Bacterial strain-tracking across the human skin landscape in health and disease

Metagenomics, or genomic sequence of the community of microbiota (bacteria, fungi, virus), enables an investigation of the full complement of genetic material, including virulence, antibiotic resistance, and strain differentiating markers. The granularity to distinguish between closely related strains is important as within one species, these strains possess distinct functions and relationships to a host. To analyze metagenomic samples, I developed a reference-based approach that utilizes both single nucleotide variants and genetic content to assign species and strain-level designations. After refining this approach with complex simulated communities, I utilized it to analyze the microbial communities present in skin samples from healthy and diseased individuals. First, to investigate strain-level heterogeneity in healthy adults, I focused on the common skin commensals Propionibacterium acnes and Staphylococcus epidermidis with well-documented sequence variation. Results indicated that an individual’s strains of P. acnes are shared across multiple sites of his or her body, and that those strains are more similar within than between individuals. For S. epidermidis, in addition to individual site similarities, there were also site-specific strains. Overall these results emphasize that both individuality and site specificity shape our bodies’ microbial communities. Based on longitudinal data, an individual’s strain signatures remain stable for up to a year despite external, environmental perturbations. I then used metagenomic data to explore microbial temporal dynamics in atopic dermatitis (AD; eczema), an inflammatory skin disease commonly associated with Staphylococcal species. Species-level investigation of AD flares demonstrated a microbial dichotomy in which S. aureus predominated on more severely affected patients while S. epidermidis predominated on less severely affected patients. Strain-level analysis determined that S. aureus-predominant patients were monocolonized with distinct S. aureus strains, while all patients had heterogeneous S. epidermidis strain communities. To assess the host immunologic effects of these species, I topically applied patient-derived strains to mice. AD strains of S. aureus were sufficient to elicit a skin immune response, characteristic of AD patients. This suggests a model whereby staphylococcal strains contribute to AD progression through activation of the host immune system. Overall, this strain-level analysis of healthy and disease communities provides previously unexplored resolution of human skin microbiome.2018-03-24T00:00:00

PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples.

BACKGROUND: Recent innovations in sequencing technologies have provided researchers with the ability to rapidly characterize the microbial content of an environmental or clinical sample with unprecedented resolution. These approaches are producing a wealth of information that is providing novel insights into the microbial ecology of the environment and human health. However, these sequencing-based approaches produce large and complex datasets that require efficient and sensitive computational analysis workflows. Many recent tools for analyzing metagenomic-sequencing data have emerged, however, these approaches often suffer from issues of specificity, efficiency, and typically do not include a complete metagenomic analysis framework. RESULTS: We present PathoScope 2.0, a complete bioinformatics framework for rapidly and accurately quantifying the proportions of reads from individual microbial strains present in metagenomic sequencing data from environmental or clinical samples. The pipeline performs all necessary computational analysis steps; including reference genome library extraction and indexing, read quality control and alignment, strain identification, and summarization and annotation of results. We rigorously evaluated PathoScope 2.0 using simulated data and data from the 2011 outbreak of Shiga-toxigenic Escherichia coli O104:H4. CONCLUSIONS: The results show that PathoScope 2.0 is a complete, highly sensitive, and efficient approach for metagenomic analysis that outperforms alternative approaches in scope, speed, and accuracy. The PathoScope 2.0 pipeline software is freely available for download at: http://sourceforge.net/projects/pathoscope/

PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples.

BACKGROUND: Recent innovations in sequencing technologies have provided researchers with the ability to rapidly characterize the microbial content of an environmental or clinical sample with unprecedented resolution. These approaches are producing a wealth of information that is providing novel insights into the microbial ecology of the environment and human health. However, these sequencing-based approaches produce large and complex datasets that require efficient and sensitive computational analysis workflows. Many recent tools for analyzing metagenomic-sequencing data have emerged, however, these approaches often suffer from issues of specificity, efficiency, and typically do not include a complete metagenomic analysis framework. RESULTS: We present PathoScope 2.0, a complete bioinformatics framework for rapidly and accurately quantifying the proportions of reads from individual microbial strains present in metagenomic sequencing data from environmental or clinical samples. The pipeline performs all necessary computational analysis steps; including reference genome library extraction and indexing, read quality control and alignment, strain identification, and summarization and annotation of results. We rigorously evaluated PathoScope 2.0 using simulated data and data from the 2011 outbreak of Shiga-toxigenic Escherichia coli O104:H4. CONCLUSIONS: The results show that PathoScope 2.0 is a complete, highly sensitive, and efficient approach for metagenomic analysis that outperforms alternative approaches in scope, speed, and accuracy. The PathoScope 2.0 pipeline software is freely available for download at: http://sourceforge.net/projects/pathoscope/

A comprehensive assessment of demographic, environmental, and host genetic associations with gut microbiome diversity in healthy individuals.

BACKGROUND: The gut microbiome is an important determinant of human health. Its composition has been shown to be influenced by multiple environmental factors and likely by host genetic variation. In the framework of the Milieu Intérieur Consortium, a total of 1000 healthy individuals of western European ancestry, with a 1:1 sex ratio and evenly stratified across five decades of life (age 20-69), were recruited. We generated 16S ribosomal RNA profiles from stool samples for 858 participants. We investigated genetic and non-genetic factors that contribute to individual differences in fecal microbiome composition. RESULTS: Among 110 demographic, clinical, and environmental factors, 11 were identified as significantly correlated with α-diversity, ß-diversity, or abundance of specific microbial communities in multivariable models. Age and blood alanine aminotransferase levels showed the strongest associations with microbiome diversity. In total, all non-genetic factors explained 16.4% of the variance. We then searched for associations between > 5 million single nucleotide polymorphisms and the same indicators of fecal microbiome diversity, including the significant non-genetic factors as covariates. No genome-wide significant associations were identified after correction for multiple testing. A small fraction of previously reported associations between human genetic variants and specific taxa could be replicated in our cohort, while no replication was observed for any of the diversity metrics. CONCLUSION: In a well-characterized cohort of healthy individuals, we identified several non-genetic variables associated with fecal microbiome diversity. In contrast, host genetics only had a negligible influence. Demographic and environmental factors are thus the main contributors to fecal microbiome composition in healthy individuals. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT01699893

Temporal Stability of the Human Skin Microbiome.

Biogeography and individuality shape the structural and functional composition of the human skin microbiome. To explore these factors\u27 contribution to skin microbial community stability, we generated metagenomic sequence data from longitudinal samples collected over months and years. Analyzing these samples using a multi-kingdom, reference-based approach, we found that despite the skin\u27s exposure to the external environment, its bacterial, fungal, and viral communities were largely stable over time. Site, individuality, and phylogeny were all determinants of stability. Foot sites exhibited the most variability; individuals differed in stability; and transience was a particular characteristic of eukaryotic viruses, which showed little site-specificity in colonization. Strain and single-nucleotide variant-level analysis showed that individuals maintain, rather than reacquire, prevalent microbes from the environment. Longitudinal stability of skin microbial communities generates hypotheses about colonization resistance and empowers clinical studies exploring alterations observed in disease states. Cell 2016 May 5; 165(4):854-66

Clinical PathoScope: Rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data

Background The use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. However, these samples contain genomic sequences from various sources that complicate the identification of pathogens. Results Here we present Clinical PathoScope, a pipeline to rapidly and accurately remove host contamination, isolate microbial reads, and identify potential disease-causing pathogens. We have accomplished three essential tasks in the development of Clinical PathoScope. First, we developed an optimized framework for pathogen identification using a computational subtraction methodology in concordance with read trimming and ambiguous read reassignment. Second, we have demonstrated the ability of our approach to identify multiple pathogens in a single clinical sample, accurately identify pathogens at the subspecies level, and determine the nearest phylogenetic neighbor of novel or highly mutated pathogens using real clinical sequencing data. Finally, we have shown that Clinical PathoScope outperforms previously published pathogen identification methods with regard to computational speed, sensitivity, and specificity. Conclusions Clinical PathoScope is the only pathogen identification method currently available that can identify multiple pathogens from mixed samples and distinguish between very closely related species and strains in samples with very few reads per pathogen. Furthermore, Clinical PathoScope does not rely on genome assembly and thus can more rapidly complete the analysis of a clinical sample when compared with current assembly-based methods. Clinical PathoScope is freely available at:http://sourceforge.net/projects/pathoscope/ webcite

Gut microbiome stability and dynamics in healthy donors and patients with non-gastrointestinal cancers

International audienceAs microbial therapeutics are increasingly being tested in diverse patient populations, it is essential to understand the host and environmental factors influencing the microbiome. Through analysis of 1,359 gut microbiome samples from 946 healthy donors of the Milieu Intérieur cohort, we detail how microbiome composition is associated with host factors, lifestyle parameters, and disease states. Using a genome-based taxonomy, we found biological sex was the strongest driver of community composition. Additionally, bacterial populations shift across decades of life (age 20-69), with Bacteroidota species consistently increased with age while Actinobacteriota species, including Bifidobacterium, decreased. Longitudinal sampling revealed that short-term stability exceeds interindividual differences. By accounting for these factors, we defined global shifts in the microbiomes of patients with non-gastrointestinal tumors compared with healthy donors. Together, these results demonstrated that the microbiome displays predictable variations as a function of sex, age, and disease state. These variations must be considered when designing microbiome-targeted therapies or interpreting differences thought to be linked to pathophysiology or therapeutic response