Search

Recent research highlights the significant impact of the gut microbiota on health and disease. Thalassemia, a hereditary blood disorder, requires regular blood transfusions, leading to an accumulation of iron in the body. Such changes could potentially alter the intestinal microbiota, thereby increasing the susceptibility of thalassemic patients to infection. In this study, we analyzed the fecal microbiota of 70 non-transfusion-dependent (NTDT) β-thalassemia/HbE patients and 30 healthy controls. Our findings indicate that iron chelation intervention had no detectable effect on the microbiome profile of thalassemic patients. However, the cross-sectional analysis revealed that the bacterial diversity and community structure in patients were significantly less diverse and distinct compared to those of healthy subjects. Using reference frames, we were also able to demonstrate that bacterial taxa that are known to produce short chain fatty acids, from the genera Alistipes, Coprococcus, and Oscillospira, and those from the family Ruminococcaceae, were less prevalent in the patients. In contrast, bacterial taxa associated with an unhealthy gut, including the genus Clostridium and those from the families Fusobacteriaceae, Enterobacteriaceae, and Peptostrptococcaceae, were more prevalent in patients and found to be correlated with higher levels of ferritin. Collectively, these changes in the microbiota could be regarded as markers of raised ferritin levels, and therefore, awareness should be exercised as they could interfere, albeit indirectly, with the treatment of the co-morbidities of thalassemia. [ABSTRACT FROM AUTHOR]

Purpose: Population-based studies on the associations of plant-based foods, red meat or dairy with gut microbiome are scarce. We examined whether the consumption of plant-based foods (vegetables, potatoes, fruits, cereals), red and processed meat (RPM) or dairy (fermented milk, cheese, other dairy products) are related to gut microbiome in Finnish adults. Methods: We utilized data from the National FINRISK/FINDIET 2002 Study (n = 1273, aged 25–64 years, 55% women). Diet was assessed with 48-hour dietary recalls. Gut microbiome was analyzed using shallow shotgun sequencing. We applied multivariate analyses with linear models and permutational ANOVAs adjusted for relevant confounders. Results: Fruit consumption was positively (beta = 0.03, SE = 0.01, P = 0.04), while a dairy subgroup including milk, cream and ice-creams was inversely associated (beta=-0.03, SE 0.01, P = 0.02) with intra-individual gut microbiome diversity (alpha-diversity). Plant-based foods (R2 = 0.001, P = 0.03) and dairy (R2 = 0.002, P = 0.01) but not RPM (R2 = 0.001, P = 0.38) contributed to the compositional differences in gut microbiome (beta-diversity). Plant-based foods were associated with several butyrate producers/cellulolytic species including Roseburia hominis. RPM associations included an inverse association with R. hominis. Dairy was positively associated with several lactic producing/probiotic species including Lactobacillus delbrueckii and potentially opportunistic pathogens including Citrobacter freundii. Dairy, fermented milk, vegetables, and cereals were associated with specific microbial functions. Conclusion: Our results suggest a potential association between plant-based foods and dairy or their subgroups with microbial diversity measures. Furthermore, our findings indicated that all the food groups were associated with distinct overall microbial community compositions. Plant-based food consumption particularly was associated with a larger number of putative beneficial species. [ABSTRACT FROM AUTHOR]

More than 100 tidal marsh creation projects were constructed throughout the Fraser River Estuary, British Columbia, Canada from the 1980s to present. Past studies described and evaluated many of these projects and found varied success, but the underlying factors that determine project outcomes remain uncertain. Combining field sampling, spatial analysis, and statistical modeling of plant communities, we aim to address this knowledge gap by asking what factors influence the resilience of created marshes, as measured by (1) persistence of marsh vegetation, (2) native species dominance, and (3) species richness. We observed marsh recession in 40 of the 78 projects visited, representing 23,666 m2 (9.3%) of the 254,357 m2 of created marsh surveyed. Increases in mean site elevation had a negative effect on percent recessed area, while sites in the north branch of the river and sites further upriver were more prone to recession. From field observations and data interpretation we suggest that wake erosion and Canada Goose (Branta canadensis) herbivory may be drivers behind these losses and warrant further investigation. Dominance of native species declined with distance upriver, though invasive cattail (Typha angustifolia, T. × glauca) defied this trend, dominating outer estuary sites, particularly closed embayments, when present. Native and non-native richness shared similar patterns and were comparable between reference and created marshes, increasing on average with elevation and distance upriver. These findings offer insight into how site design and location influence the outcome of marsh creation projects, and the challenges presented by stressors and environmental change in estuaries. [ABSTRACT FROM AUTHOR]

Phylogenetic trees provide a framework for organizing evolutionary histories across the tree of life and aid downstream comparative analyses such as metagenomic identification. Methods that rely on single-marker genes such as 16S rRNA have produced trees of limited accuracy with hundreds of thousands of organisms, whereas methods that use genome-wide data are not scalable to large numbers of genomes. We introduce updating trees using divide-and-conquer (uDance), a method that enables updatable genome-wide inference using a divide-and-conquer strategy that refines different parts of the tree independently and can build off of existing trees, with high accuracy and scalability. With uDance, we infer a species tree of roughly 200,000 genomes using 387 marker genes, totaling 42.5 billion amino acid residues. Large, updatable phylogenetic trees are constructed using a divide-and-conquer strategy. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, wastewater surveillance of the SARS CoV-2 virus has been demonstrated to be effective for population surveillance at the county level down to the building level. At the University of California, San Diego, daily high-resolution wastewater surveillance conducted at the building level is being used to identify potential undiagnosed infections and trigger notification of residents and responsive testing, but the optimal determinants for notifications are unknown. To fill this gap, we propose a pipeline for data processing and identifying features of a series of wastewater test results that can predict the presence of COVID-19 in residences associated with the test sites. Using time series of wastewater results and individual testing results during periods of routine asymptomatic testing among UCSD students from 11/2020 to 11/2021, we develop hierarchical classification/decision tree models to select the most informative wastewater features (patterns of results) which predict individual infections. We find that the best predictor of positive individual level tests in residence buildings is whether or not the wastewater samples were positive in at least 3 of the past 7 days. We also demonstrate that the tree models outperform a wide range of other statistical and machine models in predicting the individual COVID-19 infections while preserving interpretability. Results of this study have been used to refine campus-wide guidelines and email notification systems to alert residents of potential infections. [ABSTRACT FROM AUTHOR]

The gut microbiome is important for human health, yet modulation requires more insight into inter-individual variation. Here, we explored latent structures of the human gut microbiome across the human lifespan, applying partitioning, pseudotime, and ordination approaches to >35,000 samples. Specifically, three major gut microbiome branches were identified, within which multiple partitions were observed in adulthood, with differential abundances of species along branches. Different compositions and metabolic functions characterized the branches' tips, reflecting ecological differences. An unsupervised network analysis from longitudinal data from 745 individuals showed that partitions exhibited connected gut microbiome states rather than over-partitioning. Stability in the Bacteroides-enriched branch was associated with specific ratios of Faecalibacterium:Bacteroides. We also showed that associations with factors (intrinsic and extrinsic) could be generic, branch- or partition-specific. Our ecological framework for cross-sectional and longitudinal data allows a better understanding of overall variation in the human gut microbiome and disentangles factors associated with specific configurations. Here, applying an ecological framework to cross-sectional and longitudinal data, the authors identify major branches of the human gut microbiome across the lifespan that connect local ecological states, allowing to better capture associations with health, diet, and lifestyle. [ABSTRACT FROM AUTHOR]

For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that don’t rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict ~200,000 structures for diverse protein sequences from 1,003 representative genomes across the microbial tree of life and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for a shift in focus across all branches of biology, from obtaining structures to putting them into context and from sequence-based to sequence-structure-function based meta-omics analyses.Advances in protein structure prediction have led to a significant influx of protein structure data. Here the authors exploit this data to offer an unbiased overview of complex sequence-structure-function relationships in the protein universe. This work opens up new uses for 3D structure data repositories in meta-omics and other fields of biology. [ABSTRACT FROM AUTHOR]

Diabetes represents a spectrum of disease in which metabolic dysfunction damages multiple organ systems including liver, kidneys and peripheral nerves1,2. Although the onset and progression of these co-morbidities are linked with insulin resistance, hyperglycaemia and dyslipidaemia3–7, aberrant non-essential amino acid (NEAA) metabolism also contributes to the pathogenesis of diabetes8–10. Serine and glycine are closely related NEAAs whose levels are consistently reduced in patients with metabolic syndrome10–14, but the mechanistic drivers and downstream consequences of this metabotype remain unclear. Low systemic serine and glycine are also emerging as a hallmark of macular and peripheral nerve disorders, correlating with impaired visual acuity and peripheral neuropathy15,16. Here we demonstrate that aberrant serine homeostasis drives serine and glycine deficiencies in diabetic mice, which can be diagnosed with a serine tolerance test that quantifies serine uptake and disposal. Mimicking these metabolic alterations in young mice by dietary serine or glycine restriction together with high fat intake markedly accelerates the onset of small fibre neuropathy while reducing adiposity. Normalization of serine by dietary supplementation and mitigation of dyslipidaemia with myriocin both alleviate neuropathy in diabetic mice, linking serine-associated peripheral neuropathy to sphingolipid metabolism. These findings identify systemic serine deficiency and dyslipidaemia as novel risk factors for peripheral neuropathy that may be exploited therapeutically.Serine deficiency can increase small fibre neuropathy in wild-type mice and serine replacement in diabetic mice alleviates diabetic neuropathy, directly linking amino acid metabolism to peripheral nerve disorders. [ABSTRACT FROM AUTHOR]

Liver damage due to chronic alcohol use is among the most prevalent liver diseases. Alcohol consumption frequency is a strong factor of microbiota variance. Here we use isotope labeled [1-13C] ethanol, metagenomics, and metatranscriptomics in ethanol-feeding and intragastric mouse models to investigate the metabolic impacts of alcohol consumption on the gut microbiota. First, we show that although stable isotope labeled [1-13C] ethanol contributes to fatty acid pools in the liver, plasma, and cecum contents of mice, there is no evidence of ethanol metabolism by gut microbiota ex vivo under anaerobic conditions. Next, we observe through metatranscriptomics that the gut microbiota responds to ethanol-feeding by activating acetate dissimilation, not by metabolizing ethanol directly. We demonstrate that blood acetate concentrations are elevated during ethanol consumption. Finally, by increasing systemic acetate levels with glyceryl triacetate supplementation, we do not observe any impact on liver disease, but do induce similar gut microbiota alterations as chronic ethanol-feeding in mice. Our results show that ethanol is not directly metabolized by the gut microbiota, and changes in the gut microbiota linked to ethanol are a side effect of elevated acetate levels. De-trending for these acetate effects may be critical for understanding gut microbiota changes that cause alcohol-related liver disease. Chronic alcohol use is associated with intestinal bacterial overgrowth and dysbiosis, but the contribution of ethanol is unclear. Here, using mouse models, the authors find that ethanol is not directly metabolized by the gut microbiota, and that induced changes are rather a side effect of elevated acetate levels. [ABSTRACT FROM AUTHOR]

Cell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, and tissue microenvironment. Single-cell technologies measure the molecules mediating cell–cell communication, and emerging computational tools can exploit these data to decipher intercellular communication. However, current methods either disregard cellular context or rely on simple pairwise comparisons between samples, thus limiting the ability to decipher complex cell–cell communication across multiple time points, levels of disease severity, or spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which deciphers context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. To do so, Tensor-cell2cell uncovers context-driven patterns of communication associated with different phenotypic states and determined by unique combinations of cell types and ligand-receptor pairs. As such, Tensor-cell2cell robustly improves upon and extends the analytical capabilities of existing tools. We show Tensor-cell2cell can identify multiple modules associated with distinct communication processes (e.g., participating cell–cell and ligand-receptor pairs) linked to severities of Coronavirus Disease 2019 and to Autism Spectrum Disorder. Thus, we introduce an effective and easy-to-use strategy for understanding complex communication patterns across diverse conditions. Cellular contexts such as disease state, organismal life stage and tissue microenvironment, shape intercellular communication, and ultimately affect an organism's phenotypes. Here, the authors present Tensor-cell2cell, an unsupervised method for deciphering context-driven intercellular communication. [ABSTRACT FROM AUTHOR]

Preterm infants are at a greater risk for the development of asthma and atopic disease, which can lead to lifelong negative health consequences. This may be due, in part, to alterations that occur in the gut microbiome and metabolome during their stay in the Neonatal Intensive Care Unit (NICU). To explore the differential roles of family history (i.e., predisposition due to maternal asthma diagnosis) and hospital-related environmental and clinical factors that alter microbial exposures early in life, we considered a unique cohort of preterm infants born ≤ 34 weeks gestational age from two local level III NICUs, as part of the MAP (Microbiome, Atopic disease, and Prematurity) Study. From MAP participants, we chose a sub-cohort of infants whose mothers had a history of asthma and matched gestational age and sex to infants of mothers without a history of asthma diagnosis (control). We performed a prospective, paired metagenomic and metabolomic analysis of stool and milk feed samples collected at birth, 2 weeks, and 6 weeks postnatal age. Although there were clinical factors associated with shifts in the diversity and composition of stool-associated bacterial communities, maternal asthma diagnosis did not play an observable role in shaping the infant gut microbiome during the study period. There were significant differences, however, in the metabolite profile between the maternal asthma and control groups at 6 weeks postnatal age. The most notable changes occurred in the linoleic acid spectral network, which plays a role in inflammatory and immune pathways, suggesting early metabolomic changes in the gut of preterm infants born to mothers with a history of asthma. Our pilot study suggests that a history of maternal asthma alters a preterm infants' metabolomic pathways in the gut, as early as the first 6 weeks of life. [ABSTRACT FROM AUTHOR]

Throughout the COVID-19 pandemic, massive sequencing and data sharing efforts enabled the real-time surveillance of novel SARS-CoV-2 strains throughout the world, the results of which provided public health officials with actionable information to prevent the spread of the virus. However, with great sequencing comes great computation, and while cloud computing platforms bring high-performance computing directly into the hands of all who seek it, optimal design and configuration of a cloud compute cluster requires significant system administration expertise. We developed ViReflow, a user-friendly viral consensus sequence reconstruction pipeline enabling rapid analysis of viral sequence datasets leveraging Amazon Web Services (AWS) cloud compute resources and the Reflow system. ViReflow was developed specifically in response to the COVID-19 pandemic, but it is general to any viral pathogen. Importantly, when utilized with sufficient compute resources, ViReflow can trim, map, call variants, and call consensus sequences from amplicon sequence data from 1000 SARS-CoV-2 samples at 1000X depth in < 10 min, with no user intervention. ViReflow's simplicity, flexibility, and scalability make it an ideal tool for viral molecular epidemiological efforts. [ABSTRACT FROM AUTHOR]

Integration of sensory and molecular inputs from the environment shapes animal behaviour. A major site of exposure to environmental molecules is the gastrointestinal tract, in which dietary components are chemically transformed by the microbiota1 and gut-derived metabolites are disseminated to all organs, including the brain2. In mice, the gut microbiota impacts behaviour3, modulates neurotransmitter production in the gut and brain4,5, and influences brain development and myelination patterns6,7. The mechanisms that mediate the gut–brain interactions remain poorly defined, although they broadly involve humoral or neuronal connections. We previously reported that the levels of the microbial metabolite 4-ethylphenyl sulfate (4EPS) were increased in a mouse model of atypical neurodevelopment8. Here we identified biosynthetic genes from the gut microbiome that mediate the conversion of dietary tyrosine to 4-ethylphenol (4EP), and bioengineered gut bacteria to selectively produce 4EPS in mice. 4EPS entered the brain and was associated with changes in region-specific activity and functional connectivity. Gene expression signatures revealed altered oligodendrocyte function in the brain, and 4EPS impaired oligodendrocyte maturation in mice and decreased oligodendrocyte–neuron interactions in ex vivo brain cultures. Mice colonized with 4EP-producing bacteria exhibited reduced myelination of neuronal axons. Altered myelination dynamics in the brain have been associated with behavioural outcomes7,9–14. Accordingly, we observed that mice exposed to 4EPS displayed anxiety-like behaviours, and pharmacological treatments that promote oligodendrocyte differentiation prevented the behavioural effects of 4EPS. These findings reveal that a gut-derived molecule influences complex behaviours in mice through effects on oligodendrocyte function and myelin patterning in the brain.The gut-derived molecule 4-ethylphenol influences complex behaviours in mice through effects on oligodendrocyte function and myelin patterning in the brain. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new confident function predictions. DeepFRI is available as a webserver at https://beta.deepfri.flatironinstitute.org/. The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, the authors introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. [ABSTRACT FROM AUTHOR]

The collection of fecal material and developments in sequencing technologies have enabled standardised and non-invasive gut microbiome profiling. Microbiome composition from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterised due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. Here, we analyse the long-term association between gut microbiome variation and mortality in a well-phenotyped and representative population cohort from Finland (n = 7211). We report robust taxonomic and functional microbiome signatures related to the Enterobacteriaceae family that are associated with mortality risk during a 15-year follow-up. Our results extend previous cross-sectional studies, and help to establish the basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status. Gut microbiome composition has a role in health and disease. Here the authors show that microbiome signatures related to the Enterobacteriaceae family are associated with cause-specific mortality risk in a well phenotyped Finish population over a 15-year follow-up. [ABSTRACT FROM AUTHOR]

The translational power of human microbiome studies is limited by high interindividual variation. We describe a dimensionality reduction tool, compositional tensor factorization (CTF), that incorporates information from the same host across multiple samples to reveal patterns driving differences in microbial composition across phenotypes. CTF identifies robust patterns in sparse compositional datasets, allowing for the detection of microbial changes associated with specific phenotypes that are reproducible across datasets. Gut microbiome composition is associated with phenotypes as revealed by a dimensionality reduction tool. [ABSTRACT FROM AUTHOR]

Low concordance between studies that examine the role of microbiota in human diseases is a pervasive challenge that limits the capacity to identify causal relationships between host-associated microorganisms and pathology. The risk of obtaining false positives is exacerbated by wide interindividual heterogeneity in microbiota composition1, probably due to population-wide differences in human lifestyle and physiological variables2 that exert differential effects on the microbiota. Here we infer the greatest, generalized sources of heterogeneity in human gut microbiota profiles and also identify human lifestyle and physiological characteristics that, if not evenly matched between cases and controls, confound microbiota analyses to produce spurious microbial associations with human diseases. We identify alcohol consumption frequency and bowel movement quality as unexpectedly strong sources of gut microbiota variance that differ in distribution between healthy participants and participants with a disease and that can confound study designs. We demonstrate that for numerous prevalent, high-burden human diseases, matching cases and controls for confounding variables reduces observed differences in the microbiota and the incidence of spurious associations. On this basis, we present a list of host variables that we recommend should be captured in human microbiota studies for the purpose of matching comparison groups, which we anticipate will increase robustness and reproducibility in resolving the members of the gut microbiota that are truly associated with human disease. The authors use a machine-learning approach to uncover confounding variables in studies that seek to establish an association between the gut microbiota and human disease. [ABSTRACT FROM AUTHOR]

Microbial genomes are available at an ever-increasing pace, as cultivation and sequencing become cheaper and obtaining metagenome-assembled genomes (MAGs) becomes more effective. Phylogenetic placement methods to contextualize hundreds of thousands of genomes must thus be efficiently scalable and sensitive from closely related strains to divergent phyla. We present PhyloPhlAn 3.0, an accurate, rapid, and easy-to-use method for large-scale microbial genome characterization and phylogenetic analysis at multiple levels of resolution. PhyloPhlAn 3.0 can assign genomes from isolate sequencing or MAGs to species-level genome bins built from >230,000 publically available sequences. For individual clades of interest, it reconstructs strain-level phylogenies from among the closest species using clade-specific maximally informative markers. At the other extreme of resolution, it scales to large phylogenies comprising >17,000 microbial species. Examples including Staphylococcus aureus isolates, gut metagenomes, and meta-analyses demonstrate the ability of PhyloPhlAn 3.0 to support genomic and metagenomic analyses. The increasing amount of sequenced microbial genomes and metagenomes requires platforms for efficient integrated analysis. Here, Asnicar et al. present PhyloPhlAn 3.0, a pipeline allowing large-scale microbial genome characterization and phylogenetic contextualization at multiple levels of resolution. [ABSTRACT FROM AUTHOR]

Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions1–10, we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas11 (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia–IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma; 100 samples in total) solely using plasma-derived, cell-free microbial nucleic acids. This potential microbiome-based oncology diagnostic tool warrants further exploration. Microbial nucleic acids are detected in samples of tissues and blood from more than 10,000 patients with cancer, and machine learning is used to show that these can be used to discriminate between and among different types of cancer, suggesting a new microbiome-based diagnostic approach. [ABSTRACT FROM AUTHOR]

Oral microbiome dysbiosis has been associated with various local and systemic human diseases such as dental caries, periodontal disease, obesity, and cardiovascular disease. Bacterial composition may be affected by age, oral health, diet, and geography, although information about the natural variation found in the general public is still lacking. In this study, citizen-scientists used a crowdsourcing model to obtain oral bacterial composition data from guests at the Denver Museum of Nature & Science to determine if previously suspected oral microbiome associations with an individual's demographics, lifestyle, and/or genetics are robust and generalizable enough to be detected within a general population. Consistent with past research, we found bacterial composition to be more diverse in youth microbiomes when compared to adults. Adult oral microbiomes were predominantly impacted by oral health habits, while youth microbiomes were impacted by biological sex and weight status. The oral pathogen Treponema was detected more commonly in adults without recent dentist visits and in obese youth. Additionally, oral microbiomes from participants of the same family were more similar to each other than to oral microbiomes from non-related individuals. These results suggest that previously reported oral microbiome associations are observable in a human population containing the natural variation commonly found in the general public. Furthermore, these results support the use of crowdsourced data as a valid methodology to obtain community-based microbiome data. [ABSTRACT FROM AUTHOR]

Staphylococcus aureus strains have been continuously evolving resistance to numerous classes of antibiotics including methicillin, vancomycin, daptomycin and linezolid, compounding the enormous healthcare and economic burden of the pathogen. Cation-adjusted Mueller-Hinton broth (CA-MHB) is the standard bacteriological media for measuring antibiotic susceptibility in the clinical lab, but the use of media that more closely mimic the physiological state of the patient, e.g. mammalian tissue culture media, can in certain circumstances reveal antibiotic activities that may be more predictive of effectiveness in vivo. In the current study, we use both types of media to explore antibiotic resistance phenomena in hospital-acquired USA100 lineage methicillin-resistant, vancomycin-intermediate Staphylococcus aureus (MRSA/VISA) strain D712 via multidimensional high throughput analysis of growth rates, bacterial cytological profiling, RNA sequencing, and exo-metabolomics (HPLC and LC-MS). Here, we share data generated from these assays to shed light on the antibiotic resistance behavior of MRSA/VISA D712 in both bacteriological and physiological media. Measurement(s) Antibacterial Response • cDNA • transcription profiling assay • culture medium • organic acid Technology Type(s) bacterial cytological profiling • RNA sequencing • liquid chromatography-tandem mass spectrometry • high-performance liquid chromatography Factor Type(s) growth medium Sample Characteristic - Organism Staphylococcus aureus Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.10283108 [ABSTRACT FROM AUTHOR]

Antibiotic-induced microbiome depletion (AIMD) has been used frequently to study the role of the gut microbiome in pathological conditions. However, unlike germ-free mice, the effects of AIMD on host metabolism remain incompletely understood. Here we show the effects of AIMD to elucidate its effects on gut homeostasis, luminal signaling, and metabolism. We demonstrate that AIMD, which decreases luminal Firmicutes and Bacteroidetes species, decreases baseline serum glucose levels, reduces glucose surge in a tolerance test, and improves insulin sensitivity without altering adiposity. These changes occur in the setting of decreased luminal short-chain fatty acids (SCFAs), especially butyrate, and the secondary bile acid pool, which affects whole-body bile acid metabolism. In mice, AIMD alters cecal gene expression and gut glucagon-like peptide 1 signaling. Extensive tissue remodeling and decreased availability of SCFAs shift colonocyte metabolism toward glucose utilization. We suggest that AIMD alters glucose homeostasis by potentially shifting colonocyte energy utilization from SCFAs to glucose. [ABSTRACT FROM AUTHOR]

Microbes of the phylum Aigarchaeota are widely distributed in geothermal environments, but their physiological and ecological roles are poorly understood. Here we analyze six Aigarchaeota metagenomic bins from two circumneutral hot springs in Tengchong, China, to reveal that they are either strict or facultative anaerobes, and most are chemolithotrophs that can perform sulfide oxidation. Applying comparative genomics to the Thaumarchaeota and Aigarchaeota, we find that they both originated from thermal habitats, sharing 1154 genes with their common ancestor. Horizontal gene transfer played a crucial role in shaping genetic diversity of Aigarchaeota and led to functional partitioning and ecological divergence among sympatric microbes, as several key functional innovations were endowed by Bacteria, including dissimilatory sulfite reduction and possibly carbon monoxide oxidation. Our study expands our knowledge of the possible ecological roles of the Aigarchaeota and clarifies their evolutionary relationship to their sister lineage Thaumarchaeota. [ABSTRACT FROM AUTHOR]

Ocean-derived, airborne microbes play important roles in Earth’s climate system and human health, yet little is known about factors controlling their transfer from the ocean to the atmosphere. Here, we study microbiomes of isolated sea spray aerosol (SSA) collected in a unique ocean–atmosphere facility and demonstrate taxon-specific aerosolization of bacteria and viruses. These trends are conserved within taxonomic orders and classes, and temporal variation in aerosolization is similarly shared by related taxa. We observe enhanced transfer into SSA of Actinobacteria, certain Gammaproteobacteria, and lipid-enveloped viruses; conversely, Flavobacteriia, some Alphaproteobacteria, and Caudovirales are generally underrepresented in SSA. Viruses do not transfer to SSA as efficiently as bacteria. The enrichment of mycolic acid-coated Corynebacteriales and lipid-enveloped viruses (inferred from genomic comparisons) suggests that hydrophobic properties increase transport to the sea surface and SSA. Our results identify taxa relevant to atmospheric processes and a framework to further elucidate aerosolization mechanisms influencing microbial and viral transport pathways. [ABSTRACT FROM AUTHOR]

Both diet and host phylogeny shape the gut microbial community, and separating out the effects of these variables can be challenging. In this study, high-throughput sequencing was used to evaluate the impact of diet and phylogeny on the gut microbiota of nine colobine monkey species ( N = 64 individuals). Colobines are leaf-eating monkeys that fare poorly in captivity-often exhibiting gastrointestinal (GI) problems. This study included eight Asian colobines ( Rhinopithecus brelichi, Rhinopithecus roxellana, Rhinopithecus bieti, Pygathrix nemaeus, Nasalis larvatus, Trachypithecus francoisi, Trachypithecus auratus, and Trachypithecus vetulus) and one African colobine ( Colobus guereza). Monkeys were housed at five different captive institutes: Panxi Wildlife Rescue Center (Guizhou, China), Beijing Zoo, Beijing Zoo Breeding Center, Singapore Zoo, and Singapore Zoo Primate Conservation Breeding Center. Captive diets varied widely between institutions, but within an institution, all colobine monkey species were fed nearly identical or identical diets. In addition, four monkey species were present at multiple captive institutes. This allowed us to parse the effects of diet and phylogeny in these captive colobines. Gut microbial communities clustered weakly by host species and strongly by diet, and overall, colobine phylogenetic relationships were not reflected in gut microbiota analyses. Core microbiota analyses also identified several key taxa-including microbes within the Ruminococcaceae and Lachnospiraceae families-that were shared by over 90% of the monkeys in this study. Microbial species within these families include many butyrate producers that are important for GI health. These results highlight the importance of diet in captive colobines. [ABSTRACT FROM AUTHOR]

The role of adaptive immunity in early cancer development is controversial. Here we show that chronic inflammation and fibrosis in humans and mice with non-alcoholic fatty liver disease is accompanied by accumulation of liver-resident immunoglobulin-A-producing (IgA+) cells. These cells also express programmed death ligand 1 (PD-L1) and interleukin-10, and directly suppress liver cytotoxic CD8+ T lymphocytes, which prevent emergence of hepatocellular carcinoma and express a limited repertoire of T-cell receptors against tumour-associated antigens. Whereas CD8+ T-cell ablation accelerates hepatocellular carcinoma, genetic or pharmacological interference with IgA+ cell generation attenuates liver carcinogenesis and induces cytotoxic T-lymphocyte-mediated regression of established hepatocellular carcinoma. These findings establish the importance of inflammation-induced suppression of cytotoxic CD8+ T-lymphocyte activation as a tumour-promoting mechanism. [ABSTRACT FROM AUTHOR]

In order for human microbiome studies to translate into actionable outcomes for health, meta-analysis of reproducible data from population-scale cohorts is needed. Achieving sufficient reproducibility in microbiome research has proven challenging. We report a baseline investigation of variability in taxonomic profiling for the Microbiome Quality Control (MBQC) project baseline study (MBQC-base). Blinded specimen sets from human stool, chemostats, and artificial microbial communities were sequenced by 15 laboratories and analyzed using nine bioinformatics protocols. Variability depended most on biospecimen type and origin, followed by DNA extraction, sample handling environment, and bioinformatics. Analysis of artificial community specimens revealed differences in extraction efficiency and bioinformatic classification. These results may guide researchers in experimental design choices for gut microbiome studies. [ABSTRACT FROM AUTHOR]

The characterization of baseline microbial and functional diversity in the human microbiome has enabled studies of microbiome-related disease, diversity, biogeography, and molecular function. The National Institutes of Health Human Microbiome Project has provided one of the broadest such characterizations so far. Here we introduce a second wave of data from the study, comprising 1,631 new metagenomes (2,355 total) targeting diverse body sites with multiple time points in 265 individuals. We applied updated profiling and assembly methods to provide new characterizations of microbiome personalization. Strain identification revealed subspecies clades specific to body sites; it also quantified species with phylogenetic diversity under-represented in isolate genomes. Body-wide functional profiling classified pathways into universal, human-enriched, and body site-enriched subsets. Finally, temporal analysis decomposed microbial variation into rapidly variable, moderately variable, and stable subsets. This study furthers our knowledge of baseline human microbial diversity and enables an understanding of personalized microbiome function and dynamics. [ABSTRACT FROM AUTHOR]

Studies of human and domestic animal models indicate that related individuals and those that spend the most time in physical contact typically have more similar gut microbial communities. However, few studies have examined these factors in wild mammals where complex social dynamics and a variety of interacting environmental factors may impact the patterns observed in controlled systems. Here, we explore the effect of host kinship and time spent in social contact on the gut microbiota of wild, black howler monkeys ( Alouatta pigra). Our results indicate that closely related individuals had less similar gut microbial communities than non-related individuals. However, the effect was small. In contrast, as previously reported in baboons and chimpanzees, individuals that spent more time in contact (0 m) and close proximity (0-1 m) had more similar gut microbial communities. This pattern was driven by adult female-adult female dyads, which generally spend more time in social contact than adult male-adult male dyads or adult male-adult female dyads. Relative abundances of individual microbial genera such as Bacteroides, Clostridium, and Streptococcus were also more similar in individuals that spent more time in contact or close proximity. Overall, our data suggest that even in arboreal primates that live in small social groups and spend a relatively low proportion of their time in physical contact, social interactions are associated with variation in gut microbiota composition. Additionally, these results demonstrate that within a given host species, subgroups of individuals may interact with the gut microbiota differently. [ABSTRACT FROM AUTHOR]

Recent studies suggest that variation in diet across time and space results in changes in the mammalian gut microbiota. This variation may ultimately impact host ecology by altering nutritional status and health. Wild animal populations provide an excellent opportunity for understanding these interactions. However, compared to clinical studies, microbial research targeting wild animals is currently limited, and many published studies focus only on a single population of a single host species. In this study we utilize fecal samples from two species of howler monkey ( Alouatta pigra and A. palliata) collected at four sites to investigate factors influencing the gut microbiota at three scales: taxonomic (host species), ecosystemic (forest type), and local (habitat disturbance/season). The results demonstrate that the effect of host species on the gut microbiota is stronger than the effect of host forest type, which is stronger than the effect of habitat disturbance or seasonality. Nevertheless, within host species, gut microbiota composition differs in response to forest type, habitat disturbance, and season. Variations in the effect size of these factors are associated both with host species and environment. This information may be beneficial for understanding ecological and evolutionary questions associated with Mesoamerican howler monkeys, as well as determining conservation challenges facing each species. These mechanisms may also provide insight into the ecology of other species of howler monkeys, non-human primates, and mammals. [ABSTRACT FROM AUTHOR]

Little is known about how variables, such as carcass mass, affect the succession pattern of microbes in soils during decomposition. To investigate the effects of carcass mass on the soil microbial community, soils associated with swine ( Sus scrofa domesticus) carcasses of four different masses were sampled until the 15th day of decomposition during the month of June in a pasture near Lincoln, Nebraska. Soils underneath swine of 1, 20, 40, and 50 kg masses were investigated in triplicate, as well as control sites not associated with a carcass. Soil microbial communities were characterized by sequencing the archaeal, bacterial (16S), and eukaryotic (18S) rRNA genes in soil samples. We conclude that time of decomposition was a significant influence on the microbial community, but carcass mass was not. The gravesoil associated with 1 kg mass carcasses differs most compared to the gravesoil associated with other carcass masses. We also identify the 15 most abundant bacterial and eukaryotic taxa, and discuss changes in their abundance as carcass decomposition progressed. Finally, we show significant decreases in alpha diversity for carcasses of differing mass in pre-carcass rupture (days 0, 1, 2, 4, 5, and 6 postmortem) versus post-carcass rupture (days 9 and 15 postmortem) microbial communities. [ABSTRACT FROM AUTHOR]

An important fraction of microbial diversity is harbored in strain individuality, so identification of conspecific bacterial strains is imperative for improved understanding of microbial community functions. Limitations in bioinformatics and sequencing technologies have to date precluded strain identification owing to difficulties in phasing short reads to faithfully recover the original strain-level genotypes, which have highly similar sequences. We present ConStrains, an open-source algorithm that identifies conspecific strains from metagenomic sequence data and reconstructs the phylogeny of these strains in microbial communities. The algorithm uses single-nucleotide polymorphism (SNP) patterns in a set of universal genes to infer within-species structures that represent strains. Applying ConStrains to simulated and host-derived datasets provides insights into microbial community dynamics. [ABSTRACT FROM AUTHOR]

Body-associated microbes were recently shown to change significantly during decomposition, undergoing an ecological succession in experimental conditions using rodent and swine models. We investigated microbial succession in soils associated with swine carcasses under experimental field conditions in summer and winter. We demonstrate that these postmortem microbial communities change in a specific, reproducible fashion, and that soil microbes represent a significant component of the postmortem microbial community, contrary to widespread belief in forensic science. However, the effects of decomposition on soil microbial communities were different in summer and winter. We suggest that the microbial ecological succession will be useful in medicolegal death investigation; however, observations in winter might not be applicable to summer, which indicates a need for a greater understanding of the seasonality of decomposition. [ABSTRACT FROM AUTHOR]

Ancient and diverse antibiotic resistance genes (ARGs) have previously been identified from soil1, 2, 3, including genes identical to those in human pathogens4. Despite the apparent overlap between soil and clinical resistomes4, 5, 6, factors influencing ARG composition in soil and their movement between genomes and habitats remain largely unknown3. General metagenome functions often correlate with the underlying structure of bacterial communities7, 8, 9, 10, 11, 12. However, ARGs are proposed to be highly mobile4, 5, 13, prompting speculation that resistomes may not correlate with phylogenetic signatures or ecological divisions13, 14. To investigate these relationships, we performed functional metagenomic selections for resistance to 18 antibiotics from 18 agricultural and grassland soils. The 2,895 ARGs we discovered were mostly new, and represent all major resistance mechanisms15. We demonstrate that distinct soil types harbour distinct resistomes, and that the addition of nitrogen fertilizer strongly influenced soil ARG content. Resistome composition also correlated with microbial phylogenetic and taxonomic structure, both across and within soil types. Consistent with this strong correlation, mobility elements (genes responsible for horizontal gene transfer between bacteria such as transposases and integrases) syntenic with ARGs were rare in soil by comparison with sequenced pathogens, suggesting that ARGs may not transfer between soil bacteria as readily as is observed between human pathogens. Together, our results indicate that bacterial community composition is the primary determinant of soil ARG content, challenging previous hypotheses that horizontal gene transfer effectively decouples resistomes from phylogeny13, 14. [ABSTRACT FROM AUTHOR]

The use of feathers to line bird's nests has traditionally been interpreted as having a thermoregulatory function. Feather-degrading bacteria growing on feathers lining nests may have antimicrobial properties, which may provide an additional benefit to lining nests with feathers. We test the hypothesis that the production of antimicrobial substances by feather bacteria affects the microbiological environment of the nest, and therefore the bacterial density on eggshells and, indirectly, hatching success. These effects would be expected to differ between nests lined with pigmented and white feathers, because bacteria grow differently on feathers of different colors. We experimentally manipulated the composition of pigmented and unpigmented feathers in nests of the barn swallow ( Hirundo rustica) and studied the antimicrobial properties against the keratin-degrading bacterium Bacillus licheniformis of bacteria isolated from feathers of each color. Analyzed feathers were collected at the end of the incubation period, and antimicrobial activity was defined as the proportion of bacteria from the feathers that produce antibacterial substances effective against B. licheniformis. Our experimental manipulation affected antimicrobial activity, which was higher in nests with only white feathers at the beginning of incubation. Moreover, white feathers showed higher antimicrobial activity than black ones. Interestingly, antimicrobial activity in feathers of one of the colors correlated negatively with bacterial density on feather of the opposite color. Finally, antimicrobial activity of white feathers was negatively related to eggshell bacterial load. These results suggest that antimicrobial properties of feathers in general and of white feathers in particular affect the bacterial environment in nests. This environment in turn affects the bacterial load on eggshells, which may affect hatching success. [ABSTRACT FROM AUTHOR]

Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community's functional capabilities. Here we describe PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this 'predictive metagenomic' approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available. [ABSTRACT FROM AUTHOR]

The article presents the interviews of several science experts including James Brown, Willem M. de Vos and Curtis Huttenhower. They discusses on research needed for sequencing depth and microbial characterization in each body niche, the biggest current technical or study-design roadblocks and the limitations of computational analyses of metagenomic data.

One of the most exciting scientific advances in recent years has been the realization that commensal microorganisms are not simple 'passengers' in our bodies, but instead have key roles in our physiology, including our immune responses and metabolism, as well as in disease. These insights have been obtained, in part, through the work of large-scale, consortium-driven metagenomic projects. Here, five experts in the field of microbiome research discuss the most surprising and exciting new findings, and outline the future steps that will be necessary to elucidate the numerous roles of the microbiota in human health and disease and to develop viable therapeutic strategies. [ABSTRACT FROM AUTHOR]