Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era

Impact of Combined Prebiotic Galacto-Oligosaccharides and Bifidobacterium breve-Derived Postbiotic on Gut Microbiota and HbA1c in Prediabetic Adults: A Double-Blind, Randomized, Placebo-Controlled Study

The complex interactions between intestinal microbiota and metabolic disorders are well-documented, with implications for glucose metabolism, energy expenditure, and intestinal permeability. Prebiotics induce beneficial changes in gut microbiota composition in prediabetes, while postbiotics can enhance gut barrier function, complementing each other to improve glucose metabolism and insulin sensitivity. This study investigated the effects of a 12-week dietary fibre (DF) supplement on gut health, metabolic function, and diet. The supplement contained konjac glucomannan (KGM), galacto-oligosaccharides (GOSs), and exopolysaccharides (EPSs) from Bifidobacterium breve. In a randomised, double-blind, placebo-controlled, parallel-group clinical trial, 53 prediabetic volunteers were randomly assigned to either a daily DF supplement (YMETA) or a placebo (cellulose microcrystalline) for 12 weeks, followed by a 4-week follow-up. Measurements included gut microbiota composition, glycated haemoglobin (HbA1c), fasting plasma glucose (FPG), plasma lipids, anthropometry, body composition, blood pressure, and dietary intake. The intervention group showed a significant increase in alpha diversity and butyrate-producing bacteria, with reductions in HbA1c and FPG levels below prediabetes thresholds. No significant changes were observed in the placebo group. This study suggests that manipulating the human gut microbiome through dietary interventions could be a promising therapeutic approach to managing prediabetes and preventing or delaying diabetes.

Biomarker Identification for Preterm Birth Susceptibility: Vaginal Microbiome Meta-Analysis Using Systems Biology and Machine Learning Approaches

PROBLEM

The vaginal microbiome has a substantial role in the occurrence of preterm birth (PTB), which contributes substantially to neonatal mortality worldwide. However, current bioinformatics approaches mostly concentrate on the taxonomic classification and functional profiling of the microbiome, limiting their abilities to elucidate the complex factors that contribute to PTB.

METHOD OF STUDY

A total of 3757 vaginal microbiome 16S rRNA samples were obtained from five publicly available datasets. The samples were divided into two categories based on pregnancy outcome: preterm birth (PTB) (N = 966) and term birth (N = 2791). Additionally, the samples were further categorized based on the participants' race and trimester. The 16S rRNA reads were subjected to taxonomic classification and functional profiling using the Parallel-META 3 software in Ubuntu environment. The obtained abundances were analyzed using an integrated systems biology and machine learning approach to determine the key microbes, pathways, and genes that contribute to PTB. The resulting features were further subjected to statistical analysis to identify the top nine features with the greatest effect sizes.

RESULTS

We identified nine significant features, namely Shuttleworthia, Megasphaera, Sneathia, proximal tubule bicarbonate reclamation pathway, systemic lupus erythematosus pathway, transcription machinery pathway, lepA gene, pepX gene, and rpoD gene. Their abundance variations were observed through the trimesters.

CONCLUSIONS

Vaginal infections caused by Shuttleworthia, Megasphaera, and Sneathia and altered small metabolite biosynthesis pathways such as lipopolysaccharide folate and retinal may increase the susceptibility to PTB. The identified organisms, genes, pathways, and their networks may be specifically targeted for the treatment of bacterial infections that increase PTB risk.

Microbiota based personalized nutrition improves hyperglycaemia and hypertension parameters and reduces inflammation: a prospective, open label, controlled, randomized, comparative, proof of concept study

Background

Recent studies suggest that gut microbiota composition, abundance and diversity can influence many chronic diseases such as type 2 diabetes. Modulating gut microbiota through targeted nutrition can provide beneficial effects leading to the concept of personalized nutrition for health improvement. In this prospective clinical trial, we evaluated the impact of a microbiome-based targeted personalized diet on hyperglycaemic and hyperlipidaemic individuals. Specifically, BugSpeaks®-a microbiome profile test that profiles microbiota using next generation sequencing and provides personalized nutritional recommendation based on the individual microbiota profile was evaluated.

Methods

A total of 30 participants with type 2 diabetes and hyperlipidaemia were recruited for this study. The microbiome profile of the 15 participants (test arm) was evaluated using whole genome shotgun metagenomics and personalized nutritional recommendations based on their microbiota profile were provided. The remaining 15 participants (control arm) were provided with diabetic nutritional guidance for 3 months. Clinical and anthropometric parameters such as HbA1c, systolic/diastolic pressure, c-reactive protein levels and microbiota composition were measured and compared during the study.

Results

The test arm (microbiome-based nutrition) showed a statistically significant decrease in HbA1c level from 8.30 (95% confidence interval (CI), [7.74-8.85]) to 6.67 (95% CI [6.2-7.05]), p < 0.001 after 90 days. The test arm also showed a 5% decline in the systolic pressure whereas the control arm showed a 7% increase. Incidentally, a sub-cohort of the test arm of patients with >130 mm Hg systolic pressure showed a statistically significant decrease of systolic pressure by 14%. Interestingly, CRP level was also found to drop by 19.5%. Alpha diversity measures showed a significant increase in Shannon diversity measure (p < 0.05), after the microbiome-based personalized dietary intervention. The intervention led to a minimum two-fold (Log2 fold change increase in species like Phascolarctobacterium succinatutens, Bifidobacterium angulatum, and Levilactobacillus brevis which might have a beneficial role in the current context and a similar decrease in species like Alistipes finegoldii, and Sutterella faecalis which have been earlier shown to have some negative effects in the host. Overall, the study indicated a net positive impact of the microbiota based personalized dietary regime on the gut microbiome and correlated clinical parameters.

Gut microbiome signatures associated with type 2 diabetes in obesity in Mongolia

Mongolian people possess a unique dietary habit characterized by high consumption of meat and dairy products and fewer vegetables, resulting in the highest obesity rate in East Asia. Although obesity is a known cause of type 2 diabetes (T2D), the T2D rate is moderate in this population; this is known as the "Mongolian paradox." Since the gut microbiota plays a key role in energy and metabolic homeostasis as an interface between food and body, we investigated gut microbial factors involved in the prevention of the co-occurrence of T2D with obesity in Mongolians. We compared the gut microbiome and metabolome of Mongolian adults with obesity with T2D (DO: n = 31) or without T2D (NDO: n = 35). Dysbiotic signatures were found in the gut microbiome of the DO group; lower levels of Faecalibacterium and Anaerostipes which are known as short-chain fatty acid (SCFA) producers and higher levels of Methanobrevibacter, Desulfovibrio, and Solobacterium which are known to be associated with certain diseases. On the other hand, the NDO group exhibited a higher level of fecal SCFA concentration, particularly acetate. This is consistent with the results of the whole shotgun metagenomic analysis, which revealed a higher relative abundance of SCFA biosynthesis-related genes encoded largely by Anaerostipes hadrus in the NDO group. Multiple logistic regression analysis including host demographic parameters indicated that acetate had the highest negative contribution to the onset of T2D. These findings suggest that SCFAs produced by the gut microbial community participate in preventing the development of T2D in obesity in Mongolians.

Gut Dysbiosis Shaped by Cocoa Butter-Based Sucrose-Free HFD Leads to Steatohepatitis, and Insulin Resistance in Mice

BACKGROUND

High-fat diets cause gut dysbiosis and promote triglyceride accumulation, obesity, gut permeability changes, inflammation, and insulin resistance. Both cocoa butter and fish oil are considered to be a part of healthy diets. However, their differential effects on gut microbiome perturbations in mice fed high concentrations of these fats, in the absence of sucrose, remains to be elucidated. The aim of the study was to test whether the sucrose-free cocoa butter-based high-fat diet (C-HFD) feeding in mice leads to gut dysbiosis that associates with a pathologic phenotype marked by hepatic steatosis, low-grade inflammation, perturbed glucose homeostasis, and insulin resistance, compared with control mice fed the fish oil based high-fat diet (F-HFD).

RESULTS

C57BL/6 mice (5-6 mice/group) were fed two types of high fat diets (C-HFD and F-HFD) for 24 weeks. No significant difference was found in the liver weight or total body weight between the two groups. The 16S rRNA sequencing of gut bacterial samples displayed gut dysbiosis in C-HFD group, with differentially-altered microbial diversity or relative abundances. Bacteroidetes, Firmicutes, and Proteobacteria were highly abundant in C-HFD group, while the Verrucomicrobia, Saccharibacteria (TM7), Actinobacteria, and Tenericutes were more abundant in F-HFD group. Other taxa in C-HFD group included the Bacteroides, Odoribacter, Sutterella, Firmicutes bacterium (AF12), Anaeroplasma, Roseburia, and Parabacteroides distasonis. An increased Firmicutes/Bacteroidetes (F/B) ratio in C-HFD group, compared with F-HFD group, indicated the gut dysbiosis. These gut bacterial changes in C-HFD group had predicted associations with fatty liver disease and with lipogenic, inflammatory, glucose metabolic, and insulin signaling pathways. Consistent with its microbiome shift, the C-HFD group showed hepatic inflammation and steatosis, high fasting blood glucose, insulin resistance, increased hepatic de novo lipogenesis (Acetyl CoA carboxylases 1 (Acaca), Fatty acid synthase (Fasn), Stearoyl-CoA desaturase-1 (Scd1), Elongation of long-chain fatty acids family member 6 (Elovl6), Peroxisome proliferator-activated receptor-gamma (Pparg) and cholesterol synthesis (β-(hydroxy β-methylglutaryl-CoA reductase (Hmgcr). Non-significant differences were observed regarding fatty acid uptake (Cluster of differentiation 36 (CD36), Fatty acid binding protein-1 (Fabp1) and efflux (ATP-binding cassette G1 (Abcg1), Microsomal TG transfer protein (Mttp) in C-HFD group, compared with F-HFD group. The C-HFD group also displayed increased gene expression of inflammatory markers including Tumor necrosis factor alpha (Tnfa), C-C motif chemokine ligand 2 (Ccl2), and Interleukin-12 (Il12), as well as a tendency for liver fibrosis.

CONCLUSION

These findings suggest that the sucrose-free C-HFD feeding in mice induces gut dysbiosis which associates with liver inflammation, steatosis, glucose intolerance and insulin resistance.

Exercise perspective: Benefits and mechanisms of gut microbiota on the body

Gut microbiota refers to the vast and diverse community of microorganisms residing in the intestines. Factors such as genetics, environmental influences (e.g., exercise, diet), and early life experiences (e.g., infant feeding methods) can all affect the ecological balance of gut microbiota within the body. Dysbiosis of the gut microbiota is associated with extra-intestinal diseases such as Parkinson's syndrome, osteoporosis, and autoimmune diseases, suggesting that disturbances in gut microbiota may be one of the causes of these diseases. Exercise benefits various diseases, with gut microbiota playing a role in regulating the nervous, musculoskeletal, and immune systems. Gut microbiota can impact the body's health status through the gut-brain axis, gut-muscle axis, and immune pathways. Moderate-intensity aerobic exercise can increase the quantity of gut microbiota and change microbial abundance, although short-term exercise does not significantly affect the alpha diversity of the microbiota. Resistance exercise also does not have a significant regulatory effect on gut microbiota.

Effect of metformin and metformin/linagliptin on gut microbiota in patients with prediabetes

Lifestyle modifications, metformin, and linagliptin reduce the incidence of type 2 diabetes (T2D) in people with prediabetes. The gut microbiota (GM) may enhance such interventions' efficacy. We determined the effect of linagliptin/metformin (LM) vs metformin (M) on GM composition and its relationship to insulin sensitivity (IS) and pancreatic β-cell function (Pβf) in patients with prediabetes. A cross-sectional study was conducted at different times: basal, six, and twelve months in 167 Mexican adults with prediabetes. These treatments increased the abundance of GM SCFA-producing bacteria M (Fusicatenibacter and Blautia) and LM (Roseburia, Bifidobacterium, and [Eubacterium] hallii group). We performed a mediation analysis with structural equation models (SEM). In conclusion, M and LM therapies improve insulin sensitivity and Pβf in prediabetics. GM is partially associated with these improvements since the SEM models suggest a weak association between specific bacterial genera and improvements in IS and Pβf.

Updated Progress on Polysaccharides with Anti-Diabetic Effects through the Regulation of Gut Microbiota: Sources, Mechanisms, and Structure-Activity Relationships

Diabetes mellitus (DM) is a common chronic metabolic disease worldwide. The disturbance of the gut microbiota has a complex influence on the development of DM. Polysaccharides are one type of the most important natural components with anti-diabetic effects. Gut microbiota can participate in the fermentation of polysaccharides, and through this, polysaccharides regulate the gut microbiota and improve DM. This review begins by a summary of the sources, anti-diabetic effects and the gut microbiota regulation functions of natural polysaccharides. Then, the mechanisms of polysaccharides in regulating the gut microbiota to exert anti-diabetic effects and the structure-activity relationship are summarized. It is found that polysaccharides from plants, fungi, and marine organisms show great hypoglycemic activities and the gut microbiota regulation functions. The mechanisms mainly include repairing the gut burrier, reshaping gut microbiota composition, changing the metabolites, regulating anti-inflammatory activity and immune function, and regulating the signal pathways. Structural characteristics of polysaccharides, such as monosaccharide composition, molecular weight, and type of glycosidic linkage, show great influence on the anti-diabetic activity of polysaccharides. This review provides a reference for the exploration and development of the anti-diabetic effects of polysaccharides.

Gut microbiota in relationship to diabetes mellitus and its late complications with a focus on diabetic foot syndrome: A review

Diabetes mellitus is a chronic disease affecting glucose metabolism. The pathophysiological reactions underpinning the disease can lead to the development of late diabetes complications. The gut microbiota plays important roles in weight regulation and the maintenance of a healthy digestive system. Obesity, diabetes mellitus, diabetic retinopathy, diabetic nephropathy and diabetic neuropathy are all associated with a microbial imbalance in the gut. Modern technical equipment and advanced diagnostic procedures, including xmolecular methods, are commonly used to detect both quantitative and qualitative changes in the gut microbiota. This review summarises collective knowledge on the role of the gut microbiota in both types of diabetes mellitus and their late complications, with a particular focus on diabetic foot syndrome.

Impact of exercise training on gut microbiome imbalance in obese individuals: a study based on Mendelian randomization analysis

Objective: The aim of this study was to investigate the relationship between exercise and gut Microbiome and to assess its possible causality. Methods: Using Mendelian randomization (MR) research methods, we collected genetic data from different populations, including genetic variants associated with relative abundance or presence of microbial taxa as instrumental variables. At the same time, we extracted results related to obesity and gut Microbiome from existing relevant studies and used inverse variance weighting (IVW), weighted median, and MR-Egger regression to assess the causal relationship between obesity and gut Microbiome. We plotted forest plots and scatter plots of the association between obesity and gut Microbiome. Results: Gut Microbiome was positively associated with obesity, and four bacterial genera (Akkermansia, RuminococcaceaeUCG011, Holdemania, and Intestinimonas) were associated with obesity according to inverse variance-weighted estimation in at least one MR method. Inverse variance weighted estimation showed that obesity was associated with obesity in Akkermansia (OR = 0.810, 95% CI 0.608-1.079, p = 0.04), RuminococcaceaeUCG011 (OR = 1.238, 95% CI 0. 511-2.999, p = 0.04), Holdemania Intestinimonas (OR = 1.214, 95% CI 1.002-1.470, p = 0.03), and Intestinimonas (OR = 0.747, 95% CI 0.514-1.086, p = 0.01) had a relevant effect. Obesity decreased the abundance of Akkermansia, Intestinimonas microbiome and increased the abundance of RuminococcaceaeUCG011, Holdemania microbiome. Conclusion: The results of this study, conducted using a two-sample Mendelian randomization method, suggest a causal relationship between obesity and intestinal microbiome. Obesity decreased the abundance of Akkermansia, Intestinimonas microbiome and increased the abundance of RuminococcaceaeUCG011, Holdemania microbiome. More randomized controlled trials are necessary to elucidate the protective effects of exercise on gut Microbiome and its unique protective mechanisms.

Mechanism-guided fine-tuned microbiome potentiates anti-tumor immunity in HCC

Microbiome, including bacteria, fungi, and viruses, plays a crucial role in shaping distal and proximal anti-tumor immunity. Mounting evidence showed that commensal microbiome critically modulates immunophenotyping of hepatocellular carcinoma (HCC), a leading cause of cancer-related death. However, their role in anti-tumor surveillance of HCC is still poorly understood. Herein, we spotlighted growing interests in how the microbiome influences the progression and immunotherapeutic responses of HCC via changing local tumor microenvironment (TME) upon translocating to the sites of HCC through different "cell-type niches". Moreover, we summarized not only the associations but also the deep insight into the mechanisms of how the extrinsic microbiomes interplay with hosts to shape immune surveillance and regulate TME and immunotherapeutic responses. Collectively, we provided a rationale for a mechanism-guided fine-tuned microbiome to be neoadjuvant immunotherapy in the near future.

Exploring microbial worlds: a review of whole genome sequencing and its application in characterizing the microbial communities

The classical microbiology techniques have inherent limitations in unraveling the complexity of microbial communities, necessitating the pivotal role of sequencing in studying the diversity of microbial communities. Whole genome sequencing (WGS) enables researchers to uncover the metabolic capabilities of the microbial community, providing valuable insights into the microbiome. Herein, we present an overview of the rapid advancements achieved thus far in the use of WGS in microbiome research. There was an upsurge in publications, particularly in 2021 and 2022 with the United States, China, and India leading the metagenomics research landscape. The Illumina platform has emerged as the widely adopted sequencing technology, whereas a significant focus of metagenomics has been on understanding the relationship between the gut microbiome and human health where distinct bacterial species have been linked to various diseases. Additionally, studies have explored the impact of human activities on microbial communities, including the potential spread of pathogenic bacteria and antimicrobial resistance genes in different ecosystems. Furthermore, WGS is used in investigating the microbiome of various animal species and plant tissues such as the rhizosphere microbiome. Overall, this review reflects the importance of WGS in metagenomics studies and underscores its remarkable power in illuminating the variety and intricacy of the microbiome in different environments.

Lactobacillus fermentum CKCC1858 alleviates hyperlipidemia in golden hamsters on a high-fat diet via modulating gut microbiota

To investigate the effect of probiotic Lactobacillus fermentum CKCC1858, LF on the prevention of hyperlipidemia and its correlation with gut microbiota, golden hamsters were fed a high-fat diet alone or in combination with the probiotic for 6 weeks. The results showed that the LF intervention alleviated HFD-induced hyperlipidemia and liver damage, as evidenced by the reduced serum lipid profile levels and liver function markers. More importantly, the LF intervention attenuated HFD-induced microbiota dysbiosis by enhancing the abundance of SCFA-producing bacteria and reshaping the metabolic functions of the gut microbiota, likely contributing to its pronounced preventive effects on hyperlipidemia. This study elucidated the mechanism of the preventive effect of probiotics on hyperlipidemia in terms of regulating gut microbiota, and provided suggestions for regulating gut microbiota through probiotic interventions to improve lipid metabolism.

Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohort

Introduction

The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual's gut microbiota profile. Here, we explore how supervised Machine Learning (ML) methods help to distinguish taxa for individuals with prediabetes (prediabetes) or T2D.

Methods

To this aim, we analyzed the GM profile (16s rRNA gene sequencing) in a cohort of 410 Mexican naïve patients stratified into normoglycemic, prediabetes, and T2D individuals. Then, we compared six different ML algorithms and found that Random Forest had the highest predictive performance in classifying T2D and prediabetes patients versus controls.

Results

We identified a set of taxa for predicting patients with T2D compared to normoglycemic individuals, including Allisonella, Slackia, Ruminococus_2, Megaspgaera, Escherichia/Shigella, and Prevotella, among them. Besides, we concluded that Anaerostipes, Intestinibacter, Prevotella_9, Blautia, Granulicatella, and Veillonella were the relevant genus in patients with prediabetes compared to normoglycemic subjects.

Discussion

These findings allow us to postulate that GM is a distinctive signature in prediabetes and T2D patients during the development and progression of the disease. Our study highlights the role of GM and opens a window toward the rational design of new preventive and personalized strategies against the control of this disease.

A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiota

A prolonged and elevated postprandial glucose response (PPGR) is now considered a main factor contributing for the development of metabolic syndrome and type 2 diabetes, which could be prevented by dietary interventions. However, dietary recommendations to prevent alterations in PPGR have not always been successful. New evidence has supported that PPGR is not only dependent of dietary factors like the content of carbohydrates, or the glycemic index of the foods, but is also dependent on genetics, body composition, gut microbiota, among others. In recent years, continuous glucose monitoring has made it possible to establish predictions on the effect of different dietary foods on PPGRs through machine learning methods, which use algorithms that integrate genetic, biochemical, physiological and gut microbiota variables for identifying associations between them and clinical variables with aim of personalize dietary recommendations. This has allowed to improve the concept of personalized nutrition, since it is now possible to recommend through these predictions specific dietary foods to prevent elevated PPGRs that are highly variable among individuals. Additional components that can enrich the predictive algorithms are findings of nutrigenomics, nutrigenetics and metabolomics. Thus, this review aims to summarize the evidence of the components that integrate personalized nutrition focused on the prevention of PPGRs, and to show the future of personalized nutrition by laying the groundwork for the development of individualized dietary management and its impact on the improvement of metabolic diseases.

Exposure to proton pump inhibitors and risk of diabetes: A systematic review and meta-analysis

BACKGROUND

Exposure to proton pump inhibitors (PPIs) has been reported to have a potential role in the development of diabetes.

AIM

To determine the association between PPIs and diabetes.

METHODS

This meta-analysis is registered on PROSPERO (CRD42022352704). In August 2022, eligible studies were identified through a comprehensive literature search. In this study, odds ratios were combined with 95% confidence intervals using a random-effects model. The source of heterogeneity was assessed using sensitivity analysis and subgroup analysis. The publication bias was evaluated using Egger’s test and Begg’s test.

RESULTS

The meta-analysis included 9 studies with a total of 867185 participants. Results showed that the use of PPIs increased the risk of diabetes (odds ratio = 1.23, 95% confidence interval: 1.05-1.43, n = 9, I² = 96.3%). Subgroup analysis showed that geographic location and study type had significant effects on the overall results. Both Egger’s and Begg’s tests showed no publication bias (P > 0.05). Sensitivity analysis also confirmed the stability of the results.

CONCLUSION

The results of this study indicated that the use of PPIs was related to an increased risk of diabetes. However, more well-designed studies are needed to verify these results in the future.

Relationship between changes in microbiota induced by resveratrol and its anti-diabetic effect on type 2 diabetes

Although a general healthy gut microbiota cannot be defined due to numerous internal and external individual factors, such as sex, age, ethnicity, genetics, environment, diet and drugs affect its composition, certain microbial species and gut microbiota compositions seem to be related to the progression of insulin resistance to type 2 diabetes, as well as the development of microvascular and macrovascular complications of diabetes. The present review aimed at gathering the reported information describing how resveratrol induced changes in microbiota composition can mediate the positive effects of this polyphenol on glucose homeostasis under type 2 diabetic conditions, both in animals and humans. Based on the fact that some changes observed in the gut microbiota of type 2 diabetic animals and patients are reversed by resveratrol treatment, and taking into account that some resveratrol mediated changes in gut microbiota composition are similar to those induced by anti-diabetic drugs such as metformin, it can be proposed that four genera, Alistipes, Allobaculum, Desulfovibrio and Blautia could be involved in the benefits of resveratrol on glycameic control. Nevertheless some limitations are observed in this research field: (a) the number of studies analyzing both the effects of resveratrol on glucose homeostasis and microbiota composition in the same cohort of animals, in order to know the potential involvement of microbiota in the anti-diabetic effects of this phenolic compound, are very scarce and practically inexistent in the case of humans., (b) the studies present inconsistencies concerning the effects of resveratrol on gut microbiota changes, (c) the experimental design used do not allow the researchers to establish a causal relationship between the changes in microbiota and the anti-diabetic effect, in the vast majority of the studies, (d) the knowledge about the role of each type of bacteria on glycaemic control is not sufficient so far.

Machine Learning and COVID-19: Lessons from SARS-CoV-2

Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.

Intestine microbiota and SCFAs response in naturally Cryptosporidium-infected plateau yaks

Diarrhea is a severe bovine disease, globally prevalent in farm animals with a decrease in milk production and a low fertility rate. Cryptosporidium spp. are important zoonotic agents of bovine diarrhea. However, little is known about microbiota and short-chain fatty acids (SCFAs) changes in yaks infected with Cryptosporidium spp. Therefore, we performed 16S rRNA sequencing and detected the concentrations of SCFAs in Cryptosporidium-infected yaks. Results showed that over 80,000 raw and 70,000 filtered sequences were prevalent in yak samples. Shannon (p<0.01) and Simpson (p<0.01) were both significantly higher in Cryptosporidium-infected yaks. A total of 1072 amplicon sequence variants were shared in healthy and infected yaks. There were 11 phyla and 58 genera that differ significantly between the two yak groups. A total of 235 enzymes with a significant difference in abundance (p<0.001) were found between healthy and infected yaks. KEGG L3 analysis discovered that the abundance of 43 pathways was significantly higher, while 49 pathways were significantly lower in Cryptosporidium-infected yaks. The concentration of acetic acid (p<0.05), propionic acid (p<0.05), isobutyric acid (p<0.05), butyric acid (p<0.05), and isovaleric acid was noticeably lower in infected yaks, respectively. The findings of the study revealed that Cryptosporidium infection causes gut dysbiosis and results in a significant drop in the SCFAs concentrations in yaks with severe diarrhea, which may give new insights regarding the prevention and treatment of diarrhea in livestock.

A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort

Introduction

The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host.

Methods

Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment).

Results

By exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter.

Discussion

Based on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine.