Transcriptional insights into pathogenesis of cutaneous systemic sclerosis using pathway driven meta-analysis assisted by machine learning methods

Cell Type-Specific Biomarkers of Systemic Sclerosis Disease Severity Capture Cell-Intrinsic and Cell-Extrinsic Circuits

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.

Collagen V α1 Chain Decrease in Papillary Dermis from Early Systemic Sclerosis: A New Proposal in Cutaneous Fibrosis Molecular Structure

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.

The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review

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.

Cellular and Molecular Diversity in Scleroderma

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.

The PPARα and PPARγ Epigenetic Landscape in Cancer and Immune and Metabolic Disorders

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.