Biology subjects

Raina, M.

Publications and source records attributed to Raina, M..

7 recordsLinked to original sources

bioRxiv2026

A Principled Statistical Framework for Analyzing Spatial Patterns in Spatially Resolved Multi-Omics

Emerging spatial multi-omics technologies enable the profiling of molecular variation within its tissue context, yet existing methods for identifying spatially variable features lack principled approaches to experimental design and cross-sample inference. Here, we present STORM, a principled Statistical TOol for spatially Resolved Multi-omics, for rigorously analyzing spatial patterns in spatial multi-omics research. STORM incorporates a robust and efficient nonparametric test that quantifies local deviations in molecular feature measurements to detect spatial dependence across transcriptomic and proteomic data. It further estimates an interpretable spatial effect size, supports power calculations for both spatial locations and biological replicates, and enables formal group-level comparisons. In several simulated and experimental spatial multi-omics case studies, STORM demonstrates reliable performance in detecting spatial structures while offering quantitative support for study design decisions. Overall, STORM provides a principled statistical framework that unifies spatial hypothesis testing, effect size estimation, power analysis, and experimental design for spatial multi-omics data.

bioinformatics↗

bioRxiv2026

Evolution at experimental epidemic fronts speeds up parasite spread

Spreading epidemics can foster the rapid emergence of novel parasite variants. Similar to range expansions, parasites at the front of an epidemic wave may evolve traits facilitating their spatial spread. Typically, higher transmissibility and virulence are expected to evolve at the epidemic front, but predictions change if virulence reduces dispersal of infected hosts. We investigated the feedbacks between epidemiology and evolution in interconnected microcosms of the ciliate Paramecium caudatum and its bacterial parasite Holospora undulata. First, two long-term treatments mimicked the moving front of epidemic waves, with and without the natural dispersal of infected hosts. Phenotypic trait assays revealed an eco-to-evo feedback: front parasites (i.e., travelling with infected hosts) were less virulent and interfered less with host dispersal, but also showed higher infectivity, compared to parasites from the control treatment. Whole-genome resequencing and analyses of functional genomic diversity corroborated such phenotypic divergence. Second, measurement of the spread of evolved parasites in linear landscapes demonstrated an evo-to-eco feedback: front parasites produced stronger infection outbreaks and faster spreading epidemic waves than did control parasites. A simulation model assessed the relative importance of the different parasite traits in determining infectious spread over a broad parameter range. It suggests that, if dispersal is generally low, higher infectivity alone can produce the observed differences in epidemic spread speed. Our study illustrates how the need to travel with the host shapes eco-evolutionary feedbacks at parasite invasion fronts, and it highlights the importance of considering concomitant evolutionary change when predicting epidemic speed. Significance StatementWhen predicting disease spread across spatial scales it is fundamental to consider host dispersal, but also the concomitant evolution of parasite traits such as infectivity and virulence. We tested how dispersal constraints affect parasite evolution and epidemic spread in an aquatic model system, by combining experimental evolution, genetic analysis and mathematical modelling. Our results suggest that epidemics can be accelerated by the evolution of highly infectious, but not necessarily more virulent parasites, in contrast to conventional views. This illustrates the important role of eco-evolutionary feedback loops can play in shaping the spread of emerging or geographically expanding parasites.

evolutionary biology↗

bioRxiv2025

scBSP: A fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data

Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in two and three- dimensional space. However, sparse sequencing capacity and growing spatial resolution in spatial omics present significant computational challenges in identifying biologically meaningful molecules that exhibit variable spatial distributions across different omics. We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in high-resolution spatial omics data. scBSP leverages sparse matrix operation to significantly increase computational efficiency in both computational time and memory usage. In diverse spatial sequencing data and simulations, scBSP consistently and rapidly identifies spatially variable genes and spatially variable peaks across various sequencing techniques and spatial resolutions, handling two- and three-dimensional data with up to millions of cells. It can process high-definition spatial transcriptomics data for 19,950 genes across 181,367 spots within 10 seconds on a typical desktop computer, making it the fastest tool available for handling such high-resolution, sparse spatial omics data while maintaining high accuracy. In a case study of kidney disease using 10x Xenium data, scBSP identified spatially variable genes representative of critical pathological mechanisms associated with histology.

bioinformatics↗

bioRxiv2023

Exploring the Mosaic-like Tissue Architecture of Kidney Diseases Using Relation Equivariant Graph Neural Networks on Spatially Resolved Transcriptomics

Emerging spatially resolved transcriptomics (SRT) technologies provide unprecedented opportunities to discover the spatial patterns of gene expression at the cellular or tissue levels. Currently, most existing computational tools on SRT are designed and tested on the ribbon-like brain cortex. Their present expressive power often makes it challenging to identify highly heterogeneous mosaic-like tissue architectures, such as tissues from kidney diseases. This demands heightened precision in discerning the cellular and morphological changes within renal tubules and their interstitial niches. We present an empowered graph deep learning framework, REGNN (Relation Equivariant Graph Neural Networks), for SRT data analyses on heterogeneous tissue structures. To increase expressive power in the SRT lattice using graph modeling, the proposed REGNN integrates equivariance to handle the rotational and translational symmetries of the spatial space, and Positional Encoding (PE) to identify and strengthen the relative spatial relations of the nodes uniformly distributed in the lattice. Our study finds that REGNN outperforms existing computational tools in identifying inherent mosaic-like heterogenous tissue architectures in kidney samples sourced from different kidney diseases using the 10X Visium platform. In case studies on acute kidney injury and chronic kidney diseases, the results identified by REGNN are also validated by experienced nephrology physicians. This proposed framework explores the expression patterns of highly heterogeneous tissues with an enhanced graph deep learning model, and paves the way to pinpoint underlying pathological mechanisms that contribute to the progression of complex diseases. REGNN is publicly available at https://github.com/Mraina99/REGNN.

bioinformatics↗

bioRxiv2021

Synthetic dual-function RNA reveals features necessary for target regulation

Small base pairing RNAs (sRNAs) and small proteins comprise two classes of regulators that allow bacterial cells to adapt to a wide variety of growth conditions. A limited number of transcripts encoding both of these activities, regulation of mRNA expression by base pairing and a small regulatory protein, have been identified. Given that few have been characterized, little is known about the interplay between the two regulatory functions. To investigate the competition between the two activities, we constructed synthetic dual-function RNAs, hereafter referred to as MgtSR or MgtRS, comprised of the Escherichia coli sRNA MgrR and the open reading frame encoding the small protein MgtS. MgrR is a 98 nt base pairing sRNA that negatively regulates eptB encoding phosphoethanolamine transferase. MgtS is a 31 aa small inner membrane protein that is required for the accumulation of MgtA, a magnesium (Mg2+) importer. Expression of the separate genes encoding MgrR and MgtS is normally induced in response to low Mg2+ by the PhoQP two-component system. By generating various versions of this synthetic dual-function RNA, we probed how the organization of components and the distance between the coding and base pairing sequences contribute to the proper function of both activities of a dual-function RNA. By understanding the features of natural and synthetic dual-function RNAs, future synthetic molecules can be constructed to maximize their regulatory impact. IMPORTANCEDual-function RNAs encode a small protein and also base pair with mRNAs to act as small, regulatory RNAs. Given that only a limited number of dual-function RNAs have been characterized, further study of these regulators is needed to increase understanding of their features. This study demonstrates that a functional synthetic dual-regulator can be constructed from separate components and used to study the functional organization of dual-function RNAs, with the goal of exploiting these regulators.

microbiology↗

bioRxiv2021

AzuCR RNA modulates carbon metabolism as a dual-function RNA in Escherichia coli

Bacteria have evolved small RNAs (sRNAs) to regulate numerous biological processes and stress responses. While sRNAs generally are considered to be "noncoding", a few have been found to also encode a small protein. Here we describe one such dual-function RNA that modulates carbon utilization in Escherichia coli. The 164 nucleotide RNA was previously shown to encode a 28 amino acid protein (denoted AzuC). We discovered the membrane-associated AzuC protein interacts with GlpD, the aerobic glycerol-3-phosphate dehydrogenase, leading to increased GlpD activity. Overexpression of the RNA encoding AzuC results in a growth defect in glycerol and galactose medium. The defect in galactose medium was still observed for a stop codon mutant derivative, suggesting a potential regulatory role for the RNA. Consistent with this observation, we found that cadA and galE are repressed by base pairing with the RNA (denoted AzuCR). Interestingly, translation of AzuC interferes with the observed repression of cadA and galE by AzuCR and base pairing interferes with AzuC translation, demonstrating that the translation and base pairing functions compete.

microbiology↗

bioRxiv2020

CARCASS SCAVENGING RELAXES CHEMICAL-DRIVEN FEMALE INTERFERENCE COMPETITION

Female-female nonsexual interference competition is rapidly emerging as a major fitness determinant of biased sex-ratio groups with high female density. How do females overcome such competition? We used adult flour beetle Tribolium castaneum to answer this question, where females from female-biased groups suppressed each others fecundity by secreting toxic quinones from their stink glands, revealing a chemical-driven interference competition. The added natal resource did not alleviate these fitness costs. Females also did not disperse more at high female-density. Hence, the competition was neither limited by the total resource availability nor the inability to avoid chemical interference. Instead, protein sequestered via scavenging of nutrient-rich carcasses relaxed the female competition, by increasing their fecundity and reducing the quinone content. Even infected carcasses were scavenged to extract fitness benefits, despite the infection-risk. Finally, individual stink gland components triggered carcass-scavenging to increase fecundity, indicating a potentially novel chemical feedback loop to reduce the competition.

ecology↗