Nucleic Acid-Sensing and Interferon-Inducible Pathways Show Differential Methylation in MZ Twins Discordant for Lupus and Overexpression in Independent Lupus Samples: Implications for Pathogenic Mechanism and Drug Targeting.

Systemic lupus erythematosus (SLE) is a chronic, multisystem, autoimmune inflammatory disease with genomic and non-genomic contributions to risk. We hypothesize that epigenetic factors are a significant contributor to SLE risk and may be informative for identifying pathogenic mechanisms and therapeutic targets. To test this hypothesis while controlling for genetic background, we performed an epigenome-wide analysis of DNA methylation in genomic DNA from whole blood in three pairs of female monozygotic (MZ) twins of European ancestry, discordant for SLE. Results were replicated on the same array in four cell types from a set of four Danish female MZ twin pairs discordant for SLE. Genes implicated by the epigenetic analyses were then evaluated in 10 independent SLE gene expression datasets from the Gene Expression Omnibus (GEO). There were 59 differentially methylated loci between unaffected and affected MZ twins in whole blood, including 11 novel loci. All but two of these loci were hypomethylated in the SLE twins relative to the unaffected twins. The genes harboring these hypomethylated loci exhibited increased expression in multiple independent datasets of SLE patients. This pattern was largely consistent regardless of disease activity, cell type, or renal tissue type. The genes proximal to CpGs exhibiting differential methylation (DM) in the SLE-discordant MZ twins and exhibiting differential expression (DE) in independent SLE GEO cohorts (DM-DE genes) clustered into two pathways: the nucleic acid-sensing pathway and the type I interferon pathway. The DM-DE genes were also informatically queried for potential gene-drug interactions, yielding a list of 41 drugs including a known SLE therapy. The DM-DE genes delineate two important biologic pathways that are not only reflective of the heterogeneity of SLE but may also correlate with distinct IFN responses that depend on the source, type, and location of nucleic acid molecules and the activated receptors in individual patients. Cell- and tissue-specific analyses will be critical to the understanding of genetic factors dysregulating the nucleic acid-sensing and IFN pathways and whether these factors could be appropriate targets for therapeutic intervention.

Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway.

SARS-CoV2 is a previously uncharacterized coronavirus and causative agent of the COVID-19 pandemic. The host response to SARS-CoV2 has not yet been fully delineated, hampering a precise approach to therapy. To address this, we carried out a comprehensive analysis of gene expression data from the blood, lung, and airway of COVID-19 patients. Our results indicate that COVID-19 pathogenesis is driven by populations of myeloid-lineage cells with highly inflammatory but distinct transcriptional signatures in each compartment. The relative absence of cytotoxic cells in the lung suggests a model in which delayed clearance of the virus may permit exaggerated myeloid cell activation that contributes to disease pathogenesis by the production of inflammatory mediators. The gene expression profiles also identify potential therapeutic targets that could be modified with available drugs. The data suggest that transcriptomic profiling can provide an understanding of the pathogenesis of COVID-19 in individual patients.

Analysis of Trans-Ancestral SLE Risk Loci Identifies Unique Biologic Networks and Drug Targets in African and European Ancestries.

Systemic lupus erythematosus (SLE) is a multi-organ autoimmune disorder with a prominent genetic component. Individuals of African ancestry (AA) experience the disease more severely and with an increased co-morbidity burden compared to European ancestry (EA) populations. We hypothesize that the disparities in disease prevalence, activity, and response to standard medications between AA and EA populations is partially conferred by genomic influences on biological pathways. To address this, we applied a comprehensive approach to identify all genes predicted from SNP-associated risk loci detected with the Immunochip. By combining genes predicted via eQTL analysis, as well as those predicted from base-pair changes in intergenic enhancer sites, coding-region variants, and SNP-gene proximity, we were able to identify 1,731 potential ancestry-specific and trans-ancestry genetic drivers of SLE. Gene associations were linked to upstream and downstream regulators using connectivity mapping, and predicted biological pathways were mined for candidate drug targets. Examination of trans-ancestral pathways reflect the well-defined role for interferons in SLE and revealed pathways associated with tissue repair and remodeling. EA-dominant genetic drivers were more often associated with innate immune and myeloid cell function pathways, whereas AA-dominant pathways mirror clinical findings in AA subjects, suggesting disease progression is driven by aberrant B cell activity accompanied by ER stress and metabolic dysfunction. Finally, potential ancestry-specific and non-specific drug candidates were identified. The integration of all SLE SNP-predicted genes into functional pathways revealed critical molecular pathways representative of each population, underscoring the influence of ancestry on disease mechanism and also providing key insight for therapeutic selection.

The pathogenesis of systemic lupus erythematosus: Harnessing big data to understand the molecular basis of lupus.

Systemic lupus erythematosus (SLE) is a chronic, systemic autoimmune disease that causes damage to multiple organ systems. Despite decades of research and available murine models that capture some aspects of the human disease, new treatments for SLE lag behind other autoimmune diseases such as Rheumatoid Arthritis and Crohn's disease. Big data genomic assays have transformed our understanding of SLE by providing important insights into the molecular heterogeneity of this multigenic disease. Gene wide association studies have demonstrated more than 100 risk loci, supporting a model of multiple genetic hits increasing SLE risk in a non-linear fashion, and providing evidence of ancestral diversity in susceptibility loci. Epigenetic studies to determine the role of methylation, acetylation and non-coding RNAs have provided new understanding of the modulation of gene expression in SLE patients and identified new drug targets and biomarkers for SLE. Gene expression profiling has led to a greater understanding of the role of myeloid cells in the pathogenesis of SLE, confirmed roles for T and B cells in SLE, promoted clinical trials based on the prominent interferon signature found in SLE patients, and identified candidate biomarkers and cellular signatures to further drug development and drug repurposing. Gene expression studies are advancing our understanding of the underlying molecular heterogeneity in SLE and providing hope that patient stratification will expedite new therapies based on personal molecular signatures. Although big data analyses present unique interpretation challenges, both computationally and biologically, advances in machine learning applications may facilitate the ability to predict changes in SLE disease activity and optimize therapeutic strategies.

Machine learning approaches to predict lupus disease activity from gene expression data.

The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three combined data sets or by training and testing in independent data sets, the latter of which amplified the effects of technical variation. A random forest classifier achieved a peak classification accuracy of 83 percent under 10-fold cross-validation, but its performance could be severely affected by technical variation among data sets. The use of gene modules rather than raw gene expression was more robust, achieving classification accuracies of approximately 70 percent regardless of how the training and testing sets were formed. Fine-tuning the algorithms and parameter sets may generate sufficient accuracy to be informative as a standalone estimate of disease activity.

Identification of alterations in macrophage activation associated with disease activity in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is characterized by abnormalities in B cell and T cell function, but the role of disturbances in the activation status of macrophages (Mϕ) has not been well described in human patients. To address this, gene expression profiles from isolated lymphoid and myeloid populations were analyzed to identify differentially expressed (DE) genes between healthy controls and patients with either inactive or active SLE. While hundreds of DE genes were identified in B and T cells of active SLE patients, there were no DE genes found in B or T cells from patients with inactive SLE compared to healthy controls. In contrast, large numbers of DE genes were found in myeloid cells (MC) from both active and inactive SLE patients. Among the DE genes were several known to play roles in Mϕ activation and polarization, including the M1 genes STAT1 and SOCS3 and the M2 genes STAT3, STAT6, and CD163. M1-associated genes were far more frequent in data sets from active versus inactive SLE patients. To characterize the relationship between Mϕ activation and disease activity in greater detail, weighted gene co-expression network analysis (WGCNA) was used to identify modules of genes associated with clinical activity in SLE patients. Among these were disease activity-correlated modules containing activation signatures of predominantly M1-associated genes. No disease activity-correlated modules were enriched in M2-associated genes. Pathway and upstream regulator analysis of DE genes from both active and inactive SLE MC were cross-referenced with high-scoring hits from the drug discovery Library of Integrated Network-based Cellular Signatures (LINCS) to identify new strategies to treat both stages of SLE. A machine learning approach employing MC gene modules and a generalized linear model was able to predict the disease activity status in unrelated gene expression data sets. In summary, altered MC gene expression is characteristic of both active and inactive SLE. However, disease activity is associated with an alteration in the activation of MC, with a bias toward the M1 proinflammatory phenotype. These data suggest that while hyperactivity of B cells and T cells is associated with active SLE, MC potentially direct flare-ups and remission by altering their activation status toward the M1 state.

Expression of scavenger receptor-AI promotes alternative activation of murine macrophages to limit hepatic inflammation and fibrosis.

The liver maintains an immunologically tolerant environment as a result of continuous exposure to food and bacterial constituents from the digestive tract. Hepatotropic pathogens can take advantage of this niche and establish lifelong chronic infections causing hepatic fibrosis and hepatocellular carcinoma. Macrophages (Mϕ) play a critical role in regulation of immune responses to hepatic infection and regeneration of tissue. However, the factors crucial for Mϕ in limiting hepatic inflammation or resolving liver damage have not been fully understood. In this report, we demonstrate that expression of C-type lectin receptor scavenger receptor-AI (SR-AI) is crucial for promoting M2-like Mϕ activation and polarization during hepatic inflammation. Liver Mϕ uniquely up-regulated SR-AI during hepatotropic viral infection and displayed increased expression of alternative Mϕ activation markers, such as YM-1, arginase-1, and interleukin-10 by activation of mer receptor tyrosine kinase associated with inhibition of mammalian target of rapamycin. Expression of these molecules was reduced on Mϕ obtained from livers of infected mice deficient for the gene encoding SR-AI (msr1). Furthermore, in vitro studies using an SR-AI-deficient Mϕ cell line revealed impeded M2 polarization and decreased phagocytic capacity. Direct stimulation with virus was sufficient to activate M2 gene expression in the wild-type (WT) cell line, but not in the knockdown cell line. Importantly, tissue damage and fibrosis were exacerbated in SR-AI-/- mice following hepatic infection and adoptive transfer of WT bone-marrow-derived Mϕ conferred protection against fibrosis in these mice. CONCLUSION: SR-AI expression on liver Mϕ promotes recovery from infection-induced tissue damage by mediating a switch to a proresolving Mϕ polarization state. (Hepatology 2017;65:32-43).

The role of macrophage polarization in infectious and inflammatory diseases.

Macrophages, found in circulating blood as well as integrated into several tissues and organs throughout the body, represent an important first line of defense against disease and a necessary component of healthy tissue homeostasis. Additionally, macrophages that arise from the differentiation of monocytes recruited from the blood to inflamed tissues play a central role in regulating local inflammation. Studies of macrophage activation in the last decade or so have revealed that these cells adopt a staggering range of phenotypes that are finely tuned responses to a variety of different stimuli, and that the resulting subsets of activated macrophages play critical roles in both progression and resolution of disease. This review summarizes the current understanding of the contributions of differentially polarized macrophages to various infectious and inflammatory diseases and the ongoing effort to develop novel therapies that target this key aspect of macrophage biology.