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Although disinfection is key to infection control, the colonization patterns and resistomes of hospital-environment microbes remain underexplored. We report the first extensive genomic characterization of microbiomes, pathogens and antibiotic resistance cassettes in a tertiary-care hospital, from repeated sampling (up to 1.5 years apart) of 179 sites associated with 45 beds. Deep shotgun metagenomics unveiled distinct ecological niches of microbes and antibiotic resistance genes characterized by biofilm-forming and human-microbiome-influenced environments with corresponding patterns of spatiotemporal divergence. Quasi-metagenomics with nanopore sequencing provided thousands of high-contiguity genomes, phage and plasmid sequences (>60% novel), enabling characterization of resistome and mobilome diversity and dynamic architectures in hospital environments. Phylogenetics identified multidrug-resistant strains as being widely distributed and stably colonizing across sites. Comparisons with clinical isolates indicated that such microbes can persist in hospitals for extended periods (>8 years), to opportunistically infect patients. These findings highlight the importance of characterizing antibiotic resistance reservoirs in hospitals and establish the feasibility of systematic surveys to target resources for preventing infections. Spatiotemporal characterization of microbial diversity and antibiotic resistance in a tertiary-care hospital reveals broad distribution and persistence of antibiotic-resistant organisms that could cause opportunistic infections in a healthcare setting., Author(s): Kern Rei Chng [sup.1] , Chenhao Li [sup.1] , Denis Bertrand [sup.1] , Amanda Hui Qi Ng [sup.1] , Junmei Samantha Kwah [sup.1] , Hwee Meng Low [sup.1] , [...]

The ability to adapt to low-nutrient microenvironments is essential for tumor cell survival and progression in solid cancers, such as colorectal carcinoma (CRC). Signaling by the NF-[kappa]B transcription factor pathway associates with advanced disease stages and shorter survival in patients with CRC. NF-[kappa]B has been shown to drive tumor-promoting inflammation, cancer cell survival, and intestinal epithelial cell (IEC) dedifferentiation in mouse models of CRC. However, whether NF-[kappa]B affects the metabolic adaptations that fuel aggressive disease in patients with CRC is unknown. Here, we identified carboxylesterase 1 (CES1) as an essential NF-[kappa]B-regulated lipase linking obesity-associated inflammation with fat metabolism and adaptation to energy stress in aggressive CRC. CES1 promoted CRC cell survival via cell-autonomous mechanisms that fuel fatty acid oxidation (FAO) and prevent the toxic build-up of triacylglycerols. We found that elevated CES1 expression correlated with worse outcomes in overweight patients with CRC. Accordingly, NF-[kappa]B drove CES1 expression in CRC consensus molecular subtype 4 (CMS4), which is associated with obesity, stemness, and inflammation. CES1 was also upregulated by gene amplifications of its transcriptional regulator HNF4A in CMS2 tumors, reinforcing its clinical relevance as a driver of CRC. This subtype-based distribution and unfavorable prognostic correlation distinguished CES1 from other intracellular triacylglycerol lipases and suggest CES1 could provide a route to treat aggressive CRC., Introduction Tumor cells must adapt to the low nutrient concentrations in the tumor microenvironment (TME) in order to survive, proliferate, and spread to distant sites (1-4). The availability of nutrients, [...]

Rapid and robust detection assays for Salmonella Enteritidis (SE) in shell eggs are essential to enable a quick testing turnaround time (TAT) at the earliest checkpoint and to ensure effective food safety control. Real-time polymerase chain reaction (qPCR) assays provide a workaround for the protracted lead times associated with conventional Salmonella diagnostic testing. However, DNA-based analysis cannot reliably discriminate between signals from viable and dead bacteria. We developed a strategy based on an SE qPCR assay that can be integrated into system testing to accelerate the detection of viable SE in egg-enriched cultures and verify the yielded SE isolates. The specificity of the assay was evaluated against 89 Salmonella strains, and SE was accurately identified in every instance. To define the indicator for a viable bacteria readout, viable or heat-inactivated SE were spiked into shell egg contents to generate post-enriched, artificially contaminated cultures to establish the quantification cycle (Cq) for viable SE. Our study has demonstrated that this technique could potentially be applied to accurately identify viable SE during the screening stage of naturally contaminated shell eggs following enrichment to provide an early alert, and that it consistently identified the serotypes of SE isolates in a shorter time than conventional testing. [ABSTRACT FROM AUTHOR]

The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species. Conclusion: BEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of microbiome data. Author summary: Characterizing the ecological interactions among microbial members is an important step towards understanding the structure and function of diverse microbial communities. Widely used correlation based approaches for inferring interactions from cross-sectional microbiome sequencing data are not able to predict the directionality of interactions, and their results may not be interpretable. We developed an expectation-maximization algorithm (BEEM-Static) that can infer directed interaction networks from cross-sectional data based on an ecological model. Our benchmarking results showed that BEEM-Static inferred presence and directionality of interactions accurately, while correlation based methods had performance slightly better than random guesses. In addition, BEEM-Static was robust to various types of noises using statistical filters to identify and remove data points violating its assumptions. Applying BEEM-Static to a large public dataset of human gut microbiomes, we were able to identify multiple stable equilibria with distinct ecological properties. [ABSTRACT FROM AUTHOR]

Background: Nanopore sequencing provides a rapid, cheap and portable real-time sequencing platform with the potential to revolutionize genomics. However, several applications are limited by relatively high single-read error rates (>10 %), including RNA-seq, haplotype sequencing and 16S sequencing. Results: We developed the Intramolecular-ligated Nanopore Consensus Sequencing (INC-Seq) as a strategy for obtaining long and accurate nanopore reads, starting with low input DNA. Applying INC-Seq for 16S rRNA-based bacterial profiling generated full-length amplicon sequences with a median accuracy >97 %. Conclusions: INC-Seq reads enabled accurate species-level classification, identification of species at 0.1 % abundance and robust quantification of relative abundances, providing a cheap and effective approach for pathogen detection and microbiome profiling on the MinION system. [ABSTRACT FROM AUTHOR]

The unexpected foodborne outbreak in Singapore in 2015 has accentuated Group B Streptococcus (GBS, Streptococcus agalactiae ) sequence type 283 as an emerging foodborne pathogen transmitted via the consumption of contaminated raw freshwater fish. Isolation-based workflows utilizing conventional microbiological and whole-genome sequencing methods are commonly used to support biosurveillance efforts critical for the control management of this emerging foodborne pathogen. However, these isolation-based workflows tend to have relatively long turnaround times that hamper a timely response for implementing risk mitigation. To address this gap, we have developed a metagenomics-based workflow for the simultaneous detection and genomic characterization of GBS in raw freshwater fish. Notably, our validation results showed that this metagenomics-based workflow could achieve comparable accuracy and potentially better detection limits while halving the turnaround time (from 2 weeks to 5 days) relative to an isolation-based workflow. The metagenomics-based workflow was also successfully adapted for use on a portable long-read nanopore sequencer, demonstrating its potential applicability for real-time point-of-need testing. Using GBS in freshwater fish as an example, this work represents a proof-of-concept study that supports the feasibility and validity of metagenomics as a rapid and accurate test methodology for the detection and genomic characterization of foodborne pathogens in complex food matrices., Importance: The need for a rapid and accurate food microbiological testing method is apparent for a timely and effective foodborne outbreak response. This is particularly relevant for emerging foodborne pathogens such as Group B Streptococcus (GBS) whose associated food safety risk might be undercharacterized. By using GBS in raw freshwater fish as a case example, this study describes the development of a metagenomics-based workflow for rapid food microbiological safety testing and surveillance. This study can inform as a working model for various foodborne pathogens in other complex food matrices, paving the way for future methodological development of metagenomics for food microbiological safety testing., Competing Interests: The authors declare no conflict of interest.

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities., Competing Interests: Declaration of interests C.E.M. is co-founder of Biotia and Onegevity Health. D.B. is co-founder and CSO of Poppy Health Inc. The other authors declare they have no competing interests that impacted this study., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

The aberrant activities of transcription factors such as the androgen receptor (AR) underpin prostate cancer development. While the AR cis -regulation has been extensively studied in prostate cancer, information pertaining to the spatial architecture of the AR transcriptional circuitry remains limited. In this paper, we propose a novel framework to profile long-range chromatin interactions associated with AR and its collaborative transcription factor, erythroblast transformation-specific related gene (ERG), using chromatin interaction analysis by paired-end tag (ChIA-PET). We identified ERG-associated long-range chromatin interactions as a cooperative component in the AR-associated chromatin interactome, acting in concert to achieve coordinated regulation of a subset of AR target genes. Through multifaceted functional data analysis, we found that AR-ERG interaction hub regions are characterized by distinct functional signatures, including bidirectional transcription and cotranscription factor binding. In addition, cancer-associated long noncoding RNAs were found to be connected near protein-coding genes through AR-ERG looping. Finally, we found strong enrichment of prostate cancer genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) at AR-ERG co-binding sites participating in chromatin interactions and gene regulation, suggesting GWAS target genes identified from chromatin looping data provide more biologically relevant findings than using the nearest gene approach. Taken together, our results revealed an AR-ERG-centric higher-order chromatin structure that drives coordinated gene expression in prostate cancer progression and the identification of potential target genes for therapeutic intervention., (© 2019 Zhang et al.; Published by Cold Spring Harbor Laboratory Press.)

Motivation: Microbial consortia are frequently defined by numerous interactions within the community that are key to understanding their function. While microbial interactions have been extensively studied experimentally, information regarding them is dispersed in the scientific literature. As manual collation is an infeasible option, automated data processing tools are needed to make this information easily accessible., Results: We present @MInter, an automated information extraction system based on Support Vector Machines to analyze paper abstracts and infer microbial interactions. @MInter was trained and tested on a manually curated gold standard dataset of 735 species interactions and 3917 annotated abstracts, constructed as part of this study. Cross-validation analysis showed that @MInter was able to detect abstracts pertaining to one or more microbial interactions with high specificity (specificity = 95%, AUC = 0.97). Despite challenges in identifying specific microbial interactions in an abstract (interaction level recall = 95%, precision = 25%), @MInter was shown to reduce annotator workload 13-fold compared to alternate approaches. Applying @MInter to 175 bacterial species abundant on human skin, we identified a network of 357 literature-reported microbial interactions, demonstrating its utility for the study of microbial communities., Availability and Implementation: @MInter is freely available at https://github.com/CSB5/atminter, Contact: nagarajann@gis.a-star.edu.sg, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Background: Nanopore sequencing provides a rapid, cheap and portable real-time sequencing platform with the potential to revolutionize genomics. However, several applications are limited by relatively high single-read error rates (>10 %), including RNA-seq, haplotype sequencing and 16S sequencing., Results: We developed the Intramolecular-ligated Nanopore Consensus Sequencing (INC-Seq) as a strategy for obtaining long and accurate nanopore reads, starting with low input DNA. Applying INC-Seq for 16S rRNA-based bacterial profiling generated full-length amplicon sequences with a median accuracy >97 %., Conclusions: INC-Seq reads enabled accurate species-level classification, identification of species at 0.1 % abundance and robust quantification of relative abundances, providing a cheap and effective approach for pathogen detection and microbiome profiling on the MinION system.

Whole metagenome analysis has the potential to reveal functional triggers of skin diseases, but issues of cost, robustness and sampling efficacy have limited its application. Here, we have established an alternative, clinically practical and robust metagenomic analysis protocol and applied it to 80 skin microbiome samples epidemiologically stratified for atopic dermatitis (AD). We have identified distinct non-flare, baseline skin microbiome signatures enriched for Streptococcus and Gemella but depleted for Dermacoccus in AD-prone versus normal healthy skin. Bacterial challenge assays using keratinocytes and monocyte-derived dendritic cells established distinct IL-1-mediated, innate and Th1-mediated adaptive immune responses with Staphylococcus aureus and Staphylococcus epidermidis. Bacterial differences were complemented by perturbations in the eukaryotic community and functional shifts in the microbiome-wide gene repertoire, which could exacerbate a dry and alkaline phenotype primed for pathogen growth and inflammation in AD-susceptible skin. These findings provide insights into how the skin microbial community, skin surface microenvironment and immune system cross-modulate each other, escalating the destructive feedback cycle between them that leads to AD flare.

Dehalococcoides mccartyi strain JNA detoxifies highly chlorinated polychlorinated biphenyl (PCB) mixtures via 85 distinct dechlorination reactions, suggesting that it has great potential for PCB bioremediation. However, its genomic and functional gene information remain unknown due to extremely slow growth of strain JNA with PCBs. In this study, we used tetracholorethene (PCE) as an alternative electron acceptor to grow sufficient biomass of strain JNA for subsequent genome sequencing and functional gene identification. Analysis of the assembled draft genome (1 462 509 bp) revealed the presence of 29 putative reductive dehalogenase (RDase) genes. Among them, JNA&#95;RD8 and JNA&#95;RD11 genes were highly transcribed in both PCE- and PCB-fed cultures. Furthermore, in vitro assays with crude cell lysate from PCE grown cells revealed dechlorination activity against both PCE and 2,2',3,4,4',5,5'-heptachlorobiphenyl. These data suggest that both JNA&#95;RD8 and JNA&#95;RD11 may be bifunctional PCE/PCB RDases. This study deepens the knowledge of organohalide respiration of PCBs and facilitates in situ PCB-bioremediation with strain JNA.

Extensive and multi-dimensional data sets generated from recent cancer omics profiling projects have presented new challenges and opportunities for unraveling the complexity of cancer genome landscapes. In particular, distinguishing the unique complement of genes that drive tumorigenesis in each patient from a sea of passenger mutations is necessary for translating the full benefit of cancer genome sequencing into the clinic. We address this need by presenting a data integration framework (OncoIMPACT) to nominate patient-specific driver genes based on their phenotypic impact. Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively). In particular, we computationally predicted and experimentally validated the gene TRIM24 as a putative novel amplified driver in a melanoma patient. Applying OncoIMPACT to more than 1000 tumor samples, we generated patient-specific driver gene lists in five different cancer types to identify modes of synergistic action. We also provide the first demonstration that computationally derived driver mutation signatures can be overall superior to single gene and gene expression based signatures in enabling patient stratification and prognostication. Source code and executables for OncoIMPACT are freely available from http://sourceforge.net/projects/oncoimpact., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Dehalococcoides mccartyi strain SG1, isolated from digester sludge, dechlorinates polychlorinated biphenyls (PCBs) to lower congeners. Here we report the draft genome sequence of SG1, which carries a 22.65 kbp circular putative plasmid., (Copyright © 2014 Wang et al.)

The NKX3-1 gene is a homeobox gene required for prostate tumor progression, but how it functions is unclear. Here, using chromatin immunoprecipitation coupled to massively parallel sequencing (ChIP-seq) we showed that NKX3-1 colocalizes with the androgen receptor (AR) across the prostate cancer genome. We uncovered two distinct mechanisms by which NKX3-1 controls the AR transcriptional network in prostate cancer. First, NKX3-1 and AR directly regulate each other in a feed-forward regulatory loop. Second, NKX3-1 collaborates with AR and FoxA1 to mediate genes in advanced and recurrent prostate carcinoma. NKX3-1- and AR-coregulated genes include those found in the "protein trafficking" process, which integrates oncogenic signaling pathways. Moreover, we demonstrate that NKX3-1, AR, and FoxA1 promote prostate cancer cell survival by directly upregulating RAB3B, a member of the RAB GTPase family. Finally, we show that RAB3B is overexpressed in prostate cancer patients, suggesting that RAB3B together with AR, FoxA1, and NKX3-1 are important regulators of prostate cancer progression. Collectively, our work highlights a novel hierarchical transcriptional regulatory network between NKX3-1, AR, and the RAB GTPase signaling pathway that is critical for the genetic-molecular-phenotypic paradigm in androgen-dependent prostate cancer.