Recent advances in ultra-high-throughput sequencing technology and metagenomics have led to a paradigm shift in microbial genomics from few genome comparisons to large-scale pan-genome studies at different scales of phylogenetic resolution. Pan-genome studies provide a framework for estimating the genomic diversity of the dataset, determining core (conserved), accessory (dispensable) and unique (strain-specific) gene pool of a species, tracing horizontal gene-flux across strains and providing insight into species evolution. The existing pan genome software tools suffer from various limitations like limited datasets, difficult installation/requirements, inadequate functional features etc. Here we present an ultra-fast computational pipeline BPGA (Bacterial Pan Genome Analysis tool) with seven functional modules. In addition to the routine pan genome analyses, BPGA introduces a number of novel features for downstream analyses like core/pan/MLST (Multi Locus Sequence Typing) phylogeny, exclusive presence/absence of genes in specific strains, subset analysis, atypical G + C content analysis and KEGG & COG mapping of core, accessory and unique genes. Other notable features include minimum running prerequisites, freedom to select the gene clustering method, ultra-fast execution, user friendly command line interface and high-quality graphics outputs. The performance of BPGA has been evaluated using a dataset of complete genome sequences of 28 Streptococcus pyogenes strains.

One of the fundamental issues in the microbiome research is characterization of the healthy human microbiota. Recent studies have elucidated substantial divergences in the microbiome structure between healthy individuals from different race and ethnicity. This review provides a comprehensive account of such geography, ethnicity or life-style-specific variations in healthy microbiome at five major body habitats—Gut, Oral-cavity, Respiratory Tract, Skin, and Urogenital Tract (UGT). The review focuses on the general trend in the human microbiome evolution—a gradual transition in the gross compositional structure along with a continual decrease in diversity of the microbiome, especially of the gut microbiome, as the human populations passed through three stages of subsistence like foraging, rural farming and industrialized urban western life. In general, gut microbiome of the hunter-gatherer populations is highly abundant with Prevotella, Proteobacteria, Spirochaetes, Clostridiales, Ruminobacter etc., while those of the urban communities are often enriched in Bacteroides, Bifidobacterium, and Firmicutes. The oral and skin microbiome are the next most diverse among different populations, while respiratory tract and UGT microbiome show lesser variations. Higher microbiome diversity is observed for oral-cavity in hunter-gatherer group with higher prevalence of Haemophilus than agricultural group. In case of skin microbiome, rural and urban Chinese populations show variation in abundance of Trabulsiella and Propionibacterium. On the basis of published data, we have characterized the core microbiota—the set of genera commonly found in all populations, irrespective of their geographic locations, ethnicity or mode of subsistence. We have also identified the major factors responsible for geography-based alterations in microbiota; though it is not yet clear which factor plays a dominant role in shaping the microbiome—nature or nurture, host genetics or his environment. Some of the geographical/racial variations in microbiome structure have been attributed to differences in host genetics and innate/adaptive immunity, while in many other cases, cultural/behavioral features like diet, hygiene, parasitic load, environmental exposure etc. overshadow genetics. The ethnicity or population-specific variations in human microbiome composition, as reviewed in this report, question the universality of the microbiome-based therapeutic strategies and recommend for geographically tailored community-scale approaches to microbiome engineering.

Providing insight into one’s health status from a gut microbiome sample is an important clinical goal in current human microbiome research. Herein, we introduce the Gut Microbiome Health Index (GMHI), a biologically-interpretable mathematical formula for predicting the likelihood of disease independent of the clinical diagnosis. GMHI is formulated upon 50 microbial species associated with healthy gut ecosystems. These species are identified through a multi-study, integrative analysis on 4347 human stool metagenomes from 34 published studies across healthy and 12 different nonhealthy conditions, i.e., disease or abnormal bodyweight. When demonstrated on our population-scale meta-dataset, GMHI is the most robust and consistent predictor of disease presence (or absence) compared to α-diversity indices. Validation on 679 samples from 9 additional studies results in a balanced accuracy of 73.7% in distinguishing healthy from non-healthy groups. Our findings suggest that gut taxonomic signatures can predict health status, and highlight how data sharing efforts can provide broadly applicable discoveries.

BackgroundThe community composition of the human microbiome is known to vary at distinct anatomical niches. But little is known about the nature of variations, if any, at the genome/sub-genome levels of a specific microbial community across different niches. The present report aims to explore, as a case study, the variations in gene repertoire of 28 Prevotella reference genomes derived from different body-sites of human, as reported earlier by the Human Microbiome Consortium.ResultsThe pan-genome for Prevotella remains “open”. On an average, 17% of predicted protein-coding genes of any particular Prevotella genome represent the conserved core genes, while the remaining 83% contribute to the flexible and singletons. The study reveals exclusive presence of 11798, 3673, 3348 and 934 gene families and exclusive absence of 17, 221, 115 and 645 gene families in Prevotella genomes derived from human oral cavity, gastro-intestinal tracts (GIT), urogenital tract (UGT) and skin, respectively. Distribution of various functional COG categories differs significantly among the habitat-specific genes. No niche-specific variations could be observed in distribution of KEGG pathways.ConclusionsPrevotella genomes derived from different body sites differ appreciably in gene repertoire, suggesting that these microbiome components might have developed distinct genetic strategies for niche adaptation within the host. Each individual microbe might also have a component of its own genetic machinery for host adaptation, as appeared from the huge number of singletons.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1350-6) contains supplementary material, which is available to authorized users.

Background Rapid advances in the past decade have shown that dysbiosis of the gut microbiome is a key hallmark of rheumatoid arthritis (RA). Yet, the relationship between the gut microbiome and clinical improvement in RA disease activity remains unclear. In this study, we explored the gut microbiome of patients with RA to identify features that are associated with, as well as predictive of, minimum clinically important improvement (MCII) in disease activity. Methods We conducted a retrospective, observational cohort study on patients diagnosed with RA between 1988 and 2014. Whole metagenome shotgun sequencing was performed on 64 stool samples, which were collected from 32 patients with RA at two separate time-points approximately 6–12 months apart. The Clinical Disease Activity Index (CDAI) of each patient was measured at both time-points to assess achievement of MCII; depending on this clinical status, patients were distinguished into two groups: MCII+ (who achieved MCII; n = 12) and MCII− (who did not achieve MCII; n = 20). Multiple linear regression models were used to identify microbial taxa and biochemical pathways associated with MCII while controlling for potentially confounding factors. Lastly, a deep-learning neural network was trained upon gut microbiome, clinical, and demographic data at baseline to classify patients according to MCII status, thereby enabling the prediction of whether a patient will achieve MCII at follow-up. Results We found age to be the largest determinant of the overall compositional variance in the gut microbiome (R2 = 7.7%, P = 0.001, PERMANOVA). Interestingly, the next factor identified to explain the most variance in the gut microbiome was MCII status (R2 = 3.8%, P = 0.005). Additionally, by looking at patients’ baseline gut microbiome profiles, we observed significantly different microbiome traits between patients who eventually showed MCII and those who did not. Taxonomic features include alpha- and beta-diversity measures, as well as several microbial taxa, such as Coprococcus, Bilophila sp. 4_1_30, and Eubacterium sp. 3_1_31. Notably, patients who achieved clinical improvement had higher alpha-diversity in their gut microbiomes at both baseline and follow-up visits. Functional profiling identified fifteen biochemical pathways, most of which were involved in the biosynthesis of L-arginine, L-methionine, and tetrahydrofolate, to be differentially abundant between the MCII patient groups. Moreover, MCII+ and MCII− groups showed significantly different fold-changes (from baseline to follow-up) in eight microbial taxa and in seven biochemical pathways. These results could suggest that, depending on the clinical course, gut microbiomes not only start at different ecological states, but also are on separate trajectories. Finally, the neural network proved to be highly effective in predicting which patients will achieve MCII (balanced accuracy = 90.0%, leave-one-out cross-validation), demonstrating potential clinical utility of gut microbiome profiles. Conclusions Our findings confirm the presence of taxonomic and functional signatures of the gut microbiome associated with MCII in RA patients. Ultimately, modifying the gut microbiome to enhance clinical outcome may hold promise as a future treatment for RA.

Migraine has been associated with patent foramen ovale (PFO), and PFO closure has become the most high-profile nonpharmacologic invasive therapy recommended for the prevention of recurrent migraine attacks, as well as for preventing further attacks in cryptogenic stroke. The results of Migraine Intervention with STARFlex Technology (MIST), a controversial but important recent randomized clinical trial (RCT) of PFO closure for migraine, do not support PFO closure for preventing migraine attacks. All patients with migraine, however, do not have a PFO, and the characteristic periodicity and predictability of migraine cannot be explained on the basis of paradoxical embolism through the PFO. Closure of the PFO or atrial septal defect can aggravate migraine suddenly. PFO increases in size with age, but migraine generally subsides with the passage of years. Serendipity does play a role in some medical discoveries, but in the absence of a logically defensible theoretical basis, chance and statistics can both become misleading. With soft end points, RCTs in migraine patients can generate conflicting and irreconcilable data. RCTs cannot supplant or substitute clinical common sense or justify serendipity. Scientific progress mandates that any serendipitous research must ultimately conform to the principles of the basic sciences surrounding the chance discovery. PFO closure for preventing migraine attacks is an unfortunate, but sobering, chapter in the migraine research saga.

Human papillomavirus (HPV) has been implicated in the etiology of a variety of human cancers. Studies investigating the presence of high-risk (HR) HPV in breast tissue have generated considerable controversy over its role as a potential risk factor for breast cancer (BC). This is the first investigation reporting the prevalence and type distribution of high-risk HPV infection in breast tissue in the population of Qatar. A prospective comparison blind research study herein reconnoitered the presence of twelve HR-HPV types’ DNA using multiplex PCR by screening a total of 150 fresh breast tissue specimens. Data obtained shows that HR-HPV types were found in 10% of subjects with breast cancer; of which the presence of HPV was confirmed in 4/33 (12.12%) of invasive carcinomas. These findings, the first reported from the population of Qatar, suggest that the selective presence of HPV in breast tissue is likely to be a related factor in the progression of certain cases of breast cancer.

The relationship between primary biliary cholangitis (PBC), a chronic cholestatic autoimmune liver disease, and the peripheral immune system remains to be fully understood. Herein, we performed the first mass cytometry (CyTOF)-based, immunophenotyping analysis of the peripheral immune system in PBC at single-cell resolution. CyTOF was performed on peripheral blood mononuclear cells (PBMCs) from PBC patients (n = 33) and age-/sex-matched healthy controls (n = 33) to obtain immune cell abundance and marker expression profiles. Hierarchical clustering methods were applied to identify immune cell types and subsets significantly associated with PBC. Subsets of gamma-delta T cells (CD3 + TCRgd + ), CD8 + T cells (CD3 + CD8 + CD161 + PD1 + ), and memory B cells (CD3 − CD19 + CD20 + CD24 + CD27 + ) were found to have lower abundance in PBC than in control. In contrast, higher abundance of subsets of monocytes and naïve B cells were observed in PBC compared to control. Furthermore, several naïve B cell (CD3 − CD19 + CD20 + CD24 − CD27 − ) subsets were significantly higher in PBC patients with cirrhosis (indicative of late-stage disease) than in those without cirrhosis. Alternatively, subsets of memory B cells were lower in abundance in cirrhotic relative to non-cirrhotic PBC patients. Future immunophenotyping investigations could lead to better understanding of PBC pathogenesis and progression, and also to the discovery of novel biomarkers and treatment strategies.