Global Profiling of Carbohydrate Active Enzymes in Human Gut Microbiome

Motivation

Carbohydrate Active enzyme (CAZyme) families, encoded by human gut microflora, play a crucial role in breakdown of complex dietary carbohydrates into components that can be absorbed by our intestinal epithelium. Since nutritional wellbeing of an individual is dependent on the nutrient harvesting capability of the gut microbiome, it is important to understand how CAZyme repertoire in the gut is influenced by factors like age, geography and food habits.

Results

This study reports a comprehensive in-silico analysis of CAZyme profiles in the gut microbiomes of 448 individuals belonging to different geographies, using similarity searches of the corresponding gut metagenomic contigs against the carbohydrate active enzymes database. The study identifies a core group of 89 CAZyme families that are present across 85% of the gut microbiomes. The study detects several geography/age-specific trends in gut CAZyme repertoires of the individuals. Notably, a group of CAZymes having a positive correlation with BMI has been identified. Further this group of BMI-associated CAZymes is observed to be specifically abundant in the Firmicutes phyla. One of the major findings from this study is identification of three distinct groups of individuals, referred to as ''CAZotypes'', having similar CAZyme profiles. Distinct taxonomic drivers for these CAZotypes as well as the probable dietary basis for such trends have also been elucidated. The results of this study provide a global view of CAZyme profiles across individuals of various geographies and age-groups. These results re-iterate the need of a more precise understanding of the role of carbohydrate active enzymes in human nutrition.     

Interplay of Human Gut Microbiome in Health and Wellness

Indian Journal of Microbiology - The gut microbiome analysis, with specific interest on their direct impact towards the human health, is currently revolutionizing the unexplored frontiers of the...     

Short-Term Antibiotic Treatment Has Differing Long-Term Impacts on the Human Throat and Gut Microbiome

Antibiotic administration is the standard treatment for the bacterium Helicobacter pylori, the main causative agent of peptic ulcer disease and gastric cancer. However, the long-term consequences of this treatment on the human indigenous microbiota are relatively unexplored. Here we studied short- and long-term effects of clarithromycin and metronidazole treatment, a commonly used therapy regimen against H. pylori, on the indigenous microbiota in the throat and in the lower intestine. The bacterial compositions in samples collected over a four-year period were monitored by analyzing the 16S rRNA gene using 454-based pyrosequencing and terminal-restriction fragment length polymorphism (T-RFLP). While the microbial communities of untreated control subjects were relatively stable over time, dramatic shifts were observed one week after antibiotic treatment with reduced bacterial diversity in all treated subjects in both locations. While the microbiota of the different subjects responded uniquely to the antibiotic treatment some general trends could be observed; such as a dramatic decline in Actinobacteria in both throat and feces immediately after treatment. Although the diversity of the microbiota subsequently recovered to resemble the pre treatment states, the microbiota remained perturbed in some cases for up to four years post treatment. In addition, four years after treatment high levels of the macrolide resistance gene erm(B) were found, indicating that antibiotic resistance, once selected for, can persist for longer periods of time than previously recognized. This highlights the importance of a restrictive antibiotic usage in order to prevent subsequent treatment failure and potential spread of antibiotic resistance.     

Archaea and Fungi of the Human Gut Microbiome: Correlations with Diet and Bacterial Residents

Diet influences health as a source of nutrients and toxins, and by shaping the composition of resident microbial populations. Previous studies have begun to map out associations between diet and the bacteria and viruses of the human gut microbiome. Here we investigate associations of diet with fungal and archaeal populations, taking advantage of samples from 98 well-characterized individuals. Diet was quantified using inventories scoring both long-term and recent diet, and archaea and fungi were characterized by deep sequencing of marker genes in DNA purified from stool. For fungi, we found 66 genera, with generally mutually exclusive presence of either the phyla Ascomycota or Basiodiomycota. For archaea, Methanobrevibacter was the most prevalent genus, present in 30% of samples. Several other archaeal genera were detected in lower abundance and frequency. Myriad associations were detected for fungi and archaea with diet, with each other, and with bacterial lineages. Methanobrevibacter and Candida were positively associated with diets high in carbohydrates, but negatively with diets high in amino acids, protein, and fatty acids. A previous study emphasized that bacterial population structure was associated primarily with long-term diet, but high Candida abundance was most strongly associated with the recent consumption of carbohydrates. Methobrevibacter abundance was associated with both long term and recent consumption of carbohydrates. These results confirm earlier targeted studies and provide a host of new associations to consider in modeling the effects of diet on the gut microbiome and human health.     

MALDI-TOF Identification of the Human Gut Microbiome in People with and without Diarrhea in Senegal

Background

In Africa, there are several problems with the specific identification of bacteria. Recently, MALDI-TOF mass spectrometry has become a powerful tool for the routine microbial identification in many clinical laboratories.

Methodology/Principal Findings

This study was conducted using feces from 347 individuals (162 with diarrhea and 185 without diarrhea) sampled in health centers in Dakar, Senegal. Feces were transported from Dakar to Marseille, France, where they were cultured using different culture conditions. The isolated colonies were identified using MALDI-TOF. If a colony was unidentified, 16S rRNA sequencing was performed. Overall, 2,753 isolates were tested, allowing for the identification of 189 bacteria from 5 phyla, including 2 previously unknown species, 11 species not previously reported in the human gut, 10 species not previously reported in humans, and 3 fungi. 2,718 bacterial isolates (98.8%) out of 2,750 yielded an accurate identification using mass spectrometry, as did the 3 Candida albicans isolates. Thirty-two bacterial isolates not identified by MALDI-TOF (1.2%) were identified by sequencing, allowing for the identification of 2 new species. The number of bacterial species per fecal sample was significantly higher among patients without diarrhea (8.6±3) than in those with diarrhea (7.3±3.4; P = 0.0003). A modification of the gut microbiota was observed between the two groups. In individuals with diarrhea, major commensal bacterial species such as E. coli were significantly decreased (85% versus 64%), as were several Enterococcus spp. (E. faecium and E. casseliflavus) and anaerobes, such as Bacteroides spp. (B. uniformis and B. vulgatus) and Clostridium spp. (C. bifermentans, C. orbiscindens, C. perfringens, and C. symbosium). Conversely, several Bacillus spp. (B. licheniformis, B. mojavensis, and B. pumilus) were significantly more frequent among patients with diarrhea.

Conclusions/Significance

MALDI-TOF is a potentially powerful tool for routine bacterial identification in Africa, allowing for a quick identification of bacterial species.     

Sex,Body Mass Index,and Dietary Fiber Intake Influence the Human Gut Microbiome

Increasing evidence suggests that the composition of the human gut microbiome is important in the etiology of human diseases; however, the personal factors that influence the gut microbiome composition are poorly characterized. Animal models point to sex hormone-related differentials in microbiome composition. In this study, we investigated the relationship of sex, body mass index (BMI) and dietary fiber intake with the gut microbiome in 82 humans. We sequenced fecal 16S rRNA genes by 454 FLX technology, then clustered and classified the reads to microbial genomes using the QIIME pipeline. Relationships of sex, BMI, and fiber intake with overall gut microbiome composition and specific taxon abundances were assessed by permutational MANOVA and multivariate logistic regression, respectively. We found that sex was associated with the gut microbiome composition overall (p=0.001). The gut microbiome in women was characterized by a lower abundance of Bacteroidetes (p=0.03). BMI (>25 kg/m²vs. <25 kg/m²) was associated with the gut microbiome composition overall (p=0.05), and this relationship was strong in women (p=0.03) but not in men (p=0.29). Fiber from beans and from fruits and vegetables were associated, respectively, with greater abundance of Actinobacteria (p=0.006 and false discovery rate adjusted q=0.05) and Clostridia (p=0.009 and false discovery rate adjusted q=0.09). Our findings suggest that sex, BMI, and dietary fiber contribute to shaping the gut microbiome in humans. Better understanding of these relationships may have significant implications for gastrointestinal health and disease prevention.     

Focus: Microbiome: The Influence of the Gut Microbiota on Host Physiology: In Pursuit of Mechanisms

The results generated from the NIH funded Human Microbiome Project (HMP) are necessarily tied to the overall mission of the agency, which is to foster scientific discoveries as a basis for protecting and improving health. The investment in the HMP phase 1 accomplished many of its goals including the preliminary characterization of the human microbiome and the identification of links between microbiome diversity and disease states. Going forward, the next step in these studies must involve the identification of the functional molecular elements that mediate the positive influence of a eubiotic microbiome on health and disease. This review will focus on recent advances describing mechanistic events in the intestine elicited by the microbiome. These include symbiotic bacteria-induced activation of redox-dependent cell signaling, the bacterial production of short chain fatty acids and ensuing cellular responses, and the secretion of bacteriocins by bacteria that have anti-microbial activities against potential pathogens.     

Development and Validation of a Microarray for the Investigation of the CAZymes Encoded by the Human Gut Microbiome

Distal gut bacteria play a pivotal role in the digestion of dietary polysaccharides by producing a large number of carbohydrate-active enzymes (CAZymes) that the host otherwise does not produce. We report here the design of a custom microarray that we used to spot non-redundant DNA probes for more than 6,500 genes encoding glycoside hydrolases and lyases selected from 174 reference genomes from distal gut bacteria. The custom microarray was tested and validated by the hybridization of bacterial DNA extracted from the stool samples of lean, obese and anorexic individuals. Our results suggest that a microarray-based study can detect genes from low-abundance bacteria better than metagenomic-based studies. A striking example was the finding that a gene encoding a GH6-family cellulase was present in all subjects examined, whereas metagenomic studies have consistently failed to detect this gene in both human and animal gut microbiomes. In addition, an examination of eight stool samples allowed the identification of a corresponding CAZome core containing 46 families of glycoside hydrolases and polysaccharide lyases, which suggests the functional stability of the gut microbiota despite large taxonomical variations between individuals.     

Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries Using Sparse Linear Regression

Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is now possible to follow the relative abundance of microbes in a community over time. These microbial communities exhibit rich ecological dynamics and an important goal of microbial ecology is to infer the ecological interactions between species directly from sequence data. Any algorithm for inferring ecological interactions must overcome three major obstacles: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units, bias inferences of species interactions due to a statistical problem called “errors-in-variables”. Here we introduce an approach, Learning Interactions from MIcrobial Time Series (LIMITS), that overcomes these obstacles. LIMITS uses sparse linear regression with boostrap aggregation to infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested LIMITS on synthetic data and showed that it could reliably infer the topology of the inter-species ecological interactions. We then used LIMITS to characterize the species interactions in the gut microbiomes of two individuals and found that the interaction networks varied significantly between individuals. Furthermore, we found that the interaction networks of the two individuals are dominated by distinct “keystone species”, Bacteroides fragilis and Bacteroided stercosis, that have a disproportionate influence on the structure of the gut microbiome even though they are only found in moderate abundance. Based on our results, we hypothesize that the abundances of certain keystone species may be responsible for individuality in the human gut microbiome.     

Expansion of the Protein Repertoire in Newly Explored Environments: Human Gut Microbiome Specific Protein Families

The microbes that inhabit particular environments must be able to perform molecular functions that provide them with a competitive advantage to thrive in those environments. As most molecular functions are performed by proteins and are conserved between related proteins, we can expect that organisms successful in a given environmental niche would contain protein families that are specific for functions that are important in that environment. For instance, the human gut is rich in polysaccharides from the diet or secreted by the host, and is dominated by Bacteroides, whose genomes contain highly expanded repertoire of protein families involved in carbohydrate metabolism. To identify other protein families that are specific to this environment, we investigated the distribution of protein families in the currently available human gut genomic and metagenomic data. Using an automated procedure, we identified a group of protein families strongly overrepresented in the human gut. These not only include many families described previously but also, interestingly, a large group of previously unrecognized protein families, which suggests that we still have much to discover about this environment. The identification and analysis of these families could provide us with new information about an environment critical to our health and well being.     

Chicken Gut Microbiome and Human Health: Past Scenarios,Current Perspectives,and Futuristic Applications

Indian Journal of Microbiology - Sustainable poultry practices are needed to maintain an adequate supply of poultry products to the increasing human population without compromising human wellbeing....     

Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome

We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth.