**2.6. Gut microbiota and diabetes type 2**

Diet plays an important role in obesity. There are preliminary studies suggesting that the consumption of probiotic bacteria found in yogurt and other fermented milk products can beneficially alter the composition of the gut microbiome. Yogurt, a fermented dairy product containing a variety of probiotic bacteria, is found to be associated with a reduction in inflammation markers and weight loss [54]. Yogurt consumption is involved in energy balance and/or energy homeostasis, which in turn controls body weight and reduces the risk of the development of DM2 [55]. One of the causes of dysbiosis is diet, and studies have shown that diet may change the gut microbiota and contribute to obesity and diabetes [56]. Obesity and DM2 are characterized by an altered gut microbiota, inflammation, and gut barrier disruption [57]. Studies in germ-free animals have shown that shifts in the composition of the gut microbiome may play an important role in disease development, specifically obesity and diabetes [58]. There is evidence demonstrating that the composition of the gut microbiota also influences metabolism and can affect energy balance [59], gut permeability [60], and inflammation [61], all of which are associated with obesity and associated disorders, including DM2 [62]. The evidence for the role of the HIM in metabolism of dietary components and the impact on health has been obtained from 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 bacteria [71] containing more than 700 species [70]. The median bacterial load is 10<sup>6</sup> bacterial 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

*Prevotella* with carbohydrates; gut microbiota composition depends on diseases and long-term dietary interventions. GM alterations can be observed within 24 h after high-fat and low-fiber or low-fat and high-fiber diet [87]. Type 1 is characterized by high levels of *Bacteroides*, type 2 has few *Bacteroides* but *Prevotella* are common, and type 3 has high levels of *Ruminococcus* [18].

The Microbiome and the Epigenetics of Diabetes Mellitus

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

17

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

**3. Metagenomic, metatranscriptomic, metaproteomic, and** 

**metabolomic approaches to mimic the gut ecosystem**

*Prevotella* with carbohydrates; gut microbiota composition depends on diseases and long-term dietary interventions. GM alterations can be observed within 24 h after high-fat and low-fiber or low-fat and high-fiber diet [87]. Type 1 is characterized by high levels of *Bacteroides*, type 2 has few *Bacteroides* but *Prevotella* are common, and type 3 has high levels of *Ruminococcus* [18].
