Nicholas Chia

Multiple sclerosis patients have a distinct gut microbiota compared to healthy controls.

Multiple sclerosis (MS) is an immune-mediated disease, the etiology of which involves both genetic and environmental factors. The exact nature of the environmental factors responsible for predisposition to MS remains elusive; however, it’s hypothesized that gastrointestinal microbiota might play an important role in pathogenesis of MS. Therefore, this study was designed to investigate whether gut microbiota are altered in MS by comparing the fecal microbiota in relapsing remitting MS (RRMS) (n = 31) patients to that of age- and gender-matched healthy controls (n = 36). Phylotype profiles of the gut microbial populations were generated using hypervariable tag sequencing of the V3–V5 region of the 16S ribosomal RNA gene. Detailed fecal microbiome analyses revealed that MS patients had distinct microbial community profile compared to healthy controls. We observed an increased abundance of Psuedomonas, Mycoplana, Haemophilus, Blautia, and Dorea genera in MS patients, whereas control group showed increased abundance of Parabacteroides, Adlercreutzia and Prevotella genera. Thus our study is consistent with the hypothesis that MS patients have gut microbial dysbiosis and further study is needed to better understand their role in the etiopathogenesis of MS.

Diet and exercise orthogonally alter the gut microbiome and reveal independent associations with anxiety and cognition

BackgroundThe ingestion of a high-fat diet (HFD) and the resulting obese state can exert a multitude of stressors on the individual including anxiety and cognitive dysfunction. Though many studies have shown that exercise can alleviate the negative consequences of a HFD using metabolic readouts such as insulin and glucose, a paucity of well-controlled rodent studies have been published on HFD and exercise interactions with regard to behavioral outcomes. This is a critical issue since some individuals assume that HFD-induced behavioral problems such as anxiety and cognitive dysfunction can simply be exercised away. To investigate this, we analyzed mice fed a normal diet (ND), ND with exercise, HFD diet, or HFD with exercise.ResultsWe found that mice on a HFD had robust anxiety phenotypes but this was not rescued by exercise. Conversely, exercise increased cognitive abilities but this was not impacted by the HFD. Given the importance of the gut microbiome in shaping the host state, we used 16S rRNA hypervariable tag sequencing to profile our cohorts and found that HFD massively reshaped the gut microbial community in agreement with numerous published studies. However, exercise alone also caused massive shifts in the gut microbiome at nearly the same magnitude as diet but these changes were surprisingly orthogonal. Additionally, specific bacterial abundances were directly proportional to measures of anxiety or cognition.ConclusionsThus, behavioral domains and the gut microbiome are both impacted by diet and exercise but in unrelated ways. These data have important implications for obesity research aimed at modifications of the gut microbiome and suggest that specific gut microbes could be used as a biomarker for anxiety or cognition or perhaps even targeted for therapy.

Phage-bacteria relationships and CRISPR elements revealed by a metagenomic survey of the rumen microbiome

Viruses are the most abundant biological entities on the planet and play an important role in balancing microbes within an ecosystem and facilitating horizontal gene transfer. Although bacteriophages are abundant in rumen environments, little is known about the types of viruses present or their interaction with the rumen microbiome. We undertook random pyrosequencing of virus-enriched metagenomes (viromes) isolated from bovine rumen fluid and analysed the resulting data using comparative metagenomics. A high level of diversity was observed with up to 28,000 different viral genotypes obtained from each environment. The majority (~78%) of sequences did not match any previously described virus. Prophages outnumbered lytic phages approximately 2:1 with the most abundant bacteriophage and prophage types being associated with members of the dominant rumen phyla (Firmicutes and Proteobacteria). Metabolic profiling based on SEED subsystems revealed an enrichment of sequences with putative functional roles in DNA and protein metabolism, but a surprisingly low proportion of sequences assigned to carbohydrate and amino acid metabolism. We expanded our analysis to include previously described metagenomic data and 14 reference genomes. Clustered regularly interspaced short palindromic repeats (CRISPR) were detected in most of the microbial genomes, suggesting previous interactions between viral and microbial communities.

The microbiome of the chicken gastrointestinal tract.

Abstract The modern molecular biology movement was developed in the 1960s with the conglomeration of biology, chemistry, and physics. Today, molecular biology is an integral part of studies aimed at understanding the evolution and ecology of gastrointestinal microbial communities. Molecular techniques have led to significant gains in our understanding of the chicken gastrointestinal microbiome. New advances, primarily in DNA sequencing technologies, have equipped researchers with the ability to explore these communities at an unprecedented level. A reinvigorated movement in systems biology offers a renewed promise in obtaining a more complete understanding of chicken gastrointestinal microbiome dynamics and their contributions to increasing productivity, food value, security, and safety as well as reducing the public health impact of raising production animals. Here, we contextualize the contributions molecular biology has already made to our understanding of the chicken gastrointestinal microbiome and propose targeted research directions that could further exploit molecular technologies to improve the economy of the poultry industry.

Quantification of the relative roles of niche and neutral processes in structuring gastrointestinal microbiomes

The theoretical description of the forces that shape ecological communities focuses around two classes of models. In niche theory, deterministic interactions between species, individuals, and the environment are considered the dominant factor, whereas in neutral theory, stochastic forces, such as demographic noise, speciation, and immigration, are dominant. Species abundance distributions predicted by the two classes of theory are difficult to distinguish empirically, making it problematic to deduce ecological dynamics from typical measures of diversity and community structure. Here, we show that the fusion of species abundance data with genome-derived measures of evolutionary distance can provide a clear indication of ecological dynamics, capable of quantifying the relative roles played by niche and neutral forces. We apply this technique to six gastrointestinal microbiomes drawn from three different domesticated vertebrates, using high-resolution surveys of microbial species abundance obtained from carefully curated deep 16S rRNA hypervariable tag sequencing data. Although the species abundance patterns are seemingly well fit by the neutral theory of metacommunity assembly, we show that this theory cannot account for the evolutionary patterns in the genomic data; moreover, our analyses strongly suggest that these microbiomes have, in fact, been assembled through processes that involve a significant nonneutral (niche) contribution. Our results demonstrate that high-resolution genomics can remove the ambiguities of process inference inherent in classic ecological measures and permits quantification of the forces shaping complex microbial communities.

Pregnancy's Stronghold on the Vaginal Microbiome

Objective To assess the vaginal microbiome throughout full-term uncomplicated pregnancy. Methods Vaginal swabs were obtained from twelve pregnant women at 8-week intervals throughout their uncomplicated pregnancies. Patients with symptoms of vaginal infection or with recent antibiotic use were excluded. Swabs were obtained from the posterior fornix and cervix at 8–12, 17–21, 27–31, and 36–38 weeks of gestation. The microbial community was profiled using hypervariable tag sequencing of the V3–V5 region of the 16S rRNA gene, producing approximately 8 million reads on the Illumina MiSeq. Results Samples were dominated by a single genus, Lactobacillus, and exhibited low species diversity. For a majority of the patients (n = 8), the vaginal microbiome was dominated by Lactobacillus crispatus throughout pregnancy. Two patients showed Lactobacillus iners dominance during the course of pregnancy, and two showed a shift between the first and second trimester from L. crispatus to L. iners dominance. In all of the samples only these two species were identified, and were found at an abundance of higher than 1% in this study. Comparative analyses also showed that the vaginal microbiome during pregnancy is characterized by a marked dominance of Lactobacillus species in both Caucasian and African-American subjects. In addition, our Caucasian subject population clustered by trimester and progressed towards a common attractor while African-American women clustered by subject instead and did not progress towards a common attractor. Conclusion Our analyses indicate normal pregnancy is characterized by a microbiome that has low diversity and high stability. While Lactobacillus species strongly dominate the vaginal environment during pregnancy across the two studied ethnicities, observed differences between the longitudinal dynamics of the analyzed populations may contribute to divergent risk for pregnancy complications. This helps establish a baseline for investigating the role of the microbiome in complications of pregnancy such as preterm labor and preterm delivery.

Collecting Fecal Samples for Microbiome Analyses in Epidemiology Studies

Background: The need to develop valid methods for sampling and analyzing fecal specimens for microbiome studies is increasingly important, especially for large population studies. Methods: Some of the most important attributes of any sampling method are reproducibility, stability, and accuracy. We compared seven fecal sampling methods [no additive, RNAlater, 70% ethanol, EDTA, dry swab, and pre/post development fecal occult blood test (FOBT)] using 16S rRNA microbiome profiling in two laboratories. We evaluated nine commonly used microbiome metrics: abundance of three phyla, two alpha-diversities, and four beta-diversities. We determined the technical reproducibility, stability at ambient temperature, and accuracy. Results: Although microbiome profiles showed systematic biases according to sample method and time at ambient temperature, the highest source of variation was between individuals. All collection methods showed high reproducibility. FOBT and RNAlater resulted in the highest stability without freezing for 4 days. In comparison with no-additive samples, swab, FOBT, and 70% ethanol exhibited the greatest accuracy when immediately frozen. Conclusions: Overall, optimal stability and reproducibility were achieved using FOBT, making this a reasonable sample collection method for 16S analysis. Impact: Having standardized method of collecting and storing stable fecal samples will allow future investigations into the role of gut microbiota in chronic disease etiology in large population studies. Cancer Epidemiol Biomarkers Prev; 25(2); 407–16. ©2015 AACR.

Relationship Between Microbiota of the Colonic Mucosa vs Feces and Symptoms, Colonic Transit, and Methane Production in Female Patients With Chronic Constipation.

BACKGROUND & AIMS In fecal samples from patients with chronic constipation, the microbiota differs from that of healthy subjects. However, the profiles of fecal microbiota only partially replicate those of the mucosal microbiota. It is not clear whether these differences are caused by variations in diet or colonic transit, or are associated with methane production (measured by breath tests). We compared the colonic mucosal and fecal microbiota in patients with chronic constipation and in healthy subjects to investigate the relationships between microbiota and other parameters. METHODS Sigmoid colonic mucosal and fecal microbiota samples were collected from 25 healthy women (controls) and 25 women with chronic constipation and evaluated by 16S ribosomal RNA gene sequencing (average, 49,186 reads/sample). We assessed associations between microbiota (overall composition and operational taxonomic units) and demographic variables, diet, constipation status, colonic transit, and methane production (measured in breath samples after oral lactulose intake). RESULTS Fourteen patients with chronic constipation had slow colonic transit. The profile of the colonic mucosal microbiota differed between constipated patients and controls (P < .05). The overall composition of the colonic mucosal microbiota was associated with constipation, independent of colonic transit (P < .05), and discriminated between patients with constipation and controls with 94% accuracy. Genera from Bacteroidetes were more abundant in the colonic mucosal microbiota of patients with constipation. The profile of the fecal microbiota was associated with colonic transit before adjusting for constipation, age, body mass index, and diet; genera from Firmicutes (Faecalibacterium, Lactococcus, and Roseburia) correlated with faster colonic transit. Methane production was associated with the composition of the fecal microbiota, but not with constipation or colonic transit. CONCLUSIONS After adjusting for diet and colonic transit, the profile of the microbiota in the colonic mucosa could discriminate patients with constipation from healthy individuals. The profile of the fecal microbiota was associated with colonic transit and methane production (measured in breath), but not constipation.

On the suitability of short reads of 16S rRNA for phylogeny-based analyses in environmental surveys

Pyrosequencing platforms have been widely used in 16S rRNA deep sequencing of organisms sampled from environmental surveys. Despite the massive number of reads generated by these platforms, the reads only cover short regions of the gene, and the use of these short reads has recently been called into question for phylogeny-based and diversity analyses. We explore the limits of the use of short reads by quantifying the loss of information, and its effect on phylogeny. Using available nearly-full-length reads from published clone libraries and databases, and simulated short reads created from these reads, we show that for selected regions of the gene, short reads contain a surprisingly high amount of biological information, making them suitable to resolve an approximate phylogeny. In particular, we find that the V6 region is significantly poorer than the V1-V3 region in its representation of phylogenetic relationships. We conclude that the use of short reads, combined with a careful choice of the gene region used, and a thorough alignment procedure, can yield phylogenetic information comparable with that obtained from nearly-full-length 16S rRNA reads.

Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.