Biology subjects

Chhibber, S.

Publications and source records attributed to Chhibber, S..

5 recordsLinked to original sources

bioRxiv2026

YdbL directly modulates YdbH-YnbE bridge formation to maintain Escherichia coli outer membrane homeostasis

Gram-negative bacteria pose a threat to global healthcare mainly because their outer membrane (OM) provides an intrinsic barrier to many antimicrobials. Key to this barrier function is the asymmetric structure of the OM, with phospholipids constituting the inner leaflet and lipopolysaccharides the outer leaflet. Although the mechanism of phospholipid transport between the inner membrane (IM) and OM remains poorly understood, recent studies implicate TamB, YhdP, and YdbH as functionally redundant proteins mediating this process in Escherichia coli. Accordingly, collective loss of these three paralogs is lethal and any one of them is sufficient for growth. YdbH is anchored to the IM and its periplasmic repeating {beta}-sheet groove domain interacts with the OM lipoprotein YnbE via {beta}-strand augmentation to form an intermembrane bridge. Additionally, YnbE multimerizes, and the periplasmic protein YdbL is proposed to modulate YnbE multimerization to facilitate its stacking on the C-terminus of YdbH. Here, we demonstrate that excess YdbL specifically inhibits the function of the YdbH-YnbE complex since overexpression of ydbL causes lethality in the {Delta}yhdP {Delta}tamB double mutant but the presence of both ydbH and ynbE in trans abrogates this lethality. We resolve high-resolution structural data for YdbL and ascertain its interaction site with the YnbE C-terminal -helix, with residues mediating this interface highly conserved and critical for YdbL function. Finally, we show that YdbL is protected from degradation by the protease DegP when complexed with YnbE. Overall, our data supports a model in which YdbL ensures proper YdbH-YnbE intermembrane bridge formation by directly interacting with YnbE.

microbiology↗

bioRxiv2026

Transforming macromolecular structures into simulations of self-assembly

Macromolecular self-assembly is a fundamental process in living and engineered systems, producing molecular machines like the ribosome or highly symmetric viral capsids. Thanks to sources like the Protein Data Bank (PDB) and AlphaFold3, the final target complexes are often known, but these static structures do not provide information on the self-assembly process directly. Computational models provide critical tools to study these essential pathways of self-assembly, but substantial coarse-graining of assembly subunits is necessary to achieve computational tractability of these relatively slow processes while retaining multi-valency. While rule-based or local interactions overcome the often-prohibitive enumeration of all possible assembly intermediates, they must ensure global structural constraints are met. We here demonstrate ioNERDSS, a user-friendly Python package that transforms 3D atomic structures into coarse-grained models for immediate simulation with the stochastic reaction-diffusion NERDSS software, converting static structures into time-resolved assembly trajectories. NERDSS uses rule-based interactions to simulate multi-component self-assembly at the minutes timescales and without limits to complex size or growth pathways. With ioNERDSS, each protein chain is defined by a rigid subunit with discrete interfaces and explicit orientational constraints that enforce a structured assembly. Repeated subunits (such as in viral capsids) are regularized to preserve the target topology across distinct stochastic assembly pathways, supporting assembly of structures with thousands of subunits. We initialize pairwise binding affinities using open-source machine-learned prediction tools, and our default coarse-grained (CG) models are all constrained by thermodynamic reversibility to reach an equilibrium steady-state. The binding rates and subunit abundances necessary to perform simulations are initialized at default values but represent the key variables (along with affinities) that cells and thus users would tune to control productive assembly. Benchmarking on over 40,000 PDB structures shows that the majority of CG models stochastically assemble into target structures. The ioNERDSS Python library links directly to open-source tools for visualization and analysis to facilitate fast and user-friendly structure validation and analysis of output for thermodynamic, kinetic, and nonequilibrium drivers of macromolecular self-assembly.

biophysics↗

bioRxiv2025

Binding affinities for 2D protein dimerization benefit from enthalpic stabilization

Dimerization underpins all macromolecular assembly processes both on and off the membrane. While the strength of dimerization, KD, is commonly quantified in solution (3D), many proteins like the soluble BAR domain-containing proteins also reversibly dimerize while bound to a membrane surface (2D). The ratio of dissociation constants, [Formula], defines a lengthscale that is essential for determining whether dimerization is more favorable in solution or on the membrane surface, particularly for these proteins that reversibly transition between 3D and 2D. While purely entropic rigid-body estimates of h apply well to transmembrane adhesion proteins, we show here using Molecular Dynamics simulations that even moderate flexibility in BAR domains dramatically alters the free energy landscape from 3D to 2D, driving enhanced selectivity and stability of the native dimer in 2D. By simulating BAR homodimerization in three distinct environments, 1) solution (3D), 2) bound to a lipid bilayer (2D), and 3) fully solvated but restrained to a pseudo membrane (2D), we show that both 2D environments induce backbone configurations that better match the crystal structure and produce more enthalpically favorable dimer states, violating the rigid-body estimates to drive h<<hRIGID. Remarkably, contact with an explicit lipid bilayer is not necessary to drive these changes, as the solvated pseudo membrane induces this same result. We show this outcome depends on the stability of the protein interaction, as a parameterization that produces exceptionally stable binding in 3D does not induce systematic improvements on the membrane. With h lengthscales calculated here that are well below a physiological volume-to-surface-area lengthscale, assembly will be dramatically enhanced on the membrane, which aligns with BAR domain function as membrane remodelers. Our approach provides simple metrics to move beyond rigid-body estimates of 2D affinities and assess whether conformational flexibility selects for enhanced stability on membranes.

biophysics↗

bioRxiv2022

A Protocol for Single Nucleus RNAseq from Frozen Skeletal Muscle

Single cell technologies are a method of choice to obtain vast amounts of cell-specific transcriptional information under physiological and diseased states. Myogenic cells are resistant to single nucleus RNA sequencing (snRNAseq) due to their large, multinucleated nature. Here, we report a novel, reliable, and cost-effective method to analyze frozen human skeletal muscle by snRNAseq. This method yields all expected cell types for human skeletal muscle and works on tissue frozen for long periods of time and with significant pathological changes. Our method is ideal for studying banked samples with the intention of studying human muscle disease.

genomics↗

bioRxiv2017

The immunomodulatory CEA cell adhesion molecule 6 (CEACAM6/CD66c) is a candidate receptor for the influenza A virus

To establish a productive infection in host cells, viruses often use one or multiple host membrane glycoprotein as their receptors. For Influenza A virus (IAV) such a glycoprotein receptor has not been described, to date. Here we show that IAV is using the host membrane glycoprotein CD66c as a receptor for entry into human epithelial lung cells. Neuraminidase (NA), a viral spike protein binds to CD66c on the cell surface during IAV entry into the host cells. Lung cells overexpressing CD66c showed an increase in virus binding and subsequent entry into the cell. Upon comparison, CD66c demonstrated higher binding capacity than other membrane glycoproteins (EGFR and DC-SIGN) reported earlier to facilitate IAV entry into host cells. siRNA mediated knockdown of CD66c from lung cells inhibited virus binding on cell surface and entry into cells. Blocking CD66c by antibody on the cell surface resulted in decreased virus entry. We found CD66c is a specific glycoprotein receptor for influenza A virus that did not affect entry of non-IAV RNA virus (Hepatitis C virus). Finally, IAV pre-incubated with recombinant CD66c protein when administered intranasally in mice showed decreased cytopathic effects in mice lungs. This publication is the first to report CD66c (CEACAM6) as a glycoprotein receptor for Influenza A virus.\n\nSignificance StatementCells are enclosed by a semipermeable membrane that allows selective exchange of biomolecules between cells and their surroundings. A set of specialized proteins in this semipermeable membrane, work like gatekeepers to the cell and regulate entry of these biomolecules. One class of such surface proteins is termed as receptors. Viruses bind to one or more of these receptors and manipulate gatekeepers for their own successful entry into host-cells. A membrane protein that influenza A virus (Flu virus) uses for entry into the cells was not discovered till date. This study reports for the first time, a receptor for influenza A virus, that was sought after by researchers for decades. The viral receptor is a promising target that can be used to inhibit virus entry into host cells.

microbiology↗