A Microbiome-Based Index for Assessing Skin Health and Treatment Effects for Atopic Dermatitis in Children.

A quantitative and objective indicator for skin health via the microbiome is of great interest for personalized skin care, but differences among skin sites and across human populations can make this goal challenging. A three-city (two Chinese and one American) comparison of skin microbiota from atopic dermatitis (AD) and healthy pediatric cohorts revealed that, although city has the greatest effect size (the skin microbiome can predict the originated city with near 100% accuracy), a microbial index of skin health (MiSH) based on 25 bacterial genera can diagnose AD with 83 to ∼95% accuracy within each city and 86.4% accuracy across cities (area under the concentration-time curve [AUC], 0.90). Moreover, nonlesional skin sites across the bodies of AD-active children (which include shank, arm, popliteal fossa, elbow, antecubital fossa, knee, neck, and axilla) harbor a distinct but lesional state-like microbiome that features relative enrichment of Staphylococcus aureus over healthy individuals, confirming the extension of microbiome dysbiosis across body surface in AD patients. Intriguingly, pretreatment MiSH classifies children with identical AD clinical symptoms into two host types with distinct microbial diversity and treatment effects of corticosteroid therapy. These findings suggest that MiSH has the potential to diagnose AD, assess risk-prone state of skin, and predict treatment response in children across human populations.IMPORTANCE MiSH, which is based on the skin microbiome, can quantitatively assess pediatric skin health across cohorts from distinct countries over large geographic distances. Moreover, the index can identify a risk-prone skin state and compare treatment effect in children, suggesting applications in diagnosis and patient stratification

Dysfunction of the Intestinal Microbiome in Inflammatory Bowel Disease and Treatment

Background: The inflammatory bowel diseases (IBD) Crohn's disease and ulcerative colitis result from alterations in intestinal microbes and the immune system. However, the precise dysfunctions of microbial metabolism in the gastrointestinal microbiome during IBD remain unclear. We analyzed the microbiota of intestinal biopsies and stool samples from 231 IBD and healthy subjects by 16S gene pyrosequencing and followed up a subset using shotgun metagenomics. Gene and pathway composition were assessed, based on 16S data from phylogenetically-related reference genomes, and associated using sparse multivariate linear modeling with medications, environmental factors, and IBD status. Results: Firmicutes and Enterobacteriaceae abundances were associated with disease status as expected, but also with treatment and subject characteristics. Microbial function, though, was more consistently perturbed than composition, with 12% of analyzed pathways changed compared with 2% of genera. We identified major shifts in oxidative stress pathways, as well as decreased carbohydrate metabolism and amino acid biosynthesis in favor of nutrient transport and uptake. The microbiome of ileal Crohn's disease was notable for increases in virulence and secretion pathways. Conclusions: This inferred functional metagenomic information provides the first insights into community-wide microbial processes and pathways that underpin IBD pathogenesis

Exploring the interaction between the human microbiota and infections

The microbiota is a living ecosystem that is influenced by a variety of host and environmental factors. Distinct microbiota colonizes various body sites, such as the gastrointestinal system and vaginal tract, corresponding to the unique microenvironment. A healthy gut microbiota contains a stable, balanced, and highly diverse reservoir of microbes. Commensal microorganisms co-evolved with the host have conferred pathogen colonization resistance. Lactobacillus species usually dominate the vaginal microbiota of majority healthy women. The vaginal microbiota has also been linked to sexually transmitted diseases, such as the human papillomavirus (HPV) infection. MicroRNA expression was found to be associated with microbiota composition. We studied the interactions of microbiota gut and vagina with metabolites, miRNA expression, and HPV infection in our investigations. In vitro three- dimensional (3D) cell-culture methods were also analyzed and developed for mechanistic investigations of the microbiota-host interaction. Study I is a cross-sectional study investigating the microbiota features in HPV-related diseases. We defined the HPV-related microbial composition in a high-vaccination coverage population of 345 young Swedish women. The associations of microbial composition and the infection of 27 HPV types were analyzed. HPV infection, especially for the infection of oncogenic HPV types, was characterized by a higher microbial alpha-diversity with non- Lactobacillus-dominant vaginal microbiota composition. The prevalence of bacterial vaginosis-associated bacteria (BV AB), Sneathia, Prevotella, and Megasphaera were significantly higher in women with HPV infection than in uninfected individuals. Study II investigated the associations among miRNA, HPV infection, and vaginal microbiota composition. A global miRNA expression increase was identified in non-Lactobacillus- dominate women compared with Lactobacillus-dominated women. The top two differently expressed miRNA, miR-23a-3p and miR-130a-3p, showed a prediction accuracy of > 97% for distinguishing Lactobacillus-dominated and non- Lactobacillus-dominated samples. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the target genes of miR-23a-3p and miR-130a-3p found that many pathways involved in cancer and infection were enriched, such as the mitogen-activated protein kinase (MAPK) signaling pathway, and the phosphatidylinositol 3-kinase (PI3K)-Akt signaling pathway. Study III explored the interactions between commensal bacteria and pathogens. We cocultured Salmonella with commensal bacteria isolations from an anaerobically cultivated human intestinal microflora (ACHIM). Commensal bacteria isolations belonging to the Clostridium and Eubacterium family showed significant inhibition of Salmonella growth. Following metabolite extraction and liquid chromatography-mass spectrometry analysis of the commensal bacteria and Salmonella metabolites in the coculture system of the commensal bacteria and Salmonella, adenine and adenosine were significantly higher in the coculture systems that inhibited Salmonella growth compared with the expressive systems. Functional assays of metabolite activity further validated the inhibition effect of adenine and adenosine on the growth of Salmonella and other antibiotic-resistant pathogens. Study IV established an anaerobic gut 3D model to study bacteria-host interactions. The Caco2 cells showed good cell viability and formed intestinal villi-like structures in our model. Anaerobic culture for 12 hours did not show a significant effect on the viability of cells, with only six genes differently expressed between cells cultured under anaerobic and aerobic conditions in RNA sequencing. RNA sequencing of Salmonella infected Caco2 cells cocultured in anaerobic conditions showed a large set of genes differentially expressed compared to aerobic conditions with the pathways associated with cell cycle, homologous recombination, and DNA replication. This supports our gut 3D model could be used for investigating host-microbe direct interactions under the anaerobic condition, which is essential for obligate anaerobic gut microbes

Could Gut Microbiota Make a Difference?—“BiotaCancerSurvivors”: A Case-Control Study

In this first analysis, samples from 23 BC survivors (group 1) and 291 healthy female controls (group 2) were characterised through the V3 and V4 regions that encode the “16S rRNA” gene of each bacteria. The samples were sequenced by next-generation sequencing (NGS), and the taxonomy was identified by resorting to Kraken2 and improved with Bracken, using a curated database called ‘GutHealth_DB’. The α and β-diversity analyses were used to determine the richness and evenness of the gut microbiota. A non-parametric Mann-Whitney U test was applied to assess differential abundance between both groups. The Firmicutes/Bacteroidetes (F/B) ratio was calculated using a Kruskal-Wallis chi-squared test. The α-diversity was significantly higher in group 1 (p = 0.28 × 10−12 for the Chao index and p = 1.64 × 10−12 for the ACE index). The Shannon index, a marker of richness and evenness, was not statistically different between the two groups (p = 0.72). The microbiota composition was different between the two groups: a null hypothesis was rejected for PERMANOVA (p = 9.99 × 10−5) and Anosim (p = 0.04) and was not rejected for β-dispersion (p = 0.158), using Unifrac weighted distance. The relative abundance of 14 phyla, 29 classes, 25 orders, 64 families, 116 genera, and 74 species differed significantly between both groups. The F/B ratio was significantly lower in group 1 than in group 2, p < 0.001. Our study allowed us to observe significant taxonomic disparities in the two groups by testing the differences between BC survivors and healthy controls. Additional studies are needed to clarify the involved mechanisms and explore the relationship between microbiota and BC survivorship.publishersversionpublishe

The evaluation, application, and expansion of 16s amplicon metagenomics

Since the invention of high-throughput sequencing, the majority of experiments studying bacterial microbiomes have relied on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiomic sample. Several computational methods exist for analyzing 16S amplicon based metagenomics, but the most commonly used bioinformatics tools are unable to produce quality genus-level or species-level taxonomic calls and may underestimate the degree to which such calls are possible. In this thesis, I have used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, with a focus on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. With the efficacy of these tools established, I then applied them in the analysis of data from two studies into human microbiomes. I evaluated the metagenomics analysis tools Qiime 2, Mothur, PathoScope 2, and Kraken 2, in conjunction with reference libraries from GreenGenes, Silva, Kraken, and RefSeq, using publicly available mock community data from several sources, comprising 137 samples with varied species richness and evenness, several different amplified regions within the 16S gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole genome metagenomics, outperformed Qiime 2 and Mothur, which are theoretically specialized in 16S analyses. I used PathoScope 2 to analyze longitudinal 16S data from infants in Zambia, exploring the maturation of nasopharyngeal microbiomes in healthy infants, establishing a range of typical healthy taxonomic profiles, and identifying dysbiotic patterns which are associated with the development of severe lower respiratory tract infections in early childhood. I used Qiime 2 to analyze 16S data from human subjects in a controlled dietary intervention study with a focus on dietary carbohydrate quality. I correlated alterations in the gut microbiome with various cardiometabolic risk factors, and identified increases in some butyrate-producing bacteria in response to complex carbohydrates. I also constructed a metatranscriptomics pipeline to analyze paired rRNA-depleted RNAseq data. My evaluation of 16S methods should improve 16S amplicon analyses by advocating for the modernization of computational tools; my analysis of infant nasopharyngeal microbiomes lays groundwork for future predictive models for childhood disease and longitudinal microbiomic studies; my analysis of gut microbes illuminates the mechanisms through which bacteria can mediate cardiovascular health. Taken together, the research I present here represents a significant contribution to 16S metagenomics and its application to epidemiology, clinical nutritional science

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach