Human dermal fibroblast-derived exosomes induce macrophage activation in systemic sclerosis.

OBJECTIVES: Prior work demonstrates that co-cultured macrophages and fibroblasts from patients with SSc engage in reciprocal activation. However, the mechanism by which these cell types communicate and contribute to fibrosis and inflammation in SSc is unknown. METHODS: Fibroblasts were isolated from skin biopsies obtained from 7 SSc patients or 6 healthy age and gender-matched control subjects following written informed consent. Human donor-derived macrophages were cultured with exosomes isolated from control or SSc fibroblasts for an additional 48 h. Macrophages were immunophenotyped using flow cytometry, qRT-PCR and multiplex. For mutual activation studies, exosome-activated macrophages were co-cultured with SSc or healthy fibroblasts using Transwells. RESULTS: Macrophages activated with dermal fibroblast-derived exosomes from SSc patients upregulated surface expression of CD163, CD206, MHC Class II and CD16 and secreted increased levels of IL-6, IL-10, IL-12p40 and TNF compared with macrophages incubated with healthy control fibroblasts (n = 7, P < 0.05). Exosome-stimulated macrophages and SSc fibroblasts engaged in reciprocal activation, as production of collagen and fibronectin was significantly increased in SSc fibroblasts receiving signals from SSc exosome-stimulated macrophages (n = 7, P < 0.05). CONCLUSION: In this work, we demonstrate for the first time that human SSc dermal fibroblasts mediate macrophage activation through exosomes. Our findings suggest that macrophages and fibroblasts engage in cross-talk in SSc skin, resulting in mutual activation, inflammation, and extracellular matrix (ECM) deposition. Collectively, these studies implicate macrophages and fibroblasts as cooperative mediators of fibrosis in SSc and suggest therapeutic targeting of both cell types may provide maximal benefit in ameliorating disease in SSc patients.

A genomic meta-analysis of clinical variables and their association with intrinsic molecular subsets in systemic sclerosis.

OBJECTIVES: Four intrinsic molecular subsets (inflammatory, fibroproliferative, limited, normal-like) have previously been identified in SSc and are characterized by unique gene expression signatures and pathways. The intrinsic subsets have been linked to improvement with specific therapies. Here, we investigated associations between baseline demographics and intrinsic molecular subsets in a meta-analysis of published datasets. METHODS: Publicly available gene expression data from skin biopsies of 311 SSc patients measured by DNA microarray were classified into the intrinsic molecular subsets. RNA-sequencing data from 84 participants from the ASSET trial were used as a validation cohort. Baseline clinical demographics and intrinsic molecular subsets were tested for statistically significant associations. RESULTS: Males were more likely to be classified in the fibroproliferative subset (P = 0.0046). SSc patients who identified as African American/Black were 2.5 times more likely to be classified as fibroproliferative compared with White/Caucasian patients (P = 0.0378). ASSET participants sera positive for anti-RNA pol I and RNA pol III autoantibodies were enriched in the inflammatory subset (P = 5.8 × 10-5, P = 9.3 × 10-5, respectively), while anti-Scl-70 was enriched in the fibroproliferative subset. Mean modified Rodnan Skin Score (mRSS) was statistically higher in the inflammatory and fibroproliferative subsets compared with normal-like (P = 0.0027). The average disease duration for inflammatory subset was less than fibroproliferative and normal-like intrinsic subsets (P = 8.8 × 10-4). CONCLUSIONS: We identified multiple statistically significant differences in baseline demographics between the intrinsic subsets that may represent underlying features of disease pathogenesis (e.g. chronological stages of fibrosis) and have implications for treatments that are more likely to work in certain SSc populations.

Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvement.

OBJECTIVE: We sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc; scleroderma). METHODS: Fifty-eight forearm biopsies were evaluated from 26 individuals with dcSSc in two clinical trials. Histologic/immunophenotypic assessments of global severity, alpha-smooth muscle actin (aSMA), CD34, collagen, inflammatory infiltrate, follicles and thickness were compared with gene expression and clinical data. Support vector machine learning was performed using scleroderma gene expression subset (normal-like, fibroproliferative, inflammatory) as classifiers and histology scores as inputs. Comparison of w-vector mean absolute weights was used to identify histologic features most predictive of gene expression subset. We then tested for differential gene expression according to histologic severity and compared those with clinical improvement (according to the Combined Response Index in Systemic Sclerosis). RESULTS: aSMA was highest and CD34 lowest in samples with highest local Modified Rodnan Skin Score. CD34 and aSMA changed significantly from baseline to 52 weeks in clinical improvers. CD34 and aSMA were the strongest predictors of gene expression subset, with highest CD34 staining in the normal-like subset (p<0.001) and highest aSMA staining in the inflammatory subset (p=0.016). Analysis of gene expression according to CD34 and aSMA binarised scores identified a 47-gene fibroblast polarisation signature that decreases over time only in improvers (vs non-improvers). Pathway analysis of these genes identified gene expression signatures of inflammatory fibroblasts. CONCLUSION: CD34 and aSMA stains describe distinct fibroblast polarisation states, are associated with gene expression subsets and clinical assessments, and may be useful biomarkers of clinical severity and improvement in dcSSc.

SARS-CoV-2 Impairs Dendritic Cells and Regulates DC-SIGN Gene Expression in Tissues.

The current spreading coronavirus SARS-CoV-2 is highly infectious and pathogenic. In this study, we screened the gene expression of three host receptors (ACE2, DC-SIGN and L-SIGN) of SARS coronaviruses and dendritic cells (DCs) status in bulk and single cell transcriptomic datasets of upper airway, lung or blood of COVID-19 patients and healthy controls. In COVID-19 patients, DC-SIGN gene expression was interestingly decreased in lung DCs but increased in blood DCs. Within DCs, conventional DCs (cDCs) were depleted while plasmacytoid DCs (pDCs) were augmented in the lungs of mild COVID-19. In severe cases, we identified augmented types of immature DCs (CD22+ or ANXA1+ DCs) with MHCII downregulation. In this study, our observation indicates that DCs in severe cases stimulate innate immune responses but fail to specifically present SARS-CoV-2. It provides insights into the profound modulation of DC function in severe COVID-19.

BioMethyl: an R package for biological interpretation of DNA methylation data.

MOTIVATION: The accumulation of publicly available DNA methylation datasets has resulted in the need for tools to interpret the specific cellular phenotypes in bulk tissue data. Current approaches use either single differentially methylated CpG sites or differentially methylated regions that map to genes. However, these approaches may introduce biases in downstream analyses of biological interpretation, because of the variability in gene length. There is a lack of approaches to interpret DNA methylation effectively. Therefore, we have developed computational models to provide biological interpretation of relevant gene sets using DNA methylation data in the context of The Cancer Genome Atlas. RESULTS: We illustrate that Biological interpretation of DNA Methylation (BioMethyl) utilizes the complete DNA methylation data for a given cancer type to reflect corresponding gene expression profiles and performs pathway enrichment analyses, providing unique biological insight. Using breast cancer as an example, BioMethyl shows high consistency in the identification of enriched biological pathways from DNA methylation data compared to the results calculated from RNA sequencing data. We find that 12 out of 14 pathways identified by BioMethyl are shared with those by using RNA-seq data, with a Jaccard score 0.8 for estrogen receptor (ER) positive samples. For ER negative samples, three pathways are shared in the two enrichments with a slight lower similarity (Jaccard score = 0.6). Using BioMethyl, we can successfully identify those hidden biological pathways in DNA methylation data when gene expression profile is lacking. AVAILABILITY AND IMPLEMENTATION: BioMethyl R package is freely available in the GitHub repository (https://github.com/yuewangpanda/BioMethyl). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Machine learning predicts stem cell transplant response in severe scleroderma.

OBJECTIVE: The Scleroderma: Cyclophosphamide or Transplantation (SCOT) trial demonstrated clinical benefit of haematopoietic stem cell transplant (HSCT) compared with cyclophosphamide (CYC). We mapped PBC (peripheral blood cell) samples from the SCOT clinical trial to scleroderma intrinsic subsets and tested the hypothesis that they predict long-term response to HSCT. METHODS: We analysed gene expression from PBCs of SCOT participants to identify differential treatment response. PBC gene expression data were generated from 63 SCOT participants at baseline and follow-up timepoints. Participants who completed treatment protocol were stratified by intrinsic gene expression subsets at baseline, evaluated for event-free survival (EFS) and analysed for differentially expressed genes (DEGs). RESULTS: Participants from the fibroproliferative subset on HSCT experienced significant improvement in EFS compared with fibroproliferative participants on CYC (p=0.0091). In contrast, EFS did not significantly differ between CYC and HSCT arms for the participants from the normal-like subset (p=0.77) or the inflammatory subset (p=0.1). At each timepoint, we observed considerably more DEGs in HSCT arm compared with CYC arm with HSCT arm showing significant changes in immune response pathways. CONCLUSIONS: Participants from the fibroproliferative subset showed the most significant long-term benefit from HSCT compared with CYC. This study suggests that intrinsic subset stratification of patients may be used to identify patients with SSc who receive significant benefit from HSCT.

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

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.

Lysyl oxidase enzymes mediate TGF-ß1-induced fibrotic phenotypes in human skin-like tissues.

Cutaneous fibrosis is a common complication seen in mixed connective tissue diseases. It often occurs as a result of TGF-ß-induced deposition of excessive amounts of collagen in the skin. Lysyl oxidases (LOXs), a family of extracellular matrix (ECM)-modifying enzymes responsible for collagen cross-linking, are known to be increased in dermal fibroblasts from patients with fibrotic diseases, denoting a possible role of LOXs in fibrosis. To directly study this, we have developed two bioengineered, in vitro skin-like models: human skin equivalents (hSEs), and self-assembled stromal tissues (SASs) that contain either normal or systemic sclerosis (SSc; scleroderma) patient-derived fibroblasts. These tissues provide an organ-level structure that could be combined with non-invasive, label-free, multiphoton microscopy (SHG/TPEF) to reveal alterations in the organization and cross-linking levels of collagen fibers during the development of cutaneous fibrosis, which demonstrated increased stromal rigidity and activation of dermal fibroblasts in response to TGF-ß1. Specifically, inhibition of specific LOXs isoforms, LOX and LOXL4, in foreskin fibroblasts (HFFs) resulted in antagonistic effects on TGF-ß1-induced fibrogenic hallmarks in both hSEs and SASs. In addition, a translational relevance of these models was seen as similar antifibrogenic phenotypes were achieved upon knocking down LOXL4 in tissues containing SSc patient-derived-dermal fibroblasts (SScDFs). These findings point to a pivotal role of LOXs in TGF-ß1-induced cutaneous fibrosis through impaired ECM homeostasis in skin-like tissues, and show the value of these tissue platforms in accelerating the discovery of antifibrosis therapeutics.

Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data.

Motivation: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different gene expression profiling platforms present a unique problem when analyzing data generated from different studies. Currently, there is a lack of effective methods designed to eliminate platform-based bias. We present a method to normalize and classify RNA-seq data using machine learning classifiers trained on DNA microarray data and molecular subtypes in two datasets: breast invasive carcinoma (BRCA) and colorectal cancer (CRC). Results: Multiple analyses show that feature specific quantile normalization (FSQN) successfully removes platform-based bias from RNA-seq data, regardless of feature scaling or machine learning algorithm. We achieve up to 98% accuracy for BRCA data and 97% accuracy for CRC data in assigning molecular subtypes to RNA-seq data normalized using FSQN and a support vector machine trained exclusively on DNA microarray data. We find that maximum accuracy was achieved when normalizing RNA-seq datasets that contain at least 25 samples. FSQN allows comparison of RNA-seq data to existing DNA microarray datasets. Using these techniques, we can successfully leverage information from existing gene expression data in new analyses despite different platforms used for gene expression profiling. Availability and implementation: FSQN has been submitted as an R package to CRAN. All code used for this study is available on Github (https://github.com/jenniferfranks/FSQN). Contact: michael.l.whitfield@dartmouth.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

A subset of replication-dependent histone mRNAs are expressed as polyadenylated RNAs in terminally differentiated tissues.

Histone proteins are synthesized in large amounts during S-phase to package the newly replicated DNA, and are among the most stable proteins in the cell. The replication-dependent (RD)-histone mRNAs expressed during S-phase end in a conserved stem-loop rather than a polyA tail. In addition, there are replication-independent (RI)-histone genes that encode histone variants as polyadenylated mRNAs. Most variants have specific functions in chromatin, but H3.3 also serves as a replacement histone for damaged histones in long-lived terminally differentiated cells. There are no reported replacement histone genes for histones H2A, H2B or H4. We report that a subset of RD-histone genes are expressed in terminally differentiated tissues as polyadenylated mRNAs, likely serving as replacement histone genes in long-lived non-dividing cells. Expression of two genes, HIST2H2AA3 and HIST1H2BC, is conserved in mammals. They are expressed as polyadenylated mRNAs in fibroblasts differentiated in vitro, but not in serum starved fibroblasts, suggesting that their expression is part of the terminal differentiation program. There are two histone H4 genes and an H3 gene that encode mRNAs that are polyadenylated and expressed at 5- to 10-fold lower levels than the mRNAs from H2A and H2B genes, which may be replacement genes for the H3.1 and H4 proteins.

A multiprotein occupancy map of the mRNP on the 3' end of histone mRNAs.

The animal replication-dependent (RD) histone mRNAs are coordinately regulated with chromosome replication. The RD-histone mRNAs are the only known cellular mRNAs that are not polyadenylated. Instead, the mature transcripts end in a conserved stem-loop (SL) structure. This SL structure interacts with the stem-loop binding protein (SLBP), which is involved in all aspects of RD-histone mRNA metabolism. We used several genomic methods, including high-throughput sequencing of cross-linked immunoprecipitate (HITS-CLIP) to analyze the RNA-binding landscape of SLBP. SLBP was not bound to any RNAs other than histone mRNAs. We performed bioinformatic analyses of the HITS-CLIP data that included (i) clustering genes by sequencing read coverage using CVCA, (ii) mapping the bound RNA fragment termini, and (iii) mapping cross-linking induced mutation sites (CIMS) using CLIP-PyL software. These analyses allowed us to identify specific sites of molecular contact between SLBP and its RD-histone mRNA ligands. We performed in vitro crosslinking assays to refine the CIMS mapping and found that uracils one and three in the loop of the histone mRNA SL preferentially crosslink to SLBP, whereas uracil two in the loop preferentially crosslinks to a separate component, likely the 3'hExo. We also performed a secondary analysis of an iCLIP data set to map UPF1 occupancy across the RD-histone mRNAs and found that UPF1 is bound adjacent to the SLBP-binding site. Multiple proteins likely bind the 3' end of RD-histone mRNAs together with SLBP.

Molecular stratification and precision medicine in systemic sclerosis from genomic and proteomic data.

PURPOSE OF REVIEW: The goal of this review is to summarize recent advances into the pathogenesis and treatment of systemic sclerosis (SSc) from genomic and proteomic studies. RECENT FINDINGS: Intrinsic gene expression-driven molecular subtypes of SSc are reproducible across three independent datasets. These subsets are a consistent feature of SSc and are found in multiple end-target tissues, such as skin and esophagus. Intrinsic subsets as well as baseline levels of molecular target pathways are potentially predictive of clinical response to specific therapeutics, based on three recent clinical trials. A gene expression-based biomarker of modified Rodnan skin score, a measure of SSc skin severity, can be used as a surrogate outcome metric and has been validated in a recent trial. Proteome analyses have identified novel biomarkers of SSc that correlate with SSc clinical phenotypes. SUMMARY: Integrating intrinsic gene expression subset data, baseline molecular pathway information, and serum biomarkers along with surrogate measures of modified Rodnan skin score provides molecular context in SSc clinical trials. With validation, these approaches could be used to match patients with the therapies from which they are most likely to benefit and thus increase the likelihood of clinical improvement.

Downregulation of miR-193b in systemic sclerosis regulates the proliferative vasculopathy by urokinase-type plasminogen activator expression.

OBJECTIVES: To investigate the role of microRNA-193b-3p (miR-193b) in the vascular pathophysiology of systemic sclerosis (SSc). METHODS: Expression of miR-193b in skin biopsies and fibroblasts from patients with SSc and normal healthy (NH) controls were determined by real-time PCR. Transfection with miR-193b precursor and inhibitor were used to confirm targets of miR-193b. Proliferative effects of urokinase-type plasminogen activator (uPA) were determined by water-soluble tetrazolium salt-1 assay and by analysis of proliferating cell nuclear antigen expression. Fluorescence activated cell sorting analysis was performed to investigate the effect of uPA on apoptosis. For inhibition of the uPA-cellular receptor for uPA (uPAR) pathway, uPAR neutralising antibodies and low molecular weight uPA were used. RESULTS: We found that miR-193b was downregulated in SSc fibroblasts and skin sections as compared with NH controls. The expression of miR-193b was not affected by major profibrotic cytokines and hypoxia. Induction of miR-193b in SSc fibroblasts suppressed, and accordingly, knockdown of miR-193b increased the levels of messenger RNA and protein for uPA. uPA was found to be upregulated in SSc as compared with NH controls in a transforming growth factor-ß dependent manner, and uPA was strongly expressed in vascular smooth muscle cells in SSc skin section. Interestingly, uPA induced cell proliferation and inhibited apoptosis of human pulmonary artery smooth muscle cells, and these effects were independent of uPAR signalling. CONCLUSIONS: In SSc, the downregulation of miR-193b induces the expression of uPA, which increases the number of vascular smooth muscle cells in an uPAR-independent manner and thereby contributes to the proliferative vasculopathy with intimal hyperplasia characteristic for SSc.

Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms.

Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6-12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected and related to a patients underlying genetic risk.

Common markers of proliferation.

When normal tissue and tumour samples are compared by microarray analysis, the biggest differences most often occur in the expression levels of genes that control cell proliferation. However, this difference is detected whenever mRNA samples that are taken from two cell populations with different proliferation rates are compared. Although the exact genes that comprise this 'proliferation signature' often differ, they are almost always genes that are involved in the fundamental process of cell proliferation. Can the proliferation signature be used to improve our understanding of the cell cycle and cancer pathogenesis, as well as being used as a biomarker for cancer diagnosis and prognosis?

Transcription factor binding profiles reveal cyclic expression of human protein-coding genes and non-coding RNAs.

Cell cycle is a complex and highly supervised process that must proceed with regulatory precision to achieve successful cellular division. Despite the wide application, microarray time course experiments have several limitations in identifying cell cycle genes. We thus propose a computational model to predict human cell cycle genes based on transcription factor (TF) binding and regulatory motif information in their promoters. We utilize ENCODE ChIP-seq data and motif information as predictors to discriminate cell cycle against non-cell cycle genes. Our results show that both the trans- TF features and the cis- motif features are predictive of cell cycle genes, and a combination of the two types of features can further improve prediction accuracy. We apply our model to a complete list of GENCODE promoters to predict novel cell cycle driving promoters for both protein-coding genes and non-coding RNAs such as lincRNAs. We find that a similar percentage of lincRNAs are cell cycle regulated as protein-coding genes, suggesting the importance of non-coding RNAs in cell cycle division. The model we propose here provides not only a practical tool for identifying novel cell cycle genes with high accuracy, but also new insights on cell cycle regulation by TFs and cis-regulatory elements.

Increased expression of endoplasmic reticulum stress and unfolded protein response genes in peripheral blood mononuclear cells from patients with limited cutaneous systemic sclerosis and pulmonary arterial hypertension.

OBJECTIVE: Pulmonary arterial hypertension (PAH), a common complication of limited cutaneous systemic sclerosis (lcSSc), is associated with alterations of markers of inflammation and vascular damage in peripheral blood mononuclear cells (PBMCs). Endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) have been implicated in autoimmune and inflammatory diseases. The goal of this study was to assess whether markers of ER stress and the UPR are present in PBMCs from lcSSc patients with PAH. METHODS: PBMCs were purified from 36 healthy controls, 32 lcSSc patients with PAH, and 34 lcSSc patients without PAH. Gene expression in healthy control PBMCs stimulated with thapsigargin was analyzed by DNA microarray. Genes were validated by quantitative real-time reverse transcription-polymerase chain reaction in PBMCs from healthy controls and lcSSc patients. RESULTS: Several ER stress/UPR genes, including BiP, activating transcription factor 4 (ATF-4), ATF-6, and a spliced form of X-box binding protein 1, were up-regulated in PBMCs from lcSSc patients, with the highest levels in patients with PAH. Thapsigargin up-regulated heat-shock proteins (HSPs) and interferon (IFN)-regulated genes in PBMCs from healthy controls. Selected HSP genes (particularly DnaJB1) and IFN-related genes were also found at significantly elevated levels in PBMCs from lcSSc patients, while IFN regulatory factor 4 expression was significantly decreased. There was a positive correlation between DnaJB1 and severity of PAH (measured by pulmonary artery pressure) (r = 0.56, P < 0.05) and between ER stress markers and interleukin-6 levels (r = 0.53, P < 0.0001) in PBMCs from lcSSc patients. CONCLUSION: This study demonstrates an association between select ER stress/UPR markers and lcSSc with PAH, suggesting that ER stress and the UPR may contribute to the altered function of circulating immune cells in lcSSc.

Single-cell epigenomic dysregulation of Systemic Sclerosis fibroblasts via CREB1/EGR1 axis in self-assembled human skin equivalents.

Systemic sclerosis (SSc) is an autoimmune disease characterized by skin fibrosis, internal organ involvement and vascular dropout. We previously developed and phenotypically characterized an in vitro 3D skin-like tissue model of SSc, and now analyze the transcriptomic (scRNA-seq) and epigenetic (scATAC-seq) characteristics of this model at single-cell resolution. SSc 3D skin-like tissues were fabricated using autologous fibroblasts, macrophages, and plasma from SSc patients or healthy control (HC) donors. SSc tissues displayed increased dermal thickness and contractility, as well as increased α-SMA staining. Single-cell transcriptomic and epigenomic analyses identified keratinocytes, macrophages, and five populations of fibroblasts (labeled FB1 - 5). Notably, FB1 APOE-expressing fibroblasts were 12-fold enriched in SSc tissues and were characterized by high EGR1 motif accessibility. Pseudotime analysis suggests that FB1 fibroblasts differentiate from a TGF-ß1-responsive fibroblast population and ligand-receptor analysis indicates that the FB1 fibroblasts are active in macrophage crosstalk via soluble ligands including FGF2 and APP. These findings provide characterization of the 3D skin-like model at single cell resolution and establish that it recapitulates subsets of fibroblasts and macrophage phenotypes observed in skin biopsies.

Interspecies comparison of human and murine scleroderma reveals IL-13 and CCL2 as disease subset-specific targets.

Development of personalized treatment regimens is hampered by lack of insight into how individual animal models reflect subsets of human disease, and autoimmune and inflammatory conditions have proven resistant to such efforts. Scleroderma is a lethal autoimmune disease characterized by fibrosis, with no effective therapy. Comparative gene expression profiling showed that murine sclerodermatous graft-versus-host disease (sclGVHD) approximates an inflammatory subset of scleroderma estimated at 17% to 36% of patients analyzed with diffuse, 28% with limited, and 100% with localized scleroderma. Both sclGVHD and the inflammatory subset demonstrated IL-13 cytokine pathway activation. Host dermal myeloid cells and graft T cells were identified as sources of IL-13 in the model, and genetic deficiency of either IL-13 or IL-4Rα, an IL-13 signal transducer, protected the host from disease. To identify therapeutic targets, we explored the intersection of genes coordinately up-regulated in sclGVHD, the human inflammatory subset, and IL-13-treated fibroblasts; we identified chemokine CCL2 as a potential target. Treatment with anti-CCL2 antibodies prevented sclGVHD. Last, we showed that IL-13 pathway activation in scleroderma patients correlated with clinical skin scores, a marker of disease severity. Thus, an inflammatory subset of scleroderma is driven by IL-13 and may benefit from IL-13 or CCL2 blockade. This approach serves as a model for personalized translational medicine, in which well-characterized animal models are matched to molecularly stratified patient subsets.

Wnt/ß-catenin signaling is hyperactivated in systemic sclerosis and induces Smad-dependent fibrotic responses in mesenchymal cells.

OBJECTIVE: Fibrosis in human diseases and animal models is associated with aberrant Wnt/ß-catenin pathway activation. The aim of this study was to characterize the regulation, activity, mechanism of action, and significance of Wnt/ß-catenin signaling in the context of systemic sclerosis (SSc). METHODS: The expression of Wnt signaling pathway components in SSc skin biopsy specimens was analyzed. The regulation of profibrotic responses by canonical Wnt/ß-catenin was examined in explanted human mesenchymal cells. Fibrotic responses were studied using proliferation, migration, and gel contraction assays. The cell fate specification of subcutaneous preadipocytes by canonical Wnt signaling was evaluated. RESULTS: Analysis of published genome-wide expression data revealed elevated expression of the Wnt receptor FZD2 and the Wnt target LEF1 and decreased expression of Wnt antagonists DKK2 and WIF1 in skin biopsy specimens from subsets of patients with diffuse cutaneous SSc compared to the other distinct subsets. Immunohistochemical analysis showed increased nuclear ß-catenin expression in these biopsy specimens. In vitro, Wnt-3a induced ß-catenin activation, stimulated fibroblast proliferation and migration, collagen gel contraction, and myofibroblast differentiation, and enhanced profibrotic gene expression. Genetic and pharmacologic approaches were used to demonstrate that these profibrotic responses involved autocrine transforming growth factor ß signaling via Smads. In contrast, in explanted subcutaneous preadipocytes, Wnt-3a repressed adipogenesis and promoted myofibroblast differentiation. CONCLUSION: Canonical Wnt signaling was hyperactivated in SSc skin biopsy specimens. In explanted mesenchymal cells, Wnt-3a stimulated fibrogenic responses while suppressing adipogenesis. Taken together, these results indicate that Wnts have potent profibrotic effects, and that canonical Wnt signaling plays an important role in the pathogenesis of fibrosis and lipoatrophy in SSc.