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Regulator combinations identify systemic sclerosis patients with more severe disease.

ABSTRACT: Systemic sclerosis (SSc) is a heterogeneous autoimmune disorder that results in skin fibrosis, autoantibody production, and internal organ dysfunction. We previously identified 4 "intrinsic" subsets of SSc based upon skin gene expression that are found across organ systems. Gene expression regulators that underlie the SSc-intrinsic subsets, or are associated with clinical covariates, have not been systematically characterized. Here, we present a computational framework to calculate the activity scores of gene expression regulators and identify their associations with SSc clinical outcomes. We found that regulator activity scores can reproduce the intrinsic molecular subsets, with distinct sets of regulators identified for inflammatory, fibroproliferative, limited, and normal-like samples. Regulators most highly correlated with modified Rodnan skin score (MRSS) also varied by intrinsic subset. We identified subgroups of patients with fibroproliferative and inflammatory SSc with more severe pathophenotypes, such as higher MRSS and increased likelihood of interstitial lung disease (ILD). Using an independent cohort, we show that the group with more severe ILD was more likely to show forced vital capacity decline over a period of 36-54 months. Our results demonstrate an association among the activation of regulators, gene expression subsets, and clinical variables that can identify patients with SSc with more severe disease.

SUBMITTER: Wang Y

PROVIDER: S-EPMC7526449 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Regulator combinations identify systemic sclerosis patients with more severe disease.

Wang Yue Y Franks Jennifer M JM Yang Monica M Toledo Diana M DM Wood Tammara A TA Hinchcliff Monique M Whitfield Michael L ML

JCI insight 20200903 17

Systemic sclerosis (SSc) is a heterogeneous autoimmune disorder that results in skin fibrosis, autoantibody production, and internal organ dysfunction. We previously identified 4 "intrinsic" subsets of SSc based upon skin gene expression that are found across organ systems. Gene expression regulators that underlie the SSc-intrinsic subsets, or are associated with clinical covariates, have not been systematically characterized. Here, we present a computational framework to calculate the activity ...[more]

PMID: 32721949

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Project description:Systemic sclerosis is a rare connective tissue disease; and interstitial lung disease (SSc-ILD) is associated with significant morbidity and mortality. There are no clinical, radiologic features, nor biomarkers that identify the specific time when patients are at risk for progression at which the benefits from treatment outweigh the risks. Our study aimed to identify blood protein biomarkers associated with progression of interstitial lung disease in patients with SSc-ILD using an unbiased, high-throughput approach. We classified SSc-ILD as progressive or stable based on change in forced vital capacity over 12 months or less. We profiled serum proteins by quantitative mass spectrometry and analyzed the association between protein levels and progression of SSc-ILD via logistic regression. The proteins associated with at a p value of < 0.1 were queried in the ingenuity pathway analysis (IPA) software to identify interaction networks, signaling, and metabolic pathways. Through principal component analysis, the relationship between the top 10 principal components and progression was evaluated. Unsupervised hierarchical clustering with heatmapping was done to define unique groups. The cohort consisted of 72 patients, 32 with progressive SSc-ILD and 40 with stable disease with similar baseline characteristics. Of a total of 794 proteins, 29 were associated with disease progression. After adjusting for multiple testing, these associations did not remain significant. IPA identified five upstream regulators that targeted proteins associated with progression, as well as a canonical pathway with a higher signal in the progression group. Principal component analysis showed that the ten components with the highest Eigenvalues represented 41% of the variability of the sample. Unsupervised clustering analysis revealed no significant heterogeneity between the subjects. We identified 29 proteins associated with progressive SSc-ILD. While these associations did not remain significant after accounting for multiple testing, some of these proteins are part of pathways relevant to autoimmunity and fibrogenesis. Limitations included a small sample size and a proportion of immunosuppressant use in the cohort, which could have altered the expression of inflammatory and immunologic proteins. Future directions include a targeted evaluation of these proteins in another SSc-ILD cohort or application of this study design to a treatment naïve population.

Project description:BACKGROUND:Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We developed and validated EHR-based algorithms that incorporate billing codes and clinical data to identify SSc patients in the EHR. METHODS:We used a de-identified EHR with over 3 million subjects and identified 1899 potential SSc subjects with at least 1 count of the SSc ICD-9 (710.1) or ICD-10-CM (M34*) codes. We randomly selected 200 as a training set for chart review. A subject was a case if diagnosed with SSc by a rheumatologist, dermatologist, or pulmonologist. We selected the following algorithm components based on clinical knowledge and available data: SSc ICD-9 and ICD-10-CM codes, positive antinuclear antibody (ANA) (titer ??1:80), and a keyword of Raynaud's phenomenon (RP). We performed both rule-based and machine learning techniques for algorithm development. Positive predictive values (PPVs), sensitivities, and F-scores (which account for PPVs and sensitivities) were calculated for the algorithms. RESULTS:PPVs were low for algorithms using only 1 count of the SSc ICD-9 code. As code counts increased, the PPVs increased. PPVs were higher for algorithms using ICD-10-CM codes versus the ICD-9 code. Adding a positive ANA and RP keyword increased the PPVs of algorithms only using ICD billing codes. Algorithms using ??3 or ??4 counts of the SSc ICD-9 or ICD-10-CM codes and ANA positivity had the highest PPV at 100% but a low sensitivity at 50%. The algorithm with the highest F-score of 91% was ??4 counts of the ICD-9 or ICD-10-CM codes with an internally validated PPV of 90%. A machine learning method using random forests yielded an algorithm with a PPV of 84%, sensitivity of 92%, and F-score of 88%. The most important feature was RP keyword. CONCLUSIONS:Algorithms using only ICD-9 codes did not perform well to identify SSc patients. The highest performing algorithms incorporated clinical data with billing codes. EHR-based algorithms can identify SSc patients across a healthcare system, enabling researchers to examine important outcomes.

Project description:Antisense long non-coding RNAs (AS lncRNAs) have increasingly been recognized as important regulators of gene expression and they have been found to play key roles in several diseases. However, very little is known about the role of AS lncRNAs in fibrotic diseases such as systemic sclerosis (SSc). Our recent screening experiments by RNA sequencing showed that ovarian tumor domain containing 6B antisense RNA1 (OTUD6B-AS1) and its sense gene OTUD6B were significantly downregulated in SSc skin biopsies. Therefore, we aimed to identify key regulators of OTUD6B-AS1 and to analyze the functional relevance of OTUD6B-AS1 in SSc. OTUD6B-AS1 and OTUD6B expression in SSc and healthy control (HC) dermal fibroblasts (Fb) after stimulation with transforming growth factor-β (TGFβ), Interleukin (IL)-4, IL-13, and platelet-derived growth factor (PDGF) was analyzed by qPCR. To identify the functional role of OTUD6B-AS1, dermal Fb or human pulmonary artery smooth muscle cells (HPASMC) were transfected with a locked nucleic acid antisense oligonucleotide (ASO) targeting OTUD6B-AS1. Proliferation was measured by BrdU and real-time proliferation assay. Apoptosis was measured by Caspase 3/7 assay and Western blot for cleaved caspase 3. While no difference was recorded at the basal level between HC and SSc dermal Fb, the expression of OTUD6B-AS1 and OTUD6B was significantly downregulated in both SSc and HC dermal Fb after PDGF stimulation in a time-dependent manner. Only mild and inconsistent effects were observed with TGFβ, IL-4, and IL-13. OTUD6B-AS1 knockdown in Fb and HPASMC did not affect extracellular matrix or pro-fibrotic/proinflammatory cytokine production. However, OTUD6B-AS1 knockdown significantly increased Cyclin D1 expression at the mRNA and protein level. Moreover, silencing of OTUD6B-AS1 significantly reduced proliferation and suppressed apoptosis in both dermal Fb and HPASMC. OTUD6B-AS1 knockdown did not affect OTUD6B expression at the mRNA level and protein level. Our data suggest that OTUD6B-AS1 regulates proliferation and apoptosis via cyclin D1 expression in a sense gene independent manner. This is the first report investigating the function of OTUD6B-AS1. Our data shed light on a novel apoptosis resistance mechanism in Fb and vascular smooth muscle cells that might be relevant for pathogenesis of SSc.

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Regulator combinations identify systemic sclerosis patients with more severe disease.

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Regulator combinations identify systemic sclerosis patients with more severe disease.

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