Silva-Saffar, S. E. | bioRxiv Science

bioRxiv2025

The SjD Map: An interactive pathway tour into Sjogren's disease signalling mechanisms

ObjectivesSjogrens disease (SjD) remains a major unmet medical challenge, marked by biological complexity, patient heterogeneity, and a lack of curative treatments. To advance the understanding of its pathogenesis and support therapeutic discovery, we developed a comprehensive knowledgebase in the form of a Molecular Interaction Map (MIM). MethodsDifferential expression analysis was performed on peripheral blood samples from SjD patients and healthy controls across three datasets: GSE51092 (190 SjD vs 32 controls), UKPSSR (151 SjD vs 29 controls) and PRECISESADS (304 SjD vs 341 controls). Pathway enrichment analyses provided guidance for MIM construction, which was further refined through literature mining to integrate data-driven results with curated knowledge. ResultsA total of 1,625 differentially expressed genes (DEGs) were identified: 725 from PRECISESADS, 1,162 from GSE51092, and 239 from UKPSSR, with 25 DEGs shared across all three datasets. Among these, nine common DEGs were associated with interferon signalling, reinforcing experimental evidence pointing to its pivotal role in SjD. Enrichment analyses revealed 146 pathways, 43 of which were successfully incorporated into the MIM. The resulting SjD Map freely accessible at https://sjdmap.elixir-luxembourg.org/, comprises 829 molecular entities connected by 598 interactions, with 45% of the information depicted supported by transcriptomic data and 47% derived from literature. The map also includes overlays of experimental data and clinical trial information. ConclusionThis first comprehensive Sjogrens Disease Map, developed through a hybrid data-driven and literature-based approach, offers an integrative view of SjD pathogenesis. It supports visualisation of mechanistic pathways, omics-based data overlays, enables incorporation of user data and drug queries. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/674876v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@1bdc517org.highwire.dtl.DTLVardef@1d68077org.highwire.dtl.DTLVardef@18c079forg.highwire.dtl.DTLVardef@487b64_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG Key messagesO_LIThis is the first molecular interaction map specific of Sjogrens Disease C_LIO_LIIt enables comprehensive visualisation of affected pathways through integrated transcriptomic and literature-based evidence, following systems biology graphical notation schemes C_LIO_LIThe map may support therapeutic discovery by linking molecular mechanisms to clinical data and drug targets C_LI

systems biology↗

bioRxiv2023

Hybrid computational modeling highlights reverse Warburg effect in breast cancer-associated fibroblasts

Cancer-associated fibroblasts (CAFs) are key players of the tumor microenvironment (TME) involved in cancer initiation, progression, and resistance to therapy. These cells exhibit aggressive phenotypes affecting, among others, extracellular matrix remodeling, angiogenesis, immune system modulation, tumor growth, and proliferation. CAFs phenotypic changes appear to be associated with metabolic alterations, notably a reverse Warburg effect that may drive fibroblasts transformation. However, its precise molecular mechanisms and regulatory drivers are still under investigation. Deciphering the reverse Warburg effect in breast CAFs may contribute to a better understanding of the interplay between TME and tumor cells, leading to new treatment strategies. In this regard, dynamic modeling approaches able to span multiple biological layers are essential to capture the emergent properties of various biological entities when complex and intertwined pathways are involved. This work presents the first hybrid large-scale computational model for breast CAFs covering major cellular signaling, gene regulation, and metabolic processes. It was generated by combining an asynchronous cell- and disease-specific regulatory Boolean model with a generic core metabolic network leveraging both data-driven and manual curation approaches. This model reproduces the experimentally observed reverse Warburg effect in breast CAFs and further identifies Hypoxia-Inducible Factor 1 (HIF-1) as its key molecular driver. Targeting HIF-1 as part of a TME-centered therapeutic strategy may prove beneficial in the treatment of breast cancer by addressing the reverse Warburg effect. Such findings in CAFs, considering our previously published results in rheumatoid arthritis synovial fibroblasts, point to a common HIF-1-driven metabolic reprogramming of fibroblasts in breast cancer and rheumatoid arthritis. All analyses are compiled and thoroughly annotated in Jupyter notebooks and R scripts available on a GitLab repository (https://gitlab.com/genhotel/breast-cafs-reverse-warburg-effect) and a Zenodo permanent archive [1].

systems biology↗