Fig 2 - available via license: Creative Commons Attribution 4.0 International
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Schematic view of data processing and analysis. (A) 9 SSc studies were used in the current meta-analysis. (B) Preprocessed gene expression matrix of each study was projected into pathway space using GSVA algorithm (C) 9 SSc studies were projected into one pathway enrichment table (D) Multiple filters were applied to remove pathways of constant enrichment scores. (E) Consensus clustering procedure was applied and (F) optimal number of subsets were determined. (G) Machine learning procedure was used to determine best pathway modules at maximum accuracy. (H) Genes were extracted from selected top pathways modules that differentiate SSc subtypes and (I) subsequent network analysis was applied to select important gene regulators behind each subset.
Source publication
Pathophysiology of systemic sclerosis (SSc, Scleroderma), an autoimmune rheumatic disease, comprises of mechanisms that drive vasculopathy, inflammation and fibrosis. Understanding of the disease and associated clinical heterogeneity has advanced considerably in the past decade, highlighting the necessity of more specific targeted therapy. While ma...
Context in source publication
Context 1
... steps involved network analysis of each SSc subgroup and identification of latent gene regulators. The entire analytic pipeline, fully illustrated in Fig 2, featured a pathway-centered view of SSc patients' heterogeneity and an objective data driven approach delineating the genomic differences that defined SSc pathogenesis. Flow diagram of studies collection and screening process. ...
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Citations
... Multiple other investigators have analyzed bulk skin gene expression for defining patient "subsets" and for investigating disease pathogenesis (4,5). Although algorithms have been developed to deconvolute bulk gene expression, this approach cannot clearly identify the heterogeneous fibroblast, vascular, epithelial, glandular, and inflammatory cell populations in the skin (6,7). In addition, bulk gene expression cannot tell if a gene is increased because a stable cell number is expressing higher levels or the cell population expressing a gene has expanded in number. ...
Objective
Systemic sclerosis (SSc) is a multifactorial autoimmune fibrotic disorder involving complex rewiring of cell‐intrinsic and cell‐extrinsic signaling coexpression networks involving a range of cell types. However, the rewired circuits as well as corresponding cell–cell interactions remain poorly understood. To address this, we used a predictive machine learning framework to analyze single‐cell RNA‐sequencing data from 24 SSc patients across the severity spectrum as quantified by the modified Rodnan skin score (MRSS).
Methods
We used a least absolute shrinkage and selection operator (LASSO)–based predictive machine learning approach on the single‐cell RNA‐sequencing data set to identify predictive biomarkers of SSc severity, both across and within cell types. The use of L1 regularization helps prevent overfitting on high‐dimensional data. Correlation network analyses were coupled to the LASSO model to identify cell‐intrinsic and cell‐extrinsic co‐correlates of the identified biomarkers of SSc severity.
Results
We found that the uncovered cell type–specific predictive biomarkers of MRSS included previously implicated genes in fibroblast and myeloid cell subsets (e.g., SFPR2+ fibroblasts and monocytes), as well as novel gene biomarkers of MRSS, especially in keratinocytes. Correlation network analyses revealed novel cross‐talk between immune pathways and implicated keratinocytes in addition to fibroblast and myeloid cells as key cell types involved in SSc pathogenesis. We then validated the uncovered association of key gene expression and protein markers in keratinocytes, KRT6A and S100A8, with SSc skin disease severity.
Conclusion
Our global systems analyses reveal previously uncharacterized cell‐intrinsic and cell‐extrinsic signaling coexpression networks underlying SSc severity that involve keratinocytes, myeloid cells, and fibroblasts.
image
... Xu and colleagues [26] recently identified 80 pathway signatures that could stratify patients into eight subtypes. They analyzed microarrays from 221 involved skin samples of 141 SSc patients at the time of diagnosis and 80 healthy controls. ...
Background:
Systemic sclerosis (SSc) is a rare connective tissue disease that can affect different organs and has extremely heterogenous presentations. This complexity makes it difficult to perform an early diagnosis and a subsequent subclassification of the disease. This hinders a personalized approach in clinical practice. In this context, machine learning (ML), a branch of artificial intelligence (AI), is able to recognize relationships in data and predict outcomes.
Methods:
Here, we performed a narrative review concerning the application of ML in SSc to define the state of art and evaluate its role in a precision medicine context.
Results:
Currently, ML has been used to stratify SSc patients and identify those at high risk of severe complications. Additionally, ML may be useful in the early detection of organ involvement. Furthermore, ML might have a role in target therapy approach and in predicting drug response.
Conclusion:
Available evidence about the utility of ML in SSc is sparse but promising. Future improvements in this field could result in a big step toward precision medicine. Further research is needed to define ML application in clinical practice.
... Of SSc patients, 30% die within 10 years of diagnosis. One of the main challenges of SSc is the rapid worsening of the disease due to uncontrolled inflammation, collagen deposition, and dysregulation of fibroblast growth [96]. ...
Peroxisome proliferator-activated receptors (PPARs) are ligand-modulated nuclear receptors that play pivotal roles in nutrient sensing, metabolism, and lipid-related processes. Correct control of their target genes requires tight regulation of the expression of different PPAR isoforms in each tissue, and the dysregulation of PPAR-dependent transcriptional programs is linked to disorders, such as metabolic and immune diseases or cancer. Several PPAR regulators and PPAR-regulated factors are epigenetic effectors, including non-coding RNAs, epigenetic enzymes, histone modifiers, and DNA methyltransferases. In this review, we examine advances in PPARα and PPARγ-related epigenetic regulation in metabolic disorders, including obesity and diabetes, immune disorders, such as sclerosis and lupus, and a variety of cancers, providing new insights into the possible therapeutic exploitation of PPAR epigenetic modulation.
Cutaneous fibrosis is one of the main features of systemic sclerosis (SSc). Recent findings correlated abnormal collagen V (Col V) deposition in dermis with skin thickening and disease activity in SSc. Considering that Col V is an important regulator of collagen fibrillogenesis, understanding the role of Col V in the first two years of the skin fibrosis in SSc (early SSc) can help to determine new targets for future treatments. In this study, we analyzed the morphological, ultrastructural and molecular features of α1(V) and α2(V) chains and the expression of their coding genes COL5A1 and COL5A2 in collagen fibrillogenesis in early-SSc. Skin biopsies were obtained from seven consecutive treatment-naïve patients with SSc-related fibrosis and four healthy controls. Our data showed increased α1(V) and α2(V) chain expression in the reticular dermis of early-SSc patients; however, immunofluorescence and ultrastructural immunogold staining determined a significant decreased expression of the α1(V) chain along the dermoepidermal junction in the papillary dermis from early-SSc-patients in relation to the control (12.77 ± 1.34 vs. 66.84 ± 3.36; p < 0.0001). The immunoblot confirmed the decreased expression of the α1(V) chain by the cutaneous fibroblasts of early-SSc, despite the increased COL5A1 and COL5A2 gene expression. In contrast, the α2(V) chain was overexpressed in the small vessels (63.18 ± 3.56 vs. 12.16 ± 0.81; p < 0.0001) and capillaries (60.88 ± 5.82 vs. 15.11 ± 3.80; p < 0.0001) in the reticular dermis of early-SSc patients. Furthermore, COLVA2 siRNA in SSc cutaneous fibroblasts resulted in a decreased α1(V) chain expression. These results highlight an intense decrease in the α1(V) chain along the dermoepidermal junction, suggesting an altered molecular histoarchitecture in the SSc papillary dermis, with a possible decrease in the expression of the α1(V)3 homotrimeric isoform, which could interfere with the thickening and cutaneous fibrosis related to SSc.
With the increasing armamentarium of high-throughput tools available at manageable cost, it is attractive and informative to determine the molecular underpinnings of patient heterogeneity in systemic sclerosis (SSc). Given the highly variable clinical outcomes of patients labelled with the same diagnosis, unravelling the cellular and molecular basis of disease heterogeneity will be crucial to predicting disease risk, stratifying management and ultimately informing a patient-centered precision medicine approach. Herein, we summarise the findings of the past several years in the fields of genomics, transcriptomics, and proteomics that contribute to unraveling the cellular and molecular heterogeneity of SSc. Expansion of these findings and their routine integration with quantitative analysis of histopathology and imaging studies into clinical care promise to inform a scientifically driven patient-centred personalized medicine approach to SSc in the near future.
Purpose of review
The emergence of genomic data science stands poised to revolutionize our molecular understanding of the heterogeneity of complex diseases including systemic autoimmune diseases. In systemic sclerosis (SSc), bulk and single-cell transcriptomics have provided a new lens into the heterogeneity of this complex condition, both in terms of molecular heterogeneity, treatment response, and cell types important for the disease.
Recent findings
Transcriptomics has revealed reproducible patterns of gene expression among SSc patients. These conserved patterns of gene expression provide insights into SSc etiology, and evidence suggests that these groups may have important implications for treatment decisions by targeting specific patients. Integration and analyses of publicly available data are providing new insights into the disease. Single-cell technologies are illuminating cell types that may be important in pathogenesis. The disease trajectory for SSc remains difficult to predict, but the interactions between adaptive and innate immune cells with tissue-resident stromal cells may play an important role.
Summary
The heterogeneity in SSc can be broken down and quantified using molecular methods that range from bulk analysis to single cells. Further study of cellular and molecular dynamics in end-target tissues is likely to result in better disease management through personalized, data-driven treatment decisions.
Background
Systemic sclerosis (scleroderma, SSc) is a systemic autoimmune disease characterized by inflammation, fibrosis and vasculopathy and associated with high mortality and high morbidity ¹ . Stratification based on whole-genome gene expression data could provide a new basis for clinical diagnosis from a micro perspective ² .
Objectives
The objective of this study is to stratify patients with SSc, combine with clinical skin scores and clinical features, and provide a preliminary assessment and novel insights for assessing disease severity, and treatment design.
Methods
The original data mRNA expression profiles of GSE95065 (including 18 SSc patients and 4 healthy controls) and GSE130955 (including 58 SSc patients and 33 healthy controls) were downloaded from the public Gene Expression Omnibus (GEO) database. After batch correction, background adjustment, and other pre-processing, a large gene matrix was obtained to identify the differently expressed genes (DEGs) of SSc compared with healthy controls. Then the gene expression matrix decomposition was used to identify SSc subtypes by NMF algorithm. The cluster-based signature genes were applied to pathway enrichment analysis by Metascape ³ . Immune infiltrating cells and clinical skin scores were evaluated in all SSc subtypes.
Results
Total 325 DEGs were imputed to NMF unsupervised machine learning algorithm. Patients were divided into 2 subtypes (Figure 1A), one of which (sub1) was mostly enriched in the defense response to bacterium and cellular response to lipopolysaccharide pathway and another subtype (sub2) was enriched in the PPAR signaling and alcohol metabolic process pathway (Figure 1B-C). According to immune infiltration, sub1 had higher level of immune cells such as B cells, CD4+T cells, DC cells, Th2 cells and Tregs compared with sub2 (P < 0.01). Sub2 had more skin-related cells, including Epithelial cells, Fibroblasts and Sebocytes (P < 0.05). Interestingly, combined with clinical information, sub1 showed a severe clinical skin score over those of Sub2 patients (P < 0.05)(Figure 1D-E).
Conclusion
Our findings indicated that SSc patients could be stratified into 2 subtypes which had different molecular profiles of disease progression and clinical disease activities. This result could serve as a template for future studies to design stratified approaches for SSc patients.
References
[1]Xu X, Ramanujam M, Visvanathan S, et al. Transcriptional insights into pathogenesis of cutaneous systemic sclerosis using pathway driven meta-analysis assisted by machine learning methods. PLoS One 2020;15(11):e0242863. doi: 10.1371/journal.pone.0242863 [published Online First: 2020/12/01].
[2]Xu C, Meng LB, Duan YC, et al. Screening and identification of biomarkers for systemic sclerosis via microarray technology. Int J Mol Med 2019;44(5):1753-70. doi: 10.3892/ijmm.2019.4332 [published Online First: 2019/09/24].
[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].
Acknowledgements
This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).
Disclosure of Interests
None declared
