Identifying glycan consumers in human gut microbiota samples using metabolic labeling coupled with fluorescence-activated cell sorting.

The composition and metabolism of the human gut microbiota are strongly influenced by dietary complex glycans, which cause downstream effects on the physiology and health of hosts. Despite recent advances in our understanding of glycan metabolism by human gut bacteria, we still need methods to link glycans to their consuming bacteria. Here, we use a functional assay to identify and isolate gut bacteria from healthy human volunteers that take up different glycans. The method combines metabolic labeling using fluorescent oligosaccharides with fluorescence-activated cell sorting (FACS), followed by amplicon sequencing or culturomics. Our results demonstrate metabolic labeling in various taxa, such as Prevotella copri, Collinsella aerofaciens and Blautia wexlerae. In vitro validation confirms the ability of most, but not all, labeled species to consume the glycan of interest for growth. In parallel, we show that glycan consumers spanning three major phyla can be isolated from cultures of sorted labeled cells. By linking bacteria to the glycans they consume, this approach increases our basic understanding of glycan metabolism by gut bacteria. Going forward, it could be used to provide insight into the mechanism of prebiotic approaches, where glycans are used to manipulate the gut microbiota composition.

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.

Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets.

The novel coronavirus SARS-CoV-2 has spread across the world since 2019, causing a global pandemic. The pathogenesis of the viral infection and the associated clinical presentations depend primarily on host factors such as age and immunity, rather than the viral load or its genetic variations. A growing number of omics studies have been conducted to characterize the host immune and metabolic responses underlying the disease progression. Meta-analyses of these datasets have great potential to identify robust molecular signatures to inform clinical care and to facilitate therapeutics development. In this study, we performed a comprehensive meta-analysis of publicly available global metabolomics datasets obtained from three countries (United States, China and Brazil). To overcome high heterogeneity inherent in these datasets, we have (a) implemented a computational pipeline to perform consistent raw spectra processing; (b) conducted meta-analyses at pathway levels instead of individual feature levels; and (c) performed visual data mining on consistent patterns of change between disease severities for individual studies. Our analyses have yielded several key metabolic signatures characterizing disease progression and clinical outcomes. Their biological interpretations were discussed within the context of the current literature. To the best of our knowledge, this is the first comprehensive meta-analysis of global metabolomics datasets of COVID-19.

TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome.

Crosstalk between the gut microbiome and the host plays an important role in animal development and health. Small compounds are key mediators in this host-gut microbiome dialogue. For instance, tryptophan metabolites, generated by biotransformation of tryptophan through complex host-microbiome co-metabolism can trigger immune, metabolic, and neuronal effects at local and distant sites. However, the origin of tryptophan metabolites and the underlying tryptophan metabolic pathway(s) are not well characterized in the current literature. A large number of the microbial contributors of tryptophan metabolism remain unknown, and there is a growing interest in predicting tryptophan metabolites for a given microbiome. Here, we introduce TrpNet, a comprehensive database and analytics platform dedicated to tryptophan metabolism within the context of host (human and mouse) and gut microbiome interactions. TrpNet contains data on tryptophan metabolism involving 130 reactions, 108 metabolites and 91 enzymes across 1246 human gut bacterial species and 88 mouse gut bacterial species. Users can browse, search, and highlight the tryptophan metabolic pathway, as well as predict tryptophan metabolites on the basis of a given taxonomy profile using a Bayesian logistic regression model. We validated our approach using two gut microbiome metabolomics studies and demonstrated that TrpNet was able to better predict alterations in in indole derivatives compared to other established methods.

Generation of an in vitro model of the outer annulus fibrosus-cartilage interface.

Current treatments for degenerative disc disease do not restore full biological functionality of the intervertebral disc (IVD). As a result, regenerative medicine approaches are being developed to generate a biological replacement that when implanted will restore form and function of the degenerated IVD. Tissue-engineered models to date have focused on the generation of nucleus pulposus and annulus fibrosus IVD components. However, these tissues need to be integrated with a cartilage endplate in order for successful implantation to occur. The purpose of this study was to generate an in vitro annulus fibrosus-cartilage interface model which would enable us to better understand the biological and biomechanical implications of such interfaces. It was hypothesized that in vitro-formed outer annulus fibrosus (OAF) and cartilage tissues would integrate in direct-contact coculture to yield an interface containing extracellular matrix with aspects resembling the native OAF-CEP interface. In vitro-formed tissues were generated using bovine OAF cell-seeded angle-ply, multi-lamellated polycarbonate urethane scaffolds and articular chondrocytes, which were then placed in direct-contact coculture. 2-week old OAF tissues integrated with 3-day old cartilage by 1 week of coculture. Immunohistochemical staining of 2-week interfaces showed that distributions of collagen type I, collagen type II, and aggrecan were similar to the native bovine interface. The apparent tensile strength of the in vitro interface increased significantly between 2 and 4 weeks of coculture. In summary, an annulus fibrosus-cartilage interface model can be formed in vitro which will facilitate the identification of conditions required to generate an entire tissue-engineered disc replacement suitable for clinical use.

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.

Correlations between the Composition of the Bovine Microbiota and Vitamin B12 Abundance.

Vitamin B12 is synthesized by prokaryotes in the rumens of dairy cows-and this has implications in human nutrition since humans rely on consumption of dairy products for vitamin B12 acquisition. However, the concentration of vitamin B12 in milk is highly variable, and there is interest in determining what causes vitamin B12 variability. We collected 92 temporally linked rumen, fecal, blood, and milk sample sets from Holstein cows at various stages of lactation fitted with rumen cannula and attempted to define which bacterial genera correlated well with vitamin B12 abundance. The level of vitamin B12 present in each sample was measured, and the bacterial population of each rumen, fecal, and milk sample (n = 263) was analyzed by 16S rRNA-targeted amplicon sequencing of the V4 region. The bacterial populations present in the rumen, small intestine, and milk were highly dissimilar. Combined diet and lactation status had significant effects on the composition of the microbiota in the rumen and in the feces. A high ruminal concentration of vitamin B12 was correlated with the increased abundance of Prevotella, while a low ruminal concentration of vitamin B12 was correlated with increased abundance of Bacteroidetes, Ruminiclostridium, and Butyrivibrio The ultimate concentration of vitamin B12 is controlled by the complex interaction of several factors, including the composition of the microbiota. Bacterial consumption of vitamin B12 in the rumen may be more important in determining overall levels than bacterial production.IMPORTANCE In this paper, we examined the microbiome of the bovine rumen, feces, and milk and attempted to understand how the bacterial communities at each site affected the production and movement of vitamin B12 around the animal's body. It was determined that the composition of the bovine rumen microbiome correlates well with vitamin B12 concentration, indicating that the rumen microbiota may be a good target for manipulation to improve production of this important vitamin.

Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data.

MicrobiomeAnalyst is an easy-to-use, web-based platform for comprehensive analysis of common data outputs generated from current microbiome studies. It enables researchers and clinicians with little or no bioinformatics training to explore a wide variety of well-established methods for microbiome data processing, statistical analysis, functional profiling and comparison with public datasets or known microbial signatures. MicrobiomeAnalyst currently contains four modules: Marker-gene Data Profiling (MDP), Shotgun Data Profiling (SDP), Projection with Public Data (PPD), and Taxon Set Enrichment Analysis (TSEA). This protocol will first introduce the MDP module by providing a step-wise description of how to prepare, process and normalize data; perform community profiling; identify important features; and conduct correlation and classification analysis. We will then demonstrate how to perform predictive functional profiling and introduce several unique features of the SDP module for functional analysis. The last two sections will describe the key steps involved in using the PPD and TSEA modules for meta-analysis and visual exploration of the results. In summary, MicrobiomeAnalyst offers a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces. The complete protocol can be executed in ~70 min.

Using MetaboAnalyst 4.0 for Metabolomics Data Analysis, Interpretation, and Integration with Other Omics Data.

MetaboAnalyst ( www.metaboanalyst.ca ) is an easy-to-use, comprehensive web-based tool, freely available for metabolomics data processing, statistical analysis, functional interpretation, as well as integration with other omics data. This chapter first provides an introductory overview to the current MetaboAnalyst (version 4.0) with regards to its underlying design concepts and user interface structure. Subsequent sections describe three common metabolomics data analysis workflows covering targeted metabolomics, untargeted metabolomics, and multi-omics data integration.

Transforming Growth Factor ß Enhances Tissue Formation by Passaged Nucleus Pulposus Cells In Vitro.

The nucleus pulposus (NP) is composed of NP and notochord cell. It is a paucicellular tissue and if it is to be used as a source of cells for tissue engineering the cell number will have to be expanded by cell passaging. The hypothesis of this study is that passaged NP and notochordal cells grown in three-dimensional (3D) culture in the presence of transforming growth factor ß (TGFß) will show enhanced NP tissue formation compared with cells grown in the absence of this growth factor. Bovine NP cells isolated by sequential enzymatic digestion from caudal intervertebral discs were either placed directly in 3D culture (P0) or serially passaged up to passage 3 (P3) prior to placement in 3D culture. Serial cell passage in monolayer culture led to de-differentiation, increased senescence and oxidative stress and decreases in the gene expression of NP and notochordal associated markers and increases in de-differentiation markers. The NP tissue regeneration capacity of cells in 3D culture decreases with passaging as indicated by diminished tissue thickness and total collagen content when compared with tissues formed by P0 cells. Immunohistochemical studies showed that type II collagen accumulation appeared to decrease. TGFß1 or TGFß3 treatment enhanced the ability of cells at each passage to form tissue, in part by decreasing cell death. However, neither TGFß1 nor TGFß3 were able to restore the notochordal phenotype. Although TGFß1/3 recovered NP tissue formation by passaged cells, to generate NP in vitro that resembles the native tissue will require identification of conditions facilitating retention of notochordal cell differentiation. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 38:438-449, 2020.

Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis.

MetaboAnalyst (https://www.metaboanalyst.ca) is an easy-to-use web-based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever-expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta-analysis, and network-based multi-omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web-based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Data uploading, processing, and normalization Basic Protocol 2: Identification of significant variables Basic Protocol 3: Multivariate exploratory data analysis Basic Protocol 4: Functional interpretation of metabolomic data Basic Protocol 5: Biomarker analysis based on receiver operating characteristic (ROC) curves Basic Protocol 6: Time-series and two-factor data analysis Basic Protocol 7: Sample size estimation and power analysis Basic Protocol 8: Joint pathway analysis Basic Protocol 9: MS peaks to pathway activities Basic Protocol 10: Biomarker meta-analysis Basic Protocol 11: Knowledge-based network exploration of multi-omics data Basic Protocol 12: MetaboAnalystR introduction.

MetaboAnalystR 2.0: From Raw Spectra to Biological Insights.

Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.

Metabolomics investigation of dietary effects on flesh quality in grass carp (Ctenopharyngodon idellus).

Background: The ultrahigh density intensive farming model of grass carp (Ctenopharyngodon idellus) may elicit growth inhibition, decrease flesh quality, and increase disease susceptibility of fish. The degradation in quality and excessive fat accumulation in cultured C. idellus have long been attributed to possible alterations in the lipid metabolism of fish muscle tissues as a result of overnutrition from artificial diets. To investigate the effects of different diets on fish muscle quality, a large-scale metabolomics study was performed on 250 tails of C. idellus. Findings: The experimental fish were divided into four groups based on sex and diet-female artificial feed (FAF), female grass feed, male artificial feed (MAF), and male grass feed (MGF). After a 113-day rearing period, the artificial feed (AF) group showed a significantly higher total mass of muscle fat (P < 0.01), with the FAF group being the highest. Metabolomics profiling based on liquid chromatography-mass spectrometry revealed distinctive patterns of clustering according to the four groups. Overall, artificial feeding was associated with higher concentrations of docosapentaenoic acid, dihomo-gamma-linolenic acid, and arachidonic acid, whereas grass feeding was associated with elevated n-3 unsaturated fatty acids (UFAs) such as eicosapentaenoic acid, alpha-linolenic acid, and gamma-linolenic acid. Artificial feeding also resulted in significant increased docosahexaenoic acid in MAF muscle than in MGF fish, whereas there was no significance in the comparison of female samples. Metabolic pathway analyses using both targeted and untargeted approaches consistently revealed that arachidonic acid metabolism and steroid hormone biosynthesis pathways were significantly different between AF and grass fed groups. Conclusions: Our results suggest that grass is a better source of dietary fatty acid and protein when compared to artificial feed. Grass feeding could effectively lower triglycerides in serum, reduce fat accumulation, and alter lipid compositions in fish muscle by increasing the concentrations of n-3 UFAs, leading to better nutrition and health.

MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data.

Summary: The MetaboAnalyst web application has been widely used for metabolomics data analysis and interpretation. Despite its user-friendliness, the web interface has presented its inherent limitations (especially for advanced users) with regard to flexibility in creating customized workflow, support for reproducible analysis, and capacity in dealing with large data. To address these limitations, we have developed a companion R package (MetaboAnalystR) based on the R code base of the web server. The package has been thoroughly tested to ensure that the same R commands will produce identical results from both interfaces. MetaboAnalystR complements the MetaboAnalyst web server to facilitate transparent, flexible and reproducible analysis of metabolomics data. Availability and implementation: MetaboAnalystR is freely available from https://github.com/xia-lab/MetaboAnalystR.

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.

We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

CDC42 regulates the expression of superficial zone molecules in part through the actin cytoskeleton and myocardin-related transcription factor-A.

Osteoarthritis (OA) is a degenerative disease that initially manifests as loss of the superficial zone (SZ) of articular cartilage. SZ chondrocytes (SZC) differ in morphology from other chondrocytes as they are elongated and oriented parallel to the tissue surface. Proteoglycan 4 (PRG4) and tenascin C (TNC) are molecules expressed by SZC, which have been shown to be chondroprotective. Identification of the signalling pathway(s) regulating expression of SZ molecules may lead to a therapeutic target that can be used to delay or prevent the onset of OA. The hypothesis of this study is that expression of SZ molecules are regulated in part, by the CDC42-actin-myocardin-related transcription factor-A (MRTF-A) signaling pathway. SZC from bovine metacarpal-phalangeal joints were isolated and grown in monolayer culture. Each target in the CDC42-actin-MRTF-A pathway was inhibited and the effect on cell shape, actin cytoskeleton status, and expression of PRG4 and TNC were determined. Treatment with the CDC42 inhibitor ML141 decreased PRG4 and TNC expression, and correlated with increased cell circularity and G-/F-actin ratio. PRG4 and TNC expression were differentially regulated by actin depolymerizing agents, latrunculin B and cytochalasin D. Chemical inhibition of MRTF-A resulted in decreased expression of both PRG4 and TNC; however, specific knockdown by small interfering RNA only decreased expression of TNC indicating that TNC, but not PRG4, is regulated by MRTF-A. Although PRG4 and TNC expression are both regulated by CDC42 and actin, it appears to occur through different downstream signaling pathways. Further study is required to elucidate the pathway regulating PRG4. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2421-2430, 2018.

Computational Approaches for Integrative Analysis of the Metabolome and Microbiome.

The study of the microbiome, the totality of all microbes inhabiting the host or an environmental niche, has experienced exponential growth over the past few years. The microbiome contributes functional genes and metabolites, and is an important factor for maintaining health. In this context, metabolomics is increasingly applied to complement sequencing-based approaches (marker genes or shotgun metagenomics) to enable resolution of microbiome-conferred functionalities associated with health. However, analyzing the resulting multi-omics data remains a significant challenge in current microbiome studies. In this review, we provide an overview of different computational approaches that have been used in recent years for integrative analysis of metabolome and microbiome data, ranging from statistical correlation analysis to metabolic network-based modeling approaches. Throughout the process, we strive to present a unified conceptual framework for multi-omics integration and interpretation, as well as point out potential future directions.

MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data.

The widespread application of next-generation sequencing technologies has revolutionized microbiome research by enabling high-throughput profiling of the genetic contents of microbial communities. How to analyze the resulting large complex datasets remains a key challenge in current microbiome studies. Over the past decade, powerful computational pipelines and robust protocols have been established to enable efficient raw data processing and annotation. The focus has shifted toward downstream statistical analysis and functional interpretation. Here, we introduce MicrobiomeAnalyst, a user-friendly tool that integrates recent progress in statistics and visualization techniques, coupled with novel knowledge bases, to enable comprehensive analysis of common data outputs produced from microbiome studies. MicrobiomeAnalyst contains four modules - the Marker Data Profiling module offers various options for community profiling, comparative analysis and functional prediction based on 16S rRNA marker gene data; the Shotgun Data Profiling module supports exploratory data analysis, functional profiling and metabolic network visualization of shotgun metagenomics or metatranscriptomics data; the Taxon Set Enrichment Analysis module helps interpret taxonomic signatures via enrichment analysis against >300 taxon sets manually curated from literature and public databases; finally, the Projection with Public Data module allows users to visually explore their data with a public reference data for pattern discovery and biological insights. MicrobiomeAnalyst is freely available at http://www.microbiomeanalyst.ca.

Heteroresistant Vancomycin Intermediate Coagulase Negative Staphylococcus in the NICU: A Systematic Review.

CONTEXT: NICUs in the province of Québec have seen an increase in hVICoNS, detected in the clinical laboratory. OBJECTIVE: To investigate the clinical relevance of hVICoNS on the course of infection, and to determine the prevalence of hVICoNS sepsis in the NICU. METHODS: We searched MEDLINE, EMBASE, and PubMed from 1 January 1980 to 1 July 2016. Both observational and interventional studies were considered eligible if they provided data on hVICoNS in the NICU population. Two investigators independently reviewed studies for data extraction. Data extracted included: number of CoNS cultures, prevalence of hVICoNS, and clonality of strains. RESULTS: Of the 613 studies identified, 19 studies were reviewed, and 5 studies included in the final review. No studies addressed the clinical significance of hVICoNS in the NICU. The prevalence of hVICoNS in the NICU varied greatly, ranging from 2.3% to 100%. LIMITATIONS: Publication bias could not be assessed, and risk of bias in some of the included studies due to small sample size and poor methods reporting. The quality of all included studies was low according to GRADE criteria, and the inclusion criteria restricted to either English or French studies. CONCLUSIONS: Our review suggests that heteroresistance to vancomycin is much more common than previously believed. Our search however did not identify any studies that explicitly assessed any clinical implications of hVICoNS infections, thereby highlighting the need for research to assess the true impact of hVICoNS infection and to determine its significance on patient mortality and morbidity in the NICU.

Molecular Epidemiology of a Vancomycin-Intermediate Heteroresistant Staphylococcus epidermidis Outbreak in a Neonatal Intensive Care Unit.

Coagulase-negative staphylococci (CoNS) have become the leading cause of bloodstream infections (BSIs) in intensive care units (ICUs), particularly in premature neonates. Vancomycin-intermediate heteroresistant CoNS (hVICoNS) have been identified as sources of BSIs worldwide, and their potential to emerge as significant pathogens in the neonatal ICU (NICU) remains uncertain. This study describes the molecular epidemiology of an outbreak of vancomycin-heteroresistant (hV) Staphylococcus epidermidis central-line-associated BSI (CLABSI) in a single tertiary care NICU and compares it to a second tertiary care NICU that had not been associated with an outbreak. Between November 2009 and April 2014, 119 S. epidermidis CLABSIs were identified in two tertiary care NICUs in Quebec, Canada. Decreased vancomycin susceptibility was identified in about 88% of all collected strains using Etest methods. However, discrepancies were found according to the Etest and population analysis profiling-area under the concentration-time curve (PAP-AUC) methods used. All strains were susceptible to linezolid, and a few isolates were nonsusceptible to daptomycin. Great genetic diversity was observed within the collection, with 31 pulsed-field gel electrophoresis (PFGE) patterns identified. The outbreak strains were all determined to be heteroresistant to vancomycin and were polyclonal. The study identified two major clones, PFGE patterns E and G, which were found in both NICUs across the 5-year study period. This suggests the persistence of highly successful clones that are well adapted to the hospital environment. hV S. epidermidis seems more common than currently realized in the NICU, and certain hV S. epidermidis clones can become endemic to the NICU. The reservoirs for these clones remain unknown at this time, and identification of the reservoirs is needed to better understand the impact of hV S. epidermidis in the NICU and to inform infection prevention strategies. In addition, there is a need to investigate and validate hV determination protocols for different species of CoNS.