CoChat
Sign Up
Cited by 179Systemic Sclerosis and Related DiseasesDupuytren's Contracture and TreatmentsConnective tissue disorders research

Global skin gene expression analysis of early diffuse cutaneous systemic sclerosis shows a prominent innate and adaptive inflammatory profile

Open access
Skaug, Brian, Khanna, Dinesh, Swindell, William R, Hinchcliff, Monique E, Frech, Tracy M, Steen, Virginia D, Hant, Faye N, Gordon, Jessica K +20 more·2020·Annals of the Rheumatic Diseases
DOI 10.1136/annrheumdis-2019-215894PMC PMC7386329
Open this paper in CoChat — free account

Abstract

Objectives Determine global skin transcriptome patterns of early diffuse systemic sclerosis (SSc) and how they differ from later disease. Methods Skin biopsy RNA from 48 patients in the Prospective Registry for Early Systemic Sclerosis (PRESS) cohort (mean disease duration 1.3 years) and 33 matched healthy controls was examined by next-generation RNA sequencing. Data were analysed for cell type-specific signatures and compared with similarly obtained data from 55 previously biopsied patients in Genetics versus Environment in Scleroderma Outcomes Study cohort with longer disease duration (mean 7.4 years) and their matched controls. Correlations with histological features and clinical course were also evaluated. Results SSc patients in PRESS had a high prevalence of M2 (96%) and M1 (94%) macrophage and CD8 T cell (65%), CD4 T cell (60%) and B cell (69%) signatures. Immunohistochemical staining of immune cell markers correlated with the gene expression-based immune cell signatures. The prevalence of immune cell signatures in early diffuse SSc patients was higher than in patients with longer disease duration. In the multivariable model, adaptive immune cell signatures were significantly associated with shorter disease duration, while fibroblast and macrophage cell type signatures were associated with higher modified Rodnan Skin Score (mRSS). Immune cell signatures also correlated with skin thickness progression rate prior to biopsy, but did not predict subsequent mRSS progression. Conclusions Skin in early diffuse SSc has prominent innate and adaptive immune cell signatures. As a prominently affected end organ, these signatures reflect the preceding rate of disease progression. These findings could have implications in understanding SSc pathogenesis and clinical trial design.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1136/annrheumdis-2019-215894

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In human skin transcriptome analysis, FPKM values of DEGS1 and DEGS2 were 186 and 32 (17% of DEGS1), respectively.

    “In transcriptome analysis of human skin, the fragments per kilobase of transcript per million mapped reads (FPKM) values, which indicate relative gene expression levels, of DEGS1 and DEGS2 were 186 and 32 (17% of DEGS1), respectively (51).”

    From Comparative profiling and comprehensive quantification of stratum corneum ceramides in humans and mice by LC/MS/MS (2020) · cites this paper as [51]

    The Methods sections on skin biopsy and assignment to intrinsic subsets describe skin RNA sequencing and the use of FPKM values. Neither those passages nor the Results passage on transcript expression gives FPKM values for DEGS1 or DEGS2; searches for the gene names and the reported numbers found no matching passage in our copy. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 32

  1. [1]Denton CP, Khanna D. Systemic sclerosis. Lancet 2017; 390: 1685– 99. 28413064
  2. [2]Varga J, Trojanowska M, Kuwana M. Pathogenesis of systemic sclerosis: recent insights of molecular and cellular mechanisms and therapeutic opportunities. J Scleroderma Relat Disord 2017; 2: 137– 52.
  3. [3]Assassi S, Mayes MD. What does global gene expression profiling tell us about the pathogenesis of systemic sclerosis? Curr Opin Rheumatol 2013; 25: 686– 91. 24061076 PMC3929183
  4. [4]Martyanov V, Whitfield ML. Molecular stratification and precision medicine in systemic sclerosis from genomic and proteomic data. Curr Opin Rheumatol 2016; 28: 83– 8. 26555452 PMC4722537
  5. [5]Whitfield ML, Finlay DR, Murray Ji, Systemic and cell type-specific gene expression patterns in scleroderma skin. Proc Natl Acad Sci U S A 2003; 100: 12319– 24. 14530402 PMC218756
  6. [6]Gardner H, Shearstone JR, Bandaru R, Gene profiling of scleroderma skin reveals robust signatures of disease that are imperfectly reflected in the transcript profiles of explanted fibroblasts. Arthritis Rheum 2006; 54: 1961– 73. 16736506
  7. [7]Milano A, Pendergrass SA, Sargent JL, Molecular subsets in the gene expression signatures of scleroderma skin. PLoS One 2008; 3: e2696. 18648520 PMC2481301
  8. [8]Pendergrass SA, Lemaire R, Francis iP, intrinsic gene expression subsets of diffuse cutaneous systemic sclerosis are stable in serial skin biopsies. J Invest Dermatol 2012; 132: 1363– 73. 22318389 PMC3326181
  9. [9]Hinchcliff M, Huang C-C, Wood TA, Molecular signatures in skin associated with clinical improvement during mycophenolate treatment in systemic sclerosis. J Invest Dermatol 2013; 133: 1979– 89. 23677167 PMC3714324
  10. [10]Assassi S, Swindell WR, Wu M, Dissecting the heterogeneity of skin gene expression patterns in systemic sclerosis. Arthritis Rheumatol 2015; 67: 3016– 26. 26238292 PMC5394431
  11. [11]Lofgren S, Hinchcliff M, Carns M, Integrated, multicohort analysis of systemic sclerosis identifies robust transcriptional signature of disease severity. JCI Insight 2016; 1: e89073. 28018971 PMC5161207
  12. [12]Moon S-J, Bae JM, Park K-S, Compendium of skin molecular signatures identifies key pathological features associated with fibrosis in systemic sclerosis. Ann Rheum Dis 2019; 78: 817– 25. 30952646
  13. [13]Frech TM, Shanmugam VK, Shah AA, Treatment of early diffuse systemic sclerosis skin disease. Clin Exp Rheumatol 2013; 31: 166– 71. 23910619 PMC3889109
  14. [14]van den Hoogen F, Khanna D, Fransen J, 2013 classification criteria for systemic sclerosis: an American College of rheumatology/European League against rheumatism collaborative initiative. Ann Rheum Dis 2013; 72: 1747– 55. 24092682
  15. [15]Domsic RT, Rodriguez-Reyna T, Lucas M, Skin thickness progression rate: a predictor of mortality and early internal organ involvement in diffuse scleroderma. Ann Rheum Dis 2011; 70: 104– 9. 20679474 PMC3881170
  16. [16]Swindell WR, Johnston A, Voorhees JJ, Dissecting the psoriasis transcriptome: inflammatory- and cytokine-driven gene expression in lesions from 163 patients. BMC Genomics 2013; 14: 527. 23915137 PMC3751090
  17. [17]Franks JM, Cai G, Whitfield ML. Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data. Bioinformatics 2018; 34: 1868– 74. 29360996 PMC5972664
  18. [18]Franks JM, Martyanov V, Cai G, A machine learning classifier for assigning individual patients with systemic sclerosis to intrinsic molecular subsets. Arthritis Rheumatol 2019; 71: 1701– 10. 30920766 PMC6764877
  19. [19]Wood GS, Warner NL, Warnke RA. Anti-Leu-3/T4 antibodies react with cells of monocyte/macrophage and Langerhans lineage. J Immunol 1983; 131: 212– 6. 6408171
  20. [20]Barros MHM, Hauck F, Dreyer JH, Macrophage polarisation: an immunohistochemical approach for identifying M1 and M2 macrophages. PLoS One 2013; 8: e80908. 24260507 PMC3829941
  21. [21]Stifano G, Sornasse T, Rice LM, Skin gene expression is prognostic for the trajectory of skin disease in patients with diffuse cutaneous systemic sclerosis. Arthritis Rheumatol 2018; 70: 912– 9. 29858547 PMC6731768
  22. [22]Fleischmajer R, Perlish JS, Reeves JR. Cellular infiltrates in scleroderma skin. Arthritis Rheum 1977; 20: 975– 84. 861067
  23. [23]Fleischmajer R, Gay S, Meigel WN, Collagen in the cellular and fibrotic stages of scleroderma. Arthritis Rheum 1978; 21: 418– 28. 350229
  24. [24]Roumm AD, Whiteside TL, Medsger TA, Lymphocytes in the skin of patients with progressive systemic sclerosis. quantification, subtyping, and clinical correlations. Arthritis Rheum 1984; 27: 645– 53. 6375682
  25. [25]Ishikawa O, Ishikawa H. Macrophage infiltration in the skin of patients with systemic sclerosis. J Rheumatol 1992; 19: 1202– 6. 1404154
  26. [26]Kraling BM, Maul GG, Jimenez SA. Mononuclear cellular infiltrates in clinically involved skin from patients with systemic sclerosis of recent onset predominantly consist of monocytes/macrophages. Pathobiology 1995; 63: 48– 56. 7546275
  27. [27]Hinchcliff M, Toledo DM, Taroni JN, Mycophenolate mofetil treatment of systemic sclerosis reduces myeloid cell numbers and attenuates the inflammatory gene signature in skin. J Invest Dermatol 2018; 138: 1301– 10. 29391252 PMC6590516
  28. [28]Bosello S, Angelucci C, Lama G, Characterization of inflammatory cell infiltrate of scleroderma skin: B cells and skin score progression. Arthritis Res Ther 2018; 20. PMC5907298 29669578
  29. [29]Brown M, O’Reilly S. The immunopathogenesis of fibrosis in systemic sclerosis. Clin Exp Immunol 2019; 195: 310– 21. 30430560 PMC6378383
  30. [30]Toledo DM, Pioli PA. Macrophages in systemic sclerosis: novel insights and therapeutic implications. Curr Rheumatol Rep 2019; 21: 31. 31123840 PMC7444604
  31. [31]Lafyatis R Transforming growth factor β-at the centre of systemic sclerosis. Nat Rev Rheumatol 2014; 10: 706– 19. 25136781
  32. [32]Kukurba KR, Montgomery SB. RNA sequencing and analysis. Cold Spring Harb Protoc 2015; 2015: 951– 69. 25870306 PMC4863231

Cited by

Sorted by citations

Authors
Skaug, B., Khanna, D., Swindell, W. R., Hinchcliff, M. E., Frech, T. M., Steen, V. D., Hant, F. N., Gordon, J. K., Shah, A. A., Zhu, L., Zheng, W. J., Browning, J. L., Barron, A. M. S., Wu, M., Visvanathan, S., Baum, P., Franks, J. M., Whitfield, M. L., Shanmugam, V. K., Domsic, R. T., Castelino, F. V., Bernstein, E. J., Wareing, N., Lyons, M. A., Ying, J., Charles, J., Mayes, M. D., & Assassi, S.
Affiliations
  • The University of Texas Health Science Center at Houston (US)
  • University of Michigan (US)
  • Ohio University (US)
  • Yale University (US)
  • University of Utah (US)
  • MedStar Georgetown University Hospital (US)
  • Medical University of South Carolina (US)
  • Hospital for Special Surgery (US)
  • Johns Hopkins University (US)
  • Boston University (US)
  • Boehringer Ingelheim (United States) (US)
  • Boehringer Ingelheim (Germany) (DE)
  • Dartmouth College (US)
  • George Washington University (US)
  • University of Pittsburgh (US)
  • Massachusetts General Hospital (US)
  • Royal College of Physicians and Surgeons of Glasgow (GB)
Corresponding author
Skaug, Brian
ORCID
Venue
Annals of the Rheumatic Diseases
Publisher
Elsevier BV
Published
2020-03
Volume
79
Issue
3
Pages
379-386
ISSN
0003-4967 (ISSN-L)
DOI
10.1136/annrheumdis-2019-215894
PMC
PMC7386329
OpenAlex
W2991146640
Cited by
179
References
0
Citations by year
2026: 18 · 2025: 25 · 2024: 34 · 2023: 29 · 2022: 32 · 2021: 27 · 2020: 14
FWCI
13.43
Type
journal article
Language
en
Open access
Licence
Locations
  • Annals of the Rheumatic Diseases (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • HighWire Press Open Archive (submittedVersion)
Funders
  • U.S. Department of Defense (W81XWH-61-1-0296)
  • NIH/NIAMS (K23AR075112, K24 AR063120, P50AR060780-01, RO1AR073284)
  • Scleroderma Research Foundation
  • Arthritis National Research Foundation
  • Scleroderma Foundation (SCORE grant)
  • Cancer Prevention and Research Institute of Texas (RP170668)
  • Dr. Ralph and Marian Falk Medical Research Trust
  • NIH (5T32LM012204-03)
Topics
  • Health Sciences › Medicine › Pathology and Forensic Medicine › Systemic Sclerosis and Related Diseases
  • Health Sciences › Medicine › Rheumatology › Dupuytren's Contracture and Treatments
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Connective tissue disorders research
Keywords
diffuse cutaneous systemic sclerosis, skin transcriptome, skin gene expression, immune cell signatures, macrophages, macrophage polarization, CD8+ T cells, CD4+ T cells, B cells, fibroblasts, disease duration, disease progression, modified Rodnan skin score, skin thickness, innate and adaptive immunity, scleroderma pathogenesis
MeSH terms
Adult, Biopsy, Female, Humans, Immunity, Innate, Male, Middle Aged, Prospective Studies, Registries, Regression Analysis, Severity of Illness Index, Skin, Biomarkers, Multivariate Analysis, Sequence Analysis, RNA, Scleroderma, Diffuse, Adaptive Immunity, High-Throughput Nucleotide Sequencing, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review