Biology subjects

Ray, M.

Publications and source records attributed to Ray, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Mapping genome-wide RNA-RNA and RNA-DNA interactions in nuclear hubs of male and female Drosophila cells

Nuclear bodies are nucleoprotein complexes with established functions that target chromatin at specific locations and regulate specific RNA processing functions, thereby influencing gene expression. However, the mechanisms that define how nuclear bodies are targeted to specific locations within the genome where they function remain poorly understood. One significant challenge is capturing and understanding the multiple cell-specific interactions occurring in these complexes, arising from RNA components interacting with each other and with DNA and nucleic acid-binding proteins within the context of the nucleuss three-dimensional organization. Mapping these interactions is critical for elucidating mechanisms such as RNA splicing, a key driver of cell-specific transcript diversity. Here, we use RNA-DNA Split Pool Recognition of Interactions by Tag Extension (RD-SPRITE) to characterize, for the first time, sex-specific RNA-RNA and RNA-DNA interactions in Drosophila S2 (male) and Kc (female) cells. We determined the sex-specific RNA-RNA interaction map within the nucleus and, using RNA-DNA interaction data, pinpointed the target loci of various RNA molecules, including small nuclear RNAs (snRNAs), which are core components of the spliceosome-a ribonucleoprotein complex involved in RNA splicing. Based on RNA-RNA interaction data, we also identified novel long non-coding RNAs that may regulate splicing. Furthermore, we investigated the role of transcription factor (TF) CLAMP in sex-specific targeting of the spliceosome. We generated RD-SPRITE datasets in the presence and absence of CLAMP, a key TF involved in dosage compensation, sex-specific RNA splicing, and chromatin organization. We determined that CLAMP regulates global changes in spliceosomal interactions with chromatin, inhibits aberrant snRNA interactions, and regulates sex-specific interactions of RNAs involved in splicing function. Additionally, our dataset provides a valuable resource for investigating additional processes, such as miRNA-mediated silencing, nucleolar functions of snoRNAs, and Cajal body functions of scaRNAs, among others. To facilitate broad community use, we have developed a computational platform, "FlySprite," that enables Drosophila researchers to explore sex-specific RNA-RNA interactions, as well as DNA targets of RNA clusters, through a user-friendly interface.

genomics↗

bioRxiv2026

Generative AI Models Reveal Dynamic Views of Aging (DyViA) Phenotypes in Healthy Individuals

Background and objectivesIn recent years, the need to develop analytical strategies for healthy aging has assumed great importance. In this study, we introduce DyViA, a generative artificial intelligence (genAI) platform that can construct personalized trajectories capable of predicting the plausible progression of selected phenotypes with advancing age. Research design and methodsDyViA presents a suite of deep learning models covering two major GenAI approaches: DyViA-Diff, a new diffusion model; and DyViA-mGAN, an improved version of a recent Generative Adversarial Network model. It demonstrated the dynamic progression of femoral neck bone mineral density (BMD) using data from a longitudinal cohort study of women in the U.S. of age 65 years or above. ResultsUsing very few initial measurements, DyViA generated individual-specific continuous trajectories of BMD, with a corresponding region of acceptable predictions, from 66 to 89 years. The results were subjected to rigorous quality-control and comparative analysis across multiple methods. While DyViA-Diff is the superior model with more coherent and accurate predictions, DyViA-mGAN allows for encoding population- and individual-level effects with a better control. Discussion and implicationsGiven the prevalence of osteoporosis in the aging population, the main impact of DyViAs genAI-driven contribution in the form of personalized, plausible models of BMD progression with age lies in the systematic yet rigorous transition from otherwise static models of inference about a clearly dynamic phenomenon to a continuous one. The foresight offered by DyViAs outputs empowers an individual by conferring a certain degree of strategic preparedness in the course of aging.

bioinformatics↗

bioRxiv2026

Differential chromatin looping regulated by two GA-binding transcription factors creates an X-specific chromatin environment for dosage compensation

The mechanisms by which differential occupancy of transcription factors (TFs) at similar binding sites leads to context-specific targeting of large transcription complexes remain poorly understood. X chromosome upregulation (XCU), the most highly conserved step in dosage compensation and best studied in Drosophila, serves as a model for understanding how differential occupancy of similar TFs functions context-specifically. Sequence variation within GA-repeat motifs that accumulated on the X chromosome over evolutionary time promotes the binding of a specific GA-binding TF (CLAMP) that recruits the dosage compensation complex (DCC) while outcompeting another similar TF (GAF). However, the mechanism by which CLAMP-GAF competition drives specific targeting of the DCC to the X chromosome remains unknown. Because DCC binding sites cluster in 3D space, we combined Micro-C and Hi-ChIP to determine that CLAMP and GAF directly mediate largely mutually exclusive 3D genomic contacts. Specifically, we show that CLAMP but not GAF drives local short-range interactions that directly link high affinity DCC binding sites with active, dosage-compensated housekeeping genes. In contrast, GAF mediates interactions between transcriptionally silent insulator regions on the X chromosome spanning a wider range of genomic distances. Together, these findings demonstrate that CLAMP outcompetes GAF at active regions on the X chromosome, but not autosomes, to create an X-chromosome specific chromatin environment for dosage compensation. Overall, we provide new insight into how differential TF binding at similar binding sites drives context-specific targeting of transcription complexes.

molecular biology↗

bioRxiv2026

MERFISH 2.0, an ultra-sensitive single-cell spatial transcriptomics imaging chemistry across diverse tissue types

Spatial transcriptomics has emerged as a transformative approach for elucidating tissue architecture, cellular heterogeneity, and disease mechanisms by preserving the spatial context of gene expression in cells. Despite these advances, many spatial transcriptomic methods underperform in archival or clinically relevant specimens, particularly formalin-fixed, paraffin-embedded (FFPE) tissues, where RNA degradation and crosslinking hinder transcript detection. To address these challenges, we developed Multiplexed Error Robust Fluorescence In Situ Hybridization 2.0 (MERFISH 2.0), an optimized spatial transcriptomic imaging chemistry to enhance profiling of fragmented and highly crosslinked RNA. Across diverse human and mouse tissues preserved as fresh-frozen, fixed-frozen, and FFPE specimens, MERFISH 2.0 substantially increased transcript detection sensitivity by up to [~]8-fold relative to MERFISH 1.0 while preserving quantitative concordance (Pearson r [≥] 0.8 across tissues). In archived fresh-frozen human brain samples, MERFISH 2.0s enhanced sensitivity improved transcript recovery, enhanced cell type resolution and spatial analyses. In low quality archival FFPE human breast cancer specimen, MERFISH 2.0 revealed additional cell populations, novel cell clusters, refined tumor-immune architecture, and increased detection of gene-gene and cell-cell interactions relative to MERFISH 1.0, underscoring the impact of improved sensitivity on downstream spatial analysis. By substantially expanding robust transcript detection to degraded and archival samples, MERFISH 2.0 enables scalable, cohort-level spatial transcriptomic analysis across clinically relevant tissue collections.

genomics↗

bioRxiv2025

Cyanobacterial metagenomes reflect the spatiotemporal variations in a coastal brackish lagoon

Cyanobacteria play vital roles in aquatic ecosystems by driving photosynthesis, nitrogen fixation, carbon sequestration, and forming symbiotic relationships with diverse organisms. However, their proliferation can trigger harmful algal blooms, posing risks to aquatic biodiversity and public health. Despite their ecological significance, the interplay between cyanobacterial genomic traits and ecosystem dynamics remains poorly resolved. Here, we employed culture-independent metagenomic approaches to reconstruct cyanobacterial metagenome-assembled genomes (MAGs) from Chilika Lagoon, India, and investigate their spatiotemporal distribution and functions. Our analysis revealed distinct temporal patterns in cyanobacterial MAG abundance, with salinity emerging as the primary environmental driver of community structure and functional gene composition. Genes associated with biogeochemical cycling and toxin synthesis displayed pronounced seasonal variation, suggesting that functional genomic traits, rather than taxonomic identity govern species selection. Notably, five MAGs harboured the complete phosphate acetyltransferase-acetate kinase (Pta-Ack) pathway, a critical component of the Wood-Ljungdahl pathway, indicating an underappreciated potential for alternative carbon fixation mechanisms alongside the canonical Calvin-Benson-Bassham cycle. Furthermore, genomic variability, rather than phylogenetic relatedness was the dominant factor shaping cyanobacterial dynamics in the lagoon. This study establishes a direct link between physicochemical fluctuations and cyanobacterial functional diversity, offering critical insights into how climate-driven changes in salinity and nutrient regimes may influence aquatic ecosystems. By elucidating the genomic basis of cyanobacterial adaptation, these findings enhance our capacity to predict ecological outcomes of harmful algal blooms and inform strategies to safeguard ecosystem services in vulnerable coastal habitats. ImportanceThis study employs culture-independent metagenomics to reconstruct cyanobacterial metagenome-assembled genomes (MAGs) in Chilika Lagoon, India, unraveling their spatiotemporal distribution and functional traits. Salinity emerged as the primary driver shaping community structure and functional gene composition, with seasonal fluctuations influencing genes tied to biogeochemical cycling (e.g., carbon, nitrogen) and toxin synthesis. Notably, functional genomic traits--rather than taxonomic identity--governed species selection, highlighting adaptive strategies under environmental stress. Intriguingly, five MAGs harbored the complete Pta-Ack pathway, a component of the Wood-Ljungdahl pathway, suggesting cyanobacteria may employ alternative carbon fixation mechanisms alongside the Calvin-Benson-Bassham cycle under fluctuating conditions. These findings link physicochemical variables (e.g., salinity, nutrients) to functional diversity, revealing how genomic adaptations underpin ecological resilience. The study provides critical insights into cyanobacterial responses to environmental change by bridging microbial genomic plasticity with ecosystem-level impacts. This framework aids in predicting bloom dynamics and toxin risks, offering actionable tools to mitigate ecological threats in vulnerable coastal habitats, thereby informing conservation and management strategies amid climate variability.

microbiology↗

bioRxiv2025

Deep learning-driven morphology analysis enables label-free classification of therapeutic agent- naive versus resistant cancer cells

Therapeutic drug treatments of solid tumors are often undermined by various resistance mechanisms. Identification of drug-resistance phenotypes at the single cell level is challenging because conventional molecular methods are cell-destructive, labor-intensive, and cost-prohibitive. To overcome these challenges, we developed an orthogonal approach to drug-resistance phenotyping, through the use of deep-learning-driven morphology analysis of single, high resolution cell images. Specifically, we trained deep learning-based drug resistance classifiers using cell images from 5 different cell lines that were rendered resistant to 5 different therapeutic agents, using a foundation model framework. With high accuracy, the classifier correctly predicted naive or resistance phenotypes across all cancer types and across all the therapeutic agent types (chemotherapeutic, targeted) tested. These results showed that morphology can capture complex phenotype information in the context of drug treatment. To demonstrate the potential clinical utility of the drug resistance classifier, it was applied to a dissociated tumor biopsy and the resulting phenotype predictions were in close concordance with scRNASeq analysis of the biopsy. Our study highlights the potential of deep-learning-driven morphology analysis to provide complex phenotype information, and ultimately shape oncology drug treatment strategies at the patient-level in a clinical context.

cancer biology↗

bioRxiv2024

Label-Free Melanoma Phenotype Classification Using Artificial Intelligence-Based Morphological Profiling

Melanomas are the deadliest skin cancers, in part due to cellular plasticity and heterogeneity. Intratumoral heterogeneity drives varied mutable phenotypes, specifically "melanocytic" and "mesenchymal" cell states, which result in differential functional properties and drug responses. Definitive and rigorous classification of these phenotypic states has been challenging with conventional biomarker-based methods, and high-parameter molecular methods are cell-destructive, labor-intensive, and time-consuming. To overcome these technical and practical limitations, we utilized label-free artificial intelligence-based morphological profiling to classify live melanoma cells into melanocytic and mesenchymal phenotypes based on high resolution imaging of single cells. To predict the phenotypes of single melanoma cells based on morphology alone, we developed the AI-based Melanoma Phenotype Classifier trained with 19 patient-derived cell lines with known melanocytic or mesenchymal transcriptional profiles. To link phenotypic state with high-dimensional morphological profiles, cells were subjected to genetic and chemical perturbations known to shift phenotypic states. The AI classifier successfully predicted phenotypic shifts which were confirmed by single-cell RNA-Seq (scRNA-Seq). These results demonstrate that correlations between melanoma cell phenotypes and morphological changes are detectable by AI. Additionally, the Melanoma Phenotype Classifier was applied to dissociated tumor biopsy samples and characterization of phenotypic heterogeneity was supported by scRNA-Seq transcriptional profiles. This work establishes a link between cell morphology and melanoma phenotypes, laying the groundwork for the use of a label-free morphology-based method for phenotyping live melanoma cells combined with additional analyses.

cancer biology↗

bioRxiv2024

BindCompare: A Novel Integrated protein-nucleic Acid Binding Analysis Platform

SummaryAdvanced genomic technologies have generated thousands of Protein-Nucleic acid binding datasets that have the potential to identify testable gene regulatory network (GRNs) models governed by combinatorial associations between factors. Transcription factors (TFs) and RNA binding proteins (RBPs) are nucleic-acid binding proteins regulating gene expression and are key drivers of GRN function. However, the combinatorial mechanisms by which the interactions between specific TFs and RBPs regulate gene expression remain largely unknown. To identify possible combinations of TFs and RBPs that may function together, developing a tool that compares and contrasts the interactions of multiple TFs and RBPs with nucleic acids to identify their common and unique targets is necessary. Therefore, we introduce BindCompare, a user-friendly tool that can be run locally to predict new combinatorial relationships between TFs and RBPs. BindCompare can analyze data from any organism with known annotated genome information and outputs files with detailed genomic locations and gene information for targets for downstream analysis. Overall, BindCompare is a new tool that identifies TFs and RBPs that co-bind to the same DNA and/or RNA loci, generating testable hypotheses about their combinatorial regulation of target genes. Availability and ImplementationBindCompare is an open-source package that is available on the Python Packaging Index (PyPI, https://pypi.org/project/bindcompare/) with the source code available on GitHub (https://github.com/pranavmahabs/bindcompare). Complete documentation for the package can be found at both of these links.

genomics↗

bioRxiv2024

Dual DNA/RNA-binding factor regulates dynamics of hnRNP splicing condensates

How RNA splicing events are targeted to the correct genomic locations in specific cellular contexts to generate context-specific transcript diversity and prevent deleterious cryptic splicing remains very poorly understood. We show that a functionally conserved GA-rich DNA-binding transcription factor (TF), CLAMP, targets distinct RNA molecules in male and female cells to precisely regulate sex-specific splicing events through physical and functional interactions with RNA and RNA-binding proteins (RBPs). The prion-like domain of CLAMP (PrLD) and a stem-loop region in the target RNA are important for CLAMP-RNA interaction. Moreover, the CLAMP PrLD domain regulates sex-specific splicing by modulating the dynamics of an hnRNPA2/B1 family protein that regulates alternative splicing. Thus, we demonstrate that a TF targets co-transcriptional splicing to the correct genomic locations by directly linking DNA binding sites to RNA targets and modulating the dynamics of RBP partners that drive alternative splicing.

genomics↗

bioRxiv2023

The CLAMP GA-binding transcription factor regulates heat stress-induced transcriptional repression by associating with 3D loop anchors.

To survive exposure to heat stress (HS), organisms activate stress response genes and repress constitutive gene expression, thereby preventing the accumulation of potentially toxic RNA and protein products. Although many studies have elucidated the mechanisms that drive HS-induced activation of stress response genes across species, little is known about the mechanisms that repress constitutively expressed genes. Using nascent RNA-sequencing, we identify the first reported transcription factor (TF) that regulates repression of constitutive genes upon heat stress across species and define direct and indirect mechanisms of action by integrating 3D genomic approaches. We demonstrate that the CLAMP (Chromatin-linked adaptor for MSL complex proteins) GA-binding transcription factor (TF) regulates [~]75% of the HS-induced repression in Drosophila, a well-established model for understanding the mechanisms of HS-induced gene regulation. Using Micro-C, we demonstrate that heat stress induces widespread changes in local 3D chromatin looping, which are significantly associated with HS-induced transcriptional changes. Overall, we identify CLAMP as the first reported TF that induces HS repression, which modulates 3D chromatin looping through both direct mechanisms and indirect mechanisms. Moreover, we present the highest-resolution heat stress 3D genomic dataset available in Drosophila, providing a key resource for generating mechanistic insights into how temperature regulates 3D genomic contacts.

genomics↗

bioRxiv2023

Genomic potential and evolution of Dissimilatory Nitrate Reduction to Ammonium in Cyanobacteria

Cyanobacteria play an important role in primary production and nitrogen fixation. Although Cyanobacteria are well-known diazotrophic organisms, their role in other steps Nitrogen Cycle is obscure. Screening of Cyanobacterial genomes from cultured and unculturable species can help identify potentially novel functions. In this study, we assembled Cyanobacterial genomes from metagenomic data generated from environmental DNA isolated from a brackish water lagoon (Chilika, India). We annotated these Cyanobacterial metagenome-assembled genomes (MAG) for all the encoded functions using KEGG Orthology. We found two high-quality Cyanobacterial MAGs containing the nirBD gene and nifH and nifD genes involved in the nitrogen cycle. nirBD encodes for the Dissimilatory Nitrate Reduction to Ammonium (DNRA) activity, a function previously not ascribed to Cyanobacteria. We validated the presence of NirBD in publicly available isolate genomes of Cyanobacteria and examined its evolution in the phylum by phylogenetic reconciliation of species and gene trees. Our analysis revealed that both horizontal gene transfers and speciation events contributed to the dispersal of the nirBD gene in Cyanobacteria. We observed that mostly filamentous Cyanobacteria served as ancestral donors in horizontal gene transfer events. Further, we found that the nirBD gene is under a purifying selection pressure in Cyanobacteria. This study demonstrates the genomic potential and evolution of DNRA activity in Cyanobacteria for the utilisation of nitrate in the ecosystem which can help these organisms to cope with extreme environmental conditions. It expands our overall comprehension of the contribution of Cyanobacteria in the biogeochemical cycling in aquatic ecosystems.

microbiology↗

bioRxiv2022

The estrogen receptor α cistrome in human endometrium and epithelial organoids

ContextEndometrial health is impacted by molecular processes that underlie estrogen responses. ObjectiveTo define estrogen regulation of endometrial function by integrating the estrogen receptor alpha (ESR1) cistrome and transcriptome of endometrial biopsies taken from the proliferative and midsecretory phases of the menstrual cycle and hormonally stimulated endometrial epithelial organoids. DesignESR1 ChIPseq and RNAseq were performed on proliferative or mid-secretory endometrial biopsies and on hormone treated organoid cultures. SettingEndometrial samples were obtained from volunteers at outpatient research clinics for ChIPseq and for organoid culture. Patients or Other ParticipantsParticipants were fertile, reproductive aged women with normal cycle length, and without any history of infertility or irregular cycles. In total, 5 new endometrial biopsies obtained from 5 women were used in this study and were analyzed together with previously published cycle stage endometrial RNAseq data. Intervention(s)There were no interventions in this study. Main Outcome Measure(s)The cycle stage specific ESR1 binding sites and gene expression identification of human endometrium and organoid cultures were integrated with changes in gene expression. ResultsGenes with ESR1 binding in whole endometrium were enriched for chromatin modification and regulation of cell proliferation. The distribution of ESR1 binding sites in organoids was more distal to the gene promoter when compared to primary endometrium. Organoid estrogen/ESR1 candidate target genes impacted formation of cellular protrusions, and chromatin modification, ConclusionsAnalysis of the ESR1 cistromes and transcriptomes from endometrium and organoids provides important resources for understanding how estrogen impacts endometrial health and function.

genomics↗

bioRxiv2022

FTY720 requires vitamin B12-TCN2-CD320 signaling in astrocytes to reduce disease in an animal model of multiple sclerosis

FTY720 (fingolimod) is a sphingosine 1-phosphate (S1P) receptor modulator and sphingosine analogue approved for multiple sclerosis (MS) therapy, which can functionally antagonize the S1P receptor, S1P1. Vitamin B12 (B12) deficiency produces neurological manifestations resembling MS. Here, we report a new mechanism where FTY720 suppresses neuroinflammation by regulating B12 metabolic pathways. Nuclear RNA-seq of c-Fos-activated astrocytes (called ieAstrocytes) from experimental autoimmune encephalomyelitis (EAE) spinal cords identified up-regulation of CD320, a transcobalamin 2 (TCN2)-B12 receptor, by S1P1 inhibition. CD320 was reduced in MS plaques. Deficiency of CD320 or dietary B12 worsened EAE and eliminated FTY720s efficacy, while concomitantly down-regulating type I interferon signaling. TCN2 functioned as a chaperone for FTY720 and sphingosine, which induced astrocytic CD320 internalization. An accompanying paper identified a requirement for astrocyte sphingosine kinases in FTY720 efficacy and its altered expression in MS brains, molecularly linking MS and B12 deficiency that can be accessed by sphingolipid/fingolimod metabolic pathways.

neuroscience↗

bioRxiv2021

PDXNet Portal: Patient-Derived Xenograft model, data, workflow, and tool discovery

We created the PDX Network (PDXNet) Portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources (i.e., PDX models, sequencing data, treatment response data, and bioinformatics workflows), to facilitate collaboration among researchers, and to make resources easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols, and training materials for processing PDX data. The initial portal release highlights PDXNet model and data resources, including 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCIs Patient-Derived Model Repository (PDMR) for public access. These models have 2,822 associated sequencing files from 873 samples across 307 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal also includes results from standardized analysis workflows on PDXNet sequencing files and PDMR data (2,594 samples from 463 patients across 78 disease types). These 15 analysis workflows for whole-exome and RNA-Seq data are freely available, robust, validated, and standardized. The model and data lists will grow substantially over the next two years and will be continuously updated as new data are available. PDXNet models support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials. The PDXNet portal is a centralized location for these data and resources, which we expect to be of significant utility for the cancer research community.

cancer biology↗

bioRxiv2021

Integrative Epigenomic and High-Throughput Functional Enhancer Profiling Reveals Determinants of Enhancer Heterogeneity in Gastric Cancer

BackgroundEnhancers are distal cis-regulatory elements required for cell-specific gene expression and cell fate determination. In cancer, enhancer variation has been proposed as a major cause of inter-patient heterogeneity - however, most predicted enhancer regions remain to be functionally tested. ResultsAnalyzing 128 epigenomic histone modification profiles of primary GC samples, normal gastric tissues, and GC cell lines, we report a comprehensive catalog of 75,730 recurrent predicted enhancers, the majority of which are tumor-associated in vivo (>50,000) and associated with lower somatic mutation rates inferred by whole-genome sequencing. Applying Capture-based Self-Transcribing Active Regulatory Region sequencing (CapSTARR-seq) to the enhancer catalog, we observed significant correlations between CapSTARR-seq functional activity and H3K27ac/H3K4me1 levels. Super-enhancer regions exhibited increased CapSTARR-seq signals compared to regular enhancers even when decoupled from native chromatin contexture. We show that combining histone modification and CapSTARR-seq functional enhancer data improves the prediction of enhancer-promoter interactions and pinpointing of germline single nucleotide polymorphisms (SNPs), somatic copy number alterations (SCNAs), and trans-acting TFs involved in GC expression. Specifically, we identified cancer-relevant genes (e.g. ING1, ARL4C) whose expression between patients is influenced by enhancer differences in genomic copy number and germline SNPs, and HNF4 as a master trans-acting factor associated with GC enhancer heterogeneity. ConclusionsOur study indicates that combining histone modification and functional assay data may provide a more accurate metric to assess enhancer activity than either platform individually, and provides insights into the relative contribution of genetic (cis) and regulatory (trans) mechanisms to GC enhancer functional heterogeneity.

cancer biology↗

bioRxiv2021

Sex-specific transcript diversity is regulated by a maternal pioneer factor in early Drosophila embryos

Co-transcriptional splicing coordinates the processes of transcription and splicing and is driven by transcription factors (TFs) and diverse RNA-binding proteins (RBPs). Yet the mechanisms by which specific TFs and RBPs function together in context-specific ways to drive precise co-transcriptional splicing at each of thousands of genomic loci remains unknown. Therefore, we have used sex-specific splicing in Drosophila as a model to understand how the function of TFs and RBPs is coordinated to transcribe and process specific RNA transcripts at the correct genomic locations. We show widespread sex-specific transcript diversity occurs much earlier than previously thought and present a new pipeline called time2splice to quantify splicing changes over time. We define several mechanisms by which the essential and functionally-conserved CLAMP TF functions with specific RBPs to precisely regulate co-transcriptional splicing: 1) CLAMP links the DNA of gene bodies of sex-specifically spliced genes directly to the RNA of target genes and physically interacts with snRNA and protein components of the splicing machinery; 2) In males, CLAMP regulates the distribution of the highly conserved RBP Maleless (MLE) (RNA Helicase A) to prevent aberrant sex-specific splicing; 3) In females, CLAMP modulates alternative splicing by directly binding to target DNA and RNA and indirectly through regulating the splicing of sex lethal, the master regulator of sex determination. Overall, we provide new insight into how TFs function specifically with RBPs to drive alternative splicing.

genomics↗

bioRxiv2021

Does colour impact attention towards 2D images in geckos?

Animals are exposed to different visual stimuli that influence how they perceive and interact with their environment. Visual information such as shape and colour can help the animal detect, discriminate and make appropriate behavioural decisions for mate selection, communication, camouflage, and foraging. In all major vertebrate groups, it has been shown that certain species can discriminate and prefer certain colours and that colours may increase the response to a stimulus. However, because colour is often studied together with other potentially confounding factors, it is still unclear to what extent colour discrimination plays a crucial role in the perception of and attention towards biologically relevant and irrelevant stimuli. To address these questions in reptiles, we assessed the response of three gecko species Correlophus ciliatus, Eublepharis macularius, and Phelsuma laticauda to familiar and novel 2D images in colour or grayscale. We found that while all species responded more often to the novel than to the familiar images, colour information did not influence object discrimination. We also found that the duration of interaction with images was significantly longer for the diurnal species, P. laticauda, than for the two nocturnal species, but this was independent from colouration. Finally, no differences among sexes were observed within or across species. Our results indicate that geckos discriminate between 2D images of different content independent of colouration, suggesting that colouration does not increase detectability or intensity of the response. These results are essential for uncovering which visual stimuli produce a response in animals and furthering our understanding of how animals use colouration and colour vision.

animal behavior and cognition↗

bioRxiv2020

Lysophosphatidic acid (LPA)-antibody (504B3)validation by free-solution assay and interferometry identifies off-target binding

Antibody specificity and sensitivity is required in basic and clinical research for ensuring scientific rigor and reproducibility, while off-target cross-reactivity could generate erroneous conclusions. Lysophosphatidic acid (LPA) is a bioactive lipid being targeted clinically by antibody strategies. Here, we reexamined binding properties of a commercially available monoclonal antibody (504B3) reported as specific for LPA using a free-solution assay measured in a compensated interferometric reader. The antibody showed comparable binding affinities to LPA and non-LPA lipids including phosphatidic acid (PA) and lysophosphatidylcholine (LPC). These results may alter conclusions drawn from current and past basic and clinical studies employing anti-LPA antibodies.

molecular biology↗