Weighted Gene Co-Expression Network Analysis Identifies a Functional Guild and Metabolite Cluster Mediating the Relationship between Mucosal Inflammation and Adherence to the Mediterranean Diet in Ulcerative Colitis.

Diet influences the pathogenesis and clinical course of inflammatory bowel disease (IBD). The Mediterranean diet (MD) is linked to reductions in inflammatory biomarkers and alterations in microbial taxa and metabolites associated with health. We aimed to identify features of the gut microbiome that mediate the relationship between the MD and fecal calprotectin (FCP) in ulcerative colitis (UC). Weighted gene co-expression network analysis (WGCNA) was used to identify modules of co-abundant microbial taxa and metabolites correlated with the MD and FCP. The features considered were gut microbial taxa, serum metabolites, dietary components, short-chain fatty acid and bile acid profiles in participants that experienced an increase (n = 13) or decrease in FCP (n = 16) over eight weeks. WGCNA revealed ten modules containing sixteen key features that acted as key mediators between the MD and FCP. Three taxa (Faecalibacterium prausnitzii, Dorea longicatena, Roseburia inulinivorans) and a cluster of four metabolites (benzyl alcohol, 3-hydroxyphenylacetate, 3-4-hydroxyphenylacetate and phenylacetate) demonstrated a strong mediating effect (ACME: -1.23, p = 0.004). This study identified a novel association between diet, inflammation and the gut microbiome, providing new insights into the underlying mechanisms of how a MD may influence IBD. See clinicaltrials.gov (NCT04474561).

Genomic plasticity associated with antimicrobial resistance in Vibrio cholerae.

The Bay of Bengal is known as the epicenter for seeding several devastating cholera outbreaks across the globe. Vibrio cholerae, the etiological agent of cholera, has extraordinary competency to acquire exogenous DNA by horizontal gene transfer (HGT) and adapt them into its genome for structuring metabolic processes, developing drug resistance, and colonizing the human intestine. Antimicrobial resistance (AMR) in V. cholerae has become a global concern. However, little is known about the identity of the resistance traits, source of AMR genes, acquisition process, and stability of the genetic elements linked with resistance genes in V. cholerae Here we present details of AMR profiles of 443 V. cholerae strains isolated from the stool samples of diarrheal patients from two regions of India. We sequenced the whole genome of multidrug-resistant (MDR) and extensively drug-resistant (XDR) V. cholerae to identify AMR genes and genomic elements that harbor the resistance traits. Our genomic findings were further confirmed by proteome analysis. We also engineered the genome of V. cholerae to monitor the importance of the autonomously replicating plasmid and core genome in the resistance profile. Our findings provided insights into the genomes of recent cholera isolates and identified several acquired traits including plasmids, transposons, integrative conjugative elements (ICEs), pathogenicity islands (PIs), prophages, and gene cassettes that confer fitness to the pathogen. The knowledge generated from this study would help in better understanding of V. cholerae evolution and management of cholera disease by providing clinical guidance on preferred treatment regimens.

Gut microbiome diversity in acute severe colitis is distinct from mild to moderate ulcerative colitis.

BACKGROUND AND AIM: Although the gut microbiome of patients with ulcerative colitis (UC) has been characterized, no study has characterized the gut microbiome in acute severe colitis (ASC). We compared the gut microbiome of patients with UC, ASC, and healthy controls (HCs). METHODS: Patients with mild to moderate UC (n = 24), ASC (n = 19 with 21 episodes) and HCs (n = 50) were recruited prospectively. A 16SrDNA amplicon approach was used to explore gut microbial diversity and taxonomic repertoires. UC was diagnosed using European Crohn's and Colitis Organization guidelines, and ASC was diagnosed using Truelove and Witts' criteria. RESULTS: The normalized alpha diversity was significantly lower in ASC than mild-moderately active UC (P < 0.05) or HC (P < 0.001). The gut microbiome in ASC was highly unstable, as characterized by high intracohort variation (analyzed using J-divergence measure), which was significantly greater than in UC or HC. On principal coordinate analysis, the microbiome of HC and UC were similar, with the ASC cohort being distinct from both. Comparison of ranked abundances identified four distinct clusters of genera (G1, G2, G3, and G4), with specific trends in their abundance across three groups: G1/G2A clusters had the least, whereas G3 had the highest abundance in the ASC cohort. CONCLUSIONS: Gut microbial diversity is lower in ASC than mild-moderate UC or HCs. Gut microbiome composition is increasingly unstable in ASC, with a distinct abundance of specific genera varying between HCs and ASC. Mild-moderate UC lies within the spectrum.

Mediterranean diet intervention alters the gut microbiome in older people reducing frailty and improving health status: the NU-AGE 1-year dietary intervention across five European countries.

OBJECTIVE: Ageing is accompanied by deterioration of multiple bodily functions and inflammation, which collectively contribute to frailty. We and others have shown that frailty co-varies with alterations in the gut microbiota in a manner accelerated by consumption of a restricted diversity diet. The Mediterranean diet (MedDiet) is associated with health. In the NU-AGE project, we investigated if a 1-year MedDiet intervention could alter the gut microbiota and reduce frailty. DESIGN: We profiled the gut microbiota in 612 non-frail or pre-frail subjects across five European countries (UK, France, Netherlands, Italy and Poland) before and after the administration of a 12-month long MedDiet intervention tailored to elderly subjects (NU-AGE diet). RESULTS: Adherence to the diet was associated with specific microbiome alterations. Taxa enriched by adherence to the diet were positively associated with several markers of lower frailty and improved cognitive function, and negatively associated with inflammatory markers including C-reactive protein and interleukin-17. Analysis of the inferred microbial metabolite profiles indicated that the diet-modulated microbiome change was associated with an increase in short/branch chained fatty acid production and lower production of secondary bile acids, p-cresols, ethanol and carbon dioxide. Microbiome ecosystem network analysis showed that the bacterial taxa that responded positively to the MedDiet intervention occupy keystone interaction positions, whereas frailty-associated taxa are peripheral in the networks. CONCLUSION: Collectively, our findings support the feasibility of improving the habitual diet to modulate the gut microbiota which in turn has the potential to promote healthier ageing.

IPCO: Inference of Pathways from Co-variance analysis.

BACKGROUND: Key aspects of microbiome research are the accurate identification of taxa and the profiling of their functionality. Amplicon profiling based on the 16S ribosomal DNA sequence is a ubiquitous technique to identify and profile the abundance of the various taxa. However, it does not provide information on their encoded functionality. Predictive tools that can accurately extrapolate the functional information of a microbiome based on taxonomic profile composition are essential. At present, the applicability of these tools is limited due to requirement of reference genomes from known species. We present IPCO (Inference of Pathways from Co-variance analysis), a new method of inferring functionality for 16S-based microbiome profiles independent of reference genomes. IPCO utilises the biological co-variance observed between paired taxonomic and functional profiles and co-varies it with the queried dataset. RESULTS: IPCO outperforms other established methods both in terms of sample and feature profile prediction. Validation results confirmed that IPCO can replicate observed biological associations between shotgun and metabolite profiles. Comparative analysis of predicted functionality profiles with other popular 16S-based functional prediction tools showed significantly lower performances with predicted functionality showing little to no correlation with paired shotgun features across samples. CONCLUSIONS: IPCO can infer functionality from 16S datasets and significantly outperforms existing tools. IPCO is implemented in R and available from https://github.com/IPCO-Rlibrary/IPCO.

Vaginal Microbiome of Pregnant Indian Women: Insights into the Genome of Dominant Lactobacillus Species.

The trillions of microorganisms residing in the human body display varying degrees of compositional and functional diversities within and between individuals and contribute significantly to host physiology and susceptibility to disease. Microbial species present in the vaginal milieu of reproductive age women showed a large personal component and varies widely in different ethnic groups at the taxonomic, genomic, and functional levels. Lactobacillus iners, L. crispatus, L. gasseri, L. jensenii, and L. johnsonii are most frequently detected bacterial species in the vaginal milieu of reproductive age women. However, we currently lack (i) an understanding of the baseline vaginal microbiota of reproductive age Indian women, (ii) the extent of taxonomic and functional variations of vaginal microbiota between individuals and (iii) the genomic repertoires of the dominant vaginal microbiota associated with the Indian subjects. In our study, we analyzed the metagenome of high vaginal swab (HVS) samples collected from 40 pregnant Indian women enrolled in the GARBH-Ini cohort. Composition and abundance of bacterial species was characterized by pyrosequencing 16S rRNA gene. We identified 3067 OTUs with ≥ 10 reads from four different bacterial phyla. Several species of lactobacilli were clustered into three community state types (CSTs). L. iners, L. crispatus, L. gasseri, and L. jensenii are the most frequently detected Lactobacillus species in the vaginal environment of Indian women. Other than Lactobacillus, several species of Halomonas were also identified in the vaginal environment of most of the women sampled. To gain genomic and functional insights, we isolated several Lactobacillus species from the HVS samples and explored their whole genome sequences by shotgun sequencing. We analyzed the genome of dominant Lactobacillus species, L. iners, L. crispatus, L. gasseri, and L. paragesseri to represent the CSTs and identify functions that may influence the composition of complex vaginal microbial ecology. This study reports for the first time the vaginal microbial ecology of Indian women and genomic insights into L. iners, L. crispatus, L. gasseri, and L. paragesseri commonly found in the genital tract of reproductive age women.

Serum protein signature of coronary artery disease in type 2 diabetes mellitus.

BACKGROUND: Coronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose of the present study was to discriminate the Indian CAD patients with or without T2DM by using multiple pathophysiological biomarkers. METHODS: Using sensitive multiplex protein assays, we assessed 46 protein markers including cytokines/chemokines, metabolic hormones, adipokines and apolipoproteins for evaluating different pathophysiological conditions of control, T2DM, CAD and T2DM with CAD patients (T2DM_CAD). Network analysis was performed to create protein-protein interaction networks by using significantly (p < 0.05) altered protein markers in each disease using STRING 10.5 database. We used two supervised analysis methods i.e., between class analysis (BCA) and principal component analysis (PCA) to reveals distinct biomarkers profiles. Further, random forest classification (RF) was used to classify the diseases by the panel of markers. RESULTS: Our two supervised analysis methods BCA and PCA revealed a distinct biomarker profiles and high degree of variability in the marker profiles for T2DM_CAD and CAD. Thereafter, the present study identified multiple potential biomarkers to differentiate T2DM, CAD, and T2DM_CAD patients based on their relative abundance in serum. RF classified T2DM based on the abundance patterns of nine markers i.e., IL-1ß, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP and Apo-B; CAD by 14 markers i.e., resistin, PDGF-BB, PAI-1, lipocalin-2, leptin, IL-13, eotaxin, GM-CSF, Apo-E, ghrelin, adipsin, GIP, Apo-CII and IP-10; and T2DM _CAD by 12 markers i.e., insulin, resistin, PAI-1, adiponectin, lipocalin-2, GM-CSF, adipsin, leptin, Apo-AII, rantes, IL-6 and ghrelin with respect to the control subjects. Using network analysis, we have identified several cellular network proteins like PTPN1, AKT1, INSR, LEPR, IRS1, IRS2, IL1R2, IL6R, PCSK9 and MYD88, which are responsible for regulating inflammation, insulin resistance, and atherosclerosis. CONCLUSION: We have identified three distinct sets of serum markers for diabetes, CAD and diabetes associated with CAD in Indian patients using nonparametric-based machine learning approach. These multiple marker classifiers may be useful for monitoring progression from a healthy person to T2DM and T2DM to T2DM_CAD. However, these findings need to be further confirmed in the future studies with large number of samples.

Molecular Insights into Antimicrobial Resistance Traits of Commensal Human Gut Microbiota.

Antimicrobial resistance (AMR) among bacterial species that resides in complex ecosystems is a natural phenomenon. Indiscriminate use of antimicrobials in healthcare, livestock, and agriculture provides an evolutionary advantage to the resistant variants to dominate the ecosystem. Ascendency of resistant variants threatens the efficacy of most, if not all, of the antimicrobial drugs commonly used to prevent and/or cure microbial infections. Resistant phenotype is very common in enteric bacteria. The most common mechanisms of AMR are enzymatic modifications to the antimicrobials or their target molecules. In enteric bacteria, most of the resistance traits are acquired by horizontal gene transfer from closely or distantly related bacterial population. AMR traits are generally linked with mobile genetic elements (MGEs) and could rapidly disseminate to the bacterial species through horizontal gene transfer (HGT) from a pool of resistance genes. Although prevalence of AMR genes among pathogenic bacteria is widely studied in the interest of infectious disease management, the resistance profile and the genetic traits that encode resistance to the commensal microbiota residing in the gut of healthy humans are not well-studied. In the present study, we have characterized AMR phenotypes and genotypes of five dominant commensal enteric bacteria isolated from the gut of healthy Indians. Our study revealed that like pathogenic bacteria, enteric commensals are also multidrug-resistant. The genes encoding antibiotic resistance are physically linked with MGEs and could disseminate vertically to the progeny and laterally to the distantly related microbial species. Consequently, the AMR genes present in the chromosome of commensal gut bacteria could be a potential source of resistance functions for other enteric pathogens.

Igloo-Plot: a tool for visualization of multidimensional datasets.

Advances in science and technology have resulted in an exponential growth of multivariate (or multi-dimensional) datasets which are being generated from various research areas especially in the domain of biological sciences. Visualization and analysis of such data (with the objective of uncovering the hidden patterns therein) is an important and challenging task. We present a tool, called Igloo-Plot, for efficient visualization of multidimensional datasets. The tool addresses some of the key limitations of contemporary multivariate visualization and analysis tools. The visualization layout, not only facilitates an easy identification of clusters of data-points having similar feature compositions, but also the 'marker features' specific to each of these clusters. The applicability of the various functionalities implemented herein is demonstrated using several well studied multi-dimensional datasets. Igloo-Plot is expected to be a valuable resource for researchers working in multivariate data mining studies. Igloo-Plot is available for download from: http://metagenomics.atc.tcs.com/IglooPlot/.

Classification of metagenomic sequences: methods and challenges.

Characterizing the taxonomic diversity of microbial communities is one of the primary objectives of metagenomic studies. Taxonomic analysis of microbial communities, a process referred to as binning, is challenging for the following reasons. Primarily, query sequences originating from the genomes of most microbes in an environmental sample lack taxonomically related sequences in existing reference databases. This absence of a taxonomic context makes binning a very challenging task. Limitations of current sequencing platforms, with respect to short read lengths and sequencing errors/artifacts, are also key factors that determine the overall binning efficiency. Furthermore, the sheer volume of metagenomic datasets also demands highly efficient algorithms that can operate within reasonable requirements of compute power. This review discusses the premise, methodologies, advantages, limitations and challenges of various methods available for binning of metagenomic datasets obtained using the shotgun sequencing approach. Various parameters as well as strategies used for evaluating binning efficiency are then reviewed.

Community-analyzer: a platform for visualizing and comparing microbial community structure across microbiomes.

A key goal in comparative metagenomics is to identify microbial group(s) which are responsible for conferring specific characteristics to a given environment. These characteristics are the result of the inter-microbial interactions between the resident microbial groups. We present a new GUI-based comparative metagenomic analysis application called Community-Analyzer which implements a correlation-based graph layout algorithm that not only facilitates a quick visualization of the differences in the analyzed microbial communities (in terms of their taxonomic composition), but also provides insights into the inherent inter-microbial interactions occurring therein. Notably, this layout algorithm also enables grouping of the metagenomes based on the probable inter-microbial interaction patterns rather than simply comparing abundance values of various taxonomic groups. In addition, the tool implements several interactive GUI-based functionalities that enable users to perform standard comparative analyses across microbiomes. For academic and non-profit users, the Community-Analyzer is currently available for download from: http://metagenomics.atc.tcs.com/Community_Analyzer/.

Inflammatory Protein Signatures as Predictive Disease-Specific Markers for Non-Alcoholic Steatohepatitis (NASH).

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic disease worldwide, consisting of a broad spectrum of diseases such as simple steatosis (NAFL), non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma. Hepatic inflammation plays a key role in the pathophysiology of NAFLD. Inflammatory mediators such as cytokines and chemokines are considered as contributing factors to NAFLD development and progression. In the present study, we aimed to investigate the inflammatory protein signatures as predictive disease-specific markers for non-alcoholic fatty liver disease (NAFLD). This cross-sectional study included healthy control (n = 64), NAFL (n = 109), and NASH (n = 60) human subjects. Serum concentrations of various cytokines and chemokines were evaluated using sensitive multiplex assays. We used principal component analysis (PCoA) to reveal distinct differences in the levels of cytokines and chemokines between each of the study groups. Further, a random forest classification model was developed to identify the panel of markers that could predict diseases. The protein-protein network analysis was performed to determine the various signaling pathways associated with the disease-specific panel of markers. Serum concentrations of TNF-α, IL-1ß, IL-1ra, G-CSF, PDGF-BB, MCP-1, MIP-1a, MIP-1b, RANTES, eotaxin, IL-8 and IP-10 were significantly increased in NASH group as compared to control group. Furthermore, serum concentrations of IL-9 and IL-13 were significantly lower in the NASH group, whereas IL-2 levels were significantly decreased in the NAFL group when compared to the control group. PCoA results demonstrated statistically significant differences in cytokines and chemokines between each of the study groups (PERMANOVA p = 0.001; R2 = 0.102). RANTES, IL-1ra, MIP-1b, IL-2, and G-CSF could differentiate the NAFL group from the controls; G-CSF, IL-1ra, TNF-α, RANTES, and IL-9 could differentiate the NASH group from the controls; and G-CSF, IL-9, IL-13, eotaxin, and TNF- α could differentiate the NASH group from the NAFL group. Our protein-protein network revealed that these markers are involved in cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, TNF, chemokine, JAK/STAT, P13K/Akt, TLR, NOD-like receptor, NF-kB, and adipocytokine signaling pathways which might be responsible for disease pathogenesis. Our study findings revealed a set of distinct cytokine and chemokine markers and they might be considered as biomarkers in distinguishing NASH from NAFL. Future multicentre studies with larger sample size are recommended to determine the potential utility of these panels of markers.

Associations between host microbiome and inflammation suggest role for host microbiome in driving COVID-19 disease severity.

Systemic inflammation and innate immune activation are associated with COVID-19 disease severity. Knowledge gaps remain in the relationships between microbiome, inflammation and COVID-19 disease severity. To better characterise these associations, we performed 16SrDNA analysis of stool samples in COVID-19 subjects to explore diversity and taxanomic composition. We correlated these to host inflammatory profiles, derived from soluble plasma biomarkers measured by bead-based fluorescence and electrochemiluminescence immunoassays. Associations of microbial diversity and inflammatory biomarkers on maximal COVID-19 severity (mild, moderate v severe/critical) was explored using logistic regression and weighted gene correlation network analysis (WGCNA). Of 79 subjects, 58% were male and 88% were Caucasian with 36% experiencing mild disease, 22% moderate disease and 40% critical/severe COVID-19. Hierarchical clustering and principal component analysis (PCo) revealed distinct inflammatory clusters that were found to correlate with 4 modules of microbiome profiles. Modules 3 and 4 were associated with both older age and severe/critical disease outcomes. These modules were enriched in pathogenic and inflammatory bacteria that mapped to a pro-inflammatory biomarker cluster. In contrast, module 1 exhibited enrichment of anti-inflammatory bacteria, was associated with younger age and mild/moderate disease outcomes and mapped to a less-inflamed biomarker cluster. This study provides further insights into links between host microbiome, inflammatory responses to SARS-CoV-2 infection and clinical COVID-19 disease severity, suggesting a role for the microbiome in shaping distinct host inflammatory responses to infection.

C16S - a Hidden Markov Model based algorithm for taxonomic classification of 16S rRNA gene sequences.

Recent advances in high throughput sequencing technologies and concurrent refinements in 16S rDNA isolation techniques have facilitated the rapid extraction and sequencing of 16S rDNA content of microbial communities. The taxonomic affiliation of these 16S rDNA fragments is subsequently obtained using either BLAST-based or word frequency based approaches. However, the classification accuracy of such methods is observed to be limited in typical metagenomic scenarios, wherein a majority of organisms are hitherto unknown. In this study, we present a 16S rDNA classification algorithm, called C16S, that uses genus-specific Hidden Markov Models for taxonomic classification of 16S rDNA sequences. Results obtained using C16S have been compared with the widely used RDP classifier. The performance of C16S algorithm was observed to be consistently higher than the RDP classifier. In some scenarios, this increase in accuracy is as high as 34%. A web-server for the C16S algorithm is available at http://metagenomics.atc.tcs.com/C16S/.

Human Milk Microbiome of Healthy Indian Mothers is Dominated by Genus Pseudomonas.

BACKGROUND: The composition of the human milk microbiome is highly variable and multifactorial. Milk microbiota from various countries show striking differences. There is a paucity of data from healthy lactating Indian mothers. RESEARCH AIM: To describe the milk microbiota of healthy North Indian women, using a culture-independent, targeted metagenomic approach. METHODS: We recruited exclusively breastfeeding mothers (N = 22) who had vaginally delivered full-term singleton infants in a tertiary care hospital less than 1 week previously and had not recently consumed systemic antibiotics. Milk samples (5 ml) were collected aseptically, and microbial deoxyribonucleic acid was extracted. Microbial composition and diversity were determined using a 454-pyrosequencing platform. Core genera were identified, and their relative abundances ranked. Heatmaps showing the variation of the ranked abundances and Shannon index were obtained using R. RESULTS: Participants (all exclusively vegetarian) had a mean (SD) age of 27.2 (3.4) years, postnatal age of 3.9 (1.6) days and gestation 38 (1.2) weeks. The dominant phylum was Proteobacterium (relative abundance 84%) and dominant genus Pseudomonas (relative abundance 61.78%). Eleven species of Pseudomonas were identified, all generally considered nonpathogenic. Based on abundance patterns of the core genera, the milk samples could be grouped: (a) dominated by Pseudomonas with low diversity; (b) less Pseudomonas and high diversity; and (c) dominated by Pseudomonas but high diversity. All neonates were healthy and gaining weight well at 1 month of age. CONCLUSIONS: Healthy, lactating, vegetarian, North Indian women who deliver at term gestation and have no recent exposure to antibiotics, have a unique milk microbiome dominated by Pseudomonas.

Robust cross-cohort gut microbiome associations with COVID-19 severity.

Although many recent studies have examined associations between the gut microbiome and COVID-19 disease severity in individual patient cohorts, questions remain on the robustness across international cohorts of the biomarkers they reported. Here, we performed a meta-analysis of eight shotgun metagenomic studies of COVID-19 patients (comprising 1,023 stool samples) and 23 > 16S rRNA gene amplicon sequencing (16S) cohorts (2,415 total stool samples). We found that disease severity (as defined by the WHO clinical progression scale) was associated with taxonomic and functional microbiome differences. This alteration in gut microbiome configuration peaks at days 7-30 post diagnosis, after which the gut microbiome returns to a configuration that becomes more similar to that of healthy controls over time. Furthermore, we identified a core set of species that were consistently associated with disease severity across shotgun metagenomic and 16S cohorts, and whose abundance can accurately predict disease severity category of SARS-CoV-2 infected subjects, with Actinomyces oris abundance predicting population-level mortality rate of COVID-19. Additionally, we used relational diet-microbiome databases constructed from cohort studies to predict microbiota-targeted diet patterns that would modulate gut microbiota composition toward that of healthy controls. Finally, we demonstrated the association of disease severity with the composition of intestinal archaeal, fungal, viral, and parasitic communities. Collectively, this study has identified robust COVID-19 microbiome biomarkers, established accurate predictive models as a basis for clinical prognostic tests for disease severity, and proposed biomarker-targeted diets for managing COVID-19 infection.

Comparative diet-gut microbiome analysis in Crohn's disease and Hidradenitis suppurativa.

Introduction: The chronic inflammatory skin disease Hidradenitis suppurativa (HS) is strongly associated with Crohn's Disease (CD). HS and CD share clinical similarities and similar inflammatory pathways are upregulated in both conditions. Increased prevalence of inflammatory disease in industrialised nations has been linked to the Western diet. However, gut microbiota composition and diet interaction have not been compared in HS and CD. Methods: Here we compared the fecal microbiota (16S rRNA gene amplicon sequencing) and habitual diet of previously reported subjects with HS (n = 55), patients with CD (n = 102) and controls (n = 95). Results and discussion: Patients with HS consumed a Western diet similar to patients with CD. Meanwhile, habitual diet in HS and CD was significantly different to controls. Previously, we detected differences in microbiota composition among patients with HS from that of controls. We now show that 40% of patients with HS had a microbiota configuration similar to that of CD, characterised by the enrichment of pathogenic genera (Enterococcus, Veillonella and Escherichia_Shigella) and the depletion of putatively beneficial genera (Faecalibacterium). The remaining 60% of patients with HS harboured a normal microbiota similar to that of controls. Antibiotics, which are commonly used to treat HS, were identified as a co-varying with differences in microbiota composition. We examined the levels of several inflammatory markers highlighting that growth-arrest specific 6 (Gas6), which has anti-inflammatory potential, were significantly lower in the 40% of patients with HS who had a CD microbiota configuration. Levels of the pro-inflammatory cytokine IL-12, which is a modulator of intestinal inflammation in CD, were negatively correlated with the abundance of health-associated genera in patients with HS. In conclusion, the fecal microbiota may help identify patients with HS who are at greater risk for development of CD.

SPHINX--an algorithm for taxonomic binning of metagenomic sequences.

MOTIVATION: Compared with composition-based binning algorithms, the binning accuracy and specificity of alignment-based binning algorithms is significantly higher. However, being alignment-based, the latter class of algorithms require enormous amount of time and computing resources for binning huge metagenomic datasets. The motivation was to develop a binning approach that can analyze metagenomic datasets as rapidly as composition-based approaches, but nevertheless has the accuracy and specificity of alignment-based algorithms. This article describes a hybrid binning approach (SPHINX) that achieves high binning efficiency by utilizing the principles of both 'composition'- and 'alignment'-based binning algorithms. RESULTS: Validation results with simulated sequence datasets indicate that SPHINX is able to analyze metagenomic sequences as rapidly as composition-based algorithms. Furthermore, the binning efficiency (in terms of accuracy and specificity of assignments) of SPHINX is observed to be comparable with results obtained using alignment-based algorithms. AVAILABILITY: A web server for the SPHINX algorithm is available at http://metagenomics.atc.tcs.com/SPHINX/.

Emerging tools for understanding the human microbiome.

Recent advances in sequencing technologies, experimental protocols and approaches in data generation and analysis have enabled us to investigate the human microbiome at an unprecedented level of resolution. The current chapter aims to provide an understanding of the different computational and bioinformatic strategies adopted to answer the different questions of a typical microbiome investigation and how the upstream DNA sequencing methodologies can affect this. The chapter enlist the state-of-the-art in metagenomic data analysis along with the available strategies to perform an integrated investigation of the human microbiome along with other data layers.