Showing papers by "Rob Knight published in 2021"

The microbiome and human cancer.

Abstract: Microbial roles in cancer formation, diagnosis, prognosis, and treatment have been disputed for centuries. Recent studies have provocatively claimed that bacteria, viruses, and/or fungi are pervasive among cancers, key actors in cancer immunotherapy, and engineerable to treat metastases. Despite these findings, the number of microbes known to directly cause carcinogenesis remains small. Critically evaluating and building frameworks for such evidence in light of modern cancer biology is an important task. In this Review, we delineate between causal and complicit roles of microbes in cancer and trace common themes of their influence through the host's immune system, herein defined as the immuno-oncology-microbiome axis. We further review evidence for intratumoral microbes and approaches that manipulate the host's gut or tumor microbiome while projecting the next phase of experimental discovery.

Large-scale association analyses identify host factors influencing human gut microbiome composition

Abstract: To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 × 10−8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10−20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10−10 < P < 5 × 10−8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.

Structure-based protein function prediction using graph convolutional networks.

Abstract: 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/ .

High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing.

Abstract: High-throughput amplicon sequencing of large genomic regions remains challenging for short-read technologies. Here, we report a high-throughput amplicon sequencing approach combining unique molecular identifiers (UMIs) with Oxford Nanopore Technologies (ONT) or Pacific Biosciences circular consensus sequencing, yielding high-accuracy single-molecule consensus sequences of large genomic regions. We applied our approach to sequence ribosomal RNA operon amplicons (~4,500 bp) and genomic sequences (>10,000 bp) of reference microbial communities in which we observed a chimera rate <0.02%. To reach a mean UMI consensus error rate <0.01%, a UMI read coverage of 15× (ONT R10.3), 25× (ONT R9.4.1) and 3× (Pacific Biosciences circular consensus sequencing) is needed, which provides a mean error rate of 0.0042%, 0.0041% and 0.0007%, respectively. This work presents a sequencing strategy based on unique molecular identifiers that improves long-read consensus sequence accuracy of targeted amplicons as well as shotgun whole-genome fragments.

Genomic epidemiology identifies emergence and rapid transmission of SARS-CoV-2 B.1.1.7 in the United States

Abstract: Author(s): Washington, Nicole L; Gangavarapu, Karthik; Zeller, Mark; Bolze, Alexandre; Cirulli, Elizabeth T; Schiabor Barrett, Kelly M; Larsen, Brendan B; Anderson, Catelyn; White, Simon; Cassens, Tyler; Jacobs, Sharoni; Levan, Geraint; Nguyen, Jason; Ramirez, Jimmy M; Rivera-Garcia, Charlotte; Sandoval, Efren; Wang, Xueqing; Wong, David; Spencer, Emily; Robles-Sikisaka, Refugio; Kurzban, Ezra; Hughes, Laura D; Deng, Xianding; Wang, Candace; Servellita, Venice; Valentine, Holly; De Hoff, Peter; Seaver, Phoebe; Sathe, Shashank; Gietzen, Kimberly; Sickler, Brad; Antico, Jay; Hoon, Kelly; Liu, Jingtao; Harding, Aaron; Bakhtar, Omid; Basler, Tracy; Austin, Brett; Isaksson, Magnus; Febbo, Phil; Becker, David; Laurent, Marc; McDonald, Eric; Yeo, Gene W; Knight, Rob; Laurent, Louise C; de Feo, Eileen; Worobey, Michael; Chiu, Charles; Suchard, Marc A; Lu, James T; Lee, William; Andersen, Kristian G | Abstract: As of January of 2021, the highly transmissible B.1.1.7 variant of SARS-CoV-2, which was first identified in the United Kingdom (U.K.), has gained a strong foothold across the world. Because of the sudden and rapid rise of B.1.1.7, we investigated the prevalence and growth dynamics of this variant in the United States (U.S.), tracking it back to its early emergence and onward local transmission. We found that the RT-qPCR testing anomaly of S gene target failure (SGTF), first observed in the U.K., was a reliable proxy for B.1.1.7 detection. We sequenced 212 B.1.1.7 SARS-CoV-2 genomes collected from testing facilities in the U.S. from December 2020 to January 2021. We found that while the fraction of B.1.1.7 among SGTF samples varied by state, detection of the variant increased at a logistic rate similar to those observed elsewhere, with a doubling rate of a little over a week and an increased transmission rate of 35-45%. By performing time-aware Bayesian phylodynamic analyses, we revealed several independent introductions of B.1.1.7 into the U.S. as early as late November 2020, with onward community transmission enabling the variant to spread to at least 30 states as of January 2021. Our study shows that the U.S. is on a similar trajectory as other countries where B.1.1.7 rapidly became the dominant SARS-CoV-2 variant, requiring immediate and decisive action to minimize COVID-19 morbidity and mortality.

High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County

Abstract: Large-scale wastewater surveillance has the ability to greatly augment the tracking of infection dynamics especially in communities where the prevalence rates far exceed the testing capacity. However, current methods for viral detection in wastewater are severely lacking in terms of scaling up for high throughput. In the present study, we employed an automated magnetic-bead-based concentration approach for viral detection in sewage that can effectively be scaled up for processing 24 samples in a single 40-min run. The method compared favorably to conventionally used methods for viral wastewater concentrations with higher recovery efficiencies from input sample volumes as low as 10 ml and can enable the processing of over 100 wastewater samples in a day. The sensitivity of the high-throughput protocol was shown to detect 1 asymptomatic individual in a building of 415 residents. Using the high-throughput pipeline, samples from the influent stream of the primary wastewater treatment plant of San Diego County (serving 2.3 million residents) were processed for a period of 13 weeks. Wastewater estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome copies in raw untreated wastewater correlated strongly with clinically reported cases by the county, and when used alongside past reported case numbers and temporal information in an autoregressive integrated moving average (ARIMA) model enabled prediction of new reported cases up to 3 weeks in advance. Taken together, the results show that the high-throughput surveillance could greatly ameliorate comprehensive community prevalence assessments by providing robust, rapid estimates.IMPORTANCE Wastewater monitoring has a lot of potential for revealing coronavirus disease 2019 (COVID-19) outbreaks before they happen because the virus is found in the wastewater before people have clinical symptoms. However, application of wastewater-based surveillance has been limited by long processing times specifically at the concentration step. Here we introduce a much faster method of processing the samples and show its robustness by demonstrating direct comparisons with existing methods and showing that we can predict cases in San Diego by a week with excellent accuracy, and 3 weeks with fair accuracy, using city sewage. The automated viral concentration method will greatly alleviate the major bottleneck in wastewater processing by reducing the turnaround time during epidemics.

Rapid, Large-Scale Wastewater Surveillance and Automated Reporting System Enable Early Detection of Nearly 85% of COVID-19 Cases on a University Campus.

Abstract: Wastewater-based surveillance has gained prominence and come to the forefront as a leading indicator of forecasting COVID-19 (coronavirus disease 2019) infection dynamics owing to its cost-effectiveness and its ability to inform early public health interventions. A university campus could especially benefit from wastewater surveillance, as universities are characterized by largely asymptomatic populations and are potential hot spots for transmission that necessitate frequent diagnostic testing. In this study, we employed a large-scale GIS (geographic information systems)-enabled building-level wastewater monitoring system associated with the on-campus residences of 7,614 individuals. Sixty-eight automated wastewater samplers were deployed to monitor 239 campus buildings with a focus on residential buildings. Time-weighted composite samples were collected on a daily basis and analyzed on the same day. Sample processing was streamlined significantly through automation, reducing the turnaround time by 20-fold and exceeding the scale of similar surveillance programs by 10- to 100-fold, thereby overcoming one of the biggest bottlenecks in wastewater surveillance. An automated wastewater notification system was developed to alert residents to a positive wastewater sample associated with their residence and to encourage uptake of campus-provided asymptomatic testing at no charge. This system, integrated with the rest of the "Return to Learn" program at the University of California (UC) San Diego-led to the early diagnosis of nearly 85% of all COVID-19 cases on campus. COVID-19 testing rates increased by 1.9 to 13× following wastewater notifications. Our study shows the potential for a robust, efficient wastewater surveillance system to greatly reduce infection risk as college campuses and other high-risk environments reopen. IMPORTANCE Wastewater-based epidemiology can be particularly valuable at university campuses where high-resolution spatial sampling in a well-controlled context could not only provide insight into what affects campus community as well as how those inferences can be extended to a broader city/county context. In the present study, a large-scale wastewater surveillance was successfully implemented on a large university campus enabling early detection of 85% of COVID-19 cases thereby averting potential outbreaks. The highly automated sample processing to reporting system enabled dramatic reduction in the turnaround time to 5 h (sample to result time) for 96 samples. Furthermore, miniaturization of the sample processing pipeline brought down the processing cost significantly ($13/sample). Taken together, these results show that such a system could greatly ameliorate long-term surveillance on such communities as they look to reopen.

Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data

Abstract: We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

Chemically informed analyses of metabolomics mass spectrometry data with Qemistree.

Abstract: Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.

Context-aware dimensionality reduction deconvolutes gut microbial community dynamics.

Abstract: 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.

Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies

Abstract: Objective Tryptophan can be catabolised to various metabolites through host kynurenine and microbial indole pathways. We aimed to examine relationships of host and microbial tryptophan metabolites with incident type 2 diabetes (T2D), host genetics, diet and gut microbiota. Method We analysed associations between circulating levels of 11 tryptophan metabolites and incident T2D in 9180 participants of diverse racial/ethnic backgrounds from five cohorts. We examined host genome-wide variants, dietary intake and gut microbiome associated with these metabolites. Results Tryptophan, four kynurenine-pathway metabolites (kynurenine, kynurenate, xanthurenate and quinolinate) and indolelactate were positively associated with T2D risk, while indolepropionate was inversely associated with T2D risk. We identified multiple host genetic variants, dietary factors, gut bacteria and their potential interplay associated with these T2D-relaetd metabolites. Intakes of fibre-rich foods, but not protein/tryptophan-rich foods, were the dietary factors most strongly associated with tryptophan metabolites. The fibre-indolepropionate association was partially explained by indolepropionate-associated gut bacteria, mostly fibre-using Firmicutes. We identified a novel association between a host functional LCT variant (determining lactase persistence) and serum indolepropionate, which might be related to a host gene-diet interaction on gut Bifidobacterium, a probiotic bacterium significantly associated with indolepropionate independent of other fibre-related bacteria. Higher milk intake was associated with higher levels of gut Bifidobacterium and serum indolepropionate only among genetically lactase non-persistent individuals. Conclusion Higher milk intake among lactase non-persistent individuals, and higher fibre intake were associated with a favourable profile of circulating tryptophan metabolites for T2D, potentially through the host–microbial cross-talk shifting tryptophan metabolism toward gut microbial indolepropionate production.

Exploring the Composition and Functions of Plastic Microbiome Using Whole-Genome Sequencing.

Abstract: Besides the ecotoxicological consequences of microplastics and associated chemicals, the association of microbes on plastics has greater environmental implications as microplastics may select for unique microbiome participating in environmentally significant functions. Despite this, the functional potential of the microbiome associated with different types of plastics is understudied. Here, we investigate the interaction between plastic and marine biofilm-forming microorganisms through a whole-genome sequencing approach on four types of microplastics incubated in the marine environment. Taxonomic analysis suggested that the microplastic surfaces exhibit unique microbial profiles and niche partitioning among the substrates. In particular, the abundance of Vibrio alginolyticus and Vibrio campbellii suggested that microplastic pollution may pose a potential risk to the marine food chain and negatively impact aquaculture industries. Microbial genera involved in xenobiotic compound degradation, carbon cycling, and genes associated with the type IV secretion system, conjugal transfer protein TraG, plant-pathogen interaction, CusA/CzcA family heavy metal efflux transfer proteins, and TolC family proteins were significantly enriched on all the substrates, indicating the variety of processes operated by the plastic-microbiome. The present study gives a detailed characterization of the rapidly altering microbial composition and gene pools on plastics and adds new knowledge surrounding the environmental ramifications of marine plastic pollution.

Challenges in benchmarking metagenomic profilers

Abstract: Accurate microbial identification and abundance estimation are crucial for metagenomics analysis. Various methods for classification of metagenomic data and estimation of taxonomic profiles, broadly referred to as metagenomic profilers, have been developed. Nevertheless, benchmarking of metagenomic profilers remains challenging because some tools are designed to report relative sequence abundance while others report relative taxonomic abundance. Here we show how misleading conclusions can be drawn by neglecting this distinction between relative abundance types when benchmarking metagenomic profilers. Moreover, we show compelling evidence that interchanging sequence abundance and taxonomic abundance will influence both per-sample summary statistics and cross-sample comparisons. We suggest that the microbiome research community pay attention to potentially misleading biological conclusions arising from this issue when benchmarking metagenomic profilers, by carefully considering the type of abundance data that were analyzed and interpreted and clearly stating the strategy used for metagenomic profiling.

Deep metagenomics examines the oral microbiome during dental caries, revealing novel taxa and co-occurrences with host molecules.

Abstract: Dental caries, the most common chronic infectious disease worldwide, has a complex etiology involving the interplay of microbial and host factors that are not completely understood. In this study, the oral microbiome and 38 host cytokines and chemokines were analyzed across 23 children with caries and 24 children with healthy dentition. De novo assembly of metagenomic sequencing obtained 527 metagenome-assembled genomes (MAGs), representing 150 bacterial species. Forty-two of these species had no genomes in public repositories, thereby representing novel taxa. These new genomes greatly expanded the known pangenomes of many oral clades, including the enigmatic Saccharibacteria clades G3 and G6, which had distinct functional repertoires compared to other oral Saccharibacteria. Saccharibacteria are understood to be obligate epibionts, which are dependent on host bacteria. These data suggest that the various Saccharibacteria clades may rely on their hosts for highly distinct metabolic requirements, which would have significant evolutionary and ecological implications. Across the study group, Rothia, Neisseria, and Haemophilus spp. were associated with good dental health, whereas Prevotella spp., Streptococcus mutans, and Human herpesvirus 4 (Epstein-Barr virus [EBV]) were more prevalent in children with caries. Finally, 10 of the host immunological markers were significantly elevated in the caries group, and co-occurrence analysis provided an atlas of potential relationships between microbes and host immunological molecules. Overall, this study illustrated the oral microbiome at an unprecedented resolution and contributed several leads for further study that will increase the understanding of caries pathogenesis and guide therapeutic development.

Insight into the function and evolution of the Wood–Ljungdahl pathway in Actinobacteria

Abstract: Carbon fixation by chemoautotrophic microbes such as homoacetogens had a major impact on the transition from the inorganic to the organic world. Recent reports have shown the presence of genes for key enzymes associated with the Wood-Ljungdahl pathway (WLP) in the phylum Actinobacteria, which adds to the diversity of potential autotrophs. Here, we compiled 42 actinobacterial metagenome-assembled genomes (MAGs) from new and existing metagenomic datasets and propose three novel classes, Ca. Aquicultoria, Ca. Geothermincolia and Ca. Humimicrobiia. Most members of these classes contain genes coding for acetogenesis through the WLP, as well as a variety of hydrogenases (NiFe groups 1a and 3b-3d; FeFe group C; NiFe group 4-related hydrogenases). We show that the three classes acquired the hydrogenases independently, yet the carbon monoxide dehydrogenase/acetyl-CoA synthase complex (CODH/ACS) was apparently present in their last common ancestor and was inherited vertically. Furthermore, the Actinobacteria likely donated genes for CODH/ACS to multiple lineages within Nitrospirae, Deltaproteobacteria (Desulfobacterota), and Thermodesulfobacteria through multiple horizontal gene transfer events. Finally, we show the apparent growth of Ca. Geothermincolia and H2-dependent acetate production in hot spring enrichment cultures with or without the methanogenesis inhibitor 2-bromoethanesulfonate, which is consistent with the proposed homoacetogenic metabolism.

Taxonomic signatures of cause-specific mortality risk in human gut microbiome

Abstract: 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 in Schizophrenia: Altered functional pathways related to immune modulation and atherosclerotic risk.

Abstract: Emerging evidence has linked the gut microbiome changes to schizophrenia. However, there has been limited research into the functional pathways by which the gut microbiota contributes to the phenotype of persons with chronic schizophrenia. We characterized the composition and functional potential of the gut microbiota in 48 individuals with chronic schizophrenia and 48 matched (sequencing plate, age, sex, BMI, and antibiotic use) non-psychiatric comparison subjects (NCs) using 16S rRNA sequencing. Patients with schizophrenia demonstrated significant beta-diversity differences in microbial composition and predicted genetic functional potential compared to NCs. Alpha-diversity of taxa and functional pathways were not different between groups. Random forests analyses revealed that the microbiome predicts differentiation of patients with schizophrenia from NCs using taxa (75% accuracy) and functional profiles (67% accuracy for KEGG orthologs, 70% for MetaCyc pathways). We utilized a new compositionally-aware method incorporating reference frames to identify differentially abundant microbes and pathways, which revealed that Lachnospiraceae is associated with schizophrenia. Functional pathways related to trimethylamine-N-oxide reductase and Kdo2-lipid A biosynthesis were altered in schizophrenia. These metabolic pathways were associated with inflammatory cytokines and risk for coronary heart disease in schizophrenia. Findings suggest potential mechanisms by which the microbiota may impact the pathophysiology of the disease through modulation of functional pathways related to immune signaling/response and lipid and glucose regulation to be further investigated in future studies.

SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment.

Abstract: SARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting. We collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model. Sixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic. These results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment.

Naturalization of the microbiota developmental trajectory of Cesarean-born neonates after vaginal seeding

Abstract: Summary Background Early microbiota perturbations are associated with disorders that involve immunological underpinnings. Cesarean section (CS)-born babies show altered microbiota development in relation to babies born vaginally. Here we present the first statistically powered longitudinal study to determine the effect of restoring exposure to maternal vaginal fluids after CS birth. Methods Using 16S rRNA gene sequencing, we followed the microbial trajectories of multiple body sites in 177 babies over the first year of life; 98 were born vaginally, and 79 were born by CS, of whom 30 were swabbed with a maternal vaginal gauze right after birth. Findings Compositional tensor factorization analysis confirmed that microbiota trajectories of exposed CS-born babies aligned more closely with that of vaginally born babies. Interestingly, the majority of amplicon sequence variants from maternal vaginal microbiomes on the day of birth were shared with other maternal sites, in contrast to non-pregnant women from the Human Microbiome Project (HMP) study. Conclusions The results of this observational study prompt urgent randomized clinical trials to test whether microbial restoration reduces the increased disease risk associated with CS birth and the underlying mechanisms. It also provides evidence of the pluripotential nature of maternal vaginal fluids to provide pioneer bacterial colonizers for the newborn body sites. This is the first study showing long-term naturalization of the microbiota of CS-born infants by restoring microbial exposure at birth. Funding C&D, Emch Fund, CIFAR, Chilean CONICYT and SOCHIPE, Norwegian Institute of Public Health, Emerald Foundation, NIH, National Institute of Justice, Janssen.

EMPress Enables Tree-Guided, Interactive, and Exploratory Analyses of Multi-omic Data Sets.

Abstract: Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality-including ordination integration and animations-alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of 'omic data.IMPORTANCE Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.

Links between gut microbiome composition and fatty liver disease in a large population sample.

Abstract: Fatty liver disease is the most common liver disease in the world. Its connection with the gut microbiome has been known for at least 80 y, but this association remains mostly unstudied in the gene...

Assessment of the microbiome during bacteriophage therapy in combination with systemic antibiotics to treat a case of staphylococcal device infection.

Abstract: Infectious bacterial diseases exhibiting increasing resistance to antibiotics are a serious global health issue. Bacteriophage therapy is an anti-microbial alternative to treat patients with serious bacterial infections. However, the impacts to the host microbiome in response to clinical use of phage therapy are not well understood. Our paper demonstrates a largely unchanged microbiota profile during 4 weeks of phage therapy when added to systemic antibiotics in a single patient with Staphylococcus aureus device infection. Metabolomic analyses suggest potential indirect cascading ecological impacts to the host (skin) microbiome. We did not detect genomes of the three phages used to treat the patient in metagenomic samples taken from saliva, stool, and skin; however, phages were detected using endpoint-PCR in patient serum. Results from our proof-of-principal study supports the use of bacteriophages as a microbiome-sparing approach to treat bacterial infections.