1
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Mwaba J, Debes AK, Murt KN, Shea P, Simuyandi M, Laban N, Kazimbaya K, Chisenga C, Li S, Almeida M, Meisel JS, Shibemba A, Kantenga T, Mukonka V, Kwenda G, Sack DA, Chilengi R, Stine OC. Three transmission events of Vibrio cholerae O1 into Lusaka, Zambia. BMC Infect Dis 2021; 21:570. [PMID: 34126945 PMCID: PMC8200794 DOI: 10.1186/s12879-021-06259-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/27/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Cholera has been present and recurring in Zambia since 1977. However, there is a paucity of data on genetic relatedness and diversity of the Vibrio cholerae isolates responsible for these outbreaks. Understanding whether the outbreaks are seeded from existing local isolates or if the outbreaks represent separate transmission events can inform public health decisions. RESULTS Seventy-two V. cholerae isolates from outbreaks in 2009/2010, 2016, and 2017/2018 in Zambia were characterized using multilocus variable number tandem repeat analysis (MLVA) and whole genome sequencing (WGS). The isolates had eight distinct MLVA genotypes that clustered into three MLVA clonal complexes (CCs). Each CC contained isolates from only one outbreak. The results from WGS revealed both clustered and dispersed single nucleotide variants. The genetic relatedness of isolates based on WGS was consistent with the MLVA, each CC was a distinct genetic lineage and had nearest neighbors from other East African countries. In Lusaka, isolates from the same outbreak were more closely related to themselves and isolates from other countries than to isolates from other outbreaks in other years. CONCLUSIONS Our observations are consistent with i) the presence of random mutation and alternative mechanisms of nucleotide variation, and ii) three separate transmission events of V. cholerae into Lusaka, Zambia. We suggest that locally, case-area targeted invention strategies and regionally, well-coordinated plans be in place to effectively control future cholera outbreaks.
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Affiliation(s)
- John Mwaba
- Centre for Infectious Disease Research in Zambia, Lusaka, Zambia
- Department of Biomedical Sciences, University of Zambia School of Health Sciences, Lusaka, Zambia
- Department of Pathology and Microbiology, University Teaching Hospitals, Lusaka, Zambia
| | - Amanda K Debes
- Johns Hopkins Bloomberg School of Public Health, MD, Baltimore, USA
| | - Kelsey N Murt
- Johns Hopkins Bloomberg School of Public Health, MD, Baltimore, USA
| | - Patrick Shea
- Johns Hopkins Bloomberg School of Public Health, MD, Baltimore, USA
| | | | - Natasha Laban
- Centre for Infectious Disease Research in Zambia, Lusaka, Zambia
| | - Katayi Kazimbaya
- Centre for Infectious Disease Research in Zambia, Lusaka, Zambia
- Department of Biomedical Sciences, University of Zambia School of Health Sciences, Lusaka, Zambia
| | | | - Shan Li
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Mathieu Almeida
- University of Maryland, College Park, College Park, MD, USA
- Université Paris-Saclay, INRAE, MGP, 78350, Jouy-en-Josas, France
| | | | - Aaron Shibemba
- Department of Pathology and Microbiology, University Teaching Hospitals, Lusaka, Zambia
| | - Timothy Kantenga
- Department of Biomedical Sciences, University of Zambia School of Health Sciences, Lusaka, Zambia
- Department of Pathology and Microbiology, University Teaching Hospitals, Lusaka, Zambia
| | | | - Geoffrey Kwenda
- Department of Biomedical Sciences, University of Zambia School of Health Sciences, Lusaka, Zambia
| | - David A Sack
- Johns Hopkins Bloomberg School of Public Health, MD, Baltimore, USA
| | - Roma Chilengi
- Centre for Infectious Disease Research in Zambia, Lusaka, Zambia
| | - O Colin Stine
- University of Maryland School of Medicine, Baltimore, MD, USA.
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2
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Muralidharan HS, Shah N, Meisel JS, Pop M. Binnacle: Using Scaffolds to Improve the Contiguity and Quality of Metagenomic Bins. Front Microbiol 2021; 12:638561. [PMID: 33717033 PMCID: PMC7945042 DOI: 10.3389/fmicb.2021.638561] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 02/04/2021] [Indexed: 01/03/2023] Open
Abstract
High-throughput sequencing has revolutionized the field of microbiology, however, reconstructing complete genomes of organisms from whole metagenomic shotgun sequencing data remains a challenge. Recovered genomes are often highly fragmented, due to uneven abundances of organisms, repeats within and across genomes, sequencing errors, and strain-level variation. To address the fragmented nature of metagenomic assemblies, scientists rely on a process called binning, which clusters together contigs inferred to originate from the same organism. Existing binning algorithms use oligonucleotide frequencies and contig abundance (coverage) within and across samples to group together contigs from the same organism. However, these algorithms often miss short contigs and contigs from regions with unusual coverage or DNA composition characteristics, such as mobile elements. Here, we propose that information from assembly graphs can assist current strategies for metagenomic binning. We use MetaCarvel, a metagenomic scaffolding tool, to construct assembly graphs where contigs are nodes and edges are inferred based on paired-end reads. We developed a tool, Binnacle, that extracts information from the assembly graphs and clusters scaffolds into comprehensive bins. Binnacle also provides wrapper scripts to integrate with existing binning methods. The Binnacle pipeline can be found on GitHub (https://github.com/marbl/binnacle). We show that binning graph-based scaffolds, rather than contigs, improves the contiguity and quality of the resulting bins, and captures a broader set of the genes of the organisms being reconstructed.
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Affiliation(s)
- Harihara Subrahmaniam Muralidharan
- Pop Lab, Department of Computer Science, Center for Bioinformatics and Computational Biology, UMIACS, University of Maryland, College Park, MD, United States
| | - Nidhi Shah
- Pop Lab, Department of Computer Science, Center for Bioinformatics and Computational Biology, UMIACS, University of Maryland, College Park, MD, United States
| | - Jacquelyn S Meisel
- Pop Lab, Department of Computer Science, Center for Bioinformatics and Computational Biology, UMIACS, University of Maryland, College Park, MD, United States
| | - Mihai Pop
- Pop Lab, Department of Computer Science, Center for Bioinformatics and Computational Biology, UMIACS, University of Maryland, College Park, MD, United States
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3
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Abstract
The advent of high throughput sequencing has enabled in-depth characterization of human and environmental microbiomes. Determining the taxonomic origin of microbial sequences is one of the first, and frequently only, analysis performed on microbiome samples. Substantial research has focused on the development of methods for taxonomic annotation, often making trade-offs in computational efficiency and classification accuracy. A side-effect of these efforts has been a reexamination of the bacterial taxonomy itself. Taxonomies developed prior to the genomic revolution captured complex relationships between organisms that went beyond uniform taxonomic levels such as species, genus, and family. Driven in part by the need to simplify computational workflows, the bacterial taxonomies used most commonly today have been regularized to fit within a standard seven taxonomic levels. Consequently, modern analyses of microbial communities are relatively coarse-grained. Few methods make classifications below the genus level, impacting our ability to capture biologically relevant signals. Here, we present ATLAS, a novel strategy for taxonomic annotation that uses significant outliers within database search results to group sequences in the database into partitions. These partitions capture the extent of taxonomic ambiguity within the classification of a sample. The ATLAS pipeline can be found on GitHub [https://github.com/shahnidhi/outlier_in_BLAST_hits]. We demonstrate that ATLAS provides similar annotations to phylogenetic placement methods, but with higher computational efficiency. When applied to human microbiome data, ATLAS is able to identify previously characterized taxonomic groupings, such as those in the class Clostridia and the genus Bacillus. Furthermore, the majority of partitions identified by ATLAS are at the subgenus level, replacing higher-level annotations with specific groups of species. These more precise partitions improve our detection power in determining differential abundance in microbiome association studies.
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Affiliation(s)
- Nidhi Shah
- Department of Computer Science, University of Maryland, College Park, College Park, MD, United States.,Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, United States.,University of Maryland Institute for Advanced Computer Studies, College Park, MD, United States
| | - Jacquelyn S Meisel
- Department of Computer Science, University of Maryland, College Park, College Park, MD, United States.,Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, United States.,University of Maryland Institute for Advanced Computer Studies, College Park, MD, United States.,Center for Health-related Informatics and Bioimaging, University of Maryland, College Park, College Park, MD, United States
| | - Mihai Pop
- Department of Computer Science, University of Maryland, College Park, College Park, MD, United States.,Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, United States.,University of Maryland Institute for Advanced Computer Studies, College Park, MD, United States.,Center for Health-related Informatics and Bioimaging, University of Maryland, College Park, College Park, MD, United States
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4
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Sakowski E, Uritskiy G, Cooper R, Gomes M, McLaren MR, Meisel JS, Mickol RL, Mintz CD, Mongodin EF, Pop M, Rahman MA, Sanchez A, Timp W, Vela JD, Wolz CM, Zackular JP, Chopyk J, Commichaux S, Davis M, Dluzen D, Ganesan SM, Haruna M, Nasko D, Regan MJ, Sarria S, Shah N, Stacy B, Taylor D, DiRuggiero J, Preheim SP. Current State of and Future Opportunities for Prediction in Microbiome Research: Report from the Mid-Atlantic Microbiome Meet-up in Baltimore on 9 January 2019. mSystems 2019; 4:e00392-19. [PMID: 31594828 PMCID: PMC6787564 DOI: 10.1128/msystems.00392-19] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Accurate predictions across multiple fields of microbiome research have far-reaching benefits to society, but there are few widely accepted quantitative tools to make accurate predictions about microbial communities and their functions. More discussion is needed about the current state of microbiome analysis and the tools required to overcome the hurdles preventing development and implementation of predictive analyses. We summarize the ideas generated by participants of the Mid-Atlantic Microbiome Meet-up in January 2019. While it was clear from the presentations that most fields have advanced beyond simple associative and descriptive analyses, most fields lack essential elements needed for the development and application of accurate microbiome predictions. Participants stressed the need for standardization, reproducibility, and accessibility of quantitative tools as key to advancing predictions in microbiome analysis. We highlight hurdles that participants identified and propose directions for future efforts that will advance the use of prediction in microbiome research.
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Affiliation(s)
- Eric Sakowski
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Gherman Uritskiy
- Department of Biology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Rachel Cooper
- Molecular and Comparative Pathobiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Maya Gomes
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Michael R McLaren
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, North Carolina, USA
| | - Jacquelyn S Meisel
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | | | - C David Mintz
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Emmanuel F Mongodin
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, Maryland, USA
| | - Mihai Pop
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | | | - Alvaro Sanchez
- Department of Ecology and Evolutionary Biology, Yale University, New Haven Connecticut, USA
| | - Winston Timp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeseth Delgado Vela
- Department of Civil and Environmental Engineering, Howard University, Washington, DC, USA
| | - Carly Muletz Wolz
- Center for Conservation Genomics, Smithsonian National Zoological Park & Conservation Biology Institute, Washington, DC, USA
| | - Joseph P Zackular
- Department of Pathology and Laboratory Medicine, University of Pennsylvania and Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Jessica Chopyk
- School of Public Health, University of Maryland, College Park, Maryland, USA
| | - Seth Commichaux
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | - Meghan Davis
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Douglas Dluzen
- Department of Biology, Morgan State University, Baltimore, Maryland, USA
| | - Sukirth M Ganesan
- National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA
| | - Muyideen Haruna
- Department of Biology, Morgan State University, Baltimore, Maryland, USA
| | - Dan Nasko
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | - Mary J Regan
- University of Maryland School of Nursing, Baltimore, Maryland, USA
| | - Saul Sarria
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | - Nidhi Shah
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | - Brook Stacy
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | - Dylan Taylor
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | | | - Sarah P Preheim
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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5
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Deusenbery CB, Kalan L, Meisel JS, Gardner SE, Grice EA, Spiller KL. Human macrophage response to microbial supernatants from diabetic foot ulcers. Wound Repair Regen 2019; 27:598-608. [DOI: 10.1111/wrr.12752] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Accepted: 06/13/2019] [Indexed: 12/20/2022]
Affiliation(s)
- Carly B. Deusenbery
- School of Biomedical Engineering Science and Health SystemsDrexel University Philadelphia Pennsylvania
| | - Lindsay Kalan
- Department of Microbiology & ImmunologyUniversity of Wisconsin‐Madison Madison Wisconsin
| | - Jacquelyn S. Meisel
- Department of DermatologyUniversity of Pennsylvania, Perelman School of Medicine Philadelphia Pennsylvania
- The Center for Bioinformatics and Computational BiologyUniversity of Maryland College Park
| | | | - Elizabeth A. Grice
- Department of DermatologyUniversity of Pennsylvania, Perelman School of Medicine Philadelphia Pennsylvania
| | - Kara L. Spiller
- School of Biomedical Engineering Science and Health SystemsDrexel University Philadelphia Pennsylvania
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6
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Kalan LR, Meisel JS, Loesche MA, Horwinski J, Soaita I, Chen X, Uberoi A, Gardner SE, Grice EA. Strain- and Species-Level Variation in the Microbiome of Diabetic Wounds Is Associated with Clinical Outcomes and Therapeutic Efficacy. Cell Host Microbe 2019; 25:641-655.e5. [PMID: 31006638 DOI: 10.1016/j.chom.2019.03.006] [Citation(s) in RCA: 153] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 11/27/2018] [Accepted: 03/08/2019] [Indexed: 12/12/2022]
Abstract
Chronic wounds are a major complication of diabetes associated with high morbidity and health care expenditures. To investigate the role of colonizing microbiota in diabetic wound healing, clinical outcomes, and response to interventions, we conducted a longitudinal, prospective study of patients with neuropathic diabetic foot ulcers (DFU). Metagenomic shotgun sequencing revealed that strain-level variation of Staphylococcus aureus and genetic signatures of biofilm formation were associated with poor outcomes. Cultured wound isolates of S. aureus elicited differential phenotypes in mouse models that corresponded with patient outcomes, while wound "bystanders" such as Corynebacterium striatum and Alcaligenes faecalis, typically considered commensals or contaminants, also significantly impacted wound severity and healing. Antibiotic resistance genes were widespread, and debridement, rather than antibiotic treatment, significantly shifted the DFU microbiota in patients with more favorable outcomes. These findings suggest that the DFU microbiota may be a marker for clinical outcomes and response to therapeutic interventions.
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Affiliation(s)
- Lindsay R Kalan
- University of Pennsylvania, Perelman School of Medicine, Department of Dermatology, Philadelphia, PA 19014, USA; University of Wisconsin, Department of Medical Microbiology and Immunology, School of Medicine and Public Health, Madison, WI, USA
| | - Jacquelyn S Meisel
- University of Pennsylvania, Perelman School of Medicine, Department of Dermatology, Philadelphia, PA 19014, USA; University of Maryland College Park, Center for Bioinformatics and Computational Biology, College Park, MD, USA
| | - Michael A Loesche
- University of Pennsylvania, Perelman School of Medicine, Department of Dermatology, Philadelphia, PA 19014, USA
| | - Joseph Horwinski
- University of Pennsylvania, Perelman School of Medicine, Department of Dermatology, Philadelphia, PA 19014, USA
| | - Ioana Soaita
- University of Pennsylvania, Perelman School of Medicine, Department of Dermatology, Philadelphia, PA 19014, USA
| | - Xiaoxuan Chen
- University of Pennsylvania, Perelman School of Medicine, Department of Dermatology, Philadelphia, PA 19014, USA
| | - Aayushi Uberoi
- University of Pennsylvania, Perelman School of Medicine, Department of Dermatology, Philadelphia, PA 19014, USA
| | - Sue E Gardner
- University of Iowa, College of Nursing, Iowa City, IA 52242, USA
| | - Elizabeth A Grice
- University of Pennsylvania, Perelman School of Medicine, Department of Dermatology, Philadelphia, PA 19014, USA.
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7
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Meisel JS, Nasko DJ, Brubach B, Cepeda-Espinoza V, Chopyk J, Corrada-Bravo H, Fedarko M, Ghurye J, Javkar K, Olson ND, Shah N, Allard SM, Bazinet AL, Bergman NH, Brown A, Caporaso JG, Conlan S, DiRuggiero J, Forry SP, Hasan NA, Kralj J, Luethy PM, Milton DK, Ondov BD, Preheim S, Ratnayake S, Rogers SM, Rosovitz MJ, Sakowski EG, Schliebs NO, Sommer DD, Ternus KL, Uritskiy G, Zhang SX, Pop M, Treangen TJ. Current progress and future opportunities in applications of bioinformatics for biodefense and pathogen detection: report from the Winter Mid-Atlantic Microbiome Meet-up, College Park, MD, January 10, 2018. Microbiome 2018; 6:197. [PMID: 30396371 PMCID: PMC6219074 DOI: 10.1186/s40168-018-0582-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 10/18/2018] [Indexed: 06/08/2023]
Abstract
The Mid-Atlantic Microbiome Meet-up (M3) organization brings together academic, government, and industry groups to share ideas and develop best practices for microbiome research. In January of 2018, M3 held its fourth meeting, which focused on recent advances in biodefense, specifically those relating to infectious disease, and the use of metagenomic methods for pathogen detection. Presentations highlighted the utility of next-generation sequencing technologies for identifying and tracking microbial community members across space and time. However, they also stressed the current limitations of genomic approaches for biodefense, including insufficient sensitivity to detect low-abundance pathogens and the inability to quantify viable organisms. Participants discussed ways in which the community can improve software usability and shared new computational tools for metagenomic processing, assembly, annotation, and visualization. Looking to the future, they identified the need for better bioinformatics toolkits for longitudinal analyses, improved sample processing approaches for characterizing viruses and fungi, and more consistent maintenance of database resources. Finally, they addressed the necessity of improving data standards to incentivize data sharing. Here, we summarize the presentations and discussions from the meeting, identifying the areas where microbiome analyses have improved our ability to detect and manage biological threats and infectious disease, as well as gaps of knowledge in the field that require future funding and focus.
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Affiliation(s)
- Jacquelyn S Meisel
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Daniel J Nasko
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Brian Brubach
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Victoria Cepeda-Espinoza
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Jessica Chopyk
- School of Public Health, University of Maryland, College Park, College Park, MD, USA
| | - Héctor Corrada-Bravo
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Marcus Fedarko
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Jay Ghurye
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Kiran Javkar
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Nathan D Olson
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Nidhi Shah
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Sarah M Allard
- School of Public Health, University of Maryland, College Park, College Park, MD, USA
| | - Adam L Bazinet
- National Biodefense Analysis and Countermeasures Center, Frederick, MD, USA
| | - Nicholas H Bergman
- National Biodefense Analysis and Countermeasures Center, Frederick, MD, USA
| | - Alexis Brown
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - J Gregory Caporaso
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Sean Conlan
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Samuel P Forry
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Nur A Hasan
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
- CosmosID, Inc., Rockville, MD, USA
| | - Jason Kralj
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Paul M Luethy
- Department of Pathology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Donald K Milton
- Maryland Institute for Applied Environmental Health, School of Public Health, University of Maryland, College Park, College Park, MD, USA
| | - Brian D Ondov
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sarah Preheim
- Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | | | | - M J Rosovitz
- National Biodefense Analysis and Countermeasures Center, Frederick, MD, USA
| | - Eric G Sakowski
- Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | | - Daniel D Sommer
- National Biodefense Analysis and Countermeasures Center, Frederick, MD, USA
| | | | - Gherman Uritskiy
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Sean X Zhang
- Division of Medical Microbiology, Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Mihai Pop
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Todd J Treangen
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA.
- Present address: Department of Computer Science - MS-132, Rice University, P.O. Box 1892, Houston, TX, 77005-1892, USA.
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8
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Zheng Q, Bartow-McKenney C, Meisel JS, Grice EA. HmmUFOtu: An HMM and phylogenetic placement based ultra-fast taxonomic assignment and OTU picking tool for microbiome amplicon sequencing studies. Genome Biol 2018; 19:82. [PMID: 29950165 PMCID: PMC6020470 DOI: 10.1186/s13059-018-1450-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Accepted: 05/09/2018] [Indexed: 02/01/2023] Open
Abstract
Culture-independent analysis of microbial communities frequently relies on amplification and sequencing of the prokaryotic 16S ribosomal RNA gene. Typical analysis pipelines group sequences into operational taxonomic units (OTUs) to infer taxonomic and phylogenetic relationships. Here, we present HmmUFOtu, a novel tool for processing microbiome amplicon sequencing data, which performs rapid per-read phylogenetic placement, followed by phylogenetically informed clustering into OTUs and taxonomy assignment. Compared to standard pipelines, HmmUFOtu more accurately and reliably recapitulates microbial community diversity and composition in simulated and real datasets without relying on heuristics or sacrificing speed or accuracy.
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Affiliation(s)
- Qi Zheng
- Department of Dermatology and Microbiology, Perelman School of Medicine, University of Pennsylvania, 421 Curie Blvd, BRB 1046/7, Philadelphia, PA 19104 USA
| | - Casey Bartow-McKenney
- Genomics and Computational Biology Program, Department of Dermatology, University of Pennsylvania, Philadelphia, USA
| | - Jacquelyn S. Meisel
- Genomics and Computational Biology Program, Department of Dermatology, University of Pennsylvania, Philadelphia, USA
| | - Elizabeth A. Grice
- Department of Dermatology and Microbiology, Perelman School of Medicine, University of Pennsylvania, 421 Curie Blvd, BRB 1046/7, Philadelphia, PA 19104 USA
- Genomics and Computational Biology Program, Department of Dermatology, University of Pennsylvania, Philadelphia, USA
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9
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Meisel JS, Sfyroera G, Bartow-McKenney C, Gimblet C, Bugayev J, Horwinski J, Kim B, Brestoff JR, Tyldsley AS, Zheng Q, Hodkinson BP, Artis D, Grice EA. Commensal microbiota modulate gene expression in the skin. Microbiome 2018; 6:20. [PMID: 29378633 PMCID: PMC5789709 DOI: 10.1186/s40168-018-0404-9] [Citation(s) in RCA: 119] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 01/18/2018] [Indexed: 05/10/2023]
Abstract
BACKGROUND The skin harbors complex communities of resident microorganisms, yet little is known of their physiological roles and the molecular mechanisms that mediate cutaneous host-microbe interactions. Here, we profiled skin transcriptomes of mice reared in the presence and absence of microbiota to elucidate the range of pathways and functions modulated in the skin by the microbiota. RESULTS A total of 2820 genes were differentially regulated in response to microbial colonization and were enriched in gene ontology (GO) terms related to the host-immune response and epidermal differentiation. Innate immune response genes and genes involved in cytokine activity were generally upregulated in response to microbiota and included genes encoding toll-like receptors, antimicrobial peptides, the complement cascade, and genes involved in IL-1 family cytokine signaling and homing of T cells. Our results also reveal a role for the microbiota in modulating epidermal differentiation and development, with differential expression of genes in the epidermal differentiation complex (EDC). Genes with correlated co-expression patterns were enriched in binding sites for the transcription factors Klf4, AP-1, and SP-1, all implicated as regulators of epidermal differentiation. Finally, we identified transcriptional signatures of microbial regulation common to both the skin and the gastrointestinal tract. CONCLUSIONS With this foundational approach, we establish a critical resource for understanding the genome-wide implications of microbially mediated gene expression in the skin and emphasize prospective ways in which the microbiome contributes to skin health and disease.
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Affiliation(s)
- Jacquelyn S Meisel
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, 421 Curie Blvd, 1015 BRB II/III, Philadelphia, PA, 19104, USA
| | - Georgia Sfyroera
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, 421 Curie Blvd, 1015 BRB II/III, Philadelphia, PA, 19104, USA
| | - Casey Bartow-McKenney
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, 421 Curie Blvd, 1015 BRB II/III, Philadelphia, PA, 19104, USA
| | - Ciara Gimblet
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, 421 Curie Blvd, 1015 BRB II/III, Philadelphia, PA, 19104, USA
| | - Julia Bugayev
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, 421 Curie Blvd, 1015 BRB II/III, Philadelphia, PA, 19104, USA
| | - Joseph Horwinski
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, 421 Curie Blvd, 1015 BRB II/III, Philadelphia, PA, 19104, USA
| | - Brian Kim
- Department of Dermatology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Jonathan R Brestoff
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Amanda S Tyldsley
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, 421 Curie Blvd, 1015 BRB II/III, Philadelphia, PA, 19104, USA
| | - Qi Zheng
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, 421 Curie Blvd, 1015 BRB II/III, Philadelphia, PA, 19104, USA
| | - Brendan P Hodkinson
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, 421 Curie Blvd, 1015 BRB II/III, Philadelphia, PA, 19104, USA
| | - David Artis
- Jill Roberts Institute for Research in Inflammatory Bowel Disease, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, Cornell University, New York, NY, 10021, USA
| | - Elizabeth A Grice
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, 421 Curie Blvd, 1015 BRB II/III, Philadelphia, PA, 19104, USA.
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10
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Gimblet C, Meisel JS, Loesche MA, Cole SD, Horwinski J, Novais FO, Misic AM, Bradley CW, Beiting DP, Rankin SC, Carvalho LP, Carvalho EM, Scott P, Grice EA. Cutaneous Leishmaniasis Induces a Transmissible Dysbiotic Skin Microbiota that Promotes Skin Inflammation. Cell Host Microbe 2017; 22:13-24.e4. [PMID: 28669672 DOI: 10.1016/j.chom.2017.06.006] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2017] [Revised: 05/09/2017] [Accepted: 06/08/2017] [Indexed: 12/15/2022]
Abstract
Skin microbiota can impact allergic and autoimmune responses, wound healing, and anti-microbial defense. We investigated the role of skin microbiota in cutaneous leishmaniasis and found that human patients infected with Leishmania braziliensis develop dysbiotic skin microbiota, characterized by increases in the abundance of Staphylococcus and/or Streptococcus. Mice infected with L. major exhibit similar changes depending upon disease severity. Importantly, this dysbiosis is not limited to the lesion site, but is transmissible to normal skin distant from the infection site and to skin from co-housed naive mice. This observation allowed us to test whether a pre-existing dysbiotic skin microbiota influences disease, and we found that challenging dysbiotic naive mice with L. major or testing for contact hypersensitivity results in exacerbated skin inflammatory responses. These findings demonstrate that a dysbiotic skin microbiota is not only a consequence of tissue stress, but also enhances inflammation, which has implications for many inflammatory cutaneous diseases.
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Affiliation(s)
- Ciara Gimblet
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jacquelyn S Meisel
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael A Loesche
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Stephen D Cole
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joseph Horwinski
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fernanda O Novais
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ana M Misic
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Charles W Bradley
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel P Beiting
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shelley C Rankin
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lucas P Carvalho
- Centro de Pesquisas Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador 40296-710, Brazil; Serviço de Imunologia, Complexo Hospitalar Prof. Edgard Santos, Universidade Federal da Bahia, Salvador 40170-115, Brazil; Instituto Nacional de Ciências e Tecnologia-Doenças Tropicais, Salvador 40110-160, Brazil
| | - Edgar M Carvalho
- Centro de Pesquisas Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador 40296-710, Brazil; Serviço de Imunologia, Complexo Hospitalar Prof. Edgard Santos, Universidade Federal da Bahia, Salvador 40170-115, Brazil; Instituto Nacional de Ciências e Tecnologia-Doenças Tropicais, Salvador 40110-160, Brazil
| | - Phillip Scott
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Elizabeth A Grice
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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11
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Hannigan GD, Zheng Q, Meisel JS, Minot SS, Bushman FD, Grice EA. Evolutionary and functional implications of hypervariable loci within the skin virome. PeerJ 2017; 5:e2959. [PMID: 28194314 PMCID: PMC5299996 DOI: 10.7717/peerj.2959] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Accepted: 01/05/2017] [Indexed: 01/04/2023] Open
Abstract
Localized genomic variability is crucial for the ongoing conflicts between infectious microbes and their hosts. An understanding of evolutionary and adaptive patterns associated with genomic variability will help guide development of vaccines and antimicrobial agents. While most analyses of the human microbiome have focused on taxonomic classification and gene annotation, we investigated genomic variation of skin-associated viral communities. We evaluated patterns of viral genomic variation across 16 healthy human volunteers. Human papillomavirus (HPV) and Staphylococcus phages contained 106 and 465 regions of diversification, or hypervariable loci, respectively. Propionibacterium phage genomes were minimally divergent and contained no hypervariable loci. Genes containing hypervariable loci were involved in functions including host tropism and immune evasion. HPV and Staphylococcus phage hypervariable loci were associated with purifying selection. Amino acid substitution patterns were virus dependent, as were predictions of their phenotypic effects. We identified diversity generating retroelements as one likely mechanism driving hypervariability. We validated these findings in an independently collected skin metagenomic sequence dataset, suggesting that these features of skin virome genomic variability are widespread. Our results highlight the genomic variation landscape of the skin virome and provide a foundation for better understanding community viral evolution and the functional implications of genomic diversification of skin viruses.
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Affiliation(s)
- Geoffrey D Hannigan
- Department of Dermatology, University of Pennsylvania , Philadelphia, PA , USA
| | - Qi Zheng
- Department of Dermatology, University of Pennsylvania , Philadelphia, PA , USA
| | - Jacquelyn S Meisel
- Department of Dermatology, University of Pennsylvania , Philadelphia, PA , USA
| | | | - Frederick D Bushman
- Department of Microbiology, University of Pennsylvania , Philadelphia, PA , USA
| | - Elizabeth A Grice
- Department of Dermatology, University of Pennsylvania, Philadelphia, PA, USA; Department of Microbiology, University of Pennsylvania, Philadelphia, PA, USA
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12
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Meisel JS, Hannigan GD, Tyldsley AS, SanMiguel AJ, Hodkinson BP, Zheng Q, Grice EA. Skin Microbiome Surveys Are Strongly Influenced by Experimental Design. J Invest Dermatol 2016; 136:947-956. [PMID: 26829039 DOI: 10.1016/j.jid.2016.01.016] [Citation(s) in RCA: 211] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Revised: 12/21/2015] [Accepted: 01/11/2016] [Indexed: 12/30/2022]
Abstract
Culture-independent studies to characterize skin microbiota are increasingly common, due in part to affordable and accessible sequencing and analysis platforms. Compared to culture-based techniques, DNA sequencing of the bacterial 16S ribosomal RNA (rRNA) gene or whole metagenome shotgun (WMS) sequencing provides more precise microbial community characterizations. Most widely used protocols were developed to characterize microbiota of other habitats (i.e., gastrointestinal) and have not been systematically compared for their utility in skin microbiome surveys. Here we establish a resource for the cutaneous research community to guide experimental design in characterizing skin microbiota. We compare two widely sequenced regions of the 16S rRNA gene to WMS sequencing for recapitulating skin microbiome community composition, diversity, and genetic functional enrichment. We show that WMS sequencing most accurately recapitulates microbial communities, but sequencing of hypervariable regions 1-3 of the 16S rRNA gene provides highly similar results. Sequencing of hypervariable region 4 poorly captures skin commensal microbiota, especially Propionibacterium. WMS sequencing, which is resource and cost intensive, provides evidence of a community's functional potential; however, metagenome predictions based on 16S rRNA sequence tags closely approximate WMS genetic functional profiles. This study highlights the importance of experimental design for downstream results in skin microbiome surveys.
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Affiliation(s)
- Jacquelyn S Meisel
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Geoffrey D Hannigan
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Amanda S Tyldsley
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Adam J SanMiguel
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Brendan P Hodkinson
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Qi Zheng
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Elizabeth A Grice
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
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13
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Hannigan GD, Meisel JS, Tyldsley AS, Zheng Q, Hodkinson BP, SanMiguel AJ, Minot S, Bushman FD, Grice EA. The human skin double-stranded DNA virome: topographical and temporal diversity, genetic enrichment, and dynamic associations with the host microbiome. mBio 2015; 6:e01578-15. [PMID: 26489866 PMCID: PMC4620475 DOI: 10.1128/mbio.01578-15] [Citation(s) in RCA: 153] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Accepted: 09/23/2015] [Indexed: 12/18/2022] Open
Abstract
UNLABELLED Viruses make up a major component of the human microbiota but are poorly understood in the skin, our primary barrier to the external environment. Viral communities have the potential to modulate states of cutaneous health and disease. Bacteriophages are known to influence the structure and function of microbial communities through predation and genetic exchange. Human viruses are associated with skin cancers and a multitude of cutaneous manifestations. Despite these important roles, little is known regarding the human skin virome and its interactions with the host microbiome. Here we evaluated the human cutaneous double-stranded DNA virome by metagenomic sequencing of DNA from purified virus-like particles (VLPs). In parallel, we employed metagenomic sequencing of the total skin microbiome to assess covariation and infer interactions with the virome. Samples were collected from 16 subjects at eight body sites over 1 month. In addition to the microenviroment, which is known to partition the bacterial and fungal microbiota, natural skin occlusion was strongly associated with skin virome community composition. Viral contigs were enriched for genes indicative of a temperate phage replication style and also maintained genes encoding potential antibiotic resistance and virulence factors. CRISPR spacers identified in the bacterial DNA sequences provided a record of phage predation and suggest a mechanism to explain spatial partitioning of skin phage communities. Finally, we modeled the structure of bacterial and phage communities together to reveal a complex microbial environment with a Corynebacterium hub. These results reveal the previously underappreciated diversity, encoded functions, and viral-microbial dynamic unique to the human skin virome. IMPORTANCE To date, most cutaneous microbiome studies have focused on bacterial and fungal communities. Skin viral communities and their relationships with their hosts remain poorly understood despite their potential to modulate states of cutaneous health and disease. Previous studies employing whole-metagenome sequencing without purification for virus-like particles (VLPs) have provided some insight into the viral component of the skin microbiome but have not completely characterized these communities or analyzed interactions with the host microbiome. Here we present an optimized virus purification technique and corresponding analysis tools for gaining novel insights into the skin virome, including viral "dark matter," and its potential interactions with the host microbiome. The work presented here establishes a baseline of the healthy human skin virome and is a necessary foundation for future studies examining viral perturbations in skin health and disease.
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Affiliation(s)
- Geoffrey D Hannigan
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Jacquelyn S Meisel
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Amanda S Tyldsley
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Qi Zheng
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Brendan P Hodkinson
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Adam J SanMiguel
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Samuel Minot
- Department of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Frederic D Bushman
- Department of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Elizabeth A Grice
- Department of Dermatology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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