Biology subjects

Gjerga, E.

Publications and source records attributed to Gjerga, E..

11 recordsLinked to original sources

bioRxiv2026

CAPRINI-M: An AI-curated Cardiac-Specific Atlas of Protein Interactions in Mice

MotivationProtein-protein interactions are fundamental to cardiovascular disease biology, but the corresponding knowledge is dispersed across the literature and heterogeneous databases, making systematic curation time-consuming. Moreover, many existing PPI resources may be biased and lack detailed information on structural interaction interfaces or associated thermodynamic parameters. ResultsWe present CAPRINI-M (CArdiac PRotein INteractions In Mice), a web-based tool hosting an AI-curated atlas of cardiac protein interactions. We mined 9,105 cardiobiology manuscripts and used open-source LLMs (LLaMA-3.3 70B) to extract 11,189 protein-protein interactions. We then used AlphaFold3 to infer interaction interfaces, estimate thermodynamic properties related to complex stability, and predict the likelihood that each protein pair forms a complex. In our benchmarking analysis, CAPRINI-M showed stronger performance than the comparator PPI resources tested here. Predicted interaction favourability also agreed with published experimental evidence, with lower predicted Gibbs free energy associated with experimentally preferred binding partners. Overall, CAPRINI-M provides a more comprehensive, mechanistically annotated view of cardiovascular disease-relevant protein-protein interactions by integrating literature evidence with structural, interface-level, and stability-related information. AvailabilityThe CAPRINI-M web application is available at https://shiny.dieterichlab.org/app/caprinim. The source code used in this study is linked in the manuscripts Availability section.

bioinformatics↗

bioRxiv2025

Genomic G quadruplexes regulate mRNA splicing

Genomic G quadruplexes (G4) are non-canonical DNA structures that regulate gene expression primarily through transcriptional control. Here, we uncover that DNA G quadruplexes are critical determinants of pre-mRNA splicing. G4s on the DNA template strand serve as recruiting elements for the RNA-binding proteins SRSF9 and WBP11 in order to facilitate productive splicing of adjacent pre-mRNAs. This process is controlled by protein arginine methyltransferase 5 (PRMT5) that releases SRSF9 and WBP11 from G4s through arginine methylation. Acyl-CoA dehydrogenase very long-chain specific (ACADVL) is a gene highly regulated by this mechanism since targeting G4 stability prevents mis-splicing and restores ACADVL protein levels. In the heart, deletion of Prmt5 recapitulates defective splicing and results in progressive cardiac failure along with loss of ACADVL in mice. Importantly, we find that Acadvl regulation plays a critical role for cardiomyopathy as restoring Acadvl expression in Prmt5 knockout mice prevents cardiac dysfunction. This study establishes an unanticipated fundamental principle by which genomic G quadruplexes act as splicing enhancers of associated pre-mRNAs, and reveals an essential role in cardiac homeostasis.

cell biology↗

bioRxiv2025

Reading papers: Extraction of molecular interaction networks with large language models

MotivationSignalling occurs within and across cells and orchestrates essential cellular processes in complex tissues. Cell signalling involves several different components, including protein-protein interactions (PPI) and transcription factors (TF), to promoter binding in gene regulatory networks (GRNs). Dynamically changing conditions oftentimes lead to the rewiring of cellular communication networks. Computational modelling approaches typically rely on databases of possible molecular interactions. Evidently, manual curation of databases is time-consuming and automatic relation extraction from scientific literature would greatly support our strive to understand molecular mechanisms. To ease this process, we reason that prompt-based data mining with Large Language Models (LLMs) could be used to extract information from relevant scientific publications. ApproachIn our work, we use open-source LLMs to mine an annotated corpus of molecular interactions. We focus on the extraction of entity relations between proteins, as exemplified in protein-protein interaction networks, and transcription factor to target gene relations, as exemplified in gene regulatory networks. ResultsWe obtain promising evaluation results as measured by precision, recall and F1-score for the extraction of PPI relations: 87%, 70% and 71% and 77%, 57% and 62% for GRN relation extraction over a large corpus of short (average 331 tokens) scientific texts. AvailabilityCodes with scripts and results have been provided in: https://github.com/dieterich-lab/LLM_Relations.

bioinformatics↗

bioRxiv2024

Transverse Aortic COnstriction Multi-omics Analysis (TACOMA) uncovers pathophysiological cardiac molecular mechanisms

Time-course multi-omics data of a murine model of progressive heart failure induced by transverse aortic constriction (TAC) provide insights into the molecular mechanisms that are causatively involved in contractile failure and structural cardiac remodelling. We employ Illumina-based transcriptomics, Nanopore sequencing, and mass spectrometry-based proteomics on samples from the left ventricle (LV) and right ventricle (RV, RNA only) of the heart at 1, 7, 21, and 56 days following TAC and Sham surgery. Here, we present TACOMA, as an interactive web-application that integrates and visualizes transcriptomics and proteomics data collected in a TAC time-course experiment. TACOMA enables users to visualize the expression profile of known and novel genes and protein products thereof. Importantly, we capture alternative splicing events by assessing differential transcript and exon usage as well. Co-expression-based clustering algorithms and functional enrichment analysis revealed overrepresented annotations of biological processes and molecular functions at the protein and gene levels. To enhance data integration, TACOMA synchronizes transcriptomics and proteomics profiles, enabling cross-omics comparisons. With TACOMA (https://shiny.dieterichlab.org/app/tacoma), we offer a rich web-based resource to uncover molecular events and biological processes implicated in contractile failure and cardiac hypertrophy. For example, we highlight: (i) changes in metabolic genes and proteins in the time course of hypertrophic growth and contractile impairment; (ii) identification of RNA splicing changes in the expression of Tpm2 isoforms between RV and LV; and (iii) novel transcripts and genes likely contributing to the pathogenesis of heart failure. We plan to extend these data with additional environmental and genetic models of heart failure to decipher common and distinct molecular changes in heart diseases of different aetiologies. Database URLhttps://shiny.dieterichlab.org/app/tacoma

bioinformatics↗

bioRxiv2022

Adaptive Sampling as tool for Nanopore direct RNA-sequencing

ONT long-read sequencing provides real-time monitoring and controlling of individual nanopores. Adaptive sampling enriches or depletes specific sequences in Nanopore DNA sequencing, but was not applicable to direct sequencing of RNA so far. Here, we identify essential parameter settings for direct RNA sequencing (DRS). We demonstrate the superior performance of depletion over enrichment and show that adaptive sampling efficiently depletes specific transcripts in transcriptome-wide sequencing applications. Specifically, we applied our adaptive sampling approach to polyA+ RNA samples from human cardiomyocytes and mouse whole heart tissue. Herein, we show more than 2.5-fold depletion of highly abundant mitochondrial-encoded transcripts that in normal sequencing account for up to 40% of sequenced bases in heart tissue samples.

bioinformatics↗

bioRxiv2022

Magnetique: An interactive web application to explore transcriptome signatures of heart failure

Despite a recent increase in the number of RNA-seq datasets investigating heart failure (HF), accessibility and usability remain critical issues for medical researchers. We present Magnetique (https://shiny.dieterichlab.org/app/magnetique), an interactive web application to explore the transcriptional signatures of heart failure. We reanalyzed the Myocardial Applied Genomics Network RNA-seq dataset, one of the largest publicly available datasets of left ventricular RNA-seq samples from patients with dilated (DCM) or hypertrophic (HCM) cardiomyopathy, as well as unmatched non-failing hearts from organ donors and patient characteristics that allowed us to model confounding factors. Focusing on the DCM versus HCM contrast, we identified 201 differentially expressed genes and associated pathway signatures. Moreover, we predict underlying signaling networks based on inferred transcription factor activities. To the best of our knowledge, Magnetique is the first online application to provide an interactive view of the HF transcriptome by analyzing differential transcript isoform usage. Finally, another graphical view on statistically predicted RNA-binding protein to target transcript interactions complements the Magnetique web application. The source code for both the analyses (https://github.com/dieterich-lab/magnetiqueCode2022) and the web application (https://github.com/AnnekathrinSilvia/magnetique) is available to the public. We hope that our application will help users to uncover the molecular basis of heart failure.

bioinformatics↗

bioRxiv2022

Reconstructing the functional effect of TP53 somatic mutations on its regulon using causal signalling network modelling

AO_SCPLOWBSTRACTC_SCPLOWThe gene encoding tumor protein p53 (TP53) is the most frequently mutated gene in human cancer. Mutations in both coding and non-coding regions of TP53 can disrupt the regulatory function of the transcription factor, but the functional impact of different somatic mutations on the global TP53 regulon is complex and poorly understood. To address this, we first proceed with a machine learning (ML) approach, and then propose an integrated computational network modelling approach that reconstructs signalling networks using a comprehensive collection of experimental and predicted regulons, and compares their topology. We evaluate both these approaches in a scrutinized pan-cancer analysis of matched genomics and transcriptomics data from 1,457 cell lines (22 cancer types) and 12,531 clinical samples (54 cancer sub-types). Using a ML approach based on penalized generalized linear regression we were able to predict TP53 mutation, but failed to resolve different mutation types. Thus, to infer the impact of different TP53 mutations we compared the topological characteristics of the optimized and reconstructed (upwards of twenty thousand) gene networks and extracted gene signatures for each mutation type using network analysis. We demonstrate that by accounting for TP53 mutation characteristics such as i) mutation type (e.g. missense, nonsense), ii) deleterious consequences of the mutation, or iii) mapping to previously identified hotspots, we can infer a much richer understanding of gene expression regulation, than when simply grouping samples based on their mutation/wild type or gene expression status. Our study highlights a powerful strategy exploiting signalling networks to systematically characterize the functional impact of the full spectrum of somatic mutations. This approach can be applied in general to genetic variation, with clear implications for, but not limited to, the biomedical domain and precision medicine.

systems biology↗

bioRxiv2020

Metabolic control of tumour extracellular matrix production in cancer-associated fibroblasts

TElevated production of collagen-rich extracellular matrix (ECM) is a hallmark of cancer associated fibroblasts (CAFs) and a central driver of cancer aggressiveness. How to target ECM production to oppose cancer is yet unclear, since targeting CAFs has been shown to restrain but also promote cancer progression. Metabolic rewiring is a hallmark of CAFs. Here we find that proline, which is a highly abundant amino acid in collagen proteins, is newly synthesised from glutamine to make tumour collagen in breast cancer xenografts, and that its production is elevated in breast cancer CAFs. PYCR1 is the rate-limiting enzyme for proline synthesis and is highly expressed in the tumour stroma of breast cancer patients and in CAFs. Reducing PYCR1 levels in CAFs is sufficient to reduce tumour collagen production, tumour growth and metastatic spread in vivo and cancer cell proliferation in vitro. PYCR1 and COL1A1 are overexpressed in patients with invasive ductal carcinoma with poor prognosis. Both collagen and glutamine-derived proline synthesis in CAFs are enhanced by increased pyruvate dehydrogenase-derived acetyl-CoA levels, via gene expression regulation through the epigenetic regulator histone acetyl transferase EP300. Altogether, our work unveils unprecedented roles of CAF metabolism to support pro-tumorigenic collagen production. PYCR1 is a recognised cancer cell vulnerability and potential target for therapy, hence, our work provides evidence that targeting PYCR1 in tumours may have the additional benefit of halting the production of pro-tumorigenic ECM.

cancer biology↗

bioRxiv2020

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Diverse tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from nine renal cell carcinoma patients. We used COSMOS to generate novel hypotheses such as the impact of Androgen Receptor on nucleoside metabolism and the influence of the JAK-STAT pathway on propionyl coenzyme A production. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/057893v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@37d2d7org.highwire.dtl.DTLVardef@f333d4org.highwire.dtl.DTLVardef@cfcac3org.highwire.dtl.DTLVardef@90318_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Converting networks to predictive logic models from perturbation signalling data with CellNOpt

MotivationThe molecular changes induced by perturbations such as drugs and ligands are highly informative of the intracellular wiring. Our capacity to generate large data-sets is increasing steadily as new experimental approaches are developed. A useful way to extract mechanistic insight from the data is by integrating them with a prior knowledge network of signalling to obtain dynamic models. Logic models scale better with network size than alternative kinetic models, while keeping the interpretation of the model simple, making them particularly suitable for large datasets. ResultsCellNOpt is a collection of Bioconductor R packages for building logic models from perturbation data and prior knowledge of signalling networks. We have recently developed new components and refined the existing ones. These updates include (i) an Integer Linear Programming (ILP) formulation which guarantees efficient optimisation for Boolean models, (ii) a probabilistic logic implementation for semi-quantitative datasets and (iii) the integration of MaBoSS, a stochastic Boolean simulator. Furthermore, we introduce Dynamic-Feeder, a tool to identify missing links not present in the prior knowledge. We have also implemented systematic post-hoc analyses to highlight the key components and parameters of our models. Finally, we provide an R-Shiny tool to run CellNOpt interactively. AvailabilityR-package(s): https://github.com/saezlab/cellnopt Contactjulio.saez@bioquant.uni-heidelberg.de Supplementary informationSupplemental Text.

bioinformatics↗

bioRxiv2019

From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL

While gene expression profiling is commonly used to gain an overview of cellular processes, the identification of upstream processes that drive expression changes remains a challenge. To address this issue, we introduce CARNIVAL, a causal network building tool which derives network architectures from gene expression footprints. CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) integrates different sources of prior knowledge, including signed and directed protein-protein interactions, transcription factor targets, and pathway signatures. The use of prior knowledge in CARNIVAL allows the capture of a broad set of upstream cellular processes and regulators, which in turn delivered results with higher accuracy when benchmarked against related tools. Implementation as an integer linear programming (ILP) problem also guarantees efficient computation. As a case study, we applied CARNIVAL to contextualize signaling networks from gene expression data in IgA nephropathy, a chronic kidney disease. CARNIVAL identified specific signaling pathways and associated mediators with important bioactivity in IgAN including WNT and TGF-{beta}, that we subsequently validated experimentally. In summary, we demonstrated how CARNIVAL generates hypotheses on potential upstream alterations that propagate through signaling networks, providing valuable insights into disease processes. CARNIVAL, freely available as an R-package at https://saezlab.github.io/CARNIVAL, can be applied to any field of biomedical research to contextualize signaling networks and identify the causal relationships between downstream gene expression and upstream regulators.

systems biology↗