Decreased microbial co-occurrence network stability and SCFA receptor level correlates with obesity in African-origin women.

We compared the gut microbial populations in 100 women, from rural Ghana and urban US [50% lean (BMI < 25 kg/m2) and 50% obese (BMI ≥ 30 kg/m2)] to examine the ecological co-occurrence network topology of the gut microbiota as well as the relationship of short chain fatty acids (SCFAs) with obesity. Ghanaians consumed significantly more dietary fiber, had greater microbial alpha-diversity, different beta-diversity, and had a greater concentration of total fecal SCFAs (p-value < 0.002). Lean Ghanaians had significantly greater network density, connectivity and stability than either obese Ghanaians, or lean and obese US participants (false discovery rate (FDR) corrected p-value ≤ 0.01). Bacteroides uniformis was significantly more abundant in lean women, irrespective of country (FDR corrected p < 0.001), while lean Ghanaians had a significantly greater proportion of Ruminococcus callidus, Prevotella copri, and Escherichia coli, and smaller proportions of Lachnospiraceae, Bacteroides and Parabacteroides. Lean Ghanaians had a significantly greater abundance of predicted microbial genes that catalyzed the production of butyric acid via the fermentation of pyruvate or branched amino-acids, while obese Ghanaians and US women (irrespective of BMI) had a significantly greater abundance of predicted microbial genes that encoded for enzymes associated with the fermentation of amino-acids such as alanine, aspartate, lysine and glutamate. Similar to lean Ghanaian women, mice humanized with stool from the lean Ghanaian participant had a significantly lower abundance of family Lachnospiraceae and genus Bacteroides and Parabacteroides, and were resistant to obesity following 6-weeks of high fat feeding (p-value < 0.01). Obesity-resistant mice also showed increased intestinal transcriptional expression of the free fatty acid (Ffa) receptor Ffa2, in spite of similar fecal SCFAs concentrations. We demonstrate that the association between obesity resistance and increased predicted ecological connectivity and stability of the lean Ghanaian microbiota, as well as increased local SCFA receptor level, provides evidence of the importance of robust gut ecologic network in obesity

From data to science: a multi-Omics analysis of the pathobiome

Humans represent a complex ecosystem colonized not only by our cells but trillions of other microbes such as bacteria, archaea, fungi, and viruses. This microbiome gains increasing interest due to its involvement in human health and disease. While we live in symbiosis with most of these travellers, dysbiosis can lead to the growth of pathogens. Pathobionts are commensal microbes and harmless in healthy individuals until specific circumstances occur. There is increasing interest in studying this pathobiome due to the rise in infections with high mortality rates and stagnant treatment options. Due to the complexity of possible interactions between the host and microbes, studies on microbial interactions are conducted at varying scales. In this thesis, we start to study interactions in small, well-controlled model systems in vitro and then at the community level in vivo. The key technology used to identify, quantify, and characterize microbes and study host- microbe interactions throughout my studies is whole-genome and transcriptome sequencing. While an extensive body of work has focused on understanding the virulence factors of common pathogens, such as Aspergillus and Candida species, very little work has been done on understanding the interplay of those pathogens with the host’s symbionts or other pathogens at the start of my Ph.D. In my Ph.D. project, I used next- generation sequencing, advanced statistical approaches, and machine learning to significantly expanded our knowledge of the life of pathogens from an ecological point of view

Alterations in common marmoset gut microbiome associated with duodenal strictures

Chronic gastrointestinal (GI) diseases are the most common diseases in captive common marmosets (Callithrix jacchus). Despite standardized housing, diet and husbandry, a recently described gastrointestinal syndrome characterized by duodenal ulcers and strictures was observed in a subset of marmosets sourced from the New England Primate Research Center. As changes in the gut microbiome have been associated with GI diseases, the gut microbiome of 52 healthy, non-stricture marmosets (153 samples) were compared to the gut microbiome of 21 captive marmosets diagnosed with a duodenal ulcer/stricture (57 samples). No significant changes were observed using alpha diversity metrics, and while the community structure was significantly different when comparing beta diversity between healthy and stricture cases, the results were inconclusive due to differences observed in the dispersion of both datasets. Differences in the abundance of individual taxa using ANCOM, as stricture-associated dysbiosis was characterized by Anaerobiospirillum loss and Clostridium perfringens increases. To identify microbial and serum biomarkers that could help classify stricture cases, we developed models using machine learning algorithms (random forest, classification and regression trees, support vector machines and k-nearest neighbors) to classify microbiome, serum chemistry or complete blood count (CBC) data. Random forest (RF) models were the most accurate models and correctly classified strictures using either 9 ASVs (amplicon sequence variants), 4 serum chemistry tests or 6 CBC tests. Based on the RF model and ANCOM results, C. perfringens was identified as a potential causative agent associated with the development of strictures. Clostridium perfringens was also isolated by microbiological culture in 4 of 9 duodenum samples from marmosets with histologically confirmed strictures. Due to the enrichment of C. perfringens in situ, we analyzed frozen duodenal tissues using both 16S microbiome profiling and RNAseq. Microbiome analysis of the duodenal tissues of 29 marmosets from the MIT colony confirmed an increased abundance of Clostridium in stricture cases. Comparison of the duodenal gene expression from stricture and non-stricture marmosets found enrichment of genes associated with intestinal absorption, and lipid metabolism, localization, and transport in stricture cases. Using machine learning, we identified increased abundance of C. perfringens, as a potential causative agent of GI disease and intestinal strictures in marmosets.National Institutes of Health/[T32 OD010978]/NIH/Estados UnidosNational Institutes of Health/[P30-ES002109]/NIH/Estados UnidosUniversidad de Costa Rica/[803-C1-163]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Enfermedades Tropicales (CIET

Metabolic model of necrotizing enterocolitis in the premature newborn gut resulting from enteric dysbiosis

Necrotizing enterocolitis (NEC) is a leading cause of premature newborn morbidity and mortality. The clinical features of NEC consistently include prematurity, gut dysbiosis and enteral inflammation, yet the pathogenesis remains obscure. Herein we combine metagenomics and targeted metabolomics, with functional in vivo and in vitro assessment, to define a novel molecular mechanism of NEC. One thousand six hundred and forty seven publicly available metagenomics datasets were analyzed (NEC = 245; healthy = 1,402) using artificial intelligence methodologies. Targeted metabolomic profiling was used to quantify the concentration of specified fecal metabolites at NEC onset (n = 8), during recovery (n = 6), and in age matched controls (n = 10). Toxicity assays of discovered metabolites were performed in vivo in mice and in vitro using human intestinal epithelial cells. Metagenomic and targeted metabolomic analyses revealed significant differences in pyruvate fermentation pathways and associated intermediates. Notably, the short chain fatty acid formate was elevated in the stool of NEC patients at disease onset (P = 0.005) dissipated during recovery (P = 0.02) and positively correlated with degree of intestinal injury (r2 = 0.86). In vitro, formate caused enterocyte cytotoxicity in human cells through necroptosis (P \u3c 0.01). In vivo, luminal formate caused significant dose and development dependent NEC-like injury in newborn mice. Enterobacter cloacae and Klebsiella pneumoniae were the most discriminatory taxa related to NEC dysbiosis and increased formate production. Together, these data suggest a novel biochemical mechanism of NEC through the microbial production of formate. Clinical efforts to prevent NEC should focus on reducing the functional consequences of newborn gut dysbiosis associated metabolic pathways

Integration of host, pathogen and microbiome -omics data for studying infectious diseases

In an ever-growing worldwide population, human infectious diseases are an increasingly serious problem for public health. In particular, more than a million deaths and millions of infectious disease cases per year caused by fungal pathogens have been reported globally in recent years. Hence, more investments must be put into fungal research to overcome the problem. The opportunistic pathogen Candida albicans and the airborne Aspergillus fumigatus are the two most prevalent fungal pathogens causing serious issues in medical care units. Despite the recent advances in fungal research, there is little knowledge about the role of fungal metabolism in developing the infection when coexisting within the human body with microbial community members in different organs. This dissertation applied computational tools, and implemented systems biology approaches to uncover key factors in the colonization of the pathogens, especially C. albicans and A. fumigatus, from a systems biology perspective and unseen by wet-lab experiments alone. Next to multi-omics data analysis, a major effort was put into genome-scale metabolic models (GEMs) generation and analysis as a promising approach to shed light on the role of metabolism in developing the infection. In brief, this thesis sheds light on key factors leading to the inhibition or promotion of fungal growth. This especially includes the first available GEM reconstruction of C. albicans to theoretically study the intricate interaction of the fungus with the human host and the microbial community members. Lastly, a platform of 252 A. fumigatus GEMs at the strain resolution was generated. It revealed the phenotypic diversity of A. fumigatus strains isolated from different hospitals and farms in Germany and explained the contribution of the fungus to the shaping of the metabolic landscape of the lung microbiome in a favorable manner for fungal growth

Characterization of shifts of koala (Phascolarctos cinereus) intestinal microbial communities associated with antibiotic treatment.

Koalas (Phascolarctos cinereus) are arboreal marsupials native to Australia that eat a specialized diet of almost exclusively eucalyptus leaves. Microbes in koala intestines are known to break down otherwise toxic compounds, such as tannins, in eucalyptus leaves. Infections by Chlamydia, obligate intracellular bacterial pathogens, are highly prevalent in koala populations. If animals with Chlamydia infections are received by wildlife hospitals, a range of antibiotics can be used to treat them. However, previous studies suggested that koalas can suffer adverse side effects during antibiotic treatment. This study aimed to use 16S rRNA gene sequences derived from koala feces to characterize the intestinal microbiome of koalas throughout antibiotic treatment and identify specific taxa associated with koala health after treatment. Although differences in the alpha diversity were observed in the intestinal flora between treated and untreated koalas and between koalas treated with different antibiotics, these differences were not statistically significant. The alpha diversity of microbial communities from koalas that lived through antibiotic treatment versus those who did not was significantly greater, however. Beta diversity analysis largely confirmed the latter observation, revealing that the overall communities were different between koalas on antibiotics that died versus those that survived or never received antibiotics. Using both machine learning and OTU (operational taxonomic unit) co-occurrence network analyses, we found that OTUs that are very closely related to Lonepinella koalarum, a known tannin degrader found by culture-based methods to be present in koala intestines, was correlated with a koala's health status. This is the first study to characterize the time course of effects of antibiotics on koala intestinal microbiomes. Our results suggest it may be useful to pursue alternative treatments for Chlamydia infections without the use of antibiotics or the development of Chlamydia-specific antimicrobial compounds that do not broadly affect microbial communities

The Intestinal Gut Microbiome as a Biomarker and Driver of Obesity and Non-Alcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) affects almost 1 out of every 5 Americans. The development of NAFLD increases an individual’s risk for cardiovascular disease, cirrhosis, and cancer. Given that there are few treatments available for NALFD, it is imperative to understand the features that can prognosticate and alter the progression of NAFLD. One area of research that has gained significant traction is the role of the gut microbiome in the development and progression of NAFLD. By utilizing a multi’omics approach, we discovered that the gut microbiome can affect obesity through alterations of the brain-gut axis. In a study of over 100 patients, we saw that the gut microbiome was highly associated to food addiction. Patients with food addiction had significantly lower abundances of Bacteroides, Akkermansia, and Eubacterium, and a higher abundance of Megamonas. This was associated with a reduction in a neuroprotective tryptophan-related metabolite, indolepropionate, and altered connectivity in the brain’s reward regions. This data suggests that the gut microbiome plays a role in eating behavior and is likely a modifiable risk factor for obesity. Furthermore, research has shown that the microbiome and metabolite profile of patients with NAFLD differs at each stage of the disease. Using a machine learning algorithm, we created and validated a classifier based on the gut microbiome that highly predicts advanced fibrosis in patients with NAFLD. To explore the causal effects of the gut microbiome on the development of NAFLD, we utilized the microbiome of bariatric surgery patients and transplanted them into antibiotic treated mice. Through this model, we see that an obese phenotype microbiome is able to induce significant weight gain and hepatic steatosis. Associated with these changes we see a significant alteration of the expression levels of natural killer T-cells, cytotoxic T-cells, Kupffer cells, and monocyte-derived macrophages. The data shows that not only can the gut microbiome prognosticate NAFLD, it can also alter the progression of NAFLD through changes of the innate immune system of the liver. This work shows the feasibility of the gut microbiome both as a biomarker but also as a source for potential novel therapeutics against obesity and NAFLD

Shifts in the Fecal Microbiota Associated with Adenomatous Polyps

BACKGROUND: Adenomatous polyps are the most common precursor to colorectal cancer, the second leading cause of cancer-related death in the United States. We sought to learn more about early events of carcinogenesis by investigating shifts in the gut microbiota of patients with adenomas. METHODS: We analyzed 16S rRNA gene sequences from the fecal microbiota of patients with adenomas (n = 233) and without (n = 547). RESULTS: Multiple taxa were significantly more abundant in patients with adenomas, including Bilophila, Desulfovibrio, proinflammatory bacteria in the genus Mogibacterium, and multiple Bacteroidetes species. Patients without adenomas had greater abundances of Veillonella, Firmicutes (Order Clostridia), and Actinobacteria (family Bifidobacteriales). Our findings were consistent with previously reported shifts in the gut microbiota of colorectal cancer patients. Importantly, the altered adenoma profile is predicted to increase primary and secondary bile acid production, as well as starch, sucrose, lipid, and phenylpropanoid metabolism. CONCLUSIONS: These data hint that increased sugar, protein, and lipid metabolism along with increased bile acid production could promote a colonic environment that supports the growth of bile-tolerant microbes such as Bilophilia and Desulfovibrio In turn, these microbes may produce genotoxic or inflammatory metabolites such as H2S and secondary bile acids, which could play a role in catalyzing adenoma development and eventually colorectal cancer. IMPACT: This study suggests a plausible biological mechanism to explain the links between shifts in the microbiota and colorectal cancer. This represents a first step toward resolving the complex interactions that shape the adenoma-carcinoma sequence of colorectal cancer and may facilitate personalized therapeutics focused on the microbiota