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Rezaei, R.

Publications and source records attributed to Rezaei, R..

3 recordsLinked to original sources

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↗

Characterization of Critical Determinants of ACE2-RBD Interaction

Despite sequence similarity to SARS-CoV-1, SARS-CoV-2 has demonstrated greater widespread virulence and unique challenges to researchers aiming to study its pathogenicity in humans. The interaction of the viral receptor binding domain (RBD) with its main host cell receptor, angiotensin-converting enzyme 2 (ACE2), has emerged as a critical focal point for the development of anti-viral therapeutics and vaccines. Utilizing our recently developed NanoBiT technology-based biosensor, we selectively identify and characterize the impact of mutating certain amino acid residues in the RBD of SARS-CoV-2 and in ACE2. Specifically, we examine the mutational effects on RBD-ACE2 binding ability, before and after the addition of competitive inhibitors, as well as neutralizing antibody activity. These critical determinants of virus-host interactions may provide more effective targets for ongoing vaccines, drug development, and potentially pave the way for determining the genetic variation underlying disease severity.

microbiology↗

Optimizing Consensus Generation Algorithms for Highly Variable Amino Acid Sequence Clusters

Producing a functional consensus sequence is a preliminary bioinformatics task, which is a necessity for many research purposes. However, the existence of hypervariable regions in the input multiple sequence alignment files causes complications in generating a useful consensus sequence. The current methods for consensus generation, Threshold, and majority algorithms, have several problems, which exclude them as applicable algorithms for such highly variable sequence clusters. Hence, we designed a novel alternative algorithm for the same purpose. The algorithm was explained both using a mathematical formula and a practical implementation in Python programming language. A sequence set from HCV genotype 1b E2 protein has been utilized as a practical example for evaluating the algorithms performance. A few in silico tests have been performed on the output sequence and the results have been compared to results from other algorithms. Epitope-mapping analysis indicates the functionality of this algorithm, by preserving the hotspot residues in the consensus sequence, and the antigenicity index shows significant antigenicity of the consensus sequence. Moreover, phylogenetic analysis shows no significant change in the placement of the new consensus sequence on the phylogenetic tree compared to other algorithms. This approach will have several implications in designing a new vaccine for highly variable viruses such as HIV-1, Influenza, and Hepatitis C Viruses (HCV).

bioinformatics↗