Comparison of Infant Gut and Skin Microbiota, Resistome and Virulome Between Neonatal Intensive Care Unit (NICU) Environments

Hourigan, Suchitra K.; Subramanian, Poorani; Hasan, Nur A.; Ta, Allison; Klein, Elisabeth; Chettout, Nassim; Huddleston, Kathi; Deopujari, Varsha; Levy, Shira; Baveja, Rajiv; Clemency, Nicole C.; Baker, Robin; Niederhuber, John E.; Colwell, Rita R.

doi:10.3389/fmicb.2018.01361

Cited by 37 publications

(66 citation statements)

References 47 publications

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“…Subject characteristics. Subjects were classified as having PJI or aseptic failure using Infectious Diseases Society of America (IDSA) PJI diagnostic criteria (33), as described in our previous study (31).…”

Section: Methodsmentioning

confidence: 99%

“…A complete description of the k-mer-based taxonomy assignment algorithms used is provided elsewhere (35)(36)(37). Briefly, the pipeline has a first precomputation phase and a second per-sample computation (33). The phase outputs a reference microbial database to a whole-genome phylogeny tree, with sets of fixed-length k-mer fingerprints being uniquely identified with distinct nodes of the tree (33).…”

Section: Methodsmentioning

confidence: 99%

“…Briefly, the pipeline has a first precomputation phase and a second per-sample computation (33). The phase outputs a reference microbial database to a whole-genome phylogeny tree, with sets of fixed-length k-mer fingerprints being uniquely identified with distinct nodes of the tree (33). The second computational phase searches short sequence reads against the fingerprint sets and gives fine-grain composition and relative abundance estimates at all nodes of the tree; it uses edit distance-scoring techniques to compare a target sample with a reference set (33).…”

Section: Methodsmentioning

confidence: 99%

“…A lack of accurate automated interpretative systems for translating metagenomic sequencing data into clinical-grade reports in a timely fashion is a barrier to routine use (32). More efficient ways to analyze shotgun metagenomic sequencing data are needed; automated, high-performance bioinformatics tools will enable speed and accuracy in microbial identification, and curated databases will idealize resolution in identification, discrimination of pathogens from near neighbors, and accurate measurement of concentrations (33). To address this rapidly growing need, CosmosID (Rockville, MD) developed an automated bioinformatics platform to analyze metagenomic shotgun sequence data.…”

mentioning

confidence: 99%

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Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis

Yan

Thoendel

et al. 2019

J Clin Microbiol

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We previously demonstrated that shotgun metagenomic sequencing can detect bacteria in sonicate fluid, providing a diagnosis of prosthetic joint infection (PJI). A limitation of the approach that we used is that data analysis was timeconsuming and specialized bioinformatics expertise was required, both of which are barriers to routine clinical use. Fortunately, automated commercial analytic platforms that can interpret shotgun metagenomic data are emerging. In this study, we evaluated the CosmosID bioinformatics platform using shotgun metagenomic sequencing data derived from 408 sonicate fluid samples from our prior study with the goal of evaluating the platform vis-à-vis bacterial detection and antibiotic resistance gene detection for predicting staphylococcal antibacterial susceptibility. Samples were divided into a derivation set and a validation set, each consisting of 204 samples; results from the derivation set were used to establish cutoffs, which were then tested in the validation set for identifying pathogens and predicting staphylococcal antibacterial resistance. Metagenomic analysis detected bacteria in 94.8% (109/115) of sonicate fluid culture-positive PJIs and 37.8% (37/98) of sonicate fluid culture-negative PJIs. Metagenomic analysis showed sensitivities ranging from 65.7 to 85.0% for predicting staphylococcal antibacterial resistance. In conclusion, the CosmosID platform has the potential to provide fast, reliable bacterial detection and identification from metagenomic shotgun sequencing data derived from sonicate fluid for the diagnosis of PJI. Strategies for metagenomic detection of antibiotic resistance genes for predicting staphylococcal antibacterial resistance need further development.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis

Yan

Thoendel

et al. 2019

J Clin Microbiol

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“…Finally, filtered reads were mapped to custom curated bacterial, fungal, viral, and antibiotic resistance genomic databases. Taxonomic identification was assigned with an in-house K-mer based algorithm refined against a whole genome phylogenetic tree to identify unique species and strains developed at CosmoID and described in Hourigan et al(Hourigan et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

The microbial basis of impaired wound healing: differential roles for pathogens, “bystanders”, and strain-level diversification in clinical outcomes

Kalan

Meisel

Loesche

et al. 2018

Preprint

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22Chronic, non-healing wounds are a major complication of diabetes associated with high morbidity 23 and health care expenditures estimated at $9-13 billion annually in the US. Though microbial 24 infection and critical colonization is hypothesized to impair healing and contribute to severe 25 outcomes such as amputation, antimicrobial therapy is inefficacious and the role of microbes in 26 tissue repair, regeneration, and healing remains unclear. Here, in a longitudinal prospective 27 cohort study of 100 subjects with non-infected neuropathic diabetic foot ulcer (DFU), we 28 performed metagenomic shotgun sequencing to elucidate microbial temporal dynamics at strain-29 level resolution, to investigate pathogenicity and virulence of the DFU microbiome with respect to 30 outcomes, and to determine the influence of therapeutic intervention on the DFU microbiota. Slow 31 healing DFUs were associated with signatures of biofilm formation, host invasion, and virulence. 32Though antibiotic resistance was widespread at the genetic level, debridement, rather than 33 antibiotic treatment, significantly shifted the DFU microbiome in patients with more favorable 34 outcomes. Primary clinical isolates of S. aureus, C. striatum, and A. faecalis induced differential 35 biological responses in keratinocytes and in a murine model of diabetic wound healing, with the 36 S. aureus strain associated with non-healing wounds eliciting the most severe phenotype. 37Together these findings implicate strain-level diversification of the wound pathogen S. aureus in 38 chronic wound outcomes, while revealing potential contributions from skin commensals and other 39 previously underappreciated constituents of the wound microbiota. 40 41 42 chronic wounds 43 44 MAIN TEXT: 45 INTRODUCTION 46 Chronic, non-healing wounds are common and costly complications of diabetes. Up to one in four 47 persons with diabetes will develop a diabetic foot ulcer (DFU)(Boyko et al., 2015; Martins-Mendes 48 et al., 2014), and approximately 25% of hospital stays for patients with diabetes are due to infected 49 or ischemic DFU(Ramsey et al., 1999). Complications from DFUs account for two-thirds of all 50 non-traumatic lower extremity amputations performed in the United States(Hoffstad et al., 2015; 51 Martins-Mendes et al., 2014) and 5-year mortality rates surpass those of prostate and breast 52 cancer, among others(Armstrong et al., 2007; Moulik et al., 2003). Improved therapeutic 53 approaches are desperately needed, as morbidity, mortality, and health care expenditures only 54 continue to increase as the prevalence of diabetes escalates worldwide. 55 56 Microbial colonization, biofilm formation, and infection are hypothesized to impair healing of DFUs 57 and contribute to severe complications such as osteomyelitis and amputation. Wound infection is 58 believed to underlie up to 90% of amputations(Boulton et al., 2005); yet quantitative cultures of 59 uninfected DFUs were not predictive of outcomes(Gardner et al., 2014). Systemic and topical 60 antimicrobials are of...

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The infant microbiome and implications for central nervous system development

Parsons

Claud

Petrof

2020

Progress in Molecular Biology and Translational Science

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Comparison of Infant Gut and Skin Microbiota, Resistome and Virulome Between Neonatal Intensive Care Unit (NICU) Environments

Cited by 37 publications

References 47 publications

Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis

Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis

The microbial basis of impaired wound healing: differential roles for pathogens, “bystanders”, and strain-level diversification in clinical outcomes

The infant microbiome and implications for central nervous system development

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