Biology subjects

Vera Gonzalez, J.

Publications and source records attributed to Vera Gonzalez, J..

5 recordsLinked to original sources

bioRxiv2025

A computational SOX10 network-based selection strategy to identify new drug targets in uveal melanoma

Uveal melanoma (UM) is the most common intraocular malignancy in adults. In contrast to cutaneous melanoma (CM), effective treatment options for metastatic UM are limited. The transcription factor SOX10 is crucial for CM initiation and survival, making it an interesting candidate for new targeted therapies, but its relevance in UM was unclear. We found that SOX10 was widely expressed in UM and essential for proliferation, cell cycle progression, and survival. The effects were partially mediated by SOX10-related genes including MITF, highlighting high addiction of UM to the SOX10-MITF axis. Additionally, SOX10 knockdown induced massive transcriptomic changes. Due to a lack of specific inhibitors of SOX10 and MITF, a computational approach was used to identify druggable targets by curating a UM-specific protein interaction network to search for candidates downregulated upon SOX10 inhibition. Thereby, the E2F transcription factor family was identified and their potential as druggable target candidates in UM was confirmed using the pan-E2F inhibitor HLM006474, resulting in cell cycle arrest and apoptosis. Taken together, SOX10 is crucial for UM survival and SOX10-associated proteins may serve as promising targets for developing new therapeutic strategies in UM.

cancer biology↗

bioRxiv2025

Integrative network modeling of colorectal cancer reveals diagnostic signatures and therapeutic targets

Emerging evidence suggests that the interplay between multiple signaling pathways and the immune microenvironment influences tumorigenesis in cancers such as colorectal cancer (CRC). To gain an in-depth understanding of CRC mechanisms and identify novel therapeutic targets, we constructed a molecular interaction map (MIM) integrating key signaling pathways from cancer cells and immune cells within the tumor microenvironment. This map comprises 218 molecules and 328 interactions, curated using PubMed references and official gene symbols. We dynamically simulated the MIM for individual pathways and combinations via stimulus-response and perturbation analyses, calibrating the model with two CRC datasets: GSE1323 (primary tumor-to-metastasis progression) and GSE8671 (normal mucosa-to-adenoma progression), which served as experimental conditions. Simulations revealed distinct disease signatures for GSE1323, including (1) simultaneous activation of TNF/TNFRSF1A,B and EGF/EGFR with inactivation of ERE/ESR, and (2) simultaneous activation of TNF/TNFRSF1A,B and TLR4 with inactivation of ERE/ESR. For GSE8671, the signature was simultaneous activation of TNF/TNFRSF1A,B and TLR4. In silico perturbation analysis identified potent anti-cancer effects from concurrent inhibition of MAPK3 and STAT3 (GSE1323), and ELK1/ATF2 and STAT3 or MAPK14 and STAT3 (GSE8671), significantly reducing epithelial-mesenchymal transition (EMT), proliferation, and inflammation while increasing apoptosis. Disease signatures and therapeutic targets were validated using patient data through Kaplan-Meier survival analysis and machine learning. This integrative model recapitulates cancer biology, predicts biomarkers and therapeutic targets, and is extensible to other immunogenic cancers.

systems biology↗

bioRxiv2024

The integration of network biology and pharmacophore modeling suggests repurposing Clindamycin as an inhibitor of pyroptosis via Caspase-1 blockage in tumor-associated macrophages

BackgroundUveal melanoma (UM) is a highly malignant intraocular tumor with a poor prognosis and response to therapy, including immune checkpoint inhibitors (ICIs), after the onset of liver metastasis. The metastatic microenvironment contains high levels of tumor-associated macrophages (TAMs) that correlate positively with a worse patient prognosis. We hypothesized that one could increase the efficacy of ICIs in UM metastases by immunomodulating UM-associated macrophages. MethodsTo identify potential targets for the immunomodulation, we created a network-based representation of the biology of TAMs and employed (bulk and single-cell) differential gene expression analysis to obtain a regulatory core of UM macrophages-associated genes. We utilized selected targets for pharmacophore-based virtual screening against a library of FDA-approved chemical compounds, followed by refined flexible docking analysis. Finally, we ranked the interactions and selected one novel drug-target combination for in vitro validation. ResultsBased on the generated TAM-specific interaction network (3863 nodes, 9073 edges), we derived a UM macrophages-associated regulatory core (74 nodes, 286 edges). From the regulatory core genes, we selected eight potential targets for pharmacophore-based virtual screening (YBX1, GSTP1, NLRP3, ISG15, MYC, PTGS2, NFKB1, CASP1). Of 266 drug-target interactions screened, we identified the interaction between the antibiotic Clindamycin and Caspase-1 as a priority for experimental validation. Our in vitro validation experiments showed that Clindamycin specifically interferes with activated Caspase-1 and inhibits the secretion of IL-1{beta}, IL-18, and lactate dehydrogenase (LDH) in macrophages after stimulation. Our results suggest that repurposed Clindamycin could reduce pyroptosis in TAMs, a pro-inflammatory form of programmed immune cell death favouring tumor progression. ConclusionWe were able to predict a novel Clindamycin-Caspase-1 interaction that effectively blocks Caspase-1-mediated inflammasome activity and pyroptosis in vitro in macrophages. This interaction is a promising clinical immunomodulator of the tumor microenvironment for improving ICI responsivenss. This work demonstrates the power of combining network-based transcriptomic analysis with pharmacophore-guided screening for de novo drug-target repurposing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/576201v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@310a9aorg.highwire.dtl.DTLVardef@1af0dcaorg.highwire.dtl.DTLVardef@1b272aborg.highwire.dtl.DTLVardef@86572b_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

ENQUIRE RECONSTRUCTS AND EXPANDS GENE AND MESH CO-OCCURRENCE NETWORKS FROM CONTEXT-SPECIFIC LITERATURE

The accelerating growth of scientific literature overwhelms our capacity to manually distil complex phenomena like molecular networks linked to diseases. Moreover, biases in biomedical research and database annotation limit our interpretation of facts and generation of hypotheses. ENQUIRE (Expanding Networks by Querying Unexpectedly Inter-Related Entities) offers a time- and resource-efficient alternative to manual literature curation and database mining. ENQUIRE reconstructs and expands co-occurrence networks of genes and biomedical ontologies from user-selected input corpora and network-inferred PubMed queries. The integration of text mining, automatic querying, and network-based statistics mitigating literature biases makes ENQUIRE unique in its broad-scope applications. For example, ENQUIRE can generate co-occurrence gene networks that reflect high-confidence, functional networks. When tested on case studies spanning cancer, cell differentiation and immunity, ENQUIRE identified interlinked genes and enriched pathways unique to each topic, thereby preserving their underlying diversity. ENQUIRE supports biomedical researchers by easing literature annotation, boosting hypothesis formulation, and facilitating the identification of molecular targets for subsequent experimentation. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/556351v3_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@10517b6org.highwire.dtl.DTLVardef@157bb95org.highwire.dtl.DTLVardef@dc6a41org.highwire.dtl.DTLVardef@c3f8d3_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

An algorithm that combines machine learning ensemble modeling and network analysis to predict self-tolerant tumor-associated antigens for anti-cancer immunotherapy

Tumor-associated antigens (TAAs) and their derived peptides constitute the chance to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. Here, we present a computational pipeline that selects and ranks candidate antigens in a multi-pronged approach and applied it to the case of uveal melanoma. In addition to antigen expression in the tumor target and in healthy tissues, we incorporated a network analysis-derived antigen indispensability index motivated by computational modeling results, and candidate immunogenicity predictions from a machine learning ensemble model on peptide physicochemical characteristics.

bioinformatics↗