Biology subjects

Meshram, S.

Publications and source records attributed to Meshram, S..

4 recordsLinked to original sources

bioRxiv2026

CD2AP's structure and oligomerization are compromised by the K301M mutation: implications for Nephrotic syndrome

IntroductionThe podocyte slit diaphragm (SD) is a complex filtration unit localized to the blood and urine interface and governs the glomerular selectivity. However, the greater details of the SD composition and the mechanism of assembly of the SD protein as a macromolecular complex remain elusive. CD2-associated protein (CD2AP) serves as a central scaffold within the SD, and mutations in CD2AP are strongly associated with nephrotic syndrome (NS) and focal segmental glomerulosclerosis (FSGS). However, the mechanisms by which such mutations alter the architecture and higher-order organization of CD2AP are poorly understood. MethodsWe employed biophysical, structural, and proteomic approaches to investigate the impact of the disease-associated K301M mutation on CD2AP structure and its interaction with Podocin. Oligomerization was analyzed using size-exclusion chromatography, blue native PAGE, Dynamic light scattering, and small-angle X-ray scattering. Secondary and tertiary structural properties were assessed by far- and near-UV circular dichroism, thermal denaturation, and intrinsic fluorescence spectroscopy. CD2AP-podocin interactions were quantified using in vitro pulldown and surface plasmon resonance (SPR), and mutation-dependent changes in interaction networks were examined through interactome profiling. ResultsWild-type (WT) CD2AP assembled into flexible higher-order oligomers ([~]9-12-mers), whereas the K301M variant collapsed into lower-order species ([~]3-6-mers), indicating destabilization of the coiled-coil assembly interface. Spectroscopic analyses revealed subtle secondary-structure rearrangements, but profound tertiary packing defects, as well as reduced and markedly diminished thermal resilience in the mutant. SPR analysis demonstrated loss of binding between Podocin and mutant CD2AP, whereas WT CD2AP showed high-affinity interaction (KD = 211 nM) with Podocin. Complementary interactome profiling revealed widespread rewiring of protein-protein interactions in the case of mutant CD2AP, characterized by the loss of core partners and the emergence of aberrant associations. ConclusionThese findings define a mechanistic model in which the K301M mutation destabilizes CD2AP oligomerization, disrupts podocin recognition, and remodels interaction networks essential for SD stability. This work signifies the importance of CD2AP in SD assembly and the permselective filtration function of the kidney, and the impact of a single mutation in the pathogenesis of NS and FSGS. Translational Statement: Inherited nephrotic syndrome, characterized by heavy proteinuria, frequently arises from mutations in scaffolding proteins of the slit-diaphragm (SD). This study demonstrates that the nephrotic syndrome-associated K301M mutation in CD2-associated protein (CD2AP) compromises higher-order oligomerization, abolishes its binding to the binding partner (Podocin), and reshapes protein-protein interaction networks that are critical for SD assembly and stability. By establishing a direct link between mutation-induced collapse of CD2AP architecture and loss of podocyte scaffolding function, these findings provide mechanistic insight into the pathogenesis of CD2AP-associated proteinuric kidney disease. The results further identify oligomeric assembly interfaces as potential targets for therapeutic strategies aimed at preserving the integrity of the SD and glomerular filtration function. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/698362v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@1f49f1dorg.highwire.dtl.DTLVardef@fa2e06org.highwire.dtl.DTLVardef@e399c0org.highwire.dtl.DTLVardef@831c17_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

biophysics↗

bioRxiv2026

ReMeDy: A Flexible Statistical Framework For Region-based Detection of DNA Methylation Dysregulation

Region-based epigenome-wide association studies have demonstrated improved statistical power and bio-logical interpretability compared with probe-wise analyses of DNA methylation data. However, most existing region-based methods characterize methylation dysregulation primarily through changes in mean methylation levels associated with a phenotype of interest. Substantial evidence indicates that phenotype-associated methylation alterations may also manifest through changes in methylation variability or through joint shifts in mean and variability. Despite this, no existing statistical framework jointly models mean-variance methylation changes in a region-based manner. We propose ReMeDy, a flexible statistical framework that uses a hierarchical likelihood approach within a generalized linear model setting to identify differentially methylated regions, variably methylated regions, and regions exhibiting joint differential and variable methylation at a genome-wide scale. Unlike existing models, ReMeDy operates directly on biologically defined co-methylated regions, allowing it to naturally capture spatial correlation inherent in DNA methylation array data, while avoiding reliance on heuristic, user-defined tuning parameters such as smoothing spans and kernel bandwidths that can substantially influence results and introduce subjectivity. Through extensive simulation studies and comprehensive benchmarking against popular models, we demonstrate that ReMeDy maintains false discovery and type-I error rates at nominal levels while achieving consistently higher statistical power across a wide range of realistic scenarios. Application to population-level DNA methylation data further shows that ReMeDy identifies biologically meaningful regions and pathways implicated in complex human diseases that are not captured by conventional mean-based analyses alone. ReMeDy is implemented as an open-source R package and is freely available at https://github.com/SChatLab/ReMeDy.

genomics↗

bioRxiv2025

Repurposing ethacridine as a potent MMPL3 Inhibitor for the treatment of tuberculosis

Mycobacterium tuberculosis (Mtb), the pathogen responsible for tuberculosis, remains a major global health threat, particularly with the rise of multidrug-resistant and extensively drug-resistant strains. This has renewed interest in repurposing existing drugs and exploring new cellular targets. The mycobacterial cell envelope is a key barrier to antibiotics and an attractive site for therapeutic intervention. In this study, we identify the FDA-approved drug ethacridine as a strong inhibitor of MmpL3, an essential transporter required for exporting trehalose monomycolate and building the cell wall. Computational docking and molecular dynamics indicate that ethacridine engages the MmpL3 binding pocket at residues also targeted by SQ109. Ethacridine shows potent activity against drug-sensitive and resistant Mtb isolates, with an MIC of 1 g/mL, and remains effective against non-replicating bacteria and intracellular infection. Ethacridine-resistant mutants, overexpression strains, and a spheroplast TMM-flipping assay confirm MmpL3 as the target. The compound also disrupts the membrane potential, and flow-cytometry assays

microbiology↗

bioRxiv2025

A spatially-aware unsupervised pipeline to identify co-methylation regions in DNA methylation data

DNA methylation (DNAm) plays a central role in modern epigenetic research; however, the high dimensionality of DNAm data comprising hundreds of thousands of spatially ordered probes continues to present major analytical challenges. The multiple testing burden in these data introduces redundancy and reduces statistical power, contributing to the limited reproducibility often observed in association studies. Moreover, DNAm probes frequently exhibit correlated methylation patterns with neighboring sites, reflecting underlying biological co-regulation and spatial dependence along the genome. Ignoring these spatial correlations can bias parameter and standard error estimates, inflate type I error rates, and obscure biologically meaningful effects. Existing methods for detecting methylation co-regulation and reducing DNAm data dimensions, typically rely on fixed distance or correlation thresholds and arbitrary hyperparameter settings that lack data adaptivity. In this study, we introduce SACOMA (Spatially-Aware Clustering for Co-Methylation Analysis), a flexible, data-driven, and unsupervised framework designed to identify co-methylated regions which are genomic regions where adjacent sites show correlated methylation levels. SACOMA employs spatially constrained hierarchical clustering to group neighboring DNAm sites based on both spatial proximity and methylation similarity. A tunable, data-adaptive mixing parameter allows SACOMA to avoid rigid assumptions and remain robust to hyperparameter choices. Although developed for DNAm array data, SACOMA provides a generalizable framework applicable to any data exhibiting spatial dependence, enabling the identification of spatially correlated features across diverse domains. Through extensive simulations, SACOMA demonstrated superior sensitivity while maintaining effective false-positive control compared to existing methods. In population-level DNAm data analyses, SACOMA successfully identified biologically relevant co-regulated methylation regions with functional roles. Overall, SACOMA reduces the multiple-testing burden and enhances both the discovery and specificity of statistical associations, leading to improved reproducibility and more reliable biological inference.

bioinformatics↗