MYLK*FLNB and DOCK1*LAMA2 gene-gene interactions associated with rheumatoid arthritis in the focal adhesion pathway.

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease caused by a combination of genetic and environmental factors. Rare variants with low predicted effects in genes participating in the same biological function might be involved in developing complex diseases such as RA. From whole-exome sequencing (WES) data, we identified genes containing rare non-neutral variants with complete penetrance and no phenocopy in at least one of nine French multiplex families. Further enrichment analysis highlighted focal adhesion as the most significant pathway. We then tested if interactions between the genes participating in this function would increase or decrease the risk of developing RA disease. The model-based multifactor dimensionality reduction (MB-MDR) approach was used to detect epistasis in a discovery sample (19 RA cases and 11 healthy individuals from 9 families and 98 unrelated CEU controls from the International Genome Sample Resource). We identified 9 significant interactions involving 11 genes (MYLK, FLNB, DOCK1, LAMA2, RELN, PIP5K1C, TNC, PRKCA, VEGFB, ITGB5, and FLT1). One interaction (MYLK*FLNB) increasing RA risk and one interaction decreasing RA risk (DOCK1*LAMA2) were confirmed in a replication sample (200 unrelated RA cases and 91 GBR unrelated controls). Functional and genomic data in RA samples or relevant cell types argue the key role of these genes in RA.

A mathematical model of Familial Mediterranean Fever predicts mechanisms controlling inflammation.

BACKGROUND: Familial Mediterranean Fever (FMF) is a monogenic disease caused by gain-of-function mutations in the MEditerranean FeVer (MEFV) gene. The molecular dysregulations induced by these mutations and the associated causal mechanisms are complex and intricate. OBJECTIVE: We sought to provide a computational model capturing the mechanistic details of biological pathways involved in FMF physiopathology and enabling the study of the patient's immune cell dynamics. METHODS: We carried out a literature survey to identify experimental studies published from January 2000 to December 2020, and integrated its results into a molecular map and a mathematical model. Then, we studied the network of molecular interactions and the dynamic of monocytes to identify key players for inflammation phenotype in FMF patients. RESULTS: We built a molecular map of FMF integrating in a structured manner the current knowledge regarding pathophysiological processes participating in the triggering and perpetuation of the disease flares. The mathematical model derived from the map reproduced patient's monocyte behavior, in particular its proinflammatory role via the Pyrin inflammasome activation. Network analysis and in silico experiments identified NF-κB and JAK1/TYK2 as critical to modulate IL-1ß- and IL-18-mediated inflammation. CONCLUSION: The in silico model of FMF monocyte proved its ability to reproduce in vitro observations. Considering the difficulties related to experimental settings and financial investments to test combinations of stimuli/perturbation in vitro, this model could be used to test complex hypotheses in silico, thus narrowing down the number of in vitro and ex vivo experiments to perform.

Endothelial CD34 expression and regulation of immune cell response in-vitro.

Endothelial cells cover the lining of different blood vessels and lymph nodes, and have major functions including the transport of blood, vessel homeostasis, inflammatory responses, control of transendothelial migration of circulating cells into the tissues, and formation of new blood vessels. Therefore, understanding these cells is of major interest. The morphological features, phenotype and function of endothelial cells varies according to the vascular bed examined. The sialomucin, CD34, is widely used as an endothelial marker. However, CD34 is differentially expressed on endothelial cells in different organs and in pathological conditions. Little is known about regulation of endothelial CD34 expression or function. Expression of CD34 is also strongly regulated in-vitro in endothelial cell models, including human umbilical vein endothelial cells (HUVEC) and endothelial colony forming cells (ECFC). We have therefore analysed the expression and function of CD34 by comparing CD34high and CD34low endothelial cell subpopulations. Transcriptomic analysis showed that CD34 gene and protein expressions are highly correlated, that CD34high cells proliferate less but express higher levels of IL-33 and Angiopoietin 2, compared with CD34low cells. Higher secretion levels of IL-33 and Angiopoietin 2 by CD34high HUVECs was confirmed by ELISA. Finally, when endothelial cells were allowed to interact with peripheral blood mononuclear cells, CD34high endothelial cells activated stronger proliferation of regulatory T lymphocytes (Tregs) compared to CD34low cells whereas expansion of other CD4+-T cell subsets was equivalent. These results suggest that CD34 expression by endothelial cells in-vitro associates with their ability to proliferate and with an immunogenic ability that favours the tolerogenic response.

A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data.

Unlike autoimmune diseases, there is no known constitutive and disease-defining biomarker for systemic autoinflammatory diseases (SAIDs). Kawasaki disease (KD) is one of the "undiagnosed" types of SAIDs whose pathogenic mechanism and gene mutation still remain unknown. To address this issue, we have developed a sequential computational workflow which clusters KD patients with similar gene expression profiles across the three different KD phases (Acute, Subacute and Convalescent) and utilizes the resulting clustermap to detect prominent genes that can be used as diagnostic biomarkers for KD. Self-Organizing Maps (SOMs) were employed to cluster patients with similar gene expressions across the three phases through inter-phase and intra-phase clustering. Then, false discovery rate (FDR)-based feature selection was applied to detect genes that significantly deviate across the per-phase clusters. Our results revealed five genes as candidate biomarkers for KD diagnosis, namely, the HLA-DQB1, HLA-DRA, ZBTB48, TNFRSF13C, and CASD1. To our knowledge, these five genes are reported for the first time in the literature. The impact of the discovered genes for KD diagnosis against the known ones was demonstrated by training boosting ensembles (AdaBoost and XGBoost) for KD classification on common platform and cross-platform datasets. The classifiers which were trained on the proposed genes from the common platform data yielded an average increase by 4.40% in accuracy, 5.52% in sensitivity, and 3.57% in specificity than the known genes in the Acute and Subacute phases, followed by a notable increase by 2.30% in accuracy, 2.20% in sensitivity, and 4.70% in specificity in the cross-platform analysis.

Identification of biological pathways specific to phases preceding rheumatoid arthritis development through gene expression profiling.

The etiopathogenesis of rheumatoid arthritis is partially understood; however, it is believed to result from a multi-step process. The immune onset followed by pre-clinical phases will eventually lead to the development of symptomatic disease. We aim at identifying differentially expressed genes in order to highlight pathways involved in the pre-clinical stages of rheumatoid arthritis development. The study population consisted of first-degree relatives of patients with rheumatoid arthritis, known to have an increased risk of developing disease as compared to the general population. Whole transcriptome analysis was performed in four groups: asymptomatic without autoantibodies or symptoms associated with possible rheumatoid arthritis (controls); having either clinically suspect arthralgias, undifferentiated arthritis or autoimmunity associated with RA (pre-clinical stages of RA: Pcs-RA); having subsequently developed classifiable RA (pre-RA); and early untreated rheumatoid arthritis patients (RA). Differentially expressed genes were determined, and enrichment analysis was performed. Functional enrichment analysis revealed 31 pathways significantly enriched in differentially expressed genes for Pcs-RA, pre-RA and RA compared to the controls. Osteoclast pathway is among the seven pathways specific for RA. In Pcs-RA and in pre-RA, several enriched pathways include TP53 gene connections, such as P53 and Wnt signalling pathways. Analysis of whole transcriptome for phenotypes related to rheumatoid arthritis allows highlighting which pathways are requested in the pre-clinical stages of disease development. After validation in replication studies, molecules belonging to some of these pathways could be used to identify new specific biomarkers for individuals with impending rheumatoid arthritis.

RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis.

Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.

A novel nonsense variant in SUPT20H gene associated with Rheumatoid Arthritis identified by Whole Exome Sequencing of multiplex families.

The triggering and development of Rheumatoid Arthritis (RA) is conditioned by environmental and genetic factors. Despite the identification of more than one hundred genetic variants associated with the disease, not all the cases can be explained. Here, we performed Whole Exome Sequencing in 9 multiplex families (N = 30) to identify rare variants susceptible to play a role in the disease pathogenesis. We pre-selected 77 genes which carried rare variants with a complete segregation with RA in the studied families. Follow-up linkage and association analyses with pVAAST highlighted significant RA association of 43 genes (p-value < 0.05 after 106 permutations) and pinpointed their most likely causal variant. We re-sequenced the 10 most significant likely causal variants (p-value ≤ 3.78*10-3 after 106 permutations) in the extended pedigrees and 9 additional multiplex families (N = 110). Only one SNV in SUPT20H: c.73A>T (p.Lys25*), presented a complete segregation with RA in an extended pedigree with early-onset cases. In summary, we identified in this study a new variant associated with RA in SUPT20H gene. This gene belongs to several biological pathways like macro-autophagy and monocyte/macrophage differentiation, which contribute to RA pathogenesis. In addition, these results showed that analyzing rare variants using a family-based approach is a strategy that allows to identify RA risk loci, even with a small dataset.

Reconciling the evolutionary origin of bread wheat (Triticum aestivum).

The origin of bread wheat (Triticum aestivum; AABBDD) has been a subject of controversy and of intense debate in the scientific community over the last few decades. In 2015, three articles published in New Phytologist discussed the origin of hexaploid bread wheat (AABBDD) from the diploid progenitors Triticum urartu (AA), a relative of Aegilops speltoides (BB) and Triticum tauschii (DD). Access to new genomic resources since 2013 has offered the opportunity to gain novel insights into the paleohistory of modern bread wheat, allowing characterization of its origin from its diploid progenitors at unprecedented resolution. We propose a reconciled evolutionary scenario for the modern bread wheat genome based on the complementary investigation of transposable element and mutation dynamics between diploid, tetraploid and hexaploid wheat. In this scenario, the structural asymmetry observed between the A, B and D subgenomes in hexaploid bread wheat derives from the cumulative effect of diploid progenitor divergence, the hybrid origin of the D subgenome, and subgenome partitioning following the polyploidization events.