Jaclyn N. Taroni

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 patients 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.

Experimentally-Derived Fibroblast Gene Signatures Identify Molecular Pathways Associated with Distinct Subsets of Systemic Sclerosis Patients in Three Independent Cohorts

Genome-wide expression profiling in systemic sclerosis (SSc) has identified four ‘intrinsic’ subsets of disease (fibroproliferative, inflammatory, limited, and normal-like), each of which shows deregulation of distinct signaling pathways; however, the full set of pathways contributing to this differential gene expression has not been fully elucidated. Here we examine experimentally derived gene expression signatures in dermal fibroblasts for thirteen different signaling pathways implicated in SSc pathogenesis. These data show distinct and overlapping sets of genes induced by each pathway, allowing for a better understanding of the molecular relationship between profibrotic and immune signaling networks. Pathway-specific gene signatures were analyzed across a compendium of microarray datasets consisting of skin biopsies from three independent cohorts representing 80 SSc patients, 4 morphea, and 26 controls. IFNα signaling showed a strong association with early disease, while TGFβ signaling spanned the fibroproliferative and inflammatory subsets, was associated with worse MRSS, and was higher in lesional than non-lesional skin. The fibroproliferative subset was most strongly associated with PDGF signaling, while the inflammatory subset demonstrated strong activation of innate immune pathways including TLR signaling upstream of NF-κB. The limited and normal-like subsets did not show associations with fibrotic and inflammatory mediators such as TGFβ and TNFα. The normal-like subset showed high expression of genes associated with lipid signaling, which was absent in the inflammatory and limited subsets. Together, these data suggest a model by which IFNα is involved in early disease pathology, and disease severity is associated with active TGFβ signaling.

A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis

BackgroundSystemic sclerosis (SSc) is a multi-organ autoimmune disease characterized by skin fibrosis. Internal organ involvement is heterogeneous. It is unknown whether disease mechanisms are common across all involved affected tissues or if each manifestation has a distinct underlying pathology.MethodsWe used consensus clustering to compare gene expression profiles of biopsies from four SSc-affected tissues (skin, lung, esophagus, and peripheral blood) from patients with SSc, and the related conditions pulmonary fibrosis (PF) and pulmonary arterial hypertension, and derived a consensus disease-associate signature across all tissues. We used this signature to query tissue-specific functional genomic networks. We performed novel network analyses to contrast the skin and lung microenvironments and to assess the functional role of the inflammatory and fibrotic genes in each organ. Lastly, we tested the expression of macrophage activation state-associated gene sets for enrichment in skin and lung using a Wilcoxon rank sum test.ResultsWe identified a common pathogenic gene expression signature—an immune–fibrotic axis—indicative of pro-fibrotic macrophages (MØs) in multiple tissues (skin, lung, esophagus, and peripheral blood mononuclear cells) affected by SSc. While the co-expression of these genes is common to all tissues, the functional consequences of this upregulation differ by organ. We used this disease-associated signature to query tissue-specific functional genomic networks to identify common and tissue-specific pathologies of SSc and related conditions. In contrast to skin, in the lung-specific functional network we identify a distinct lung-resident MØ signature associated with lipid stimulation and alternative activation. In keeping with our network results, we find distinct MØ alternative activation transcriptional programs in SSc-associated PF lung and in the skin of patients with an “inflammatory” SSc gene expression signature.ConclusionsOur results suggest that the innate immune system is central to SSc disease processes but that subtle distinctions exist between tissues. Our approach provides a framework for examining molecular signatures of disease in fibrosis and autoimmune diseases and for leveraging publicly available data to understand common and tissue-specific disease processes in complex human diseases.

Identification of Optimal Mouse Models of Systemic Sclerosis by Interspecies Comparative Genomics

Understanding the pathogenesis of systemic sclerosis (SSc) is confounded by considerable disease heterogeneity. Animal models of SSc that recapitulate distinct subsets of disease at the molecular level have not been delineated. We applied interspecies comparative analysis of genomic data from multiple mouse models of SSc and patients with SSc to determine which animal models best reflect the SSc intrinsic molecular subsets.

Interspecies comparative genomics identifies optimal mouse models of systemic sclerosis

Understanding the pathogenesis of systemic sclerosis (SSc) is confounded by considerable disease heterogeneity. Animal models of SSc that recapitulate distinct subsets of disease at the molecular level have not been delineated. We applied interspecies comparative analysis of genomic data from multiple mouse models of SSc and patients with SSc to determine which animal models best reflect the SSc intrinsic molecular subsets.

Molecular characterization of systemic sclerosis esophageal pathology identifies inflammatory and proliferative signatures

IntroductionEsophageal involvement in patients with systemic sclerosis (SSc) is common, but tissue-specific pathological mechanisms are poorly understood. There are no animal scleroderma esophagus models and esophageal smooth muscle cells dedifferentiate in culture prohibiting in vitro studies. Esophageal fibrosis is thought to disrupt smooth muscle function and lead to esophageal dilatation, but autopsy studies demonstrate esophageal smooth muscle atrophy and the absence of fibrosis in the majority of SSc cases. Herein, we perform a detailed characterization of SSc esophageal histopathology and molecular signatures at the level of gene expression.MethodsEsophageal biopsies were prospectively obtained during esophagogastroduodenoscopy in 16 consecutive SSc patients and 7 subjects without SSc. Upper and lower esophageal biopsies were evaluated for histopathology and gene expression.ResultsIndividual patient’s upper and lower esophageal biopsies showed nearly identical patterns of gene expression. Similar to skin, inflammatory and proliferative gene expression signatures were identified suggesting that molecular subsets are a universal feature of SSc end-target organ pathology. The inflammatory signature was present in biopsies without high numbers of infiltrating lymphocytes. Molecular classification of esophageal biopsies was independent of SSc skin subtype, serum autoantibodies and esophagitis.ConclusionsProliferative and inflammatory molecular gene expression subsets in tissues from patients with SSc may be a conserved, reproducible component of SSc pathogenesis. The inflammatory signature is observed in biopsies that lack large inflammatory infiltrates suggesting that immune activation is a major driver of SSc esophageal pathogenesis.

Stress granules and RNA processing bodies are novel autoantibody targets in systemic sclerosis

BackgroundAutoantibody profiles represent important patient stratification markers in systemic sclerosis (SSc). Here, we performed serum-immunoprecipitations with patient antibodies followed by mass spectrometry (LC-MS/MS) to obtain an unbiased view of all possible autoantibody targets and their associated molecular complexes recognized by SSc.MethodsHeLa whole cell lysates were immunoprecipitated (IP) using sera of patients with SSc clinically positive for autoantibodies against RNA polymerase III (RNAP3), topoisomerase 1 (TOP1), and centromere proteins (CENP). IP eluates were then analyzed by LC-MS/MS to identify novel proteins and complexes targeted in SSc. Target proteins were examined using a functional interaction network to identify major macromolecular complexes, with direct targets validated by IP-Western blots and immunofluorescence.ResultsA wide range of peptides were detected across patients in each clinical autoantibody group. Each group contained peptides representing a broad spectrum of proteins in large macromolecular complexes, with significant overlap between groups. Network analyses revealed significant enrichment for proteins in RNA processing bodies (PB) and cytosolic stress granules (SG) across all SSc subtypes, which were confirmed by both Western blot and immunofluorescence.ConclusionsWhile strong reactivity was observed against major SSc autoantigens, such as RNAP3 and TOP1, there was overlap between groups with widespread reactivity seen against multiple proteins. Identification of PB and SG as major targets of the humoral immune response represents a novel SSc autoantigen and suggests a model in which a combination of chronic and acute cellular stresses result in aberrant cell death, leading to autoantibody generation directed against macromolecular nucleic acid-protein complexes.

A Multimodal Strategy Used By A Large c-di-GMP Network

The Pseudomonas fluorescens genome encodes more than 50 proteins predicted to be involved in c-di-GMP signaling. Here, we demonstrated that, tested across 188 nutrients, these enzymes and effectors appeared capable of impacting biofilm formation. Transcriptional analysis of network members across ∼50 nutrient conditions indicates that altered gene expression can explain a subset of but not all biofilm formation responses to the nutrients. Additional organization of the network is likely achieved through physical interaction, as determined via probing ∼2,000 interactions by bacterial two-hybrid assays. Our analysis revealed a multimodal regulatory strategy using combinations of ligand-mediated signals, protein-protein interaction, and/or transcriptional regulation to fine-tune c-di-GMP-mediated responses. These results create a profile of a large c-di-GMP network that is used to make important cellular decisions, opening the door to future model building and the ability to engineer this complex circuitry in other bacteria.IMPORTANCE Cyclic diguanylate (c-di-GMP) is a key signaling molecule regulating bacterial biofilm formation, and many microbes have up to dozens of proteins that make, break, or bind this dinucleotide. A major open issue in the field is how signaling specificity is conferred in the unpartitioned space of a bacterial cell. Here, we took a systems approach, using mutational analysis, transcriptional studies, and bacterial two-hybrid analysis to interrogate this network. We found that a majority of enzymes are capable of impacting biofilm formation in a context-dependent manner, and we revealed examples of two or more modes of regulation (i.e., transcriptional control with protein-protein interaction) being utilized to generate an observable impact on biofilm formation.

Integrative Networks Illuminate Biological Factors Underlying Gene–Disease Associations

Purpose of ReviewIntegrative networks combine multiple layers of biological data into a model of how genes work together to carry out cellular processes. Such networks become more valuable as they become more context-specific, for example, by capturing how genes work together in a certain tissue or cell type. We discuss the applications of these networks to the study of human disease.Recent FindingsOnce constructed, these networks provide the means to identify broad biological patterns underlying genes associated with complex traits and diseases. We cover the different types of integrative networks that currently exist, and how such networks that encompass multiple biological layers are constructed. We highlight how specificity can be incorporated into the reconstruction of different types of biomolecular interactions between genes, using tissue specificity as a motivating example. We discuss examples of cases where networks have been applied to study human diseases and opportunities for new applications.SummaryIntegrative networks with specificity to tissue or other biological features provide new capabilities to researchers engaged in the study of human disease. We expect improved data and algorithms to continue and improve such networks, allowing them to provide more detailed and mechanistic predictions into the context-specific genetic etiology of common diseases.

Mycophenolate Mofetil Treatment of Systemic Sclerosis Reduces Myeloid Cell Numbers and Attenuates the Inflammatory Gene Signature in Skin

Fewer than half of patients with systemic sclerosis demonstrate modified Rodnan skin score improvement during mycophenolate mofetil (MMF) treatment. To understand the molecular basis for this observation, we extended our prior studies and characterized molecular and cellular changes in skin biopsies from subjects with systemic sclerosis treated with MMF. Eleven subjects completed ≥24 months of MMF therapy. Two distinct skin gene expression trajectories were observed across six of these subjects. Three of the six subjects showed attenuation of the inflammatory signature by 24 months, paralleling reductions in CCL2 mRNA expression in skin and reduced numbers of macrophages and myeloid dendritic cells in skin biopsies. MMF cessation at 24 months resulted in an increased inflammatory score, increased CCL2 mRNA and protein levels, modified Rodnan skin score rebound, and increased numbers of skin myeloid cells in these subjects. In contrast, three other subjects remained on MMF >24 months and showed a persistent decrease in inflammatory score, decreasing or stable modified Rodnan skin score, CCL2 mRNA reductions, sera CCL2 protein levels trending downward, reduction in monocyte migration, and no increase in skin myeloid cell numbers. These data summarize molecular changes during MMF therapy that suggest reduction of innate immune cell numbers, possibly by attenuating expression of chemokines, including CCL2.