**3. Metagenomic, metatranscriptomic, metaproteomic, and metabolomic approaches to mimic the gut ecosystem**

comparative studies in germ-free animals, by using conventional microbiome, or by animals with human microbiome-associated, and from *in vitro* studies using human fecal incubations. In this sense, gastric bypass surgery leads to a substantial shift in the gut microbiota, which may contribute to weight loss in part by HIM modifications [63]. One of the most important situations is that the immune system faces microbiome continuously and it affects the host immunity and inflammation control. In this line, GM can affect the immune system by metabolites like SCFAs [64] and toxin production, such as LPS [61], modifying the adipogenesis and influence in the insulin resistance. LPS induces generation of pro-inflammatory cytokines by the immune system and adipocytes. Acetate, butyrate, and propionate (SCFA) modulate the gene expression in host, modifying the infant microbiome and stimulating white blood cells [65]. Some studies suggested that infants born by cesarean section are at greater risk of developing obesity and/ or diabetes than those born vaginally [66]. Other studies with preschool children showed overweight or obesity in children born by cesarean [67], while in other showed the opposite [68]. On the other hand, infant feeding is also important to develop GM because mother milk is not sterile and is the first bacteria to colonize the gut [69]. Breast milk is a source of probiotics and other

16 Diabetes Food Plan

bacteria [71] containing more than 700 species [70]. The median bacterial load is 10<sup>6</sup>

cells/Ml [71]. *Streptococci* and *Staphylococci* are predominant bacterial genera in human milk [69]. *Weissella*, *Leuconostoc*, *Staphylococcus*, *Streptococcus*, and *Lactococcus* are predominant in colostrum and are thought to modify the lactation to increase *Veillonella*, *Leptotrichia*, and *Prevotella* for over 6 months [71]. In this line, milk from obese mothers contains less-diverse bacteria than normal-weight mothers and has pro-inflammatory properties [72, 73, 74]. Another import issue is the infection of virus or bacteria pathogens. For instance, *Clostridium difficile* patients and asymptomatic carriers with the use of 16S ribosomal RNA gene pyrosequencing found that both had reduced microbial richness, diversity, and dysbiosis state compared with healthy subjects [75]. Gut microbiota transplants can help to increase the richness and diversity of GM [76]. For example, clearance of hepatitis B virus infection requires the reestablishment of the gut microbiota. Drugs also affect the microbiome including the drugs used to treat DM2 [77]. But also in the opposite direction. Broad-spectrum antibiotics reduce bacterial diversity and provoke the augmentation of some species like opportunistic pathogens [78], predisposing to inflammatory bowel disease [79]. *Clindamycin* produces a prolonged effect of modifying the microbiome in infants [80]. Studies in both mice and humans have found that the use of antibiotics early in life could promote obesity later in life, mediated by the alteration of the gut microbiota [81]. In the same line, antibiotics can reduce body weight and increase insulin sensitivity [82]. *Berberine* is recognized for its antidiabetic effect by modulating the gut microbiota and diminishing glucose and insulin resistance [83]. Metformin increases the insulin sensitivity in fat cells and hepatocytes and also reduces the overproduction of glucose in hepatocytes. Recent studies showed that metformin alters the GM [84, 85]. In obese mice, metformin caused the increase of mucin-degrading *Akkermansia* [85]. In human GM, altered gut microbiota can be the cause of common metformin side effects and could have a role in drug efficacy. There is a link between high-calorie diets contributing to obesity and DM2 and GM [55]. Dietary changes can result in substantial and rapid changes in the GM [86]. High-fat diet reduces the α diversity in GM. For instance, *A. muciniphila* decreased in obese mice and DM2, and it can be normalized by prebiotic consumption [62]. Treatments with *A. muciniphila* reduced fat mass, inflammation, and insulin resistance induced/caused by high-fat diet [62]. An enterotype is a classification of living organisms based on their bacteriological ecosystem in the gut microbiome. Changes in GM enterotypes were strongly associated with long-term diets, *Bacteroides* with protein and animal fat, and

bacterial

Metagenomics is used to study differences in microbiome composition having diseases and compared with healthy people. Recently a technique was developed (Ecemble; enzyme classification using ensemble approach) to predict enzymes from protein sequences in gut microbiome from metagenomic samples and study the role of GM in metabolism; 48 pathways having at least one bacteria-encoded enzyme were found [88]. The carbohydrate active enzymes are important due to their role in dietary fiber and non-absorbed carbohydrate metabolism; 81 families of glycoside hydrolases have been identified. On the other hand, single-cell genomics uses isolated colonies to shotgun sequencing and put in phylogenetic context to complement metagenomic analysis. Is important to note that the presence of a gene does not mean it amounts to their expression; in this sense, metatranscriptomics, metaproteomics, and metabonomics are needed. Metatranscriptomics involves the generation of cDNA by reverse transcription and permits to identify noncoding RNAs and small RNAs that control quorum sensing and stress response [89]. Metatranscriptomics of fecal microbiome analysis of the 16S rRNA transcripts showed *Firmicutes* (49%) and *Bacteroidetes* (31%) are the main source of RNA and smaller proportion of *Proteobacteria* (3.7%), *Actinobacteria* (0.4%), and *Lentisphaerae* (0.2%) and *Lachnospiraceae* and *Ruminococcaceae* are the major proportion of *Firmicutes*, whereas *Bacteroidaceae, Prevotellaceae,* and *Rikenellaceae* for *Bacteroidetes phylum* [90]. Other transcripts were compared with COG database to obtain a functional distribution. Results showed similar behavior for carbohydrate transport, energy production, and synthesis of cellular components. Nevertheless acid and lipid metabolism, motibily, and secondary metabolite biosynthesis were underregulated. Unfortunately, short half time of bacteria RNA makes the detection of all RNAs in fecal samples difficult. Metaproteomics permits to determine gene translation and post-transductional modifications and permits to classify microorganism to a specific catalytic function [91, 92]. Temporal stability of the fecal metaproteome was assessed, and it was determined that glutamate dehydrogenase showed high level of redundancy in *Lachnospiraceae*, *Bacteroidaceae*, *Ruminococcaceae*, and *Bifidobacteriaceae*. Ten percent of total proteome is involved to ABC sugar transport and glycolytic enzymes; the main functional categories were metabolism of carbohydrates, nucleotides, energy, amino acids, and cofactors and vitamins (especially B12 and folic acid) [87]. Finally, metabolomic approach allows to determine low-molecular-weight compounds in fecal sample and can be influenced by environmental inputs and metabolic interactions between host and environment. For example, SCFA content in the gut can be modified by diet; after that, absorption from the gut initiates the metabolism of the host and results in downstream metabolic perturbations and the generation of microbial-host co-metabolites [93]. For instance, intake of choline (meat and eggs) can form trimethylamine and dimethylamine by GM, trimethylamine is toxic and should be converted to trimethylamine-*N*-oxide (TMAO), and the latter is an electron acceptor for anaerobic metabolism *of E. coli* and is implicated in cardiovascular disease (CVD) [94–98]. Genomic

and dietary components regulate genetic information. Nutrigenomics involves understanding how diet components affect gene expression, meaning which genes are induced and which are repressed against a particular nutrient [103]. Chronic diseases, such as obesity, DM2, and cancer, are expressed from complex polygenic reactions with the environment. The most influential environmental interaction in the development of these diseases is given by the consumed nutrients. Evidence of gene-nutrient interaction is substantially demonstrated, estimating that a balanced healthy nutrition reduces the overall incidence of cancer by 35%. On the other hand, polymorphisms that predispose to certain diseases have been identified under unhealthy diet; this is the case of DM2, osteoporosis, vascular disease, and others, which can be prevented by modifying the diet [104]. The regulation of gene expression is performed through specific proteins that interact with DNA through posttranscriptional or posttranslational modifications. Regulation can occur at the level of mRNA during splicing; it would result from the interaction of certain molecules with specific nutrients, whose result could be potentially preventive [104]. The diet and the GM composition have also been associated with different characteristics of the metabolic syndrome (MS) (obesity, DM2, cardiovascular diseases, and nonalcoholic steatohepatitis). Increasing evidence suggests that the GM contributes to the onset of its characteristic low-grade inflammation, through mechanisms associated with intestinal barrier dysfunction [105]. The GM of an obese person in comparison with a normal-weight person presents a greater percentage of *Firmicutes* and smaller percentage of *Bacteroidetes*, causing dysbiosis in most of the obese and/or diabetic patients (**Figure 2**). Due to its physiological impact, GM is now recognized as an organ and can be transplanted from one individual to another [106]. Recent evidence suggests that the intestinal microbiome affects nutrient acquisition, energy storage, and metabolic pathways of the host [10]. *Bacteroidetes* have been shown to easily assimilate dietary carbohydrates. In a study in mice lacking Toll-like receptors (TLRs), which are receptors that recognize important patterns of inflammation and immunity, it is shown that these mice present hyperphagia and obesity and develop metabolic syndrome, when intestinal microbiome of these

The Microbiome and the Epigenetics of Diabetes Mellitus

http://dx.doi.org/10.5772/intechopen.76201

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**Figure 2.** The GM of an obese person in comparison with a normal-weight person presents a greater percentage of *Firmicutes* and smaller percentage of *Bacteroidetes*, causing dysbiosis in most of the obese and/or diabetic patients.

**Figure 1.** Changes in the composition of the gut microbiome are associated with an increased risk of DM2: In patients with DM2, the intestinal dysbiosis causes a decrease of short-chain fatty acid content, molecules that stimulate the secretion of peptide similar to glucagon type 1 (GLP1) and YY peptide by intestinal cells, proteins that control glucose homeostasis and regulate the intake of nutrients in intestinal cells.

analyses of the GM of subjects suffering from DM2 allowed to identify bacterial genes that are differentially expressed in those subjects; these changes in the gene expression of microbiome are related to the metabolic dysfunction and inflammation that these patients suffer from [99]. Bacterial genes including *Clostridiales* sp. SS3/4, *Faecalibacterium prausnitzii*, *Roseburia intestinalis*, and *Roseburia inulinivorans* are decreased in patients with DM2, whereas genes corresponding to *Bacteroides* sp., *Clostridium hathewayi*, *Clostridium ramosum*, *Clostridium symbiosum*, and *Eggerthella lenta*, are increased in these subjects [100]. Functional analyses suggest enriched genes in samples of DM2 patients are involved in plasmatic membrane sugar transport, branched-chain amino acids transport, methane metabolism, xenobiotic degradation and metabolism, biosynthesis of hydrogen sulfide, and oxidative stress. In contrast, decreased genes are related to functions such as chemotaxis, flagellum assembly, butyrate biosynthesis, and the metabolism of cofactors and vitamins [100]. The depletion of bacterial strains producing butyrate in patients with DM2 may be related to the ability of this fatty acid to increase secretion of peptide similar to glucagon type 1 (GLP1) and peptide YY, whose function is to promote intestinal gluconeogenesis, which leads to a better control of glucose homeostasis and cellular energy (**Figure 1**) [101, 102].
