Project description:While metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.
Project description:16S rRNA amplicon sequencing of skin microbial communities sampled from Antarctic fur seal mother-offspring pairs from two breeding colonies with differing social densities.
Project description:As microbiome research moves away from model organisms to wildlife, new challenges for microbiome high-throughput sequencing arise caused by the variety of wildlife diets. High levels of contamination are commonly observed emanating from the host (mitochondria) or diet (chloroplast). Such high contamination levels affect the overall sequencing depth of wildlife samples thus decreasing statistical power and leading to poor performance in downstream analysis. We developed an amplification protocol utilizing PNA-DNA clamps to maximize the use of resources and to increase the sampling depth of true microbiome sequences in samples with high levels of plastid contamination. We chose two study organisms, a bat (Leptonyteris yerbabuenae) and a bird (Mimus parvulus), both relying on heavy plant-based diets that sometimes lead to traces of plant-based fecal material producing high contamination signals from chloroplasts and mitochondria. On average, our protocol yielded a 13-fold increase in bacterial sequence amplification compared with the standard protocol (Earth Microbiome Protocol) used in wildlife research. For both focal species, we were able to increase significantly the percentage of sequences available for downstream analyses after the filtering of plastids and mitochondria. Our study presents the first results obtained by using PNA-DNA clamps to block the PCR amplification of chloroplast and mitochondrial DNA from the diet in the gut microbiome of wildlife. The method involves a cost-effective molecular technique instead of the filtering out of unwanted sequencing reads. As 33% and 26% of birds and bats, respectively, have a plant-based diet, the tool that we present here will optimize the sequencing and analysis of wild microbiomes.
Project description:The human skin is colonized by a wide array of microorganisms playing a role in skin disorders. Studying the skin microbiome provides unique obstacles such as low microbial biomass. The objective of this study was to establish methodology for skin microbiome analyses, focusing on sampling technique and DNA extraction. Skin swabs and scrapes were collected from 9 healthy adult subjects, and DNA extracted using 12 commercial kits. All 165 samples were sequenced using the 16S rRNA gene. Comparing the populations captured by eSwabs and scrapes, 99.3% of sequences overlapped. Using eSwabs yielded higher consistency. The success rate of library preparation applying different DNA extraction kits ranged from 39% to 100%. Some kits had higher Shannon alpha-diversity. Metagenomic shotgun analyses were performed on a subset of samples (N = 12). These data indicate that a reduction of human DNA from 90% to 57% is feasible without lowering the success of 16S rRNA library preparation and without introducing taxonomic bias. Using swabs is a reliable technique to investigate the skin microbiome. DNA extraction methodology is crucial for success of sequencing and adds a substantial amount of variation in microbiome analyses. Reduction of host DNA is recommended for interventional studies applying metagenomics.
Project description:BackgroundDental implants replace missing teeth in at least 100 million people, yet over one million implants fail every year due to peri-implantitis, a bacterially induced inflammatory disease. Our ability to treat peri-implantitis is hampered by a paucity of information on host-microbiome interactions that underlie the disease. Here, we present the first open-ended characterization of transcriptional events at the mucosal-microbial interface in the peri-implant crevice.MethodsWe simultaneously sequenced microbial and human mRNA from five pairs of healthy and diseased implants from the same patient and used graph theoretics to examine correlations between microbial and host gene expression in the peri-implant crevice.ResultsWe identified a transcriptionally active peri-implant microbiome surrounding healthy implants. Microbial genes encoding phenylalanine, tyrosine, and tryptophan biosynthesis, cysteine, methionine, arginine, proline, and histidine metabolism correlated to human genes encoding cell development, metabolism, morphogenesis, adhesion, gap junctions, cell-cell signaling, and immunoinflammatory pathways, suggesting a role for commensals in protecting epithelial integrity. In disease, we found 4- to 200-fold upregulation in microbial genes encoding biofilm thickness, heme transport and utilization, and Gram-negative cell membrane synthesis. These genes correlated with mucosal zinc finger proteins, apoptosis, membrane transport, inflammation, and cell-cell communication.ConclusionsWithin the limitations of a small sample size, our data suggest that microbial dysbiosis in the peri-implant sulcus might promote abandonment of host-bacterial transactions that dictate health and instead drive a move towards chronic programming of a non-healing wound.
Project description:Microbiome studies of the lower airways based on bacterial 16S rRNA gene sequencing assess microbial community structure but can only infer functional characteristics. Microbial products, such as short-chain fatty acids (SCFAs), in the lower airways have significant impact on the host's immune tone. Thus, functional approaches to the analyses of the microbiome are necessary. Here we used upper and lower airway samples from a research bronchoscopy smoker cohort. In addition, we validated our results in an experimental mouse model. We extended our microbiota characterisation beyond 16S rRNA gene sequencing with the use of whole-genome shotgun (WGS) and RNA metatranscriptome sequencing. SCFAs were also measured in lower airway samples and correlated with each of the sequencing datasets. In the mouse model, 16S rRNA gene and RNA metatranscriptome sequencing were performed. Functional evaluations of the lower airway microbiota using inferred metagenome, WGS and metatranscriptome data were dissimilar. Comparison with measured levels of SCFAs shows that the inferred metagenome from the 16S rRNA gene sequencing data was poorly correlated, while better correlations were noted when SCFA levels were compared with WGS and metatranscriptome data. Modelling lower airway aspiration with oral commensals in a mouse model showed that the metatranscriptome most efficiently captures transient active microbial metabolism, which was overestimated by 16S rRNA gene sequencing. Functional characterisation of the lower airway microbiota through metatranscriptome data identifies metabolically active organisms capable of producing metabolites with immunomodulatory capacity, such as SCFAs.
Project description:BackgroundStudies on the impact of the skin microbiota on human health have been gaining more attention. Bacteria are associated with various diseases, although certain strains of bacteria, which are known as probiotics, are considered beneficial. Mixtures of several bacteria (bacterial cocktail) isolated from targeted organs have shown promising modulatory activities for use in skin therapeutics. The objectives of this study were to determine and identify the microbial communities on the skin that can potentially be used as probiotics, as determined by bacterial isolation and cultivation, followed by next-generation sequencing (NGS).ResultsSamples were collected by swabbing on forehead and cheek skin. Genomic DNA from bacterial swab samples were directly extracted to be further processed into NGS. Cultivation of skin bacteria was carried out in subsequent medium. Thus, around twenty bacterial isolates with different characteristics were selected and identified by both culture-based method and 16sRNA sequencing. We found that Actinobacteria and Firmicutes are the most abundant phylum present on the skin as presented by NGS data, which constitute to 67% and 28.59% of the whole bacterial population, consecutively. However, Staphylococcus hominis, Staphylococcus warneri, and Micrococcus luteus (AN MK968325.1; AN MK968315.1; and MK968318.1 respectively) were able to be obtained in the samples of cultivable, and could be potentially developed as probiotics in skin microbiome therapeutic as well as for postbiotic formulation.ConclusionSkin microbiome is considered to provide several probiotics for skin therapeutic. However, some opportunistic pathogens were discovered in this study population. Thus, the promising formula of bacterial cocktail for skin microbiome therapeutic must be thoroughly elucidated to avoid unwanted species. Our study is the first human skin microbiome profile of Indonesia resulted from a Next Generation Sequencing as an effort to show a representative of tropical country profile.
Project description:Historical and future trends in net primary productivity (NPP) and its sensitivity to global change are largely unknown because of the lack of long-term, high-resolution data. Here we test whether annually resolved tree-ring stable carbon (δ13C) and oxygen (δ18O) isotopes can be used as proxies for reconstructing past NPP. Stable isotope chronologies from four sites within three distinct hydroclimatic environments in the eastern United States (US) were compared in time and space against satellite-derived NPP products, including the long-term Global Inventory Modeling and Mapping Studies (GIMMS3g) NPP (1982-2011), the newest high-resolution Landsat NPP (1986-2015), and the Moderate Resolution Imaging Spectroradiometer (MODIS, 2001-2015) NPP. We show that tree-ring isotopes, in particular δ18O, correlate strongly with satellite NPP estimates at both local and large geographical scales in the eastern US. These findings represent an important breakthrough for estimating interannual variability and long-term changes in terrestrial productivity at the biome scale.
Project description:Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome's role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics.
Project description:Skin samples collected from underarm w/ PDMS for 30 seconds. Samples used for optimization of GC headspace methodology wrt desorption time, cryofocusing, and size of PDMS patch (10mL vials were used).