Biology subjects

Lall, S.

Publications and source records attributed to Lall, S..

14 recordsLinked to original sources

bioRxiv2026

The transmembrane domain regulates the kinetics of the SARS-CoV-2 spike conformational transition

ABSTRACTThe homotrimeric SARS-CoV-2 spike glycoprotein comprises two subunits: S1, which recognizes host-receptors through its receptor-binding domains (RBDs), and S2, anchored to the viral membrane through its transmembrane domain (TMD), which facilitates the fusion of the viral envelope with the host cell membrane. Upon host-receptor engagement and proteolytic activation, S1 dissociates and triggers a large conformational transition in S2, involving structural rearrangements in the S2 ectomembrane-domains and the TMD. While studies have focused on the ectomembrane-domain dynamics, the TMD has typically been modeled as being in a trimeric state. Here, we use molecular dynamics simulations of a coarse-grained structure-based model (SBM) with an implicit membrane to investigate the role of TMD dynamics in modulating S2 conformational conversion. We first recapitulate previous results from an all-atom SBM with a trimeric TMD and re-emphasize that the extended pre-hairpin intermediate state of S2, which brings the two membranes into apposition, is a byproduct of the prefusion-to-postfusion transition. Next, by introducing dynamics into the TMD, we find a late fusion intermediate structurally similar to a recent cryo-EM structure. A dynamic TMD also makes the conformational transition faster. Simulations including the S1/S2 complex reveal coupled RBD-TMD dynamics: when all three RBDs are in the closed state, they can stabilize the TMD in a trimeric configuration, whereas the opening of a single RBD can trigger a transition to a dynamic TMD. So, the dynamics and the conformational preferences of the TMD can be tuned by the presence and conformation of S1. There is some evidence that the TMDs of class I viral fusion proteins, such as spike, contribute to viral fusion by modulating membrane properties. Our simulations indicate an expanded role for the function of the TMD, where it can directly regulate the kinetics of S2 conformational transitions. SIGNIFICANCE STATEMENTThe SARS-CoV-2 fusion protein, spike, undergoes a large conformational transition, which facilitates the fusion of the viral and host-cell membranes and the delivery of the viral genome into the host cell. Despite extensive studies of the spike conformational conversion, its transmembrane domain (TMD) has largely been viewed as a viral membrane anchor. Using coarse-grained structure-based model simulations, we show that TMD dynamics can modulate the timing of the spike-S2 (fusion subunit) prefusion-to-postfusion conformational conversion. The presence of spike-S1 (receptor-recognition subunit) can suppress TMD dynamics, potentially reducing the rate of spike conformational conversion and viral fusion. Thus, the spike TMD regulates the kinetics of spike-mediated membrane fusion, and TMD-targeting strategies can be an additional avenue for antiviral intervention.

biophysics↗

bioRxiv2026

RareCapsNet: An explainable capsule networks enable robust discovery of rare cell populations from large-scale single-cell transcriptomics

In-silico analysis of single cell data (downstream analysis) seeks considerable attention to the machine learning researchers in the last few years. Recent technological advances and increases in throughput capabilities open up great new chances to discover rare cell types. We develop RareCapsNet, a rare cell identification technique through capsule network in large single cell RNA-seq data. RareCapsNet aiming to leverage the landmark advantages of capsule networks in single cell domain, by identifying novel rare cell population through markers genes explained from human-mind-friendly interpretation of lower-level (primary) capsules. We demonstrate the explainability of capsule network for identifying novel markers that are act as signature of certain cell population of rare type. A comprehensive evaluation in simulated and real life single cell data demonstrate the efficacy of RareCapsNet for finding out rare population in large scRNA-seq data. RareCapsNet outperforms the other state-of-the-art not only in specificity and selectivity for identifying rare cell types, it can also successfully extract transcriptomic signature of the cell population. We demonstrate RareCapsNet to the dataset of multiple batch, where the model can store the knowledge of one batch which can be transferred to find out rare cells of other batch without training the model. Availability and ImplementationRareCapsNet is available at: https://github.com/sumantaray/RareCapsNet.

bioinformatics↗

bioRxiv2025

Generative Reconstruction of Unobserved Cellular Dynamics using Single-Cell Transcriptomic Trajectories

The primary challenge in cellular dynamics is to capture the intermediate states between two distinct biological stages. This is because of the technical constraints in capturing transient states or the nature of single-cell sequencing protocols. To address this challenge, we introduce a new computational framework, GRAIL (Generative Reconstruction of Artificial Intermediate Lineages), which aims to bridge these missing transitions. GRAIL reconstructs biologically plausible intermediate cell states between two distinct developmental stages (A and B) through a Locality Sensitive Hashing (LSH) based cell selection strategy followed by a smooth interpolation in learned latent space. The interpolated latent representations are subsequently decoded into gene expression profiles using a trained generator from a Generative Adversarial Network (GAN). The framework consists of three components: (a) a pretrained autoencoder that learns latent representations of stage specific transcriptomes, (b) an LSH-guided interpolation procedure that identifies anchor cells and performed interpolation in latent space, and (c) a GAN generator that extrapolates realistic intermediate expression from the interpolated samples consistent with the underlying trajectory. We benchmarked GRAIL with different state-of-the-arts (SOTA) in diverse setup of simulated dataets as well as in real life scRNA-seq datasets. Generated samples from GRAIL preserve expected marker gene expression patterns and also improve downstream analyses, including differential expression and cell clustering. Our method addresses the critical gap in studying cellular transitions when experimental intermediate samples are unavailable.

bioinformatics↗

bioRxiv2025

The longer transmembrane helices of class I viral fusion proteins may facilitate viral fusion

The viral envelope-resident viral fusion proteins (VFPs) fuse the envelope with the host cell membrane, which allows virus entry into host cells. The homotrimeric class I VFPs (cI-VFPs) are anchored to the envelope by three single-pass transmembrane helices (spTMHs). It is generally accepted that the hydrophobic length of a TMH matches the thickness of the hydrophobic membrane core, and this reduces TMH dynamics by stabilizing both the TMHs and the membrane. However, to enable fusion, a cI-VFP undergoes a large conformational transition which includes the disassembly of the spTMHs from a pre-fusion trimer and their subsequent reassembly with the cI-VFP fusion peptides into a post-fusion complex. Given the potential functional relevance of the cI-VFP spTMH disassembly-reassembly dynamics, we hypothesized that these spTMH lengths may be mismatched with the thickness of biological membranes. Here, we examine the predicted lengths of a curated dataset of cI-VFP spTMHs and find that they are indeed about 10% longer than the hydrophobic thickness of model membranes. The cI-VFP spTMHs are on average also longer than the spTMHs of human fusion and human non-fusion proteins. Since our dataset contains cI-VFPs from diverse enveloped viruses, and viral proteins are fast evolving, we conclude that the longer spTMHs are functionally relevant and thus, preserved. Our observation that the spTMH lengths of retrovirus-derived human fusogenic proteins, syncytins, are similar to those of cI-VFPs, supports this conclusion. The longer spTMHs may facilitate the cI-VFP conformational change and perturb the fusing membrane, reducing the barrier to fusion.

biochemistry↗

bioRxiv2025

Family-based Selection: An Efficient Method for Increasing Phenotypic Variability

Persistent idiosyncrasies in behavioral phenotypes have been documented across animal taxa. These individual differences among organisms from the same genotype and reared in identical environments can result in phenotypic variability in the absence of genetic variation. While there is strong evidence to suggest that variability of traits can be heritable and determined by the genotype of an organism, little is known about how selection can specifically shape this heritable variance. Here, we describe a Python-based model of directional artificial selection for increasing the variability of a polygenic trait of interest. Specifically, our model focuses on variability in left-vs-right turn bias in D. melanogaster. While the mean value of turn bias for a genotype is non-heritable and constant across genotypes, the variability of turn bias is a heritable and polygenic trait, varying dramatically among different genetic lines. Using our model, we compare different selection regimes and predict selection dynamics at population and genetic levels. We find that introducing population structure via a family-based selection regime can significantly affect selection response. When selection for increased variability is implemented on the basis of independently measured traits of individuals, the response is slower, but leads to a population with a greater genetic diversity. In contrast, when selection is implemented by measuring traits of families with half or full siblings, the response is faster, albeit with a reduced final genetic diversity in the population. Our model provides a useful starting point to study the effect of different selection regimes on any polygenic trait of interest. We can use this model to predict responses of laboratory-based selection experiments and implement feasible experiments for selection of complex polygenic traits in the lab.

evolutionary biology↗

bioRxiv2022

Niacinamide enhances cathelicidin mediated SARS-CoV-2 membrane disruption

The continual emergence of new SARS-CoV-2 variants threatens the effectiveness of worldwide vaccination programs and highlights the need for complementary strategies for a sustainable containment plan. A promising approach is to mobilize the bodys own antimicrobial peptides (AMPs), to combat SARS-CoV-2 infection and propagation. We have found that human cathelicidin (LL37), an AMP found at epithelial barriers as well as in various bodily fluids, has the capacity to neutralise multiple strains of SARS-CoV-2. Biophysical and computational studies indicate that LL37s mechanism of action is through the disruption of the viral membrane. This antiviral activity of LL37 is enhanced by the hydrotropic action of niacinamide, which may increase the bioavailability of the AMP. Interestingly, we observed inverse correlation between LL37 levels and disease severity of COVID-19 positive patients, suggesting enhancement of AMP response would be an effective therapeutic avenue to mitigate disease severity and overcome vaccine escape.

pathology↗

bioRxiv2021

A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data

Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. There are various issues in single cell sequencing that effect homogeneous grouping (clustering) of cells, such as small amount of starting RNA, limited per-cell sequenced reads, cell-to-cell variability due to cell-cycle, cellular morphology, and variable reagent concentrations. Moreover, single cell data is susceptible to technical noise, which affects the quality of genes (or features) selected/extracted prior to clustering. Here we introduce sc-CGconv (copula based graph convolution network for single cell clustering), a stepwise robust unsupervised feature extraction and clustering approach that formulates and aggregates cell-cell relationships using copula correlation (Ccor), followed by a graph convolution network based clustering approach. sc-CGconv formulates a cell-cell graph using Ccor that is learned by a graph-based artificial intelligence model, graph convolution network. The learned representation (low dimensional embedding) is utilized for cell clustering. sc-CGconv features the following advantages. a. sc-CGconv works with substantially smaller sample sizes to identify homogeneous clusters. b. sc-CGconv can model the expression co-variability of a large number of genes, thereby outperforming state-of-the-art gene selection/extraction methods for clustering. c. sc-CGconv preserves the cell-to-cell variability within the selected gene set by constructing a cell-cell graph through copula correlation measure. d. sc-CGconv provides a topology-preserving embedding of cells in low dimensional space. The source code and usage information are available at https://github.com/Snehalikalall/CopulaGCN Contact: sumanta.ray@cwi.nl

bioinformatics↗

bioRxiv2021

Dynamics and self-assembly of the SARS-CoV-2 spike transmembrane domain

The homotrimeric SARS-CoV-2 spike protein enables viral infection by mediating the fusion of the viral envelope with the host membrane. The spike protein is anchored to the SARS-CoV-2 envelope by its transmembrane domain (TMD), which is composed of three TM helices, each contributed by one of the protomers of the homotrimeric spike. Although the TMD is important for SARS-CoV-2 viral fusion and is well-conserved across the Coronaviridae family, it is unclear whether it is a passive anchor of the spike or actively promotes viral fusion. Specifically, the nature of the TMD dynamics and how these dynamics couple to the large pre- to post-fusion conformational transition of the spike ectomembrane domains remains unknown. Here, we computationally study the SARS-CoV-2 spike TMD in both homogenous POPC and cholesterol containing membranes to characterize its structure, dynamics, and self-assembly. Different tools identify distinct segments of the spike sequence as its TM helix. Atomistic simulations of a spike protomer segment that includes the superset of the TM helix predictions show that the membrane-embedded TM sequence bobs, tilts and gains and loses helicity at the membrane edges. Coarse-grained multimerization simulations using representative TM helix structures from the atomistic simulations exhibit diverse trimer populations whose architecture depends on the structure of the TM helix protomer. Multiple overlapping and conflicting dimerization interfaces stabilized these trimeric populations. An asymmetric conformation is populated in addition to a symmetric conformation and several in-between trimeric conformations. While the symmetric conformation reflects the symmetry of the resting spike, the asymmetric TMD conformation could promote viral membrane fusion through the stabilization of a fusion intermediate. Together, our simulations demonstrate that the SARS-CoV-2 spike TM anchor sequence is inherently dynamic, trimerization does not abrogate these dynamics and the various observed TMD conformations may enable viral fusion.

biophysics↗

bioRxiv2020

RgCop-A regularized copula based method for gene selection in single cell rna-seq data

Gene selection in unannotated large single cell RNA sequencing (scRNA-seq) data is important and crucial step in the preliminary step of downstream analysis. The existing approaches are primarily based on high variation (highly variable genes) or significant high expression (highly expressed genes) failed to provide stable and predictive feature set due to technical noise present in the data. Here, we propose RgCop, a novel regularized copula based method for gene selection from large single cell RNA-seq data. RgCop utilizes copula correlation (Ccor), a robust equitable dependence measure that captures multivariate dependency among a set of genes in single cell expression data. We raise an objective function by adding a l1 regularization term with Ccor to penalizes the redundant co-efficient of features/genes, resulting non-redundant effective features/genes set. Results show a significant improvement in the clustering/classification performance of real life scRNA-seq data over the other state-of-the-art. RgCop performs extremely well in capturing dependence among the features of noisy data due to the scale invariant property of copula, thereby improving the stability of the method. Moreover, the differentially expressed (DE) genes identified from the clusters of scRNA-seq data are found to provide an accurate annotation of cells. Finally, the features/genes obtained from RgCop can able to annotate the unknown cells with high accuracy. AvailabilityCorresponding software is available in: https://github.com/Snehalikalall/RgCop Contactsumanta.ray@cwi.nl

bioinformatics↗

bioRxiv2020

sc-REnF: An entropy guided robust feature selection for clustering of single-cell rna-seq data

Many single-cell typing methods require pure clustering of cells, which is susceptible towards the technical noise, and heavily dependent on high quality informative genes selected in the preliminary steps of downstream analysis. Techniques for gene selection in single-cell RNA sequencing (scRNA-seq) data are seemingly simple which casts problems with respect to the resolution of (sub-)types detection, marker selection and ultimately impacts towards cell annotation. We introduce sc-REnF, a novel and robust entropy based feature (gene) selection method, which leverages the landmark advantage of Renyi and Tsallis entropy achieved in their original application, in single cell clustering. Thereby, gene selection is robust and less sensitive towards the technical noise present in the data, producing a pure clustering of cells, beyond classifying independent and unknown sample with utmost accuracy. The corresponding software is available at: https://github.com/Snehalikalall/sc-REnF

bioinformatics↗

bioRxiv2020

Genetic basis of offspring number and body weight variation in Drosophila melanogaster

Drosophila melanogaster egg production, a proxy for fecundity, is an extensively studied life-history trait with a strong genetic basis. As eggs develop into larvae and adults, space and resource constraints can put pressure on the developing offspring, leading to a decrease in viability, body size, and lifespan. Our goal was to map the genetic basis of offspring number and weight under the restriction of a standard laboratory vial. We screened 143 lines from the Drosophila Genetic Reference Panel for offspring numbers and weights to create an offspring index that captured the number vs. weight trade-off. We found 30 associated variants in 18 genes. Validation of hid, Sox21b, CG8312, and mub candidate genes using gene disruption mutants demonstrated a role in adult stage viability, while mutations in Ih and Rbp increased offspring number and increased weight, respectively. The polygenic basis of offspring number and weight, with many variants of small effect, as well as the involvement of genes with varied functional roles, support the notion of Fishers "infinitesimal model" for this life-history trait.

genetics↗

bioRxiv2019

CODC: A copula based model to identify differential coexpression.

Differential coexpression has recently emerged as a new way to establish a fundamental difference in expression pattern among a group of genes between two populations. Earlier methods used some scoring techniques to detect changes in correlation patterns of a gene pair in two conditions. However, modeling differential coexpression by mean of finding differences in the dependence structure of the gene pair has hitherto not been carried out. We exploit a copula-based framework to model differential coexpression between gene pair in two different conditions. The Copula is used to model the dependency between expression profiles of a gene pair. For a gene pair, the distance between two joint distributions produced by copula is served as differential coexpression. We used five pan-cancer TCGA RNA-Seq data to evaluate the model which outperforms the existing state-of-the-art. Moreover, the proposed model can detect a mild change in the coexpression pattern across two conditions. For noisy expression data, the proposed method performs well because of the popular scale-invariant property of copula. Additionally, we have identified differentially coexpressed modules by applying hierarchical clustering on the distance matrix. The identified modules are analyzed through Gene Ontology terms and KEGG pathway enrichment analysis.

bioinformatics↗

bioRxiv2019

Adult crowding induces sexual dimorphism in chronic stress-response in Drosophila melanogaster

Stress-induced mood disorders such as depression and anxiety are sexually dimorphic in human beings. Studying behavioural stress-responses in non-human animal models can help better understand the behavioural manifestations of these disorders and the dimorphism in their prevalence. Here we explore how sexes show differential behavioural responses to different chronic stressors, both abiotic and biotic, by using outbred populations of Drosophila melanogaster. The behaviours studied - namely, anhedonia, motivation to explore a novel habitat, locomotor activity and sleep levels - have been well-investigated in human and rodent-based models of stress disorders. These behaviours were studied in the context of two different stressors - mechanical perturbation and adult crowding. Responses to stress were found to be sexually dimorphic, and stressed females showed more behavioural changes, such as a reduced motivation to explore a novel habitat. Furthermore, adult crowding caused a greater number of sexually dimorphic behavioural changes than mechanical perturbation. For instance, while mechanical perturbation caused anhedonia across sexes, only females were anhedonic after crowding. We thus make a case for Drosophila melanogaster as a model system for studying sexual dimorphism in stress-induced mood disorders in humans.\n\nSUMMARY STATEMENTFemale fruit flies, like their human counterparts, are more prone to chronic stress-induced mood disorders like anhedonia or reduced activity. This sexual dimorphism was more evident in a biotic stress.

ecology↗

bioRxiv2017

Sleep deprivation negatively impacts reproductive output in Drosophila melanogaster

Most animals sleep or exhibit a sleep-like state, yet the adaptive significance of this phenomenon remains unclear. Although reproductive deficits are associated with lifestyle induced sleep deficiencies, how sleep loss affects reproductive physiology is poorly understood, even in model organisms. We aimed to bridge this mechanistic gap by impairing sleep in female fruit flies and testing its effect on egg output. We find that sleep deprivation by feeding caffeine or by mechanical perturbation results in decreased egg output. Transient activation of wake-promoting dopaminergic neurons decreases egg output in addition to sleep levels, thus demonstrating a direct negative impact of sleep deficit on reproductive output. Similarly, loss-of-function mutation in dopamine transporter fumin (fmn)leads to both significant sleep loss and lowered fecundity. This demonstration of a direct relationship between sleep and reproductive fitness indicates a strong driving force for the evolution of sleep.

neuroscience↗