapamycin (Rp) is widely used in the treatment of breast cancer. However, its efficacy has been significantly reduced by the increasing drug resistance and serious metabolic disorders. Dietary omega-3 polyunsaturated fatty acids (ω-3 PUFAs) have been reported to markedly inhibit breast cancer. To explore whether combined treatment of Rp and ω-3 PUFAs has better efficacy, a GC/MS-based metabolomics of MCF7 cells in vitro was done [46]. It was found that glycolysis and glutamine metabolism pathways were markedly reduced when treated with the combination of Rp and ω-3 PUFAs, suggesting that ω-3 PUFA could increase the anti-breast cancer potential of Rp. Curcumin, as the primary bioactive compound from the spice turmeric, was found to be a potent anticancer agent [47]. In detail, based on the serum metabolomics analysis, curcumin attenu-

H NMR metabolomics analysis of MiaPaCa2

*DOI: http://dx.doi.org/10.5772/intechopen.88924*

Based on the combined GC/MS and <sup>1</sup>

#### *Chinese Medicines for Cancer Treatment from the Metabolomics Perspective DOI: http://dx.doi.org/10.5772/intechopen.88924*

*Metabolomics - New Insights into Biology and Medicine*

were also discussed.

**human cancers**

**2.1 Glucose metabolism**

opment of anticancer therapies.

Increasing excellent reviews have been focused on the application of metabolomics in the metabolic changes and the possible underlying mechanisms behind these alterations in the pathogenesis of different kinds of cancer [33–35]. Little reviews have been highlighted on the metabolism-based anticancer therapies. Since Chinese medicine has been suggested to be a great source of alternative treatments to human cancers, in this chapter we systematically reviewed recent studies from 2015 to March 2019 on metabolism-targeting anticancer therapies based on metabolomics in terms of glucose, lipid, amino acid, and nucleotide metabolisms and other altered metabolisms, with special emphasis on the potential of metabolic treatment with pure compounds, herb extracts, and formulations from Chinese medicines. The typical flowchart of metabolomics studies on antineoplastic Chinese medicines is shown in **Figure 1**. **Table 1** summarized the recent metabolomics studies on anticancer therapies of pure compounds from Chinese medicines. At the same time, the trends of future development of metabolism-targeting anticancer therapies

**2. Review on metabolism-targeting Chinese medicine treatment on** 

As mentioned above, cancer cells tend to acquire energy via glycolysis rather than the much more efficient oxidative phosphorylation pathway even in aerobic conditions. Glucose and energy metabolisms play an important role in the tumorigenesis of cancer and could be the therapeutic targets for cancer treatment. Pure compounds, herb extracts, and formulations from Chinese medicines, which target glucose and energy metabolisms, are attracting increasing attention for the devel-

Geranylgeranoic acid (GGA), a kind of acyclic diterpenoids, is derived from some medicinal herbs such as turmeric. UPLC/TOF/MS-based metabolomics analysis was used to study the underlying anticancer mechanism of GGA in human hepatoma-derived HuH-7 cells [36]. It was found that GGA may shift the energetic state of HuH-7 cells from aerobic glycolysis to mitochondrial respiration, which was revealed by a time-dependent augment of fructose 6-phosphate and decline of fructose 1,6-diphosphate in HuH-7 cells after GGA treatment.

Halofuginone (HF) is an active compound derived from the febrifugine which can be extracted from the Chinese herb *Dichroa febrifuga* Lour. Chen and his colleagues used the combination of UPLC-MS/MS, GC/MS, and UPLC/LTQ-Orbitrap MS metabolomics from HCT116 cells in vitro to study the anti-colorectal cancer (CRC) properties of HF [37]. They found the slower rates in the fluxes of both glycolytic and glucose-derived TCA cycle after HF treatment mainly via Akt/ mTORC1 signaling pathway. (−)-5-Hydroxy-equol, as an isoflavone derived from microbial biotransformation, was shown to exhibit anti-hepatocellular carcinoma

H NMR-based metabo-

(HCC) potential. To explore the underlying mechanism, a <sup>1</sup>

lomics of SMMC-7721 cells in vitro was conducted [38]. It was found that (−)-5-hydroxy-equol treatment significantly altered energy and amino acid metabolisms, which revealed that integrated metabolomics and further verifica-

(−)-5-hydroxy-equol. Nummularic acid (NA) is a triterpenoid isolated from a medicinal plant *Fraxinus xanthoxyloides*. To explore its anticancer potential, a ALEX-CIS GC–TOF-MS-based metabolomics analysis of DU-145 and C4-2 cells in vitro was performed [39]. It was shown that the metabolism pathways related to

tions may facilitate the exploration of the anti-HCC mechanisms of

**84**

glycolysis, TCA, and glutamine metabolisms were changed after NA treatment, which suggested NA may induce energy crisis to inhibit prostate cancer. Magnoline is the primary compound derived from *Cortex Phellodendri amurensis*, which exhibits significant therapeutic potential for PCa. Sun et al. conducted a UPLC-MS metabolomics of 22RV1 cells in vitro on PCa [40]. It was found that magnoline markedly restored the energy metabolism, amino acid metabolism, and fatty acid metabolism, which revealed that cancer cells may result in death because of insufficient material basis to favor their rapid proliferation. 1,25-Dihydroxyvitamin D3 (1,25(OH)2D3), also known as calcitriol, is one of the bioactive forms of nutraceutical vitamin D. Recently, its metabolism-modulating effects against PCa have been reported [41]. Based on the metabolomics analysis of LNCaP cells in vitro, 1,25(OH)2D3 inhibited glucose uptake and increased citrate/isocitrate because of TCA cycle truncation. The re-wiring glucose metabolizing pathways by 1,25(OH)2D3 may prove its metabolism-modulating effects against PCa. Yun et al. found that high exposed level of vitamin C could selectively kill CRC cells harboring KRAS or BRAF mutations [42]. In detail, based on the LC-MS/MS metabolomics between KRAS and BRAF mutant lines and isogenic wild-type counterparts in vitro, high level of exposure of vitamin C could increase uptake of dehydroascorbic acid by GLUT1 transporter and then decrease glutathione, which could inactivate glyceraldehyde 3-phosphate dehydrogenase (GAPDH). β-Lapachone (β-lap), as a quinone-containing compound derived from the *lapacho* tree located in South America, is bioactivated by NAD(P)H: quinone oxidoreductase 1 (NQO1). Recently, its effects on energy metabolism due to NAD<sup>+</sup> depletion on pancreatic ductal adenocarcinoma (PDA) have been shown [43]. Based on the combined GC/MS and <sup>1</sup> H NMR metabolomics analysis of MiaPaCa2 cells in vitro, β-lap treatment was found to decrease the NAD-sensitive pathways, such as glycolysis and TCA cycle, which revealed that targeting NQO1 may sensitize the treatment of β-lap. Diethylstilbestrol (DES), as a nonsteroidal estrogen, is the pharmacological inhibitor to HIF-1a. Arminan et al. employed NMR-based metabolomics of PC3 cells in vitro to explore the metabolic responses of PCa cells to hypoxia and the treatment of DES or its polyacetal conjugate tert-DES [44]. It was shown that lactate, phosphocreatine, and glutathione were the biomarkers for DES treatment. What is more, compared with tert-DES, the cell metabolome had a more extensive impact in the free DES treatment, which revealed that DES upon conjugation had a more specific effect and less toxicity. Koningic acid (KA), as an active natural product derived from the *Trichoderma fungus*, is a selective inhibitor of GAPDH. Recently Liberti et al. employed integrated pharmacogenomics and LC-HRMS metabolomics of HCT116 cells to explore the response of KA to CRC [45]. As a result, they found that partial GAPDH suppression is more selective for highly glycolytic tumors, underscoring the potential of targeting glucose metabolism therapy could be an integral part of precision medicine. Rapamycin (Rp) is widely used in the treatment of breast cancer. However, its efficacy has been significantly reduced by the increasing drug resistance and serious metabolic disorders. Dietary omega-3 polyunsaturated fatty acids (ω-3 PUFAs) have been reported to markedly inhibit breast cancer. To explore whether combined treatment of Rp and ω-3 PUFAs has better efficacy, a GC/MS-based metabolomics of MCF7 cells in vitro was done [46]. It was found that glycolysis and glutamine metabolism pathways were markedly reduced when treated with the combination of Rp and ω-3 PUFAs, suggesting that ω-3 PUFA could increase the anti-breast cancer potential of Rp. Curcumin, as the primary bioactive compound from the spice turmeric, was found to be a potent anticancer agent [47]. In detail, based on the serum metabolomics analysis, curcumin attenuated the metabolic disorders of diethylnitrosamine (DEN)-induced

hepatocarcinogenesis by elevating the levels of glucose and fructose and reducing the levels of glycine and proline. 6,7-Dimethoxy-1,2,3,4-tetrahydro-isoquinoline-3-carboxylic acid (M1) is an isoquinoline alkaloid isolated from *Mucuna pruriens* seeds. To evaluate the anti-CRC effects of M1, <sup>1</sup> H NMR-based metabolomics of serum from dimethylhydrazine (DMH)-induced CRC albino Wistar rat model in vivo was conducted [48]. As a result, M1 exhibited to reverse the perturbed metabolism pathways in CRC condition, including glycolysis, TCA cycle, choline, and phosphatidylinositol and gluconeogenesis metabolisms. Taken together, this study offered that M1 had the anti-CRC potential via the blockade of IL-6/JAK2/ STAT3 oncogenic signaling. Physapubenolide (PB) is a withanolide derived from *Physalis pubescens*. Recently its potential as a promising therapeutic drug has been put forward. However, the underlying mechanism of how it works remains to be explored. Ma et al. employed GC/MS-based metabolomics of both HepG2 cells in vitro and tumor tissues and plasma from a mouse-xenograft model bearing liver carcinoma H22 cells in vivo [49]. It was found that PB reversed the disturbed metabolic pattern by markedly decreasing the lactate production, suggesting PB may exhibit anti-HCC activities through suppression of glycolysis via the Akt-p53 pathway. Gefitinib (Gnb), as a tyrosine kinase inhibitor, is widely used for the treatment of lung cancer. However, the increasing drug resistance and serious metabolic disorders have significantly reduced its efficacy. Naringenin (Nar), as flavonoid isolated from citrus fruits, has been reported to show antioxidant, antimutagenic, and anticarcinogenic activities. To explore whether co-therapy through biotin-modified nanoparticles (NPs) of Gnb and Nar, a <sup>1</sup> H NMR-based metabolomics of serum from the urethane-induced lung cancer rat model in vivo was conducted [50]. It was found that the glycolysis was restored to normal levels with co-therapy of Gnb and Nar, which showed that co-therapy had the superiority over treatment only to improve the therapeutic efficacy.

Silymarin, extracted from the seeds of milk thistle (*Silybum marianum*), has the anti-inflammation activity. To explore the mechanism of how it suppresses inflammation, a combined transcriptional profiling and GC/MS metabolomics was conducted on Huh7-TLR3 cells [51]. It was found that the glycolytic, TCA cycle, and amino acid metabolism pathways were inhibited after silymarin treatment, which revealed that silymarin may have potential in defining how metabolic pathways mediate cellular inflammation. Rhizoma Paridis saponins (RPS) are the effective parts of Rhizoma Paridis, which have been found to show strong antihepatocarcinoma activities. However, the anticancer mechanism remains not clear. To search for the potential biomarkers for the evaluation of treatment, 1 H NMR metabolomics was employed to distinguish the serum metabolic profiling of the RPS treatment group from that of the model group [52]. As a result, RPS decreased the serum levels of lactate, acetate, N-acetyl amino acid, and glutamine, which has shown that RPS was a potential anticancer drug by inhibiting the aerobic glycolysis, lipogenesis, and glutamine metabolism. As one of the rarest plants, *Camellia nitidissima Chi* was reported to have various pharmacological activities, including anti-CRC. However, its anti-CRC efficacies remained to be confirmed due to its complex components and underlying complicated mechanisms. To address these issues, Li and his colleagues employed <sup>1</sup> H NMR-based metabolomics of the intestine, kidney, and spleen from azoxymethane/dextran sodium sulfate (AOM/DSS)-induced CRC mice model [53]. They found that *C. nitidissima Chi* extracts could markedly suppress AOM/DSS-induced CRC via reversing the disturbed metabolic profiling to the normal state. What is more, compared with the water-soluble fraction of *C. nitidissima Chi*, its butanol fraction exhibited a better efficacy. Gnb was widely used in the treatment of lung carcinoma (LLC) with increasing drug resistance and serious metabolic disorders. Si Jun Zi Tang (SJZ) is a four-herb Chinese medicine

**87**

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective*

formula and has shown potential of anticancer properties. To explore the underlying mechanisms of the co-therapy of Gnb and SJZ, Li et al. conducted an integrated network pharmacology and Q-TOF LC/MS-based metabolomics of plasma from LLC-bearing mice model in vivo [54]. SJZ was shown to increase the anti-LLC effects of Gnb via restoring TCA cycle, linoleic acid metabolism, and tyrosine and tryptophan metabolism, revealing that co-therapy of Gnb and SJZ may increase the

Besides the glucose and energy metabolisms having an essential role in the tumorigenesis process of cancer, it has been also reported that lipid metabolism such as de novo lipogenesis regulates the synthesis of cellular membranes and the important signaling molecules of rapidly proliferating tumor cells [55]. Targeting the lipid metabolism could be a novel therapeutics for cancer treatment. Here the recent metabolomics studies of pure compounds, herb extracts, and formulations from Chinese medicines, which target lipid metabolism, have been reviewed. Flexibilide is a natural compound derived from the soft coral *Sinularia flexibilis* with tumor inhibitory effects. To clarify the pharmacological mechanism, a UPLC/Q-TOF MS-based metabolomics of HCT-116 cells in vitro on colon cancer was conducted [56]. It was found that flexibilide treatment greatly elevated lysophosphatidylcholine (LysoPC) and diminished phosphocholine and phosphatidylcholine (PC), revealing that flexibilide exhibited the therapeutic effect on colon cancer mainly via downregulating PC biosynthesis pathway. Englerin A is a guaiane sesquiterpene derived from the plant *Phyllanthus engleri* with potential antineoplastic property. To uncover the therapeutic role of englerin A on clear cell renal carcinoma, Batova et al. conducted a LC-MS/MS-based metabolomics of A498 cells in vitro [57]. It was found that englerin A significantly reversed lipid metabolism and increased ceramide levels. Then the increasing ceramides inhibited renal carcinoma cells. Isoquercitrin is a kind of flavonoid derived from various plants, such as *Psidium guajava* and *Fagopyrum tataricum*. It has potential antitumor activities. To decipher its therapeutic role in bladder cancer, a UPLC/Q-TOF MS-based metabolomics of T24 cells in vitro was conducted [58]. Isoquercitrin treatment was found to regulate lipid and anaerobic glycolysis via activating the AMPK pathway. Peiminine is an active substance derived from the bulbs of *Fritillaria thunbergii* with potential antineoplastic property against CRC. To investigate the molecular mechanisms of how it worked, a combined UPLC-MS- and GC/MS-based metabolomics of HCT-116 cells in vitro was used [59]. Peiminine treatment altered several metabolites, including lignocerate (24:0), oleate (18:1n9), glutamine, and glucose, indicating peiminine exerted the predominant therapeutic effect mainly via the metabolic regulation of lipids, amino acids, and carbohydrates. 8u is an acridine derivative with potential antiproliferative activity against cancer. To explore its therapeutic effects on HCC, a combined proteomics and UPLC/Q-TOF MS-based metabolomics of HepG2 cells in vitro was used [60]. 8u was found to significantly inhibit the invasion and metastasis of HepG2 cells and regulate intracellular lipid metabolism mainly via suppressing the PI3K/Akt signaling pathway. Genistein is a kind of isoflavone with antineoplastic property. However, high concentration of genistein shows promotional role in cancer. Calcitriol (1α,25(OH)2 vitamin D3) is a primary bioactive hormonal form of vitamin D3. It also shows the antitumor effect. To explore the synergism effects of co-therapy of genistein and calcitriol on osteosarcoma, a GC/MS-based metabolomics of MG-63 cells in vitro was conducted [61]. Co-therapy of genistein and calcitriol was found to regulate lipids and amino acids rather than energy metabolism. Taken together, the promotional effects of

*DOI: http://dx.doi.org/10.5772/intechopen.88924*

anti-LLC potential of Gnb.

**2.2 Lipid metabolism**

formula and has shown potential of anticancer properties. To explore the underlying mechanisms of the co-therapy of Gnb and SJZ, Li et al. conducted an integrated network pharmacology and Q-TOF LC/MS-based metabolomics of plasma from LLC-bearing mice model in vivo [54]. SJZ was shown to increase the anti-LLC effects of Gnb via restoring TCA cycle, linoleic acid metabolism, and tyrosine and tryptophan metabolism, revealing that co-therapy of Gnb and SJZ may increase the anti-LLC potential of Gnb.

#### **2.2 Lipid metabolism**

*Metabolomics - New Insights into Biology and Medicine*

seeds. To evaluate the anti-CRC effects of M1, <sup>1</sup>

hepatocarcinogenesis by elevating the levels of glucose and fructose and reducing the levels of glycine and proline. 6,7-Dimethoxy-1,2,3,4-tetrahydro-isoquinoline-3-carboxylic acid (M1) is an isoquinoline alkaloid isolated from *Mucuna pruriens*

serum from dimethylhydrazine (DMH)-induced CRC albino Wistar rat model in vivo was conducted [48]. As a result, M1 exhibited to reverse the perturbed metabolism pathways in CRC condition, including glycolysis, TCA cycle, choline, and phosphatidylinositol and gluconeogenesis metabolisms. Taken together, this study offered that M1 had the anti-CRC potential via the blockade of IL-6/JAK2/ STAT3 oncogenic signaling. Physapubenolide (PB) is a withanolide derived from *Physalis pubescens*. Recently its potential as a promising therapeutic drug has been put forward. However, the underlying mechanism of how it works remains to be explored. Ma et al. employed GC/MS-based metabolomics of both HepG2 cells in vitro and tumor tissues and plasma from a mouse-xenograft model bearing liver carcinoma H22 cells in vivo [49]. It was found that PB reversed the disturbed metabolic pattern by markedly decreasing the lactate production, suggesting PB may exhibit anti-HCC activities through suppression of glycolysis via the Akt-p53 pathway. Gefitinib (Gnb), as a tyrosine kinase inhibitor, is widely used for the treatment of lung cancer. However, the increasing drug resistance and serious metabolic disorders have significantly reduced its efficacy. Naringenin (Nar), as flavonoid isolated from citrus fruits, has been reported to show antioxidant, antimutagenic, and anticarcinogenic activities. To explore whether co-therapy

through biotin-modified nanoparticles (NPs) of Gnb and Nar, a <sup>1</sup>

To search for the potential biomarkers for the evaluation of treatment, 1

metabolomics was employed to distinguish the serum metabolic profiling of the RPS treatment group from that of the model group [52]. As a result, RPS decreased the serum levels of lactate, acetate, N-acetyl amino acid, and glutamine, which has shown that RPS was a potential anticancer drug by inhibiting the aerobic glycolysis, lipogenesis, and glutamine metabolism. As one of the rarest plants, *Camellia nitidissima Chi* was reported to have various pharmacological activities, including anti-CRC. However, its anti-CRC efficacies remained to be confirmed due to its complex components and underlying complicated mechanisms. To address these issues, Li

and spleen from azoxymethane/dextran sodium sulfate (AOM/DSS)-induced CRC mice model [53]. They found that *C. nitidissima Chi* extracts could markedly suppress AOM/DSS-induced CRC via reversing the disturbed metabolic profiling to the normal state. What is more, compared with the water-soluble fraction of *C. nitidissima Chi*, its butanol fraction exhibited a better efficacy. Gnb was widely used in the treatment of lung carcinoma (LLC) with increasing drug resistance and serious metabolic disorders. Si Jun Zi Tang (SJZ) is a four-herb Chinese medicine

H NMR-based metabolomics of the intestine, kidney,

ity over treatment only to improve the therapeutic efficacy.

metabolomics of serum from the urethane-induced lung cancer rat model in vivo was conducted [50]. It was found that the glycolysis was restored to normal levels with co-therapy of Gnb and Nar, which showed that co-therapy had the superior-

Silymarin, extracted from the seeds of milk thistle (*Silybum marianum*), has the anti-inflammation activity. To explore the mechanism of how it suppresses inflammation, a combined transcriptional profiling and GC/MS metabolomics was conducted on Huh7-TLR3 cells [51]. It was found that the glycolytic, TCA cycle, and amino acid metabolism pathways were inhibited after silymarin treatment, which revealed that silymarin may have potential in defining how metabolic pathways mediate cellular inflammation. Rhizoma Paridis saponins (RPS) are the effective parts of Rhizoma Paridis, which have been found to show strong antihepatocarcinoma activities. However, the anticancer mechanism remains not clear.

H NMR-based metabolomics of

H NMR-based

H NMR

**86**

and his colleagues employed 1

Besides the glucose and energy metabolisms having an essential role in the tumorigenesis process of cancer, it has been also reported that lipid metabolism such as de novo lipogenesis regulates the synthesis of cellular membranes and the important signaling molecules of rapidly proliferating tumor cells [55]. Targeting the lipid metabolism could be a novel therapeutics for cancer treatment. Here the recent metabolomics studies of pure compounds, herb extracts, and formulations from Chinese medicines, which target lipid metabolism, have been reviewed.

Flexibilide is a natural compound derived from the soft coral *Sinularia flexibilis* with tumor inhibitory effects. To clarify the pharmacological mechanism, a UPLC/Q-TOF MS-based metabolomics of HCT-116 cells in vitro on colon cancer was conducted [56]. It was found that flexibilide treatment greatly elevated lysophosphatidylcholine (LysoPC) and diminished phosphocholine and phosphatidylcholine (PC), revealing that flexibilide exhibited the therapeutic effect on colon cancer mainly via downregulating PC biosynthesis pathway. Englerin A is a guaiane sesquiterpene derived from the plant *Phyllanthus engleri* with potential antineoplastic property. To uncover the therapeutic role of englerin A on clear cell renal carcinoma, Batova et al. conducted a LC-MS/MS-based metabolomics of A498 cells in vitro [57]. It was found that englerin A significantly reversed lipid metabolism and increased ceramide levels. Then the increasing ceramides inhibited renal carcinoma cells. Isoquercitrin is a kind of flavonoid derived from various plants, such as *Psidium guajava* and *Fagopyrum tataricum*. It has potential antitumor activities. To decipher its therapeutic role in bladder cancer, a UPLC/Q-TOF MS-based metabolomics of T24 cells in vitro was conducted [58]. Isoquercitrin treatment was found to regulate lipid and anaerobic glycolysis via activating the AMPK pathway. Peiminine is an active substance derived from the bulbs of *Fritillaria thunbergii* with potential antineoplastic property against CRC. To investigate the molecular mechanisms of how it worked, a combined UPLC-MS- and GC/MS-based metabolomics of HCT-116 cells in vitro was used [59]. Peiminine treatment altered several metabolites, including lignocerate (24:0), oleate (18:1n9), glutamine, and glucose, indicating peiminine exerted the predominant therapeutic effect mainly via the metabolic regulation of lipids, amino acids, and carbohydrates. 8u is an acridine derivative with potential antiproliferative activity against cancer. To explore its therapeutic effects on HCC, a combined proteomics and UPLC/Q-TOF MS-based metabolomics of HepG2 cells in vitro was used [60]. 8u was found to significantly inhibit the invasion and metastasis of HepG2 cells and regulate intracellular lipid metabolism mainly via suppressing the PI3K/Akt signaling pathway. Genistein is a kind of isoflavone with antineoplastic property. However, high concentration of genistein shows promotional role in cancer. Calcitriol (1α,25(OH)2 vitamin D3) is a primary bioactive hormonal form of vitamin D3. It also shows the antitumor effect. To explore the synergism effects of co-therapy of genistein and calcitriol on osteosarcoma, a GC/MS-based metabolomics of MG-63 cells in vitro was conducted [61]. Co-therapy of genistein and calcitriol was found to regulate lipids and amino acids rather than energy metabolism. Taken together, the promotional effects of

high level of genistein on osteosarcoma could be decreased by the co-treatment of calcitriol. Silibinin, as a kind of natural flavonoid, is derived from the milk thistle (*Silybum marianum*) seeds with strong hepatoprotective activity. To clarify the pharmacological mechanism of how silibinin exerted antineoplastic property, a 1 H-NMR-based metabolomics of tumor tissues from 22Rv1 xenograft model in vivo was used [62]. Silibinin treatment did not greatly affect glucose uptake of PCa tumor but decreased the lipid synthesis via suppressing hypoxia-induced signaling. Acyclic retinoid (ACR), as a synthetic vitamin A-like compound, exhibits antineoplastic property against HCC. To decipher the molecular mechanisms, comprehensive cationic and lipophilic metabolomics of liver tissues from mouse DEN-induced HCC model in vivo was conducted by CE-TOFMS and LC-TOFMS [63]. ACR predominantly reversed lipogenesis but not glucose metabolism by inhibiting linoleic acid metabolites, revealing lipid metabolic reprogramming played a critical role in the protective effects of ACR on HCC.

Soft coral, *Sinularia* sp., is reported to show potential antineoplastic property. To decipher the molecular mechanisms, a MS-based metabolomics of Hep 3B cells in vitro was conducted [64]. It was found that the *Bornean Sinularia* sp. extract could regulate the sphingolipids and ceramide, revealing that the regulation of dysregulated lipids may account for the antineoplastic property of *Bornean Sinularia* sp. against HCC. *Forsythiae Fructus* (FAE), as the dry fruit of *Forsythia suspensa* (*Thunb.*) Vahl. of Oleaceae family, shows potential anticancer properties. To characterize in detail the action mechanism, Bao et al. conducted a UPLC/Q-TOF MS-based metabolomics of serum from B16-F10 melanoma-bearing mice model in vivo [65]. Aqueous extract of FAE was found to restore the disturbed metabolic profile by increasing several LysoPCs in glycerophospholipid metabolisms, revealing that the regulation of glycerophospholipid metabolisms may have an essential role in the antineoplastic property of FAE. Nutmeg is a seed of the fruit of *Myristica fragrans* with antimicrobial and anticancer activities. To explore the role of its antimicrobial activity in cancer protection, a UPLC/ESI-QTOF-MS-based metabolomics of serum from colon cancer model was investigated [66]. Nutmeg extract treatment was found to regulate lipid metabolism by decreasing four uremic toxins generated from the gut microbiota, revealing that the regulation of lipid metabolism and gut microbiota may be an effective therapy for colon cancer treatment. Volatile oil is extracted from *Saussurea lappa Decne* (VOSL), and costunolide and dehydrocostus lactone (Cos–Dehy), accounting for almost 75% of VOSL by weight, are the primary active chemical compositions of VOSL. It has been reported that they all can suppress the MCF-7cells in vitro. To characterize in detail the action mechanism of how they worked, a combined GC × GC–TOF/MS and UPLC/Q-TOF MS metabolomics of serum and urine from MCF-7 xenograft mice in vivo was conducted [67]. It was revealed that both VOSL and Cos–Dehy could relieve metabolic disturbance by decreasing glycolysis and steroid hormone metabolism and increasing unsaturated fatty acids metabolism, suggesting that VOSL is a potential therapeutics against breast cancer. Shuihonghuazi formula (SHHZF) is a famous formula which has been widely used clinically for the treatment of liver cancer. To explore its action mechanism, a DEN-induced HCC rat model was built, and a HPLC/ESI-TOF-MS-based metabolomics of plasma from this model was conducted [68]. SHHZF was found to elevate the levels of arachidonic acid-like substances and the shift of phosphatidylethanolamine (PE) to PC, revealing the reversion of the disturbed fatty acid and bile acid metabolism played an important role in the therapeutic effects of SHHZF on HCC. Qi-Yu-San-Long Decoction (QYSLD) is a classic formula, which has been widely used clinically for LLC treatment. To characterize in detail the action mechanism of how it works, a UPLC/Q-TOF MS-based metabolomics was conducted [69]. Lewis LLC mice model was firstly built, and plasma

**89**

combinations.

et al. used <sup>1</sup>

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective*

was collected for metabolomics analysis. QYSLD was found to regulate sphingolipid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism, fatty acid degradation, and steroid hormone biosynthesis. *Rhizoma Curcumae* and *Rhizoma Sparganii* (RCRS) is a famous Chinese medicine drug pair to treat hysteromyoma. To investigate the molecular mechanisms of how this drug pair works on hysteromyoma, a UPLC/Q-TOF MS-based metabolomics was conducted using the serum and urine from hysteromyoma rat model [70]. RCRS treatment characterized 16 and 18 potential biomarkers from serum and urine, respectively, which were associated with glyoxylate, dicarboxylate, and linoleic acid metabolisms.

As mentioned above, besides the consumption of glucose, cancer cells have also been reported to favor glutamine as a preferential fuel. Glutamine metabolism has an essential role in the pathological progression of cancer and could be a potential therapeutic option for cancer. Besides the key metabolite glutamine, it has been

Delta-tocotrienol (δT) is one of the isomers of vitamin E with antineoplastic

Polyphenols are characterized as a hydroalcoholic chestnut shell extract. Sorice

H-NMR-based metabolomics of HepG2 cells in vitro to study the anti-HCC activity of polyphenols extracted from chestnut shell (PECS) [76]. PECS was found to regulate the levels of some amino acids. Annonaceous acetogenins (ACGs) are a group of C35 or C37 secondary metabolites isolated from plants in *Annonaceae*. To explore underlying action mechanism of the anti-HCC activity of ACGs, a UPLC-ESI-Q-TOF-MS-based metabolomics of SMMC 7721 cells in vitro was conducted [77]. ACG treatment could regulate the metabolisms of sphingolipid, arginine, glutathione, and proline, which further reversed the resistance of

lomics of A549 and H1299 cells in vitro was used [71]. In detail, δT treatment could suppress the glutamine uptake via suppressing glutamine transporters and then resulted in the induction of apoptosis and suppression of cell proliferation. Celastrol is a bioactive compound derived from *Trypterygium wilfordii HOOK F.* with potential antineoplastic property. To explore underlying action mechanism involved in its anti-colon cancer activity, a UPLC/MS-based metabolomics of HCT116 cells in vitro was conducted [72]. Metabolomics analysis found celastrol changed the levels of lipid markers, carnitine, and amino acids. Tryptophan was further identified as special biomarker by targeted metabolite analysis. Melittin, as a cytotoxic peptide isolated from bee venom, was shown to sensitize the response of ovarian cancer cells to cisplatin treatment. To explore an underlying action mechanism, a LC-MS metabolomics of A2780 and A2780CR cell lines in vitro was employed [73]. It was found that melittin treatment of cisplatin-sensitive cells decreased glutamine, proline, and arginine pathways. Chlorogenic acid (ChA) and caffeic acid (CaA), both as a kind of polyphenol, have shown anti-HCC activities. To decipher the molecular mechanisms, a combined 16S rRNA and metabolomics was conducted [74]. It was found that both CaA and ChA treatments reverse 28 metabolites. In detail, the levels of ethanolamine, L-methionine, L-tyrosine, and bilirubin were associated with diminished *Prevotella* 9 and *Lachnospiraceae incertae sedis* and elevated *Rumincoccaceae* UCG-004. Lodi et al. used untargeted metabolomics and metabolic flux analysis to investigate the synergistic effects of resveratrol, curcumin, and ursolic acid [75]. It was found that compared with the individual treatment, the combined treatment had the greater antineoplastic property. Mechanically, glutamine metabolism was regulated by the compound

H-NMR-based metabo-

reported many other amino acids also play an essential role in cancer.

property. To explore underlying action mechanism, a <sup>1</sup>

*DOI: http://dx.doi.org/10.5772/intechopen.88924*

**2.3 Amino acid metabolism**

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective DOI: http://dx.doi.org/10.5772/intechopen.88924*

was collected for metabolomics analysis. QYSLD was found to regulate sphingolipid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism, fatty acid degradation, and steroid hormone biosynthesis. *Rhizoma Curcumae* and *Rhizoma Sparganii* (RCRS) is a famous Chinese medicine drug pair to treat hysteromyoma. To investigate the molecular mechanisms of how this drug pair works on hysteromyoma, a UPLC/Q-TOF MS-based metabolomics was conducted using the serum and urine from hysteromyoma rat model [70]. RCRS treatment characterized 16 and 18 potential biomarkers from serum and urine, respectively, which were associated with glyoxylate, dicarboxylate, and linoleic acid metabolisms.

#### **2.3 Amino acid metabolism**

*Metabolomics - New Insights into Biology and Medicine*

role in the protective effects of ACR on HCC.

1

high level of genistein on osteosarcoma could be decreased by the co-treatment of calcitriol. Silibinin, as a kind of natural flavonoid, is derived from the milk thistle (*Silybum marianum*) seeds with strong hepatoprotective activity. To clarify the pharmacological mechanism of how silibinin exerted antineoplastic property, a

H-NMR-based metabolomics of tumor tissues from 22Rv1 xenograft model in vivo was used [62]. Silibinin treatment did not greatly affect glucose uptake of PCa tumor but decreased the lipid synthesis via suppressing hypoxia-induced signaling. Acyclic retinoid (ACR), as a synthetic vitamin A-like compound, exhibits antineoplastic property against HCC. To decipher the molecular mechanisms, comprehensive cationic and lipophilic metabolomics of liver tissues from mouse DEN-induced HCC model in vivo was conducted by CE-TOFMS and LC-TOFMS [63]. ACR predominantly reversed lipogenesis but not glucose metabolism by inhibiting linoleic acid metabolites, revealing lipid metabolic reprogramming played a critical

Soft coral, *Sinularia* sp., is reported to show potential antineoplastic property. To decipher the molecular mechanisms, a MS-based metabolomics of Hep 3B cells in vitro was conducted [64]. It was found that the *Bornean Sinularia* sp. extract could regulate the sphingolipids and ceramide, revealing that the regulation of dysregulated lipids may account for the antineoplastic property of *Bornean Sinularia* sp. against HCC. *Forsythiae Fructus* (FAE), as the dry fruit of *Forsythia suspensa* (*Thunb.*) Vahl. of Oleaceae family, shows potential anticancer properties. To characterize in detail the action mechanism, Bao et al. conducted a UPLC/Q-TOF MS-based metabolomics of serum from B16-F10 melanoma-bearing mice model in vivo [65]. Aqueous extract of FAE was found to restore the disturbed metabolic profile by increasing several LysoPCs in glycerophospholipid metabolisms, revealing that the regulation of glycerophospholipid metabolisms may have an essential role in the antineoplastic property of FAE. Nutmeg is a seed of the fruit of *Myristica fragrans* with antimicrobial and anticancer activities. To explore the role of its antimicrobial activity in cancer protection, a UPLC/ESI-QTOF-MS-based metabolomics of serum from colon cancer model was investigated [66]. Nutmeg extract treatment was found to regulate lipid metabolism by decreasing four uremic toxins generated from the gut microbiota, revealing that the regulation of lipid metabolism and gut microbiota may be an effective therapy for colon cancer treatment. Volatile oil is extracted from *Saussurea lappa Decne* (VOSL), and costunolide and dehydrocostus lactone (Cos–Dehy), accounting for almost 75% of VOSL by weight, are the primary active chemical compositions of VOSL. It has been reported that they all can suppress the MCF-7cells in vitro. To characterize in detail the action mechanism of how they worked, a combined GC × GC–TOF/MS and UPLC/Q-TOF MS metabolomics of serum and urine from MCF-7 xenograft mice in vivo was conducted [67]. It was revealed that both VOSL and Cos–Dehy could relieve metabolic disturbance by decreasing glycolysis and steroid hormone metabolism and increasing unsaturated fatty acids metabolism, suggesting that VOSL is a potential therapeutics against breast cancer. Shuihonghuazi formula (SHHZF) is a famous formula which has been widely used clinically for the treatment of liver cancer. To explore its action mechanism, a DEN-induced HCC rat model was built, and a HPLC/ESI-TOF-MS-based metabolomics of plasma from this model was conducted [68]. SHHZF was found to elevate the levels of arachidonic acid-like substances and the shift of phosphatidylethanolamine (PE) to PC, revealing the reversion of the disturbed fatty acid and bile acid metabolism played an important role in the therapeutic effects of SHHZF on HCC. Qi-Yu-San-Long Decoction (QYSLD) is a classic formula, which has been widely used clinically for LLC treatment. To characterize in detail the action mechanism of how it works, a UPLC/Q-TOF MS-based metabolomics was conducted [69]. Lewis LLC mice model was firstly built, and plasma

**88**

As mentioned above, besides the consumption of glucose, cancer cells have also been reported to favor glutamine as a preferential fuel. Glutamine metabolism has an essential role in the pathological progression of cancer and could be a potential therapeutic option for cancer. Besides the key metabolite glutamine, it has been reported many other amino acids also play an essential role in cancer.

Delta-tocotrienol (δT) is one of the isomers of vitamin E with antineoplastic property. To explore underlying action mechanism, a <sup>1</sup> H-NMR-based metabolomics of A549 and H1299 cells in vitro was used [71]. In detail, δT treatment could suppress the glutamine uptake via suppressing glutamine transporters and then resulted in the induction of apoptosis and suppression of cell proliferation. Celastrol is a bioactive compound derived from *Trypterygium wilfordii HOOK F.* with potential antineoplastic property. To explore underlying action mechanism involved in its anti-colon cancer activity, a UPLC/MS-based metabolomics of HCT116 cells in vitro was conducted [72]. Metabolomics analysis found celastrol changed the levels of lipid markers, carnitine, and amino acids. Tryptophan was further identified as special biomarker by targeted metabolite analysis. Melittin, as a cytotoxic peptide isolated from bee venom, was shown to sensitize the response of ovarian cancer cells to cisplatin treatment. To explore an underlying action mechanism, a LC-MS metabolomics of A2780 and A2780CR cell lines in vitro was employed [73]. It was found that melittin treatment of cisplatin-sensitive cells decreased glutamine, proline, and arginine pathways. Chlorogenic acid (ChA) and caffeic acid (CaA), both as a kind of polyphenol, have shown anti-HCC activities. To decipher the molecular mechanisms, a combined 16S rRNA and metabolomics was conducted [74]. It was found that both CaA and ChA treatments reverse 28 metabolites. In detail, the levels of ethanolamine, L-methionine, L-tyrosine, and bilirubin were associated with diminished *Prevotella* 9 and *Lachnospiraceae incertae sedis* and elevated *Rumincoccaceae* UCG-004. Lodi et al. used untargeted metabolomics and metabolic flux analysis to investigate the synergistic effects of resveratrol, curcumin, and ursolic acid [75]. It was found that compared with the individual treatment, the combined treatment had the greater antineoplastic property. Mechanically, glutamine metabolism was regulated by the compound combinations.

Polyphenols are characterized as a hydroalcoholic chestnut shell extract. Sorice et al. used <sup>1</sup> H-NMR-based metabolomics of HepG2 cells in vitro to study the anti-HCC activity of polyphenols extracted from chestnut shell (PECS) [76]. PECS was found to regulate the levels of some amino acids. Annonaceous acetogenins (ACGs) are a group of C35 or C37 secondary metabolites isolated from plants in *Annonaceae*. To explore underlying action mechanism of the anti-HCC activity of ACGs, a UPLC-ESI-Q-TOF-MS-based metabolomics of SMMC 7721 cells in vitro was conducted [77]. ACG treatment could regulate the metabolisms of sphingolipid, arginine, glutathione, and proline, which further reversed the resistance of

SMMC 7721 cells to adriamycin. *Hedyotis diffusa* is a famous Chinese herbal medicine with antineoplastic property. To predict the potential underlying mechanism, a 1 H NMR-based metabolomics was conducted to use plasma and urine from rats bearing Walker 256 tumor [78]. *Hedyotis diffusa* treatment was found to reverse lactate, acetate, choline, 3-hydroxybutyrate, and L-glutamine in plasma as well as creatinine, L-aspartate, N-acetyl-L-aspartate, and ornithine in urine. Wang et al. developed a combined gut microbiota and metabolomics analysis to investigate the anti-CRC activity of *American ginseng* [79]. By GC/TOF-MS-based metabolomics, *American ginseng* was found to regulate the metabolisms of carbohydrates, lipids, and amino acids. By the 16S rRNA data analysis, *American ginseng* was found to inhibit the changes of microbiome community caused by azoxymethane/dextran sulfate sodium. Kushen injection (CKI) is a famous Chinese medicine preparation and widely used for treating multiple kinds of solid tumors. To evaluate the anti-HCC mechanisms of CKI, a combined network analysis and 1 H-NMR-based metabolomics were used [80]. Network pharmacology analysis found the primary active compounds, the potential targets, and pathways associated with the anti-HCC effects of CKI, which was further validated by metabolomics. Metabolomics analysis validated the primary pathways associated with the anti-HCC effects of CKI were amino acid metabolism and glycometabolism.

#### **2.4 Nucleotide metabolism**

To support the rapid proliferation of cancer cells, nucleic acid synthesis is shown to be accelerated. Accordingly, the anticancer therapy targeting nucleotide metabolism has obtained numerous attentions. Here the recent metabolomics studies of Chinese medicines targeting nucleotide metabolism have been reviewed.

Glaucocalyxin A (GLA) is an ent-kaurene diterpenoid derived from *Rabdosia japonica* and has shown to have antineoplastic property. To explore underlying action mechanism underlying the anti-HCC activity of GLA, a combined GC/ MS- and LC/MS-based metabolomics was conducted using SMMC7721 cells in vitro [81]. It was found GLA treatment diminished amino acid metabolism and elevated the metabolisms of sphingolipid, purine, and pyrimidine. Taurine, as the most abundant free amino acid, has the antineoplastic property against breast cancer. To elucidate the mechanisms underlying the therapeutic benefits of taurine against breast cancer, a GC–TOF-MS-based metabolomics of plasma from dimethylbenz[a] anthracene-induced breast carcinogenesis in rats was conducted [82]. It was found that taurine treatment regulated 23 differential metabolites, which were associated with glucose, energy and amino acid, as well as nucleic acid metabolisms. Celastrol is a bioactive compound derived from *Trypterygium wilfordii HOOK F.* with potential antineoplastic property. To explore underlying action mechanism involved in its anti-acute promyelocytic leukemia activity, a UPLC-MS-based metabolomics of HL-60 cells in vitro and tumor tissue from mice xenograft model in vivo was conducted [83]. It was found that celastrol treatment regulated uridine metabolite, which further enhanced apoptosis. The development of radioprotector to reduce the serious side effects and complications caused by radiotherapy is important. Gamma-tocotrienol (GT3) is one of the isomers of vitamin E with antineoplastic property. To explore the radioprotective mechanism of GT3, a UPLC-QTF MS-based metabolomics of serum from nonhuman primate models in vivo was conducted [84]. It was found that GT3 could regulate the changed fatty acid betaoxidation and amino acid and purine catabolism metabolisms caused by irradiation.

Red kidney bean, also named as *Phaseolus vulgaris L.*, possesses antineoplastic property. To evaluate its anti-melanoma activity, a combined network pharmacology and LC-MS-based metabolomics analysis was conducted using B16-F10 cells

**91**

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective*

in vitro [85]. It was found that the kernel of red kidney bean (RKBC) extract treatment markedly elevated cellular level of cGMP. Network pharmacology analysis showed that quercetin might act as the main effective ingredient of RKBC extract. Ku-jin tea (KJT) is a famous beverage derived from the leaves of the plant *Acer tataricum subsp. ginnala* with antineoplastic property. A UPLC/Q-TOF MS-based metabolomics of urine from azoxymethane-induced precancerous colorectal lesion model in rats was conducted to investigate molecular modes of inhibitory effects of KJT against CRC [86]. It was found that KJT treatment modulated amino acid and purine metabolisms, which accounted for the chemopreventive effects of KJT.

Except for the anticancer therapies of Chinese medicine targeting the changed

Liu et al. developed a UHPLC-MS/MS-based targeted metabolomics to evaluate the efficacy of anticancer drugs, including a traditional Chinese medicine injection Aidi injections and fluorouracil [90]. It was found that with the progression of squamous cell carcinoma of the lung, the levels of 1,3-diaminopropane, cadaverine, and N-acetylputrescine altered. The two-drug treatment alone or co-therapy reversed the levels of 1,3-diaminopropane, cadaverine, and N-acetylputrescine. The team also used this metabolomics method to evaluate the efficacy of Aidi injections on CRC [91]. It was found that Aidi injection treatment could reverse polyamine metabolism, especially agmatine and putrescine, revealing that plasma polyamine could be a biomarker for both early diagnosis and medical treatment of CRC.

In accordance with the holistic perspective of Chinese medicines, metabolomics can help to explain the underlying mechanisms of the anticancer effects of Chinese medicines or the reversion of the drug resistance of chemotherapy and radiotherapy. It can also help to rapidly compare the anticancer effects of multiple compounds from Chinese medicines and act as a quick preliminary platform to screen the most dominant compound related to anticancer bioactivity. Based on the metabolomics analyses of modern studies of Chinese medicines with antineoplastic properties,

metabolisms mentioned above, there are also some other related metabolisms which are the targets by Chinese medicine. Fisetin is a kind of plant flavonoid with antineoplastic property. A HPLC/ESI-MS-based metabolomics of tumor tissues from PCa xenografts in vivo was conducted to explore its therapeutic benefit for PCa [87]. Fisetin treatment was shown to downregulate secreted and intracellular hyaluronan (HA), which conferred resistance to prostate oncogenesis. Yang et al. developed a LC-MS/MS-based metabolomics to study the bioefficacy of a plant galactolipid 1,2-di-O-α-linolenoyl-3-O-β-D- galactopyranosyl-sn-glycerol (dLGG) against melanoma [88]. dLGG treatment markedly elevated 12/15-LOX catalyzed oxylipin products in serum, revealing the novel therapeutic mechanism of phytoagent dLGG against melanoma. Derived from the medicinal plant *Elephantopus scaber*, deoxyelephantopin (DET) is a germacranolide sesquiterpene lactone with antineoplastic property. To study whether the co-therapy of DET and cisplatin could reduce the cisplatin-induced nephrotoxicity, a UPLC/ESI-QTOF MS-based metabolomics of kidney tissues from murine B16 metastatic allograft model in vivo was conducted [89]. It was shown that co-therapy of DET and cisplatin could reverse the changed urea cycle metabolites and hippuric acid in renal tissues caused by cisplatin, revealing that the co-therapy of DET and cisplatin could be an effective treatment with

*DOI: http://dx.doi.org/10.5772/intechopen.88924*

**2.5 Other related metabolisms**

low toxicity for melanoma.

**3. Current perspectives and future challenges**

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective DOI: http://dx.doi.org/10.5772/intechopen.88924*

in vitro [85]. It was found that the kernel of red kidney bean (RKBC) extract treatment markedly elevated cellular level of cGMP. Network pharmacology analysis showed that quercetin might act as the main effective ingredient of RKBC extract. Ku-jin tea (KJT) is a famous beverage derived from the leaves of the plant *Acer tataricum subsp. ginnala* with antineoplastic property. A UPLC/Q-TOF MS-based metabolomics of urine from azoxymethane-induced precancerous colorectal lesion model in rats was conducted to investigate molecular modes of inhibitory effects of KJT against CRC [86]. It was found that KJT treatment modulated amino acid and purine metabolisms, which accounted for the chemopreventive effects of KJT.

#### **2.5 Other related metabolisms**

*Metabolomics - New Insights into Biology and Medicine*

a 1

SMMC 7721 cells to adriamycin. *Hedyotis diffusa* is a famous Chinese herbal medicine with antineoplastic property. To predict the potential underlying mechanism,

H NMR-based metabolomics was conducted to use plasma and urine from rats bearing Walker 256 tumor [78]. *Hedyotis diffusa* treatment was found to reverse lactate, acetate, choline, 3-hydroxybutyrate, and L-glutamine in plasma as well as creatinine, L-aspartate, N-acetyl-L-aspartate, and ornithine in urine. Wang et al. developed a combined gut microbiota and metabolomics analysis to investigate the anti-CRC activity of *American ginseng* [79]. By GC/TOF-MS-based metabolomics, *American ginseng* was found to regulate the metabolisms of carbohydrates, lipids, and amino acids. By the 16S rRNA data analysis, *American ginseng* was found to inhibit the changes of microbiome community caused by azoxymethane/dextran sulfate sodium. Kushen injection (CKI) is a famous Chinese medicine preparation and widely used for treating multiple kinds of solid tumors. To evaluate the

metabolomics were used [80]. Network pharmacology analysis found the primary active compounds, the potential targets, and pathways associated with the anti-HCC effects of CKI, which was further validated by metabolomics. Metabolomics analysis validated the primary pathways associated with the anti-HCC effects of

To support the rapid proliferation of cancer cells, nucleic acid synthesis is shown to be accelerated. Accordingly, the anticancer therapy targeting nucleotide metabolism has obtained numerous attentions. Here the recent metabolomics studies of

Glaucocalyxin A (GLA) is an ent-kaurene diterpenoid derived from *Rabdosia japonica* and has shown to have antineoplastic property. To explore underlying action mechanism underlying the anti-HCC activity of GLA, a combined GC/ MS- and LC/MS-based metabolomics was conducted using SMMC7721 cells in vitro [81]. It was found GLA treatment diminished amino acid metabolism and elevated the metabolisms of sphingolipid, purine, and pyrimidine. Taurine, as the most abundant free amino acid, has the antineoplastic property against breast cancer. To elucidate the mechanisms underlying the therapeutic benefits of taurine against breast cancer, a GC–TOF-MS-based metabolomics of plasma from dimethylbenz[a] anthracene-induced breast carcinogenesis in rats was conducted [82]. It was found that taurine treatment regulated 23 differential metabolites, which were associated with glucose, energy and amino acid, as well as nucleic acid metabolisms. Celastrol is a bioactive compound derived from *Trypterygium wilfordii HOOK F.* with potential antineoplastic property. To explore underlying action mechanism involved in its anti-acute promyelocytic leukemia activity, a UPLC-MS-based metabolomics of HL-60 cells in vitro and tumor tissue from mice xenograft model in vivo was conducted [83]. It was found that celastrol treatment regulated uridine metabolite, which further enhanced apoptosis. The development of radioprotector to reduce the serious side effects and complications caused by radiotherapy is important. Gamma-tocotrienol (GT3) is one of the isomers of vitamin E with antineoplastic property. To explore the radioprotective mechanism of GT3, a UPLC-QTF MS-based metabolomics of serum from nonhuman primate models in vivo was conducted [84]. It was found that GT3 could regulate the changed fatty acid betaoxidation and amino acid and purine catabolism metabolisms caused by irradiation. Red kidney bean, also named as *Phaseolus vulgaris L.*, possesses antineoplastic property. To evaluate its anti-melanoma activity, a combined network pharmacology and LC-MS-based metabolomics analysis was conducted using B16-F10 cells

Chinese medicines targeting nucleotide metabolism have been reviewed.

H-NMR-based

anti-HCC mechanisms of CKI, a combined network analysis and 1

CKI were amino acid metabolism and glycometabolism.

**2.4 Nucleotide metabolism**

**90**

Except for the anticancer therapies of Chinese medicine targeting the changed metabolisms mentioned above, there are also some other related metabolisms which are the targets by Chinese medicine. Fisetin is a kind of plant flavonoid with antineoplastic property. A HPLC/ESI-MS-based metabolomics of tumor tissues from PCa xenografts in vivo was conducted to explore its therapeutic benefit for PCa [87]. Fisetin treatment was shown to downregulate secreted and intracellular hyaluronan (HA), which conferred resistance to prostate oncogenesis. Yang et al. developed a LC-MS/MS-based metabolomics to study the bioefficacy of a plant galactolipid 1,2-di-O-α-linolenoyl-3-O-β-D- galactopyranosyl-sn-glycerol (dLGG) against melanoma [88]. dLGG treatment markedly elevated 12/15-LOX catalyzed oxylipin products in serum, revealing the novel therapeutic mechanism of phytoagent dLGG against melanoma. Derived from the medicinal plant *Elephantopus scaber*, deoxyelephantopin (DET) is a germacranolide sesquiterpene lactone with antineoplastic property. To study whether the co-therapy of DET and cisplatin could reduce the cisplatin-induced nephrotoxicity, a UPLC/ESI-QTOF MS-based metabolomics of kidney tissues from murine B16 metastatic allograft model in vivo was conducted [89]. It was shown that co-therapy of DET and cisplatin could reverse the changed urea cycle metabolites and hippuric acid in renal tissues caused by cisplatin, revealing that the co-therapy of DET and cisplatin could be an effective treatment with low toxicity for melanoma.

Liu et al. developed a UHPLC-MS/MS-based targeted metabolomics to evaluate the efficacy of anticancer drugs, including a traditional Chinese medicine injection Aidi injections and fluorouracil [90]. It was found that with the progression of squamous cell carcinoma of the lung, the levels of 1,3-diaminopropane, cadaverine, and N-acetylputrescine altered. The two-drug treatment alone or co-therapy reversed the levels of 1,3-diaminopropane, cadaverine, and N-acetylputrescine. The team also used this metabolomics method to evaluate the efficacy of Aidi injections on CRC [91]. It was found that Aidi injection treatment could reverse polyamine metabolism, especially agmatine and putrescine, revealing that plasma polyamine could be a biomarker for both early diagnosis and medical treatment of CRC.

#### **3. Current perspectives and future challenges**

In accordance with the holistic perspective of Chinese medicines, metabolomics can help to explain the underlying mechanisms of the anticancer effects of Chinese medicines or the reversion of the drug resistance of chemotherapy and radiotherapy. It can also help to rapidly compare the anticancer effects of multiple compounds from Chinese medicines and act as a quick preliminary platform to screen the most dominant compound related to anticancer bioactivity. Based on the metabolomics analyses of modern studies of Chinese medicines with antineoplastic properties,

the potential of metabolic treatment with pure compounds, herb extracts, and formulations from Chinese medicines is gaining numerous attentions. However, many challenges still exist in the metabolomics study of antineoplastic Chinese medicines, and there is still a long way for the wide application of metabolomics of Chinese medicines into the treatment of cancer. Firstly, it is critical to make good experimental design before starting the experiment, such as the choices of samples, analytical platforms, and methodological approaches. Secondly, it is quite essential for researchers to develop metabolomics, such as the development of data excavation and the identification and quantification of more metabolites. Thirdly, it is important for us to validate the results from metabolomics studies with more mechanical biological experiments. Fourthly, as no one single technology could achieve a comprehensive result, it is strongly suggested to combine metabolomics with some other advanced technologies for better investigation of the action mechanisms of antineoplastic Chinese medicines, such as other "omics" technologies, network pharmacology, and gut microbiome analyses. Last but not least, more attentions will be drawn to personalized treatment based on metabolomics. It has been reported that because of the interaction between genes and environment (polypharmacy, gut microbiota, xenobiotics), not all patients present the same response to drug treatment [92]. Personalized treatment has been put forward and of great importance nowadays. Although pharmacogenomics is still the only means in terms of personalized treatment, its limitation of ignoring the environmental influences has been increasingly recognized. As an alternative and complementary manner, pharmacometabolomics is an emerging "omics" and has been proposed for personalized treatment [16]. As the results of both genetic and environmental influences, pharmacometabolomics can help to understand individual phenotypic variations in drug responses by providing individual metabolic signatures of both gene-derived endogenous and environment-derived exogenous metabolites [93]. Pharmacometabolomics will offer an intriguingly avenue for personalized treatment in the future.

#### **4. Conclusions**

In this chapter, we systematically reviewed recent studies on metabolism-targeting anticancer therapies based on metabolomics in terms of glucose, lipid, amino acid, and nucleotide metabolisms and other altered metabolisms, with special emphasis on the potential of metabolic treatment with pure compounds, herb extracts, and formulations from Chinese medicines. The trends of future development of metabolism-targeting anticancer therapies were also discussed. Hopefully, we expect that through the systematic review on the recent metabolomics studies targeting Chinese medicine treatment on human cancers, more attention will be drawn to the promising candidates from the resourceful Chinese medicine as effective neoadjuvant therapies for cancer treatment clinically.

#### **Acknowledgements**

The study was financially supported by grants from the research council of the University of Hong Kong (Project Codes: 104004092, 104004460, 104004746), the Research Grants Committee (RGC) of Hong Kong, HKSAR (Project Codes: 764708, 766211, 17152116), Wong's Donation on Modern Oncology of Chinese Medicine (Project code: 200006276), Gala Family Trust (Project Code: 200007008), Innovation Technology Fund of Hong Kong (ITF. Project code: 260900263), and HMRF (Project code: 16172751).

**93**

**Author details**

Hong Kong, Hong Kong, China

Wei Guo, Hor-Yue Tan, Ning Wang and Yibin Feng\*

\*Address all correspondence to: yfeng@hku.hk

provided the original work is properly cited.

School of Chinese Medicine, Li Ka Shing Faculty of Medicine, The University of

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective*

*DOI: http://dx.doi.org/10.5772/intechopen.88924*

*Chinese Medicines for Cancer Treatment from the Metabolomics Perspective DOI: http://dx.doi.org/10.5772/intechopen.88924*

#### **Author details**

*Metabolomics - New Insights into Biology and Medicine*

the potential of metabolic treatment with pure compounds, herb extracts, and formulations from Chinese medicines is gaining numerous attentions. However, many challenges still exist in the metabolomics study of antineoplastic Chinese medicines, and there is still a long way for the wide application of metabolomics of Chinese medicines into the treatment of cancer. Firstly, it is critical to make good experimental design before starting the experiment, such as the choices of samples, analytical platforms, and methodological approaches. Secondly, it is quite essential for researchers to develop metabolomics, such as the development of data excavation and the identification and quantification of more metabolites. Thirdly, it is important for us to validate the results from metabolomics studies with more mechanical biological experiments. Fourthly, as no one single technology could achieve a comprehensive result, it is strongly suggested to combine metabolomics with some other advanced technologies for better investigation of the action mechanisms of antineoplastic Chinese medicines, such as other "omics" technologies, network pharmacology, and gut microbiome analyses. Last but not least, more attentions will be drawn to personalized treatment based on metabolomics. It has been reported that because of the interaction between genes and environment (polypharmacy, gut microbiota, xenobiotics), not all patients present the same response to drug treatment [92]. Personalized treatment has been put forward and of great importance nowadays. Although pharmacogenomics is still the only means in terms of personalized treatment, its limitation of ignoring the environmental influences has been increasingly recognized. As an alternative and complementary manner, pharmacometabolomics is an emerging "omics" and has been proposed for personalized treatment [16]. As the results of both genetic and environmental influences, pharmacometabolomics can help to understand individual phenotypic variations in drug responses by providing individual metabolic signatures of both gene-derived endogenous and environment-derived exogenous metabolites [93]. Pharmacometabolomics will offer

an intriguingly avenue for personalized treatment in the future.

tive neoadjuvant therapies for cancer treatment clinically.

In this chapter, we systematically reviewed recent studies on metabolism-targeting anticancer therapies based on metabolomics in terms of glucose, lipid, amino acid, and nucleotide metabolisms and other altered metabolisms, with special emphasis on the potential of metabolic treatment with pure compounds, herb extracts, and formulations from Chinese medicines. The trends of future development of metabolism-targeting anticancer therapies were also discussed. Hopefully, we expect that through the systematic review on the recent metabolomics studies targeting Chinese medicine treatment on human cancers, more attention will be drawn to the promising candidates from the resourceful Chinese medicine as effec-

The study was financially supported by grants from the research council of the University of Hong Kong (Project Codes: 104004092, 104004460, 104004746), the Research Grants Committee (RGC) of Hong Kong, HKSAR (Project Codes: 764708, 766211, 17152116), Wong's Donation on Modern Oncology of Chinese Medicine (Project code: 200006276), Gala Family Trust (Project Code: 200007008), Innovation Technology Fund of Hong Kong (ITF. Project code: 260900263), and

**92**

**4. Conclusions**

**Acknowledgements**

HMRF (Project code: 16172751).

Wei Guo, Hor-Yue Tan, Ning Wang and Yibin Feng\* School of Chinese Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China

\*Address all correspondence to: yfeng@hku.hk

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Oncology. 2017;**1**:18

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and Biomedical Analysis.

Chemistry. 2018;**56**(1):5-17

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2018;**8**(1):624

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metabolic profiling. Analytical and Bioanalytical Chemistry. 2018;**410**(14):3325-3335

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2018;**1102-1103**:23-33

2019;**9**(3):50

**98**

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nephrotoxicity. Phytomedicine. 2018;**56**:194-206

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[91] Liu R et al. Quantitative metabolomics for investigating the value of polyamines in the early diagnosis and therapy of colorectal cancer. Oncotarget. 2018;**9**(4):4583-4592

[92] Katsila T, Patrinos GP. Editorial: (Pharmaco)Metabolomics in drug discovery and individualisation of treatment. Current Pharmaceutical Design. 2017;**23**(14):2027

[93] Nicholson JK, Wilson ID, Lindon JC. Pharmacometabonomics as an effector for personalized medicine. Pharmacogenomics. 2011;**12**(1):103-111

### *Edited by Wael N. Hozzein*

This book is mainly for researchers interested in the new developments and applications of metabolomics. It is also important for physicians using metabolomic approaches in the diagnosis of diseases or treatment, and for postgraduate students starting their research projects on metabolomics. The book is divided into two sections as indicated from its title, namely: new insights into biology and new insights into medicine. It gives examples of the different applications of metabolomics from the production of biosurfactants by marine microorganisms to the applications of data from fecal metabolomics, serum metabolomics, and metabolomics of microbiota, as well as the use of Chinese medicines for cancer treatment. Overall, this is a wellwritten book, containing some very interesting research avenues and cutting-edge approaches. Finally, the editing of this book was of special interest to me and I hope that readers will also find it stimulating.

Published in London, UK © 2020 IntechOpen © Whitepointer / iStock

Metabolomics - New Insights into Biology and Medicine

IntechOpen Book Series

Biochemistry, Volume 16

Metabolomics

New Insights into Biology and Medicine

*Edited by Wael N. Hozzein*