Skip to main content
Top
Published in: Arthritis Research & Therapy 1/2019

Open Access 01-12-2019 | Systemic Sclerosis | Research article

Microbiome dysbiosis is associated with disease duration and increased inflammatory gene expression in systemic sclerosis skin

Authors: Michael E. Johnson, Jennifer M. Franks, Guoshuai Cai, Bhaven K. Mehta, Tammara A. Wood, Kimberly Archambault, Patricia A. Pioli, Robert W. Simms, Nicole Orzechowski, Sarah Arron, Michael L. Whitfield

Published in: Arthritis Research & Therapy | Issue 1/2019

Login to get access

Abstract

Background

Infectious agents have long been postulated to be disease triggers for systemic sclerosis (SSc), but a definitive link has not been found. Metagenomic analyses of high-throughput data allows for the unbiased identification of potential microbiome pathogens in skin biopsies of SSc patients and allows insight into the relationship with host gene expression.

Methods

We examined skin biopsies from a diverse cohort of 23 SSc patients (including lesional forearm and non-lesional back samples) by RNA-seq. Metagenomic filtering and annotation was performed using the Integrated Metagenomic Sequencing Analysis (IMSA). Associations between microbiome composition and gene expression were analyzed using single-sample gene set enrichment analysis (ssGSEA).

Results

We find the skin of SSc patients exhibits substantial changes in microbial composition relative to controls, characterized by sharp decreases in lipophilic taxa, such as Propionibacterium, combined with increases in a wide range of gram-negative taxa, including Burkholderia, Citrobacter, and Vibrio.

Conclusions

Microbiome dysbiosis is associated with disease duration and increased inflammatory gene expression. These data provide a comprehensive portrait of the SSc skin microbiome and its association with local gene expression, which mirrors the molecular changes in lesional skin.
Appendix
Available only for authorised users
Literature
1.
go back to reference Milano A, Pendergrass SA, Sargent JL, George LK, McCalmont TH, Connolly MK, Whitfield ML. Molecular subsets in the gene expression signatures of scleroderma skin. PLoS One. 2008;3(7):e2696.CrossRef Milano A, Pendergrass SA, Sargent JL, George LK, McCalmont TH, Connolly MK, Whitfield ML. Molecular subsets in the gene expression signatures of scleroderma skin. PLoS One. 2008;3(7):e2696.CrossRef
2.
go back to reference Pendergrass SA, Lemaire R, Francis IP, Mahoney JM, Lafyatis R, Whitfield ML. Intrinsic gene expression subsets of diffuse cutaneous systemic sclerosis are stable in serial skin biopsies. J Invest Dermatol. 2012;132(5):1363–73.CrossRef Pendergrass SA, Lemaire R, Francis IP, Mahoney JM, Lafyatis R, Whitfield ML. Intrinsic gene expression subsets of diffuse cutaneous systemic sclerosis are stable in serial skin biopsies. J Invest Dermatol. 2012;132(5):1363–73.CrossRef
3.
go back to reference Hinchcliff M, Huang C-C, Wood TA, Mahoney JM, Martyanov V, Bhattacharyya S, Tamaki Z, Lee J, Carns M, Podlusky S. Molecular signatures in skin associated with clinical improvement during mycophenolate treatment in systemic sclerosis. J Invest Dermatol. 2013;133(8):1979–89. Hinchcliff M, Huang C-C, Wood TA, Mahoney JM, Martyanov V, Bhattacharyya S, Tamaki Z, Lee J, Carns M, Podlusky S. Molecular signatures in skin associated with clinical improvement during mycophenolate treatment in systemic sclerosis. J Invest Dermatol. 2013;133(8):1979–89.
4.
go back to reference Johnson M, Mahoney J, Taroni J, Sargent J, Marmarelis E, Wu M, Varga J, Hinchcliff M, Whitfield M. Experimentally-derived fibroblast gene signatures identify molecular pathways associated with distinct subsets of systemic sclerosis patients in three independent cohorts. PLoS One. 2015;10(1):e0114017.CrossRef Johnson M, Mahoney J, Taroni J, Sargent J, Marmarelis E, Wu M, Varga J, Hinchcliff M, Whitfield M. Experimentally-derived fibroblast gene signatures identify molecular pathways associated with distinct subsets of systemic sclerosis patients in three independent cohorts. PLoS One. 2015;10(1):e0114017.CrossRef
5.
go back to reference Mahoney JM, Taroni J, Martyanov V, Wood TA, Greene CS, Pioli PA, Hinchcliff ME, Whitfield ML. Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms. PLoS Comput Biol. 2015;11(1):e1004005.CrossRef Mahoney JM, Taroni J, Martyanov V, Wood TA, Greene CS, Pioli PA, Hinchcliff ME, Whitfield ML. Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms. PLoS Comput Biol. 2015;11(1):e1004005.CrossRef
6.
go back to reference Weyrich LS, Dixit S, Farrer AG, Cooper AJ. The skin microbiome: associations between altered microbial communities and disease. Australas J Dermatol. 2015;56(4):268–74.CrossRef Weyrich LS, Dixit S, Farrer AG, Cooper AJ. The skin microbiome: associations between altered microbial communities and disease. Australas J Dermatol. 2015;56(4):268–74.CrossRef
7.
go back to reference Kong HH, Oh J, Deming C, Conlan S, Grice EA, Beatson MA, Nomicos E, Polley EC, Komarow HD, Murray PR. Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res. 2012;22(5):850–9.CrossRef Kong HH, Oh J, Deming C, Conlan S, Grice EA, Beatson MA, Nomicos E, Polley EC, Komarow HD, Murray PR. Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res. 2012;22(5):850–9.CrossRef
8.
go back to reference Grossman C, Dovrish Z, Shoenfeld Y, Amital H. Do infections facilitate the emergence of systemic sclerosis? Autoimmun Rev. 2011;10(5):244–7.CrossRef Grossman C, Dovrish Z, Shoenfeld Y, Amital H. Do infections facilitate the emergence of systemic sclerosis? Autoimmun Rev. 2011;10(5):244–7.CrossRef
9.
go back to reference Hamamdzic D, Kasman LM, LeRoy EC. The role of infectious agents in the pathogenesis of systemic sclerosis. Curr Opin Rheumatol. 2002;14(6):694–8.CrossRef Hamamdzic D, Kasman LM, LeRoy EC. The role of infectious agents in the pathogenesis of systemic sclerosis. Curr Opin Rheumatol. 2002;14(6):694–8.CrossRef
10.
go back to reference Radic M, Kaliterna DM, Radic J. Helicobacter pylori infection and systemic sclerosis-is there a link? Joint Bone Spine. 2011;78(4):337–40.CrossRef Radic M, Kaliterna DM, Radic J. Helicobacter pylori infection and systemic sclerosis-is there a link? Joint Bone Spine. 2011;78(4):337–40.CrossRef
11.
go back to reference Farina A, Cirone M, York M, Lenna S, Padilla C, McLaughlin S, Faggioni A, Lafyatis R, Trojanowska M, Farina GA. Epstein-Barr virus infection induces aberrant TLR activation pathway and fibroblast-myofibroblast conversion in scleroderma. J Invest Dermatol. 2014;134(4):954–64.CrossRef Farina A, Cirone M, York M, Lenna S, Padilla C, McLaughlin S, Faggioni A, Lafyatis R, Trojanowska M, Farina GA. Epstein-Barr virus infection induces aberrant TLR activation pathway and fibroblast-myofibroblast conversion in scleroderma. J Invest Dermatol. 2014;134(4):954–64.CrossRef
12.
go back to reference Csiki Z, Gal I, Sebesi J, Szegedi G. Raynaud syndrome and eradication of helicobacter pylori. Orv Hetil. 2000;141(52):2827–9.PubMed Csiki Z, Gal I, Sebesi J, Szegedi G. Raynaud syndrome and eradication of helicobacter pylori. Orv Hetil. 2000;141(52):2827–9.PubMed
13.
go back to reference Danese S, Zoli A, Cremonini F, Gasbarrini A. High prevalence of Helicobacter pylori type I virulent strains in patients with systemic sclerosis. J Rheumatol. 2000;27(6):1568–9.PubMed Danese S, Zoli A, Cremonini F, Gasbarrini A. High prevalence of Helicobacter pylori type I virulent strains in patients with systemic sclerosis. J Rheumatol. 2000;27(6):1568–9.PubMed
14.
go back to reference Arron ST, Dimon MT, Li Z, Johnson ME, Wood TA, Feeney L, Angeles JG, Lafyatis R, Whitfield ML. High Rhodotorula sequences in skin transcriptome of patients with diffuse systemic sclerosis. J Invest Dermatol. 2014;134(8):2138–45.CrossRef Arron ST, Dimon MT, Li Z, Johnson ME, Wood TA, Feeney L, Angeles JG, Lafyatis R, Whitfield ML. High Rhodotorula sequences in skin transcriptome of patients with diffuse systemic sclerosis. J Invest Dermatol. 2014;134(8):2138–45.CrossRef
15.
go back to reference Preliminary criteria for the classification of systemic sclerosis (scleroderma). Subcommittee for scleroderma criteria of the American Rheumatism Association Diagnostic and Therapeutic Criteria Committee. Arthritis Rheum. 1980;23(5):581–90. Preliminary criteria for the classification of systemic sclerosis (scleroderma). Subcommittee for scleroderma criteria of the American Rheumatism Association Diagnostic and Therapeutic Criteria Committee. Arthritis Rheum. 1980;23(5):581–90.
16.
go back to reference LeRoy EC, Black C, Fleischmajer R, Jablonska S, Krieg T, Medsger T Jr, Rowell N, Wollheim F. Scleroderma (systemic sclerosis): classification, subsets and pathogenesis. J Rheumatol. 1988;15(2):202.PubMed LeRoy EC, Black C, Fleischmajer R, Jablonska S, Krieg T, Medsger T Jr, Rowell N, Wollheim F. Scleroderma (systemic sclerosis): classification, subsets and pathogenesis. J Rheumatol. 1988;15(2):202.PubMed
17.
go back to reference Mayes MD. Classification and epidemiology of scleroderma. Semin Cutan Med Surg. 1998;1(1):22–6. Mayes MD. Classification and epidemiology of scleroderma. Semin Cutan Med Surg. 1998;1(1):22–6.
18.
go back to reference Gordon JK, Martyanov V, Magro C, Wildman HF, Wood TA, Huang W-T, Crow MK, Whitfield ML, Spiera RF. Nilotinib (Tasigna™) in the treatment of early diffuse systemic sclerosis: an open-label, pilot clinical trial. Arthritis Res Ther. 2015;17(1):1.CrossRef Gordon JK, Martyanov V, Magro C, Wildman HF, Wood TA, Huang W-T, Crow MK, Whitfield ML, Spiera RF. Nilotinib (Tasigna™) in the treatment of early diffuse systemic sclerosis: an open-label, pilot clinical trial. Arthritis Res Ther. 2015;17(1):1.CrossRef
19.
go back to reference Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21.CrossRef Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21.CrossRef
20.
go back to reference Franks JM, Cai G, Whitfield ML. Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data. Bioinformatics. 2018. Franks JM, Cai G, Whitfield ML. Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data. Bioinformatics. 2018.
21.
go back to reference de Hoon MJ, Imoto S, Nolan J, Miyano S. Open source clustering software. Bioinformatics. 2004;20(9):1453–4.CrossRef de Hoon MJ, Imoto S, Nolan J, Miyano S. Open source clustering software. Bioinformatics. 2004;20(9):1453–4.CrossRef
22.
go back to reference Saldanha AJ. Java Treeview—extensible visualization of microarray data. Bioinformatics. 2004;20(17):3246–8.CrossRef Saldanha AJ. Java Treeview—extensible visualization of microarray data. Bioinformatics. 2004;20(17):3246–8.CrossRef
23.
go back to reference Dimon MT, Wood HM, Rabbitts PH, Arron ST. IMSA: integrated metagenomic sequence analysis for identification of exogenous reads in a host genomic background. PLoS One. 2013;8(5):e64546.CrossRef Dimon MT, Wood HM, Rabbitts PH, Arron ST. IMSA: integrated metagenomic sequence analysis for identification of exogenous reads in a host genomic background. PLoS One. 2013;8(5):e64546.CrossRef
24.
go back to reference Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6.CrossRef Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6.CrossRef
25.
go back to reference Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995:289–300. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995:289–300.
26.
go back to reference Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, Schinzel AC, Sandy P, Meylan E, Scholl C. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462(7269):108–12.CrossRef Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, Schinzel AC, Sandy P, Meylan E, Scholl C. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462(7269):108–12.CrossRef
27.
go back to reference Whitfield ML, Finlay DR, Murray JI, Troyanskaya OG, Chi JT, Pergamenschikov A, McCalmont TH, Brown PO, Botstein D, Connolly MK. Systemic and cell type-specific gene expression patterns in scleroderma skin. Proc Natl Acad Sci U S A. 2003;100(21):12319–24.CrossRef Whitfield ML, Finlay DR, Murray JI, Troyanskaya OG, Chi JT, Pergamenschikov A, McCalmont TH, Brown PO, Botstein D, Connolly MK. Systemic and cell type-specific gene expression patterns in scleroderma skin. Proc Natl Acad Sci U S A. 2003;100(21):12319–24.CrossRef
28.
go back to reference Volkmann ER, Chang YL, Barroso N, Furst DE, Clements PJ, Gorn AH, Roth BE, Conklin JL, Getzug T, Borneman J. Association of Systemic Sclerosis With a Unique Colonic Microbial Consortium. Arthritis Rheum. 2016;68(6):1483–92.CrossRef Volkmann ER, Chang YL, Barroso N, Furst DE, Clements PJ, Gorn AH, Roth BE, Conklin JL, Getzug T, Borneman J. Association of Systemic Sclerosis With a Unique Colonic Microbial Consortium. Arthritis Rheum. 2016;68(6):1483–92.CrossRef
29.
go back to reference Grice EA, Kong HH, Conlan S, Deming CB, Davis J, Young AC, Bouffard GG, Blakesley RW, Murray PR, Green ED. Topographical and temporal diversity of the human skin microbiome. Science. 2009;324(5931):1190–2.CrossRef Grice EA, Kong HH, Conlan S, Deming CB, Davis J, Young AC, Bouffard GG, Blakesley RW, Murray PR, Green ED. Topographical and temporal diversity of the human skin microbiome. Science. 2009;324(5931):1190–2.CrossRef
30.
go back to reference Calonje JE, Brenn T, Lazar AJ, McKee PH. Pathology of the skin: Elsevier Health Sciences; 2011. Calonje JE, Brenn T, Lazar AJ, McKee PH. Pathology of the skin: Elsevier Health Sciences; 2011.
31.
go back to reference Tanghetti EA. The role of inflammation in the pathology of acne. J Clin Aesthet Dermatol. 2013;6(9):27–35. Tanghetti EA. The role of inflammation in the pathology of acne. J Clin Aesthet Dermatol. 2013;6(9):27–35.
32.
go back to reference Schmid-Wendtner M-H, Korting HC. The pH of the skin surface and its impact on the barrier function. Skin Pharmacol Physiol. 2006;19(6):296–302.CrossRef Schmid-Wendtner M-H, Korting HC. The pH of the skin surface and its impact on the barrier function. Skin Pharmacol Physiol. 2006;19(6):296–302.CrossRef
Metadata
Title
Microbiome dysbiosis is associated with disease duration and increased inflammatory gene expression in systemic sclerosis skin
Authors
Michael E. Johnson
Jennifer M. Franks
Guoshuai Cai
Bhaven K. Mehta
Tammara A. Wood
Kimberly Archambault
Patricia A. Pioli
Robert W. Simms
Nicole Orzechowski
Sarah Arron
Michael L. Whitfield
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Arthritis Research & Therapy / Issue 1/2019
Electronic ISSN: 1478-6362
DOI
https://doi.org/10.1186/s13075-019-1816-z

Other articles of this Issue 1/2019

Arthritis Research & Therapy 1/2019 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

Highlights from the ACC 2024 Congress

Year in Review: Pediatric cardiology

Watch Dr. Anne Marie Valente present the last year's highlights in pediatric and congenital heart disease in the official ACC.24 Year in Review session.

Year in Review: Pulmonary vascular disease

The last year's highlights in pulmonary vascular disease are presented by Dr. Jane Leopold in this official video from ACC.24.

Year in Review: Valvular heart disease

Watch Prof. William Zoghbi present the last year's highlights in valvular heart disease from the official ACC.24 Year in Review session.

Year in Review: Heart failure and cardiomyopathies

Watch this official video from ACC.24. Dr. Biykem Bozkurt discusses last year's major advances in heart failure and cardiomyopathies.