Evidence map›Paper›PMID 33482907›Full record

ArticleMicrobiome2021

A mixed community of skin microbiome representatives influences cutaneous processes more than individual members.

Kristin H Loomis, Susan K Wu, Amanda Ernlund, Kristina Zudock, Allison Reno, Kianna Blount, David K Karig

Abstract readVideo-Audio Media
In one paragraph

Article in Microbiome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

41 citing papers in PubMed.

  1. Article
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  7. Skin bacteriota ameliorates androgenetic alopecia via harmonizing skin immuno inflammatory balance.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2026
    Article
  8. Review
  9. Review
  10. Review
  11. Article
  12. Engineered Tissue Models to Decode Host-Microbiota Interactions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  13. Article
  14. Review
  15. Article
  16. Plasma Treatment - Results of Skin Microbiome Analysis.Clinical, cosmetic and investigational dermatology · 2025
    Article
  17. Article
  18. Article
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kristin H LoomisResearch and Exploratory Development, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Susan K WuResearch and Exploratory Development, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Amanda ErnlundResearch and Exploratory Development, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Kristina ZudockResearch and Exploratory Development, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Allison RenoDepartment of Bioengineering, Clemson University, Clemson, SC, USA.
Kianna BlountResearch and Exploratory Development, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
David K KarigResearch and Exploratory Development, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA. dkarig@clemson.edu.ORCID 0000-0002-9508-6411

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSkin, the largest organ of the human body by weight, hosts a diversity of microorganisms that can influence health. The microbial residents of the skin are now appreciated for their roles in host immune interactions, wound healing, colonization resistance, and various skin disorders. Still, much remains to be discovered in terms of the host pathways influenced by skin microorganisms, as well as the higher-level skin properties impacted through these microbe-host interactions. Towards this direction, recent efforts using mouse models point to pronounced changes in the transcriptional profiles of the skin in response to the presence of a microbial community. However, there is a need to quantify the roles of microorganisms at both the individual and community-level in healthy human skin. In this study, we utilize human skin equivalents to study the effects of individual taxa and a microbial community in a precisely controlled context. Through transcriptomics analysis, we identify key genes and pathways influenced by skin microbes, and we also characterize higher-level impacts on skin processes and properties through histological analyses.

resultsThe presence of a microbiome on a 3D skin tissue model led to significantly altered patterns of gene expression, influencing genes involved in the regulation of apoptosis, proliferation, and the extracellular matrix (among others). Moreover, microbiome treatment influenced the thickness of the epidermal layer, reduced the number of actively proliferating cells, and increased filaggrin expression. Many of these findings were evident upon treatment with the mixed community, but either not detected or less pronounced in treatments by single microorganisms, underscoring the impact that a diverse skin microbiome has on the host.

conclusionsThis work contributes to the understanding of how microbiome constituents individually and collectively influence human skin processes and properties. The results show that, while it is important to understand the effect of individual microbes on the host, a full community of microbes has unique and pronounced effects on the skin. Thus, in its impacts on the host, the skin microbiome is more than the sum of its parts. Video abstract.

Indexed as

Host Microbial InteractionsMicrobiotaSkin Physiological PhenomenaFilaggrin ProteinsGene Expression ProfilingHealthy VolunteersHumansSkinFilaggrin ProteinsCutaneous transcriptomeHost-microbiome interactionHuman microbiomeHuman skin equivalentsRNA sequencingSkin microbiomeSkin proliferation

Identifiers

PMID33482907
PMCPMC7825201

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.