Biology subjects

Sanyal, R.

Publications and source records attributed to Sanyal, R..

3 recordsLinked to original sources

bioRxiv2025

Genome-Wide Identification of Conditionally Essential Genes Supporting Streptococcus suis Growth in Serum and Cerebrospinal Fluid

Streptococcus suis is a major cause of sepsis and meningitis in pigs, and zoonosis through the emergence of disease-associated lineages. The ability of S. suis to adapt and survive in host environments, such as blood and cerebrospinal fluid (CSF), is important for pathogenesis. Here, we used Tn-Seq coupled with Nanopore sequencing to identify conditionally essential genes for growth of S. suis P1/7 in active porcine serum (APS) and CSF derived from choroid plexus organoids (Zhao et al., 2025). Through comparative fitness analyses we identified 33 conditionally essential genes (CEGs) supporting growth in APS and 25 CEGs in CSF, highlighting the importance of pathways involved in amino acid transport, nucleotide metabolism, and cell envelope integrity. Notably, the LiaFSR regulatory system and multiple ABC transporters were important for proliferation. We also identified several genes of unknown function as essential for growth, pointing to previously unrecognized genetic factors involved in S. suis adaptation during infection. These findings provide new insights into the genetic requirements for S. suis survival in host-like environments and a deeper understanding of its ability to adapt to distinct physiological niches.

microbiology↗

bioRxiv2024

scDecorr - Feature decorrelation representation learning with domain adaptation enables self-supervised alignment of multiple single-cell experiments

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular het-erogeneity in complex biological systems. However, analyzing and integrating scRNA-seq data poses unique computational challenges due to sparsity, high variability, and technical batch effects. Here, we propose a novel framework called scDecorr for robust representation learning and data integration for scRNA-seq analysis. Our approach leverages the idea of feature decorrelation-based self-supervised learning (SSL) to obtain efficient low-dimensional representations of individual cells without relying on negative samples. By maximizing similarity among distorted embeddings while decorrelating their components, scDecorr captures the biological signature while eliminating technical noise. Furthermore, scDecorr incorporates unsupervised domain adaptation to bridge the gap between batches with different probability distributions, enabling effective integration of scRNA-seq data from diverse sources. Our framework achieves domain-invariant representations by learning cell embeddings independently across domains and employing domain-specific batch normalization. We evaluate scDecorr on a variety of single cell datasets and demonstrate its ability to integrate batches without losing the inherent biological variance, thereby facilitating optimal clustering. The representations generated by scDecorr also exhibit robustness in label transfer tasks, allowing for effective transfer of cell-type labels from reference to query datasets. Overall, scDecorr offers a powerful tool for efficient analysis and integration of large and complex scRNA-seq datasets, advancing our understanding of cellular processes and disease mechanisms. The code is available here https://github.com/hayatlab/scdecorr.

bioinformatics↗

bioRxiv2018

SpoT and GppA hydrolases prevent the gratuitous activation of RelA by pppGpp in Escherichia coli

Stringent response, a conserved regulation seen in bacteria, is effected through the modified nucleotides (p)ppGpp. The metabolic cycle of these molecules is driven by the synthase activity of RelA and SpoT and the hydrolase activity of SpoT and GppA which together sets the basal (p)ppGpp pool. Growth arrest due to (p)ppGpp accumulation from basal RelA activity apparently explained the essentiality of SpoT hydrolase function. We found, pppGpp degradation was enhanced when the SpoT hydrolase activity was lowered or eliminated and when this was alleviated by inactivation of the GppA hydrolase, gratuitous synthesis of (p)ppGpp by RelA was activated, leading to growth arrest. The RelA-ribosome interaction was not mandatory for these phenotypes. Our results show, for the first time, elevated pppGpp promoted the amplification of RelA-mediated stringent response in the absence of established RelA activating signals in the cell and the SpoT and GppA hydrolases prevented this. The accumulation of pppGpp inhibited the SpoT hydrolase activity. We propose this autocatalytic activation of RelA by pppGpp is likely to be an allosteric regulation and can result in a bistable switch.

microbiology↗