Biology subjects

Gheisari, Y.

Publications and source records attributed to Gheisari, Y..

4 recordsLinked to original sources

bioRxiv2026

Model-Dependent Renal Phenotypes in Diabetic Kidney Disease: Comparative Histopathological Characterization of Commonly Used Animal Models

BackgroundDiabetic kidney disease (DKD) remains a leading cause of end-stage renal disease worldwide, characterized by progressive structural and metabolic alterations secondary to chronic hyperglycemia. While numerous type 1 and type 2 rodent models have been developed to study the pathophysiology of DKD, no single model perfectly recapitulates the full clinical spectrum of human disease. The selection of an optimal model depends deeply on the specific research objective, as phenotypic expression and histopathological severity vary significantly across different strains and induction methods. The present study provides a comparative analysis of the renal histological of three widely utilized murine models: the chemically induced streptozotocin (STZ) model and the genetic Akita (type 1) and db/db (type 2) models. MethodsMale STZ-induced (28 weeks post-induction), heterozygous Akita (28 weeks old), and db/db mice at two different age intervals (18-21 and 16-24 weeks old) were assessed. Renal injury was quantified using four light-microscopic parameters: glomerulomegaly, mesangial hypercellularity, tubular vacuolization and arteriolar hyalinosis. Due to observed discrepancies between metabolic and structural findings in the db/db strain, transmission electron microscopy (TEM) was employed for subcellular characterization. ResultsAll models exhibited significant hyperglycemia and albuminuria. At the light-microscopic level, STZ and Akita mice demonstrated consistent and pronounced renal lesions. In contrast, db/db mice despite increasing albuminuria and obesity, light microscopy revealed heterogeneous and inconsistent histopathological changes. However, TEM analysis of db/db mice kidneys successfully captured early ultrastructural injury, including irregular glomerular basement membrane (GBM) thickening and focal podocyte foot process effacement, which were undetectable by light microscopy. ConclusionsOur findings indicate that the Akita and STZ-induced models exhibit prominent structural alterations detectable by conventional light microscopy, whereas the db/db model requires ultrastructural evaluation by TEM to reliably confirm renal injury. This study underscores the limitation of routine histology in certain type 2 diabetes models and highlights the complementary value of TEM for accurate histopathological characterization. Collectively, the alternative histopathological markers identified herein offer sensitive and readily accessible indices for monitoring early-to-moderate DKD progression, providing a more robust framework for preclinical model selection and therapeutic evaluation in future studies.

pathology↗

bioRxiv2024

DrugTar Improves Druggability Prediction by Integrating Large Language Models and Gene Ontologies

Target discovery is crucial in drug development, especially for complex chronic diseases. Recent advances in high-throughput technologies and the explosion of biomedical data have highlighted the potential of computational druggability prediction methods. However, most current methods rely on sequence-based features with machine learning, which often face challenges related to hand-crafted features, reproducibility, and accessibility. Moreover, the potential of raw sequence and protein structure has not been fully investigated. Here, we leveraged both protein sequence and structure using deep learning techniques, revealing that protein sequence, especially pre- trained embeddings, is more informative than protein structure. Next, we developed DrugTar, a highl7lperformance deep learning algorithm integrating sequence embeddings from the ESM-2 pre-trained protein language model with protein ontologies to predict druggability. DrugTar achieved areas under the curve and precision-recall curve values above 0.90, outperforming state-of-the-art methods. In conclusion, DrugTar streamlines target discovery as a bottleneck in developing novel therapeutics.

systems biology↗

bioRxiv2022

A Novel Strategy for Dynamic Modelling of Genome-Scale Interaction Networks

BackgroundModern medicine is equipped with huge amounts of big biological datasets and a wide range of computational methods to understand the molecular events underlying complex disorders. The recent availability of omics data allows a holistic view towards the interactions of various biomolecule types. However, the constructed maps are static, ignoring the dynamicity of molecular processes. On the other hand, the dynamic models of biological systems are commonly generated in small scales. Hence, the construction of large scale dynamic models that can quantitatively predict the time-course cellular behaviors is a big challenge. This study was aimed at developing a pipeline for automatic construction of such models from time-course experimental data. ResultsInformation of interactions between input genes is retrieved from SIGNORE 2.0 database and an interaction network is constructed which then is translated to biochemistry language and converted to a biochemical reactions network. In the next step, a large-scale ODE system is constructed by generating the ODE equivalent of each biochemical reaction. To estimate the kinetics parameters of the ODE model, a novel large-scale parameter approximation method is proposed. This method gives an estimation of system parameters by fitting model outputs to time-course experimental measurements. The total pipeline is provided as a MATLAB toolbox called SPADAN, standing for Systematic Protein Association Dynamic ANalyzer. Using multilayer time-series experimental data, the performance of the pipeline was checked by modeling 4379 regulatory interactions between 768 molecules in colon cancer cells exposed to chemotherapy agents. ConclusionStarting from time-series experimental data, SPADAN automatically constructs map of interactions, generates an ODE system, and performs a parameter approximation procedure. It constructs genome-scale dynamic models, filling the gap between large-scale static and small-scale dynamic modeling strategies. This simulation approach allows for holistic quantitative predictions which is critical for the simulation of therapeutic interventions in precision medicine.

systems biology↗

bioRxiv2020

Systems and classical biology approaches unraveled role of olfactory receptors in progression of kidney fibrosis

The olfactory receptors (ORs) which are mainly known as odor-sensors in the olfactory epithelium are distributed in several non-sensory tissues. Despite the specified role of some of these receptors in normal physiology of the kidney, little is known about their potential effect in renal disorders. In this study, using the holistic view of systems biology, it was determined that ORs are significantly changed during the progression of kidney fibrosis. For further validation, common differentially expressed ORs resulted from reanalysis of two time-course microarray datasets were selected for experimental evaluation in a validated murine model of unilateral ureteral obstruction. Transcriptional analysis demonstrated considerable changes in the expression pattern of Olfr433, Olfr129, Olfr1393, and Olfr161 during the progression of kidney fibrosis. In conclusion, our results highlight the impact of systems biology in determination of the underlying mechanisms of chronic diseases and indicate the importance of time-course approaches to unravel the patterns of gene expression. The novel ORs proposed in this study could be the subject of further functional investigations in the kidney.

systems biology↗