bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.10.06.680804

Boolean Logic Coupled with Overrepresentation Analysis Reveals Activation of the Platelet-derived Growth Factor Receptor Beta Pathway in a Model of Oliguric Acute Kidney Injury; Implications for Transition to Chronic Kidney Disease

Chavre, H. M.·Bissoondial, T.·Narayan, M.·Narayan, P.
View original source ↗Export metadata ↓

Abstract

Acute Kidney Injury (AKI) can occur secondary to insults including sepsis, ischemia and contrast dye administration. A time-sensitive increase in serum creatinine (SCr) or reduction in urine output (UO) has been used to define AKI and stage its severity. Oliguria or significantly reduced UO in AKI or oliguric AKI can have a major impact on outcomes including a transition to chronic kidney disease (CKD). We used Boolean logic coupled with overrepresentation analysis to identify the pathway activation signature associated with oliguric AKI in a published study of rat kidney-ischemia reperfusion. In the reperfused kidney, bulk transcriptomic analysis revealed 1068 differentially expressed genes (DEGs). Those DEGs that correlated with UO and SCr were submitted to gene ontology biological process overexpression analysis. The pathway activation signature associated with oliguric AKI included positive regulation of profibrotic platelet-derived growth factor receptor beta signaling (fold-enrichment >44) driven by src, hip1 and hip1r. Together these findings not only suggest that oliguric AKI may be associated with activation of a pathway leading to fibrosis and CKD but also informs an array of targets to potentially mitigate transition to CKD. HighlightsMechanistic insights should illuminate therapies. A model of rat kidney-ischemia reperfusion injury was queried by correlating kidney transcriptomics with kidney function to identify the pathway activation signature of oliguric AKI. The most striking feature associated with oliguric AKI was positive regulation of platelet-derived growth factor receptor {beta} driven by src, hip1, and hip1r. The pathway activation signature in oliguric AKI informs not only the sequel to injury but also an array of targets to mitigate the potential transition to kidney fibrosis and CKD. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=47 SRC="FIGDIR/small/680804v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@121edc1org.highwire.dtl.DTLVardef@80383borg.highwire.dtl.DTLVardef@1ebc25dorg.highwire.dtl.DTLVardef@1258145_HPS_FORMAT_FIGEXP M_FIG C_FIG

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chavre, H. M., Bissoondial, T., Narayan, M., Narayan, P.. 2025-10-07. Boolean Logic Coupled with Overrepresentation Analysis Reveals Activation of the Platelet-derived Growth Factor Receptor Beta Pathway in a Model of Oliguric Acute Kidney Injury; Implications for Transition to Chronic Kidney Disease. https://doi.org/10.1101/2025.10.06.680804

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗