Ladislav Hovan

• Belova, Tatiana; Hsieh, Ping-Han; Hovan, Ladislav; Osorio, Daniel & Kuijjer, Marieke L. (2025). Pan-cancer analysis of patient-specific gene regulatory landscapes identifies recurrent PD-1 pathway dysregulation linked to outcome. BioRxiv. ISSN 0362-4331. doi: 10.1101/2025.10.03.680219. Full text in Research Archive Show summary

  Cancer heterogeneity remains a major challenge to the development of broadly effective therapies. Although gene expression-based stratification has advanced our understanding of molecular subtypes, it often overlooks the regulatory mechanisms driving tumor progression and therapeutic resistance. These mechanisms are governed by gene regulatory networks—complex systems of transcription factors and other regulators that control gene expression. To investigate regulatory heterogeneity at the patient level, we inferred individual gene regulatory networks for over 9,000 tumors across 33 cancer types from The Cancer Genome Atlas enabling a comprehensive pancancer analysis of inter-patient regulatory variation. Our analysis uncovered novel regulatory subtypes and revealed recurrent dysregulation of the PD-1 signaling pathway in 23 cancer types. This dysregulation was associated with patient outcomes in 12 cancers and appeared largely independent of established molecular subtypes and immune cell composition, suggesting alternative regulatory mechanisms. We identified both well-characterized (FOS, JUNB, ATF3) and previously unrecognized (RFX7, ELF1, NFYA, NFYC) transcriptional regulators of PD-L1 ( CD274 ), highlighting new candidate targets for modulating immune checkpoint activity across tumor types. Our work demonstrates the power of single-sample gene regulatory networks to uncover hidden layers of tumor biology, improve patient stratification, and inform future research in immunotherapy.

• Arcos-Encarnación, Bolivar; Cortes-Flores, Eladio; Barón-Mendoza, Isabel; Almazán, Jorge L.; Valle-García, David & León-Guerrero, Sol Díaz de [Show all 15 contributors for this article] (2025). KChIP3 fosters neuroinflammation and synaptic dysfunction in the 5XFAD mouse model of Alzheimer’s disease. Journal of Neuroinflammation. doi: 10.1186/s12974-025-03426-2. Full text in Research Archive Show summary

  Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by β-amyloid (βA) accumulation, neuroinflammation, excessive synaptic pruning, and cognitive decline. Despite extensive research, effective treatments remain elusive. Here, we identify potassium channel-interacting protein 3 (KChIP3) as a key driver of AD pathology using the 5XFAD mouse model. KChIP3 levels were significantly elevated in the hippocampus of 5XFAD mice, correlating with βA burden and neuroinflammation. This upregulation was triggered by inflammatory signaling via the NLRP3 inflammasome and Caspase-1 activation. Notably, genetic deletion of KChIP3 (5XFAD/KChIP3−/−) markedly reduced βA plaque deposition, pro-inflammatory cytokines, reactive gliosis, and expression of inflammation-related proteins (APO, CLU, MDK). Transcriptomic and proteomic analyses revealed restored synaptic markers (CD47, CD200, CACNB4, GDA) and a shift of the disease-associated microglial (DAM-1) phenotype. Mechanistically, we propose that KChIP3 amplifies AD pathology through two key mechanisms: (1) sustaining neuroinflammation by upregulating pro-inflammatory genes and (2) impairing synaptic integrity by repressing genes critical for neuronal function. Consistently, KChIP3 deletion enhanced dendritic complexity, synaptic plasticity, and cognitive performance in 5XFAD mice. These findings position KChIP3 as a potential therapeutic target for mitigating neuroinflammation and synaptic dysfunction in AD and highlight its potential as a biomarker for disease progression. Graphical abstract

• Hovan, Ladislav & Kuijjer, Marieke (2025). SPONGE: Simple Prior Omics Network GEnerator. Bioinformatics. ISSN 1367-4803. 41(7). doi: 10.1093/bioinformatics/btaf320. Full text in Research Archive Show summary

  Gene regulatory networks modelled from experimental data can be improved through the use of prior biological knowledge, e.g. transcription factor binding. There are several tools that utilize this information. However, the prior networks used with them are often not updated and may fail to reflect the most up-to-date information. Here we present SPONGE, a Python module designed to access information across biological databases, chiefly JASPAR and STRING, to model two types of networks—a prior gene regulatory network mapping transcription factors to genes based on their predicted binding sites, and a prior protein-protein interaction network mapping potential interactions between transcription factors. SPONGE is mainly designed to work with the PANDA algorithm and the corresponding NetZoo family of tools. However, the networks are provided in an easily adaptable format for other tools. SPONGE was designed with ease of use in mind, and it provides sensible default values for all of its parameters while giving the users the freedom to fine-tune them. Availability and implementation The code for the Python module and the documentation can be found in our GitHub repository.

• Luis, Almazán, Jorge; Bolivar, Arcos-Encarnación,; Isabel, Barón-Mendoza,; Nohemi, Camacho-Concha,; Eladio, Cortes-Flores, & Sol, Díaz de León-Guerrero, [Show all 15 contributors for this article] (2025). KChIP3 fosters neuroinflammation and synaptic dysfunction in the 5XFAD mouse model of Alzheimer’s disease | Journal of Neuroinflammation | Full Text. Journal of Neuroinflammation. 22(160). doi: 10.1186/s12974-025-03426-2. Full text in Research Archive Show summary

  Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by β-amyloid (βA) accumulation, neuroinflammation, excessive synaptic pruning, and cognitive decline. Despite extensive research, effective treatments remain elusive. Here, we identify potassium channel-interacting protein 3 (KChIP3) as a key driver of AD pathology using the 5XFAD mouse model. KChIP3 levels were significantly elevated in the hippocampus of 5XFAD mice, correlating with βA burden and neuroinflammation. This upregulation was triggered by inflammatory signaling via the NLRP3 inflammasome and Caspase-1 activation. Notably, genetic deletion of KChIP3 (5XFAD/KChIP3−/−) markedly reduced βA plaque deposition, pro-inflammatory cytokines, reactive gliosis, and expression of inflammation-related proteins (APO, CLU, MDK). Transcriptomic and proteomic analyses revealed restored synaptic markers (CD47, CD200, CACNB4, GDA) and a shift of the disease-associated microglial (DAM-1) phenotype. Mechanistically, we propose that KChIP3 amplifies AD pathology through two key mechanisms: (1) sustaining neuroinflammation by upregulating pro-inflammatory genes and (2) impairing synaptic integrity by repressing genes critical for neuronal function. Consistently, KChIP3 deletion enhanced dendritic complexity, synaptic plasticity, and cognitive performance in 5XFAD mice. These findings position KChIP3 as a potential therapeutic target for mitigating neuroinflammation and synaptic dysfunction in AD and highlight its potential as a biomarker for disease progression.

• Ferenc, Katalin Terezia; Rauluseviciute, Ieva; Hovan, Ladislav; Kumar, Vipin; Kuijjer, Marieke & Mathelier, Anthony (2024). Improving bioinformatics software quality through teamwork. Bioinformatics. ISSN 1367-4803. 40(11). doi: 10.1093/bioinformatics/btae632. Full text in Research Archive Show summary

  Since high-throughput techniques became a staple in biological science laboratories, computational algorithms, and scientific software have boomed. However, the development of bioinformatics software usually lacks software development quality standards. The resulting software code is hard to test, reuse, and maintain. We believe that the root of inefficiency in implementing the best software development practices in academic settings is the individualistic approach, which has traditionally been the norm for recognizing scientific achievements and, by extension, for developing specialized software. Software development is a collective effort in most software-heavy endeavors. Indeed, the literature suggests teamwork directly impacts code quality through knowledge sharing, collective software development, and established coding standards. In our computational biology research groups, we sustainably involve all group members in learning, sharing, and discussing software development while maintaining the personal ownership of research projects and related software products. We found that group members involved in this endeavor improved their coding skills, became more efficient bioinformaticians, and obtained detailed knowledge about their peers’ work, triggering new collaborative projects. We strongly advocate for improving software development culture within bioinformatics through collective effort in computational biology groups or institutes with three or more bioinformaticians.

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• Hovan, Ladislav (2023). Role of computational tools in drug discovery. Full text in Research Archive
• Hovan, Ladislav (2022). STOAT: Generating gene regulatory networks from spatially resolved transcriptomic data. Full text in Research Archive

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