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

A Two-Gene Balance Regulates Salmonella Typhimurium Tolerance in the Nematode Caenorhabditis elegans

Lysozymes are antimicrobial enzymes that perform a critical role in resisting infection in a wide-range of eukaryotes. However, using the nematode Caenorhabditis elegans as a model host we now demonstrate that deletion of the protist type lysozyme LYS-7 renders animals susceptible to killing by the fatal fungal human pathogen Cryptococcus neoformans, but, remarkably, enhances tolerance to the enteric bacteria Salmonella Typhimurium. This trade-off in immunological susceptibility in C. elegans is further mediated by the reciprocal activity of lys-7 and the tyrosine kinase abl-1. Together this implies a greater complexity in C. elegans innate immune function than previously thought

Promoter Complexity and Tissue-Specific Expression of Stress Response Components in Mytilus galloprovincialis, a Sessile Marine Invertebrate Species

The mechanisms of stress tolerance in sessile animals, such as molluscs, can offer fundamental insights into the adaptation of organisms for a wide range of environmental challenges. One of the best studied processes at the molecular level relevant to stress tolerance is the heat shock response in the genus Mytilus. We focus on the upstream region of Mytilus galloprovincialis Hsp90 genes and their structural and functional associations, using comparative genomics and network inference. Sequence comparison of this region provides novel evidence that the transcription of Hsp90 is regulated via a dense region of transcription factor binding sites, also containing a region with similarity to the Gamera family of LINE-like repetitive sequences and a genus-specific element of unknown function. Furthermore, we infer a set of gene networks from tissue-specific expression data, and specifically extract an Hsp class-associated network, with 174 genes and 2,226 associations, exhibiting a complex pattern of expression across multiple tissue types. Our results (i) suggest that the heat shock response in the genus Mytilus is regulated by an unexpectedly complex upstream region, and (ii) provide new directions for the use of the heat shock process as a biosensor system for environmental monitoring

Mise en évidence et évolution d'une nouvelle famille de lysozymes (le lysozyme de type i)

PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Molecular endotypes of type 1 and type 2 SLE

Objective To character the molecular landscape of patients with type 1 and type 2 SLE by analysing gene expression profiles from peripheral blood.Methods Full transcriptomic RNA sequencing was carried out on whole blood samples from 18 subjects with SLE selected by the presence of manifestations typical of type 1 and type 2 SLE. The top 5000 row variance genes were analysed by Multiscale Embedded Gene Co-expression Network Analysis to generate gene co-expression modules that were functionally annotated and correlated with various demographic traits, clinical features and laboratory measures.Results Expression of specific gene co-expression modules correlated with individual features of type 1 and type 2 SLE and also effectively segregated samples from patients with type 1 SLE from those with type 2 SLE. Unique type 1 SLE enrichment included interferon, monocytes, T cells, cell cycle and neurotransmitter pathways, whereas unique type 2 SLE enrichment included B cells and metabolic and neuromuscular pathways. Gene co-expression modules of patients with type 2 SLE were identified in subsets of previously reported patients with inactive SLE and idiopathic fibromyalgia (FM) and also identified subsets of patients with active SLE with a greater frequency of severe fatigue.Conclusion Gene co-expression analysis successfully identified unique transcriptional patterns that segregate type 1 SLE from type 2 SLE and further identified type 2 molecular features in patients with inactive SLE or FM and with active SLE with severe fatigue

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

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

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