Integrated, multicohort analysis of systemic sclerosis identifies robust transcriptional signature of disease severity

Skin Gene Expression Profiles in Systemic Sclerosis: From Clinical Stratification to Precision Medicine

Systemic sclerosis, also known as scleroderma or SSc, is a condition characterized by significant heterogeneity in clinical presentation, disease progression, and response to treatment. Consequently, the design of clinical trials to successfully identify effective therapeutic interventions poses a major challenge. Recent advancements in skin molecular profiling technologies and stratification techniques have enabled the identification of patient subgroups that may be relevant for personalized treatment approaches. This narrative review aims at providing an overview of the current status of skin gene expression analysis using computational biology approaches and highlights the benefits of stratifying patients upon their skin gene signatures. Such stratification has the potential to lead toward a precision medicine approach in the management of SSc.

Characterising the autoantibody repertoire in systemic sclerosis following myeloablative haematopoietic stem cell transplantation

OBJECTIVES

Results from the SCOT (Scleroderma: Cyclophosphamide Or Transplantation) clinical trial demonstrated significant benefits of haematopoietic stem cell transplant (HSCT) versus cyclophosphamide (CTX) in patients with systemic sclerosis. The objective of this study was to test the hypothesis that transplantation stabilises the autoantibody repertoire in patients with favourable clinical outcomes.

METHODS

We used a bead-based array containing 221 protein antigens to profile serum IgG autoantibodies in participants of the SCOT trial.

RESULTS

Comparison of autoantibody profiles at month 26 (n=23 HSCT; n=22 CTX) revealed antibodies against two viral antigens and six self-proteins (SSB/La, CX3CL1, glycyl-tRNA synthetase (EJ), parietal cell antigen, bactericidal permeability-increasing protein and epidermal growth factor receptor (EGFR)) that were significantly different between treatment groups. Linear mixed model analysis identified temporal increases in antibody levels for hepatitis B surface antigen, CCL3 and EGFR in HSCT-treated patients. Eight of 32 HSCT-treated participants and one of 31 CTX-treated participants had temporally varying serum antibody profiles for one or more of 14 antigens. Baseline autoantibody levels against 20 unique antigens, including 9 secreted proteins (interleukins, IL-18, IL-22, IL-23 and IL-27), interferon-α2A, stem cell factor, transforming growth factor-β, macrophage colony-stimulating factor and macrophage migration inhibitory factor were significantly higher in patients who survived event-free to month 54.

CONCLUSIONS

Our results suggest that HSCT favourably alters the autoantibody repertoire, which remains virtually unchanged in CTX-treated patients. Although antibodies recognising secreted proteins are generally thought to be pathogenic, our results suggest a subset could potentially modulate HSCT in scleroderma.

Three Distinct Transcriptional Profiles of Monocytes Associate with Disease Activity in Scleroderma Patients

OBJECTIVE

Patients with diffuse cutaneous systemic sclerosis (dcSSc) display a complex clinical phenotype. Transcriptional profiling of whole blood or tissue from patients are affected by changes in cellular composition that drive gene expression and an inability to detect minority cell populations. We undertook this study to focus on the 2 main subtypes of circulating monocytes, classical monocytes (CMs) and nonclassical monocytes (NCMs) as a biomarker of SSc disease severity.

METHODS

SSc patients were recruited from the Prospective Registry for Early Systemic Sclerosis. Clinical data were collected, as well as peripheral blood for isolation of CMs and NCMs. Age-, sex-, and race-matched healthy volunteers were recruited as controls. Bulk macrophages were isolated from the skin in a separate cohort. All samples were assayed by RNA sequencing (RNA-seq).

RESULTS

We used an unbiased approach to cluster patients into 3 groups (groups A-C) based on the transcriptional signatures of CMs relative to controls. Each group maintained their characteristic transcriptional signature in NCMs. Genes up-regulated in group C demonstrated the highest expression compared to the other groups in SSc skin macrophages, relative to controls. Patients from groups B and C exhibited worse lung function than group A, although there was no difference in SSc skin disease at baseline, relative to controls. We validated our approach by applying our group classifications to published bulk monocyte RNA-seq data from SSc patients, and we found that patients without skin disease were most likely to be classified as group A.

CONCLUSION

We are the first to show that transcriptional signatures of CMs and NCMs can be used to unbiasedly stratify SSc patients and correlate with disease activity outcome measures.

Loss of Protein Kinase D2 Activity Protects Against Bleomycin-Induced Dermal Fibrosis in Mice

Protein kinase D (PKD) has been linked to inflammatory responses in various pathologic conditions; however, its role in inflammation-induced dermal fibrosis has not been evaluated. In this study, we aimed to investigate the roles and mechanisms of protein kinase D2 (PKD2) in inflammation-induced dermal fibrosis and evaluate the therapeutic potential of PKD inhibitors in this disease. Using homozygous kinase-dead PKD2 knock-in (KI) mice, we examined whether genetic ablation or pharmacologic inhibition of PKD2 activity affected dermal inflammation and fibrosis in a bleomycin (BLM)-induced skin fibrosis model. Our data showed that dermal thickness and collagen fibers were significantly reduced in BLM-treated PKD2 KI mice compared with that in wild-type mice, and so was the expression of α-smooth muscle actin and collagens and the mRNA levels of transforming growth factor-β1 and interleukin-6 in the KI mice. Corroboratively, pharmacologic inhibition of PKD by CRT0066101 also significantly blocked BLM-induced dermal fibrosis and reduced α-smooth muscle actin, collagen, and interleukin-6 expression. Further analyses indicated that loss of PKD2 activity significantly blocked BLM-induced infiltration of monocytes/macrophages and neutrophils in the dermis. Moreover, using bone marrow-derived macrophages, we demonstrated that PKD activity was required for cytokine production and migration of macrophages. We have further identified Akt as a major downstream target of PKD2 in the early inflammatory phase of the fibrotic process. Taken together, our findings indicate that PKD2 promotes dermal fibrosis via regulating immune cell infiltration, cytokine production, and downstream activation of Akt in lesional skin, and targeted inhibition of PKD2 may benefit the treatment of this condition.

Polygenic scores for psychiatric disorders in a diverse postmortem brain tissue cohort

A new era of human postmortem tissue research has emerged thanks to the development of 'omics technologies that measure genes, proteins, and spatial parameters in unprecedented detail. Also newly possible is the ability to construct polygenic scores, individual-level metrics of genetic risk (also known as polygenic risk scores/PRS), based on genome-wide association studies, GWAS. Here, we report on clinical, educational, and brain gene expression correlates of polygenic scores in ancestrally diverse samples from the Human Brain Collection Core (HBCC). Genotypes from 1418 donors were subjected to quality control filters, imputed, and used to construct polygenic scores. Polygenic scores for schizophrenia predicted schizophrenia status in donors of European ancestry (p = 4.7 × 10^-8, 17.2%) and in donors with African ancestry (p = 1.6 × 10^-5, 10.4% of phenotypic variance explained). This pattern of higher variance explained among European ancestry samples was also observed for other psychiatric disorders (depression, bipolar disorder, substance use disorders, anxiety disorders) and for height, body mass index, and years of education. For a subset of 223 samples, gene expression from dorsolateral prefrontal cortex (DLPFC) was available through the CommonMind Consortium. In this subgroup, schizophrenia polygenic scores also predicted an aggregate gene expression score for schizophrenia (European ancestry: p = 0.0032, African ancestry: p = 0.15). Overall, polygenic scores performed as expected in ancestrally diverse samples, given historical biases toward use of European ancestry samples and variable predictive power of polygenic scores across phenotypes. The transcriptomic results reported here suggest that inherited schizophrenia genetic risk influences gene expression, even in adulthood. For future research, these and additional polygenic scores are being made available for analyses, and for selecting samples, using postmortem tissue from the Human Brain Collection Core.

Epiregulin is a dendritic cell-derived EGFR ligand that maintains skin and lung fibrosis

Immune cells are fundamental regulators of extracellular matrix (ECM) production by fibroblasts and have important roles in determining extent of fibrosis in response to inflammation. Although much is known about fibroblast signaling in fibrosis, the molecular signals between immune cells and fibroblasts that drive its persistence are poorly understood. We therefore analyzed skin and lung samples of patients with diffuse cutaneous systemic sclerosis, an autoimmune disease that causes debilitating fibrosis of the skin and internal organs. Here, we define a critical role of epiregulin-EGFR signaling between dendritic cells and fibroblasts to maintain elevated ECM production and accumulation in fibrotic tissue. We found that epiregulin expression marks an inducible state of DC3 dendritic cells triggered by type I interferon and that DC3-derived epiregulin activates EGFR on fibroblasts, driving a positive feedback loop through NOTCH signaling. In mouse models of skin and lung fibrosis, epiregulin was essential for persistence of fibrosis in both tissues, which could be abrogated by epiregulin genetic deficiency or a neutralizing antibody. Therapeutic administration of epiregulin antibody reversed fibrosis in patient skin and lung explants, identifying it as a previously unexplored biologic drug target. Our findings reveal epiregulin as a crucial immune signal that maintains skin and lung fibrosis in multiple diseases and represents a promising antifibrotic target.

A roadmap for translational cancer glycoimmunology at single cell resolution

Cancer cells can evade immune responses by exploiting inhibitory immune checkpoints. Immune checkpoint inhibitor (ICI) therapies based on anti-CTLA-4 and anti-PD-1/PD-L1 antibodies have been extensively explored over the recent years to unleash otherwise compromised anti-cancer immune responses. However, it is also well established that immune suppression is a multifactorial process involving an intricate crosstalk between cancer cells and the immune systems. The cancer glycome is emerging as a relevant source of immune checkpoints governing immunosuppressive behaviour in immune cells, paving an avenue for novel immunotherapeutic options. This review addresses the current state-of-the-art concerning the role played by glycans controlling innate and adaptive immune responses, while shedding light on available experimental models for glycoimmunology. We also emphasize the tremendous progress observed in the development of humanized models for immunology, the paramount contribution of advances in high-throughput single-cell analysis in this context, and the importance of including predictive machine learning algorithms in translational research. This may constitute an important roadmap for glycoimmunology, supporting careful adoption of models foreseeing clinical translation of fundamental glycobiology knowledge towards next generation immunotherapies.

Hub genes associated with immune cell infiltration in breast cancer, identified through bioinformatic analyses of multiple datasets

OBJECTIVE

The aim of this study was to identify hub genes associated with immune cell infiltration in breast cancer through bioinformatic analyses of multiple datasets.

METHODS

Nonparametric (NOISeq) and robust rank aggregation-ranked parametric (EdgeR) methods were used to assess robust differentially expressed genes across multiple datasets. Protein-protein interaction network, GO, KEGG enrichment, and sub-network analyses were performed to identify immune-associated hub genes in breast cancer. Immune cell infiltration was evaluated with the CIBERSORT, XCELL, and TIMER methods. The association between the hub gene-based risk signature and survival was determined through Kaplan-Meier survival analysis, multivariate Cox analysis, and a nomogram with external verification.

RESULTS

We identified 163 robust differentially expressed genes in breast cancer through applying both nonparametric and parametric methods to multiple GEO (n = 2,212) and TCGA (n = 1,045) datasets. Integrated bioinformatic analyses further identified 10 hub genes: CXCL10, CXCL9, CXCL11, SPP1, POSTN, MMP9, DPT, COL1A1, ADAMDEC1, and RGS1. The 10 hub-gene-based risk signature significantly correlated with the prognosis of patients with breast cancer. Moreover, these hub genes were strongly associated with the extent of infiltration of CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and myeloid dendritic cells into breast tumors.

CONCLUSIONS

Integrated analyses of multiple databases led to the discovery of 10 robust hub genes that together may serve as a risk factor characteristic of the immune microenvironment in breast cancer.

Tenascin-C in fibrosis in multiple organs: Translational implications

Systemic sclerosis (SSc, scleroderma) is a complex disease with a pathogenic triad of autoimmunity, vasculopathy, and fibrosis involving the skin and multiple internal organs [1]. Because fibrosis accounts for as much as 45% of all deaths worldwide and appears to be increasing in prevalence [2], understanding its pathogenesis and progression is an urgent scientific challenge. Fibroblasts and myofibroblasts are the key effector cells executing physiologic tissue repair on one hand, and pathological fibrogenesis leading to chronic fibrosing conditions on the other. Recent studies identify innate immune signaling via toll-like receptors (TLRs) as a key driver of persistent fibrotic response in SSc. Repeated injury triggers the in-situ generation of "damage-associated molecular patterns" (DAMPs) or danger signals. Sensing of these danger signals by TLR4 on resident cells elicits potent stimulatory effects on fibrotic gene expression and myofibroblast differentiation triggering the self-limited tissue repair response to self-sustained pathological fibrosis characteristic of SSc. Our unbiased survey for DAMPs associated with SSc identified extracellular matrix glycoprotein tenascin-C as one of the most highly up-regulated ECM proteins in SSc skin and lung biopsies [3,4]. Furthermore, tenascin C is responsible for driving sustained fibroblasts activation, thereby progression of fibrosis [3]. This review summarizes recent studies examining the regulation and complex functional role of tenascin C, presenting tenascin-TLR4 axis in pathological fibrosis, and novel anti-fibrotic approaches targeting their signaling.

Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity

Muscle diseases share common pathological features suggesting common underlying mechanisms. We hypothesized there is a common set of genes dysregulated across muscle diseases compared to healthy muscle and that these genes correlate with severity of muscle disease. We performed meta-analysis of transcriptional profiles of muscle biopsies from human muscle diseases and healthy controls. Studies obtained from public microarray repositories fulfilling quality criteria were divided into six categories: (i) immobility, (ii) inflammatory myopathies, (iii) intensive care unit (ICU) acquired weakness (ICUAW), (iv) congenital muscle diseases, (v) chronic systemic diseases, (vi) motor neuron disease. Patient cohorts were separated in discovery and validation cohorts retaining roughly equal proportions of samples for the disease categories. To remove bias towards a specific muscle disease category we repeated the meta-analysis five times by removing data sets corresponding to one muscle disease class at a time in a "leave-one-disease-out" analysis. We used 636 muscle tissue samples from 30 independent cohorts to identify a 52 gene signature (36 up-regulated and 16 down-regulated genes). We validated the discriminatory power of this signature in 657 muscle biopsies from 12 additional patient cohorts encompassing five categories of muscle diseases with an area under the receiver operating characteristic curve of 0.91, 83% sensitivity, and 85.3% specificity. The expression score of the gene signature inversely correlated with quadriceps muscle mass (r = -0.50, p-value = 0.011) in ICUAW and shoulder abduction strength (r = -0.77, p-value = 0.014) in amyotrophic lateral sclerosis (ALS). The signature also positively correlated with histologic assessment of muscle atrophy in ALS (r = 0.88, p-value = 1.62 × 10^-3) and fibrosis in muscular dystrophy (Jonckheere trend test p-value = 4.45 × 10^-9). Our results identify a conserved transcriptional signature associated with clinical and histologic muscle disease severity. Several genes in this conserved signature have not been previously associated with muscle disease severity.

A robust gene expression signature for NASH in liver expression data

Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despite its epidemic proportions, there is no reliable diagnostics that generalizes to global patient population for distinguishing NASH from NAFLD. We performed a comprehensive multicohort analysis of publicly available transcriptome data of liver biopsies from Healthy Controls (HC), NAFLD and NASH patients. Altogether we analyzed 812 samples from 12 different datasets across 7 countries, encompassing real world patient heterogeneity. We used 7 datasets for discovery and 5 datasets were held-out for independent validation. Altogether we identified 130 genes significantly differentially expressed in NASH versus a mixed group of NAFLD and HC. We show that our signature is not driven by one particular group (NAFLD or HC) and reflects true biological signal. Using a forward search we were able to downselect to a parsimonious set of 19 mRNA signature with mean AUROC of 0.98 in discovery and 0.79 in independent validation. Methods for consistent diagnosis of NASH relative to NAFLD are urgently needed. We showed that gene expression data combined with advanced statistical methodology holds the potential to serve basis for development of such diagnostic tests for the unmet clinical need.

Large-scale analysis of longitudinal skin gene expression in systemic sclerosis reveals relationships of immune cell and fibroblast activity with skin thickness and a trend towards normalisation over time

OBJECTIVES

Determine relationships between skin gene expression and systemic sclerosis (SSc) clinical disease features, and changes in skin gene expression over time.

METHODS

A total of 339 forearm skin biopsies were obtained from 113 SSc patients and 44 matched healthy controls. 105 SSc patients had a second biopsy, and 76 had a third biopsy. Global gene expression profiling was performed, and differentially expressed genes and cell type-specific signatures in SSc were evaluated for relationships to modified Rodnan Skin Score (mRSS) and other clinical variables. Changes in skin gene expression over time were analysed by mixed effects models and principal component analysis. Immunohistochemical staining was performed to validate conclusions.

RESULTS

Gene expression dysregulation was greater in SSc patients with affected skin than in those with unaffected skin. Immune cell and fibroblast signatures positively correlated with mRSS. High baseline immune cell and fibroblast signatures predicted higher mRSS over time, but were not independently predictive of longitudinal mRSS after adjustment for baseline mRSS. In early diffuse cutaneous SSc, immune cell and fibroblast signatures declined over time, and overall skin gene expression trended towards normalisation. On immunohistochemical staining, most early diffuse cutaneous SSc patients with high baseline T cell and macrophage numbers had declines in these numbers at follow-up.

CONCLUSIONS

Skin thickness in SSc is related to dysregulated immune cell and fibroblast gene expression. Skin gene expression changes over time in early diffuse SSc, with a tendency towards normalisation. These observations are relevant for understanding SSc pathogenesis and could inform treatment strategies and clinical trial design.

Multiomic study of skin, peripheral blood, and serum: is serum proteome a reflection of disease process at the end-organ level in systemic sclerosis?

BACKGROUND

Serum proteins can be readily assessed during routine clinical care. However, it is unclear to what extent serum proteins reflect the molecular dysregulations of peripheral blood cells (PBCs) or affected end-organs in systemic sclerosis (SSc). We conducted a multiomic comparative analysis of SSc serum profile, PBC, and skin gene expression in concurrently collected samples.

METHODS

Global gene expression profiling was carried out in skin and PBC samples obtained from 49 SSc patients enrolled in the GENISOS observational cohort and 25 unaffected controls. Levels of 911 proteins were determined by Olink Proximity Extension Assay in concurrently collected serum samples.

RESULTS

Both SSc PBC and skin transcriptomes showed a prominent type I interferon signature. The examination of SSc serum profile revealed an upregulation of proteins involved in pro-fibrotic homing and extravasation, as well as extracellular matrix components/modulators. Notably, several soluble receptor proteins such as EGFR, ERBB2, ERBB3, VEGFR2, TGFBR3, and PDGF-Rα were downregulated. Thirty-nine proteins correlated with severity of SSc skin disease. The differential expression of serum protein in SSc vs. control comparison significantly correlated with the differential expression of corresponding transcripts in skin but not in PBCs. Moreover, the differentially expressed serum proteins were significantly more connected to the Well-Associated-Proteins in the skin than PBC gene expression dataset. The assessment of the concordance of between-sample similarities revealed that the molecular profile of serum proteins and skin gene expression data were significantly concordant in patients with SSc but not in healthy controls.

CONCLUSIONS

SSc serum protein profile shows an upregulation of profibrotic cytokines and a downregulation of soluble EGF and other key receptors. Our multilevel comparative analysis indicates that the serum protein profile in SSc correlates more closely with molecular dysregulations of skin than PBCs and might serve as a reflection of disease severity at the end-organ level.

A Multi-mRNA Prognostic Signature for Anti-TNFα Therapy Response in Patients with Inflammatory Bowel Disease

BACKGROUND

Anti-TNF-alpha (anti-TNFα) therapies have transformed the care and management of inflammatory bowel disease (IBD). However, they are expensive and ineffective in greater than 50% of patients, and they increase the risk of infections, liver issues, arthritis, and lymphoma. With 1.6 million Americans suffering from IBD and global prevalence on the rise, there is a critical unmet need in the use of anti-TNFα therapies: a test for the likelihood of therapy response. Here, as a proof-of-concept, we present a multi-mRNA signature for predicting response to anti-TNFα treatment to improve the efficacy and cost-to-benefit ratio of these biologics.

METHODS

We surveyed public data repositories and curated four transcriptomic datasets (n = 136) from colonic and ileal mucosal biopsies of IBD patients (pretreatment) who were subjected to anti-TNFα therapy and subsequently adjudicated for response. We applied a multicohort analysis with a leave-one-study-out (LOSO) approach, MetaIntegrator, to identify significant differentially expressed (DE) genes between responders and non-responders and then used a greedy forward search to identify a parsimonious gene signature. We then calculated an anti-TNFα response (ATR) score based on this parsimonious gene signature to predict responder status and assessed discriminatory performance via an area-under-receiver operating-characteristic curve (AUROC).

RESULTS

We identified 324 significant DE genes between responders and non-responders. The greedy forward search yielded seven genes that robustly distinguish anti-TNFα responders from non-responders, with an AUROC of 0.88 (95% CI: 0.70-1). The Youden index yielded a mean sensitivity of 91%, mean specificity of 76%, and mean accuracy of 86%.

CONCLUSIONS

Our findings suggest that there is a robust transcriptomic signature for predicting anti-TNFα response in mucosal biopsies from IBD patients prior to treatment initiation. This seven-gene signature should be further investigated for its potential to be translated into a predictive test for clinical use.

Current Concepts on the Pathogenesis of Systemic Sclerosis

From the clinical standpoint, systemic sclerosis (SSc) is characterized by skin and internal organ fibrosis, diffuse fibroproliferative vascular modifications, and autoimmunity. Clinical presentation and course are highly heterogenous and life expectancy variably affected mostly dependent on lung and heart involvement. SSc touches more women than men with differences in disease severity and environmental exposure. Pathogenetic events originate from altered homeostasis favored by genetic predisposition, environmental cues and a variety of endogenous and exogenous triggers. Epigenetic modifications modulate SSc pathogenesis which strikingly associate profound immune-inflammatory dysregulation, abnormal endothelial cell behavior, and cell trans-differentiation into myofibroblasts. SSc myofibroblasts show enhanced survival and enhanced extracellular matrix deposition presenting altered structure and altered physicochemical properties. Additional cell types of likely pathogenic importance are pericytes, platelets, and keratinocytes in conjunction with their relationship with vessel wall cells and fibroblasts. In SSc, the profibrotic milieu is favored by cell signaling initiated in the one hand by transforming growth factor-beta and related cytokines and in the other hand by innate and adaptive type 2 immune responses. Radical oxygen species and invariant receptors sensing danger participate to altered cell behavior. Conventional and SSc-specific T cell subsets modulate both fibroblasts as well as endothelial cell dysfunction. Beside autoantibodies directed against ubiquitous antigens important for enhanced clinical classification, antigen-specific agonistic autoantibodies may have a pathogenic role. Recent studies based on single-cell RNAseq and multi-omics approaches are revealing unforeseen heterogeneity in SSc cell differentiation and functional states. Advances in system biology applied to the wealth of data generated by unbiased screening are allowing to subgroup patients based on distinct pathogenic mechanisms. Deciphering heterogeneity in pathogenic mechanisms will pave the way to highly needed personalized therapeutic approaches.

Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy controls. We developed a robust machine learning feature selection pipeline with validation on five independent datasets culminating in 13 genes: TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, HSP90AB1, NCL and CIRBP which define the RA score and demonstrate its clinical utility: the score tracks the disease activity DAS28 (p = 7e-9), distinguishes osteoarthritis (OA) from RA (OR 0.57, p = 8e-10) and polyJIA from healthy controls (OR 1.15, p = 2e-4) and monitors treatment effect in RA (p = 2e-4). Finally, the immunoblotting analysis of six proteins on an independent cohort confirmed two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90.

Large-Scale Characterization of Systemic Sclerosis Serum Protein Profile: Comparison to Peripheral Blood Cell Transcriptome and Correlations With Skin/Lung Fibrosis

OBJECTIVE

To provide a large-scale assessment of serum protein dysregulation in diffuse cutaneous systemic sclerosis (dcSSc) and to investigate serum protein correlates of SSc fibrotic features.

METHODS

We investigated serum protein profiles of 66 participants with dcSSc at baseline who were enrolled in the Scleroderma: Cyclophosphamide or Transplant Trial and 66 age- and sex-matched healthy control subjects. A panel of 230 proteins, including several cytokines and chemokines, was investigated. Whole blood gene expression profiling in concomitantly collected samples was performed.

RESULTS

Among the participants with dcSSc, the mean disease duration was 2.3 years. All had interstitial lung disease (ILD), and none were being treated with immunosuppressive agents at baseline. Ninety proteins were differentially expressed in participants with dcSSc compared to healthy control subjects. Similar to previous global skin transcript results, hepatic fibrosis, granulocyte and agranulocyte adhesion, and diapedesis were the top overrepresented pathways. Eighteen proteins correlated with the modified Rodnan skin thickness score (MRSS). Soluble epidermal growth factor receptor was significantly down-regulated in dcSSc and showed the strongest negative correlation with the MRSS, being predictive of the score's course over time, whereas α₁ -antichymotrypsin was significantly up-regulated in dcSSc and showed the strongest positive correlation with the MRSS. Furthermore, higher levels of cancer antigen 15-3 correlated with more severe ILD, based on findings of reduced forced vital capacity and higher scores of disease activity on high-resolution computed tomography. Only 14 genes showed significant differential expression in the same direction in serum protein and whole blood RNA gene expression analyses.

CONCLUSION

Diffuse cutaneous SSc has a distinct serum protein profile with prominent dysregulation of proteins related to fibrosis and immune cell adhesion/diapedesis. The differential expression for most serum proteins in SSc is likely to originate outside the peripheral blood cells.

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.

Mesenchymal stromal cells for systemic sclerosis treatment

Systemic sclerosis (SSc) is a rare chronic autoimmune disease characterized by vasculopathy, dysregulation of innate and adaptive immune responses, and progressive fibrosis. SSc remains an orphan disease, with high morbity and mortality in SSc patients. The mesenchymal stromal cells (MSC) demonstrate in vitro and in vivo pro-angiogenic, immuno-suppressive, and anti-fibrotic properties and appear as a promising stem cell therapy type, that may target the key pathological features of SSc disease. This review aims to summarize acquired knowledge in the field of :1) MSC definition and in vitro and in vivo functional properties, which vary according to the donor type (allogeneic or autologous), the tissue sources (bone marrow, adipose tissue or umbilical cord) or inflammatory micro-environment in the recipient; 2) preclinical studies in various SSc animal models , which showed reduction in skin and lung fibrosis after MSC infusion; 3) first clinical trials in human, with safety and early efficacy results reported in SSc patients or currently tested in several ongoing clinical trials.

A machine learning-based clinical tool for diagnosing myopathy using multi-cohort microarray expression profiles

BACKGROUND

Myopathies are a heterogenous collection of disorders characterized by dysfunction of skeletal muscle. In practice, myopathies are frequently encountered by physicians and precise diagnosis remains a challenge in primary care. Molecular expression profiles show promise for disease diagnosis in various pathologies. We propose a novel machine learning-based clinical tool for predicting muscle disease subtypes using multi-cohort microarray expression data.

MATERIALS AND METHODS

Muscle tissue samples originating from 1260 patients with muscle weakness. Data was curated from 42 independent cohorts with expression profiles in public microarray gene expression repositories, which represent a broad range of patient ages and peripheral muscles. Cohorts were categorized into five muscle disease subtypes: immobility, inflammatory myopathies, intensive care unit acquired weakness (ICUAW), congenital, and chronic systemic disease. The data contains expression data on 34,099 genes. Data augmentation techniques were used to address class imbalances in the muscle disease subtypes. Support vector machine (SVM) models were trained on two-thirds of the 1260 samples based on the top selected gene signature using analysis of variance (ANOVA). The model was validated in the remaining samples using area under the receiver operator curve (AUC). Gene enrichment analysis was used to identify enriched biological functions in the gene signature.

RESULTS

The AUC ranges from 0.611 to 0.649 in the observed imbalanced data. Overall, using the augmented data, chronic systemic disease was the best predicted class with AUC 0.872 (95% confidence interval (CI): 0.824-0.920). The least discriminated classes were ICUAW with AUC 0.777 (95% CI: 0.668-0.887) and immobility with AUC 0.789 (95% CI: 0.716-0.861). Disease-specific gene set enrichment results showed that the gene signature was enriched in biological processes including neural precursor cell proliferation for ICUAW and aerobic respiration for congenital (false discovery rate q-value < 0.001).

CONCLUSION

Our results present a well-performing molecular classification tool with the selected gene markers for muscle disease classification. In practice, this tool addresses an important gap in the literature on myopathies and presents a potentially useful clinical tool for muscle disease subtype diagnosis.

Genomics of Human Fibrotic Diseases: Disordered Wound Healing Response

Fibrotic disease, which is implicated in almost half of all deaths worldwide, is the result of an uncontrolled wound healing response to injury in which tissue is replaced by deposition of excess extracellular matrix, leading to fibrosis and loss of organ function. A plethora of genome-wide association studies, microarrays, exome sequencing studies, DNA methylation arrays, next-generation sequencing, and profiling of noncoding RNAs have been performed in patient-derived fibrotic tissue, with the shared goal of utilizing genomics to identify the transcriptional networks and biological pathways underlying the development of fibrotic diseases. In this review, we discuss fibrosing disorders of the skin, liver, kidney, lung, and heart, systematically (1) characterizing the initial acute injury that drives unresolved inflammation, (2) identifying genomic studies that have defined the pathologic gene changes leading to excess matrix deposition and fibrogenesis, and (3) summarizing therapies targeting pro-fibrotic genes and networks identified in the genomic studies. Ultimately, successful bench-to-bedside translation of observations from genomic studies will result in the development of novel anti-fibrotic therapeutics that improve functional quality of life for patients and decrease mortality from fibrotic diseases.

Data Heterogeneity: The Enzyme to Catalyze Translational Bioinformatics?

Up to 95% of novel interventions demonstrating significant effects at the bench fail to translate to the bedside. In recent years, the windfalls of “big data” have afforded investigators more substrate for research than ever before. However, issues with translation have persisted: although countless biomarkers for diagnostic and therapeutic targeting have been proposed, few of these generalize effectively. We assert that inadequate heterogeneity in datasets used for discovery and validation causes their nonrepresentativeness of the diversity observed in real-world patient populations. This nonrepresentativeness is contrasted with advantages rendered by the solicitation and utilization of data heterogeneity for multisystemic disease modeling. Accordingly, we propose the potential benefits of models premised on heterogeneity to promote the Institute for Healthcare Improvement’s Triple Aim. In an era of personalized medicine, these models can confer higher quality clinical care for individuals, increased access to effective care across all populations, and lower costs for the health care system.

Big data in systemic sclerosis: Great potential for the future

Since it was first used in 1997, the term “big data” has been popularized; however, the concept of big data is relatively new to medicine. Big data refers to a method and technique to systematically retrieve, collect, manage, and analyze very large and complex sets of structured and unstructured data that cannot be sufficiently processed using traditional methods of processing data. Integrating big data in rare diseases with low prevalence and incidence, like systemic sclerosis is of particular importance. We conducted a literature review of use of big data in systemic sclerosis. The volume of data on systemic sclerosis has grown steadily in the recent years; however, big data methods have not been readily used. This inexhaustible source of data needs to be used more to unleash its full potential.

High-throughput quantitative histology in systemic sclerosis skin disease using computer vision

BACKGROUND

Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time. The most widely used outcome measure in SSc clinical trials is the modified Rodnan skin score (mRSS), which is a semi-quantitative assessment of skin stiffness at seventeen body sites. However, the mRSS is confounded by obesity, edema, and high inter-rater variability. In order to develop a new histopathological outcome measure for SSc, we applied a computer vision technology called a deep neural network (DNN) to stained sections of SSc skin. We tested the hypotheses that DNN analysis could reliably assess mRSS and discriminate SSc from normal skin.

METHODS

We analyzed biopsies from two independent (primary and secondary) cohorts. One investigator performed mRSS assessments and forearm biopsies, and trichrome-stained biopsy sections were photomicrographed. We used the AlexNet DNN to generate a numerical signature of 4096 quantitative image features (QIFs) for 100 randomly selected dermal image patches/biopsy. In the primary cohort, we used principal components analysis (PCA) to summarize the QIFs into a Biopsy Score for comparison with mRSS. In the secondary cohort, using QIF signatures as the input, we fit a logistic regression model to discriminate between SSc vs. control biopsy, and a linear regression model to estimate mRSS, yielding Diagnostic Scores and Fibrosis Scores, respectively. We determined the correlation between Fibrosis Scores and the published Scleroderma Skin Severity Score (4S) and between Fibrosis Scores and longitudinal changes in mRSS on a per patient basis.

RESULTS

In the primary cohort (n = 6, 26 SSc biopsies), Biopsy Scores significantly correlated with mRSS (R = 0.55, p = 0.01). In the secondary cohort (n = 60 SSc and 16 controls, 164 biopsies; divided into 70% training and 30% test sets), the Diagnostic Score was significantly associated with SSc-status (misclassification rate = 1.9% [training], 6.6% [test]), and the Fibrosis Score significantly correlated with mRSS (R = 0.70 [training], 0.55 [test]). The DNN-derived Fibrosis Score significantly correlated with 4S (R = 0.69, p = 3 × 10- 17).

CONCLUSIONS

DNN analysis of SSc biopsies is an unbiased, quantitative, and reproducible outcome that is associated with validated SSc outcomes.

A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections

Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable host-gene-expression-based classifier for acute bacterial and viral infections. We use training data (N = 1069) from 18 retrospective transcriptomic studies. Using only 29 preselected host mRNAs, we train a neural-network classifier with a bacterial-vs-other area under the receiver-operating characteristic curve (AUROC) 0.92 (95% CI 0.90–0.93) and a viral-vs-other AUROC 0.92 (95% CI 0.90–0.93). We then apply this classifier, inflammatix-bacterial-viral-noninfected-version 1 (IMX-BVN-1), without retraining, to an independent cohort (N = 163). In this cohort, IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.86 (95% CI 0.77–0.93), and viral-vs.-other 0.85 (95% CI 0.76–0.93). In patients enrolled within 36 h of hospital admission (N = 70), IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.92 (95% CI 0.83–0.99), and viral-vs.-other 0.91 (95% CI 0.82–0.98). With further study, IMX-BVN-1 could provide a tool for assessing patients with suspected infection and sepsis at hospital admission.

Diagnosing acute infections based on transcriptional host response shows promise, but generalizability is wanting. Here, the authors use a co-normalization framework to train a classifier to diagnose acute infections and apply it to independent data on a targeted diagnostic platform.

Integrated, multicohort analysis reveals unified signature of systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that follows an unpredictable disease course and affects multiple organs and tissues. We performed an integrated, multicohort analysis of 7,471 transcriptomic profiles from 40 independent studies to identify robust gene expression changes associated with SLE. We identified a 93-gene signature (SLE MetaSignature) that is differentially expressed in the blood of patients with SLE compared with healthy volunteers; distinguishes SLE from other autoimmune, inflammatory, and infectious diseases; and persists across diverse tissues and cell types. The SLE MetaSignature correlated significantly with disease activity and other clinical measures of inflammation. We prospectively validated the SLE MetaSignature in an independent cohort of pediatric patients with SLE using a microfluidic quantitative PCR (qPCR) array. We found that 14 of the 93 genes in the SLE MetaSignature were independent of IFN-induced and neutrophil-related transcriptional profiles that have previously been associated with SLE. Pathway analysis revealed dysregulation associated with nucleic acid biosynthesis and immunometabolism in SLE. We further refined a neutropoiesis signature and identified underappreciated transcripts related to immune cells and oxidative stress. In our multicohort, transcriptomic analysis has uncovered underappreciated genes and pathways associated with SLE pathogenesis, with the potential to advance clinical diagnosis, biomarker development, and targeted therapeutics for SLE.

Towards Predicting Progression to Severe Dengue

There is an urgent need for prognostic assays to predict progression to severe dengue infection, which is a major global threat. While the majority of symptomatic dengue patients experience an acute febrile illness, 5-20% progress to severe infection associated with significant morbidity and mortality. Early monitoring and administration of supportive care reduce mortality and clinically usable biomarkers to predict severe dengue are needed. Here, we review recent discoveries of gene sets, anti-dengue antibody properties, and inflammatory markers with potential utility as predictors of disease progression. Upon larger scale validation and development of affordable sample-to-answer technologies, some of these biomarkers may be utilized to develop the first prognostic assay for improving patient care and allocating healthcare resources more effectively in dengue endemic countries.

Systemic sclerosis: Advances towards stratified medicine

Epidemiological studies reporting demographic, clinical and serological factors predictive of various outcomes in systemic sclerosis (SSc) range from the prediction of mortality to the development and progression of disease manifestations. However, predicting the disease trajectory in the individual patient is a challenging but important step towards a stratified approach to disease management. Recent technological advances provide the opportunity for new subgroupings of disease based on risk stratification, through the systematic analysis of high-dimensional clinical data combined with genes, their transcription products and their corresponding translated proteins. In addition, these variables offer a rich vein of research to identify non-invasive biomarkers for predicting organ involvement and to assess disease activity and response to therapy. Selection of patients with a clinical phenotype or molecular signature relevant to the therapy under study combined with recent efforts to standardise outcome measures, show promise for improving clinical trial design and the identification of effective targeted therapies.

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.

Moving towards a molecular taxonomy of autoimmune rheumatic diseases

Autoimmune rheumatic diseases pose many problems that have, in general, already been solved in the field of cancer. The heterogeneity of each disease, the clinical similarities and differences between different autoimmune rheumatic diseases and the large number of patients that remain without a diagnosis underline the need to reclassify these diseases via new approaches. Knowledge about the molecular basis of systemic autoimmune diseases, along with the availability of bioinformatics tools capable of handling and integrating large volumes of various types of molecular data at once, offer the possibility of reclassifying these diseases. A new taxonomy could lead to the discovery of new biomarkers for patient stratification and prognosis. Most importantly, this taxonomy might enable important changes in clinical trial design to reach the expected outcomes or the design of molecularly targeted therapies. In this Review, we discuss the basis for a new molecular taxonomy for autoimmune rheumatic diseases. We highlight the evidence surrounding the idea that these diseases share molecular features related to their pathogenesis and development and discuss previous attempts to classify these diseases. We evaluate the tools available to analyse and combine different types of molecular data. Finally, we introduce PRECISESADS, a project aimed at reclassifying the systemic autoimmune diseases.

Existing and novel biomarkers for precision medicine in systemic sclerosis

The discovery and validation of biomarkers resulting from technological advances in the analysis of genomic, transcriptomic, lipidomic and metabolomic pathways involved in the pathogenesis of complex human diseases have led to the development of personalized and rationally designed approaches for the clinical management of such disorders. Although some of these approaches have been applied to systemic sclerosis (SSc), an unmet need remains for validated, non-invasive biomarkers to aid in the diagnosis of SSc, as well as in the assessment of disease progression and response to therapeutic interventions. Advances in global transcriptomic technology over the past 15 years have enabled the assessment of microRNAs that circulate in the blood of patients and the analysis of the macromolecular content of a diverse group of lipid bilayer membrane-enclosed extracellular vesicles, such as exosomes and other microvesicles, which are released by all cells into the extracellular space and circulation. Such advances have provided new opportunities for the discovery of biomarkers in SSc that could potentially be used to improve the design and evaluation of clinical trials and that will undoubtedly enable the development of personalized and individualized medicine for patients with SSc.

Evolving insights into the cellular and molecular pathogenesis of fibrosis in systemic sclerosis

Systemic sclerosis (SSc, scleroderma) is a complex multisystem disease characterized by autoimmunity, vasculopathy, and most notably, fibrosis. Multiple lines of evidence demonstrate a variety of emerging cellular and molecular pathways which are relevant to fibrosis in SSc. The myofibroblast remains the key effector cell in SSc. Understanding the development, differentiation, and function of the myofibroblast is therefore crucial to understanding the fibrotic phenotype of SSc. Studies now show that (1) multiple cell types give rise to myofibroblasts, (2) fibroblasts and myofibroblasts are heterogeneous, and (3) that a large number of (primarily immune) cells have important influences on the transition of fibroblasts to an activated myofibroblasts. In SSc, this differentiation process involves multiple pathways, including well known signaling cascades such as TGF-β and Wnt/β-Catenin signaling, as well as epigenetic reprogramming and a number of more recently defined cellular pathways. After reviewing the major and emerging cellular and molecular mechanisms underlying SSc, this article looks to identify clinical applications where this new molecular knowledge may allow for targeted treatment and personalized medicine approaches.

Unmet Needs in Systemic Sclerosis Understanding and Treatment: the Knowledge Gaps from a Scientist's, Clinician's, and Patient's Perspective

Systemic sclerosis (SSc) is a highly heterogeneous disease caused by a complex molecular circuitry. For decades, clinical and molecular research focused on understanding the primary process of fibrosis. More recently, the inflammatory, immunological and vascular components that precede the actual onset of fibrosis, have become a matter of increasing scientific scrutiny. As a consequence, the field has started to realize that the early identification of this syndrome is crucial for optimal clinical care as well as for understanding its pathology. The cause of SSc cannot be appointed to a single molecular pathway but to a multitude of molecular aberrances in a spatial and temporal matter and on the backbone of the patient's genetic predisposition. These alterations underlie the plethora of signs and symptoms which patients experience and clinicians look for, ultimately culminating in fibrotic features. To solve this complexity, a close interaction among the patient throughout its "journey," the clinician through its clinical assessments and the researcher with its experimental design, seems to be required. In this review, we aimed to highlight the features of SSc through the eyes of these three professionals, all with their own expertise and opinions. With this unique setup, we underscore the importance of investigating the role of environmental factors in the onset and perpetuation of SSc, of focusing on the earliest signs and symptoms preceding fibrosis and on the application of holistic research approaches that include a multitude of potential molecular alterations in time in an unbiased fashion, in the search for a patient-tailored cure.

A 20-Gene Set Predictive of Progression to Severe Dengue

There is a need to identify biomarkers predictive of severe dengue. Single-cohort transcriptomics has not yielded generalizable results or parsimonious, predictive gene sets. We analyzed blood samples of dengue patients from seven gene expression datasets (446 samples, five countries) using an integrated multi-cohort analysis framework and identified a 20-gene set that predicts progression to severe dengue. We validated the predictive power of this 20-gene set in three retrospective dengue datasets (84 samples, three countries) and a prospective Colombia cohort (34 patients), with an area under the receiver operating characteristic curve of 0.89, 100% sensitivity, and 76% specificity. The 20-gene dengue severity scores declined during the disease course, suggesting an infection-triggered host response. This 20-gene set is strongly associated with the progression to severe dengue and represents a predictive signature, generalizable across ages, host genetic factors, and virus strains, with potential implications for the development of a host response-based dengue prognostic assay.

Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases

In silico quantification of cell proportions from mixed-cell transcriptomics data (deconvolution) requires a reference expression matrix, called basis matrix. We hypothesize that matrices created using only healthy samples from a single microarray platform would introduce biological and technical biases in deconvolution. We show presence of such biases in two existing matrices, IRIS and LM22, irrespective of deconvolution method. Here, we present immunoStates, a basis matrix built using 6160 samples with different disease states across 42 microarray platforms. We find that immunoStates significantly reduces biological and technical biases. Importantly, we find that different methods have virtually no or minimal effect once the basis matrix is chosen. We further show that cellular proportion estimates using immunoStates are consistently more correlated with measured proportions than IRIS and LM22, across all methods. Our results demonstrate the need and importance of incorporating biological and technical heterogeneity in a basis matrix for achieving consistently high accuracy.

Advanced model systems and tools for basic and translational human immunology

There are fundamental differences between humans and the animals we typically use to study the immune system. We have learned much from genetically manipulated and inbred animal models, but instances in which these findings have been successfully translated to human immunity have been rare. Embracing the genetic and environmental diversity of humans can tell us about the fundamental biology of immune cell types and the elasticity of the immune system. Although people are much more immunologically diverse than conventionally housed animal models, tools and technologies are now available that permit high-throughput analysis of human samples, including both blood and tissues, which will give us deep insights into human immunity in health and disease. As we gain a more detailed picture of the human immune system, we can build more sophisticated models to better reflect this complexity, both enabling the discovery of new immunological mechanisms and facilitating translation into the clinic.

Proteomic Analysis of Human Scleroderma Fibroblasts Response to Transforming Growth Factor-ß

PURPOSE

Systemic sclerosis (SSc) is characterized by autoimmunity, vasculopathy and fibrosis. Fibrosis is due to an activation of fibroblasts by the transforming growth factor-ß (TGF-ß). This study investigates the proteomic response of SSc fibroblasts to TGF-ß.

EXPERIMENTAL DESIGN

Skin fibroblasts from diffuse SSc patients and healthy controls (HC) are cultured with or without TGF-ß. Two-dimensional differential in-gel electrophoresis and mass spectrometry (MS) combined with Ingenuity Pathway analysis (IPA) and Panther/David software analyze proteins differentially expressed between groups. Real-time cell analyzer (RTCA) assesses fibroblast proliferation and viability.

RESULTS

Two-hundred-and-seventy-nine proteins are differentially expressed between groups. Principal component analysis shows significant differences between groups. IPA shows specific process networks such as actin cytoskeleton and integrin signaling. Panther and David software show predominant biological processes such as cellular and metabolic processes. TGF-ß enhances protein synthesis and protein pathways. IPA and RTCA suggest the involvement of epidermal growth factor receptor (EGFR) and phosphatidylinositol 3 kinase (Pi3K).

CONCLUSIONS AND CLINICAL RELEVANCE

That the proteome of fibroblasts differs between SSc patients and HC is confirmed, and it is demonstrated that fibroblasts exacerbate their proteomic phenotype upon stimulation with TGF-ß. EGFR and Pi3K are highlighted as proteins of interest in SSc fibroblasts.

Single-cell epigenetics - Chromatin modification atlas unveiled by mass cytometry

Modifications of histone proteins are fundamental to the regulation of epigenetic phenotypes. Dysregulations of histone modifications have been linked to the pathogenesis of diverse human diseases. However, identifying differential histone modifications in patients with immune-mediated diseases has been challenging, in part due to the lack of a powerful analytic platform to study histone modifications in the complex human immune system. We recently developed a highly multiplexed platform, Epigenetic landscape profiling using cytometry by Time-Of-Flight (EpiTOF), to analyze the global levels of a broad array of histone modifications in single cells using mass cytometry. In this review, we summarize the development of EpiTOF and discuss its potential applications in biomedical research. We anticipate that this platform will provide new insights into the roles of epigenetic regulation in hematopoiesis, immune cell functions, and immune system aging, and reveal aberrant epigenetic patterns associated with immune-mediated diseases.

KLRD1-expressing natural killer cells predict influenza susceptibility

BACKGROUND

Influenza infects tens of millions of people every year in the USA. Other than notable risk groups, such as children and the elderly, it is difficult to predict what subpopulations are at higher risk of infection. Viral challenge studies, where healthy human volunteers are inoculated with live influenza virus, provide a unique opportunity to study infection susceptibility. Biomarkers predicting influenza susceptibility would be useful for identifying risk groups and designing vaccines.

METHODS

We applied cell mixture deconvolution to estimate immune cell proportions from whole blood transcriptome data in four independent influenza challenge studies. We compared immune cell proportions in the blood between symptomatic shedders and asymptomatic nonshedders across three discovery cohorts prior to influenza inoculation and tested results in a held-out validation challenge cohort.

RESULTS

Natural killer (NK) cells were significantly lower in symptomatic shedders at baseline in both discovery and validation cohorts. Hematopoietic stem and progenitor cells (HSPCs) were higher in symptomatic shedders at baseline in discovery cohorts. Although the HSPCs were higher in symptomatic shedders in the validation cohort, the increase was statistically nonsignificant. We observed that a gene associated with NK cells, KLRD1, which encodes CD94, was expressed at lower levels in symptomatic shedders at baseline in discovery and validation cohorts. KLRD1 expression in the blood at baseline negatively correlated with influenza infection symptom severity. KLRD1 expression 8 h post-infection in the nasal epithelium from a rhinovirus challenge study also negatively correlated with symptom severity.

CONCLUSIONS

We identified KLRD1-expressing NK cells as a potential biomarker for influenza susceptibility. Expression of KLRD1 was inversely correlated with symptom severity. Our results support a model where an early response by KLRD1-expressing NK cells may control influenza infection.

Thinking BIG rheumatology: how to make functional genomics data work for you

High-throughput sequencing assays have become an increasingly common part of biological research across multiple fields. Even as the resulting sequences pile up in public databases, it is not always obvious how to make use of these data sets. Functional genomics offers approaches to integrate these "big" data into our understanding of rheumatic diseases. This review aims to provide a primer on thinking about big data from functional genomics in the context of rheumatology, using examples from the field's literature as well as the author's own work to illustrate the execution of functional genomics research. Study design is crucial to ensure the right samples are used to address the question of interest. In addition, sequencing assays produce a variety of data types, from gene expression to 3D chromatin structure and single-cell technologies, that can be integrated into a model of the underlying gene regulatory networks. The best approach for this analysis uses the scientific process: bioinformatic methods should be used in an iterative, hypothesis-driven manner to uncover the disease mechanism. Finally, the future of functional genomics will see big data fully integrated into rheumatology, leading to computationally trained researchers and interactive databases. The goal of this review is not to provide a manual, but to enhance the familiarity of readers with functional genomic approaches and provide a better sense of the challenges and possibilities.

Gene annotation bias impedes biomedical research

We found tremendous inequality across gene and protein annotation resources. We observed that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate that researchers reduce these biases by pursuing data-driven hypotheses.

Antisense Long Non-Coding RNAs Are Deregulated in Skin Tissue of Patients with Systemic Sclerosis

Systemic sclerosis is an autoimmune disease characterized by fibrosis of skin and multiple organs of which the pathogenesis is poorly understood. We studied differentially expressed coding and non-coding genes in relation to systemic sclerosis pathogenesis with a specific focus on antisense non-coding RNAs. Skin biopsy-derived RNAs from 14 early systemic sclerosis patients and six healthy individuals were sequenced with ion-torrent and analyzed using DEseq2. Overall, 4,901 genes with a fold change >1.5 and a false discovery rate <5% were detected in patients versus controls. Upregulated genes clustered in immunologic, cell adhesion, and keratin-related processes. Interestingly, 676 deregulated non-coding genes were detected, 257 of which were classified as antisense genes. Sense genes expressed opposite of these antisense genes were also deregulated in 42% of the observed sense-antisense gene pairs. The majority of the antisense genes had a similar effect sizes in an independent North American dataset with three genes (CTBP1-AS2, OTUD6B-AS1, and AGAP2-AS1) exceeding the study-wide Bonferroni-corrected P-value (P_Bonf < 0.0023, P_combined = 1.1 × 10^-9, 1.4 × 10^-8, 1.7 × 10^-6, respectively). In this study, we highlight that together with coding genes, (antisense) long non-coding RNAs are deregulated in skin tissue of systemic sclerosis patients suggesting a novel class of genes involved in pathogenesis of systemic sclerosis.

Endogenous ligands of TLR4 promote unresolving tissue fibrosis: Implications for systemic sclerosis and its targeted therapy

Fibrosis, the hallmark of scleroderma or systemic sclerosis (SSc), is a complex, dynamic and generally irreversible pathophysiological process that leads to tissue disruption, and lacks effective therapy. While early-stage fibrosis resembles normal wound healing, in SSc fibrosis fails to resolve. Innate immune signaling via toll-like receptors (TLRs) has recently emerged as a key driver of persistent fibrotic response in SSc. Recurrent injury in genetically predisposed individual causes generation of "damage-associated molecular patterns" (DAMPs) such as fibronectin-EDA and tenascin-C. Sensing of these danger signals by TLR4 on resident cells elicits potent stimulatory effects on fibrotic gene expression and myofibroblast differentiation, and appears to sensitize fibroblasts to the profibrotic stimulatory effect of TGF-β. Thus, DAMPs induce TLR4-mediated innate immune signaling on resident mesenchymal cells which drives the emergence and persistence of fibrotic cells in tissues, and underlies the switch from a self-limited repair response to non-resolving pathological fibrosis characteristic of SSc. In this review, we present current views of the DAMP-TLR4 axis in driving sustained fibroblasts activation and its pathogenic roles in fibrosis progression in SSc, and potential anti-fibrotic approaches for selective therapeutic targeting of TLR4 signaling.