Biology subjects

Larschan, E.

Publications and source records attributed to Larschan, E..

15 recordsLinked to original sources

bioRxiv2026

Common and Sex-Biased Muscle Transcriptomic Aging Signatures Across Diverse Species

Sex differences in lifespan are widespread yet inconsistent across the animal kingdom. In mammals, XX females outlive XY males in over 60% of species, while this pattern reverses in birds, where ZZ males have a higher life expectancy. These sex differences raise the question of whether sex-biased aging biomarkers (age-associated molecular signatures that reflect biological aging processes) can be identified based on transcriptomic data and whether such markers are shared across taxa. To identify sex-biased aging biomarkers, we analyzed sex-specific skeletal muscle transcriptomes (RNA-seq) from young and old individuals of six species spanning major evolutionary lineages. Four female-biased and 18 male-biased aging biomarkers were identified across all species. Three shared aging biomarkers were also identified in both sexes of all species: Na Channel, Heat Shock Protein 70 (HSP70), and Secreted Protein Acidic and Rich in Cysteine (SPARC). Particularly, age-associated changes in the HSP70 family are suggestive of a shared decline in proteostasis, a hallmark of aging observed across both model and non-model organisms. Furthermore, using an Ornstein-Uhlenbeck (OU) model applied to orthogroup phylogenies, we identified lineage- and sex-dependent expression shifts in shared aging biomarkers. In addition, sex-biased orthogroups revealed age-associated alterations in muscle structure and function in females, whereas mitochondrial and metabolic pathways were predominantly changed in males, indicating divergent mechanisms in muscle aging between sexes. Together, these findings identified a set of transcriptional aging signatures, both shared and sex-biased, that are detectable across diverse evolutionary lineages, providing insight into shared and divergent molecular mechanisms underlying aging.

genomics↗

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

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↗

bioRxiv2024

TimeFlies: an snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging

Although multiple high-performing epigenetic aging clocks exist, few are based directly on gene expression. Such transcriptomic aging clocks allow us to identify potential age-associated genes directly. However, most existing transcriptomic clocks model a subset of genes and are limited in their ability to predict novel biomarkers. With the growing application of single-cell sequencing, there is a need for robust single-cell transcriptomic aging clocks. Moreover, aging clocks have yet to be applied to investigate the elusive phenomenon of sex differences in aging. We introduce TimeFlies, a pan-cell-type snRNA-seq aging clock for the Drosophila melanogaster head. TimeFlies uses deep learning to classify the donor age of cells based on genome-wide gene expression profiles. Using explainability methods, we identified key marker genes contributing to the classification, with lncRNAs showing up as highly enriched among predicted biomarkers. lncRNA:roX1 and lncRNA:roX2 are top clock genes across cell types. Both are regulators of X chromosome dosage compensation, a pathway previously found to be significantly affected by aging in the mouse brain. We validated these findings experimentally in Drosophila, showing a decrease in survival when dosage compensation is inhibited in vivo. Furthermore, we trained sex-specific TimeFlies clocks and noted significant differences in model predictions and explanations between male and female clocks, suggesting that different pathways drive aging in males and females.

genomics↗

bioRxiv2024

Synaptic gene expression is coordinated across development by conserved chromatin regulators

Synapses are the primary sites of communication in the nervous system. Synapse formation requires the simultaneous expression of hundreds of proteins across cells. Through genomic and functional studies in Drosophila, we investigated the regulatory mechanisms underlying the spatiotemporal coordination of synaptic gene expression during neurodevelopment. We find that chromatin opening is coordinated across hundreds of synaptic genes immediately preceding transcription, whereas repression of these genes outside developmental periods of synaptogenesis is regulated by distinct mechanisms. We identify the conserved DEAF1 transcription factor (TF) as a direct repressor of synaptic genes. Disruption of DEAF1 or pioneer TF CLAMP, which promotes DEAF1 binding, increases synaptic gene expression across neuronal subtypes, leading to excess synapse formation. DEAF1, which is linked to syndromic intellectual disability, is also sufficient to block synaptogenesis. These findings reveal the importance of coordinated synaptic gene repression in sculpting neuronal connectivity and broaden our understanding of gene regulation in neurodevelopment.

neuroscience↗

bioRxiv2024

Optimal transport reveals dynamic gene regulatory networks via gene velocity estimation

Inferring gene regulatory networks from gene expression data is an important and challenging problem in the biology community. We propose OTVelo, a methodology that takes time-stamped single-cell gene expression data as input and predicts gene regulation across two time points. It is known that the rate of change of gene expression, which we will refer to as gene velocity, provides crucial information that enhances such inference; however, this information is not always available due to the limitations in sequencing depth. Our algorithm overcomes this limitation by estimating gene velocities using optimal transport. We then infer gene regulation using time-lagged correlation and Granger causality via regularized linear regression. Instead of providing an aggregated network across all time points, our method uncovers the underlying dynamical mechanism across time points. We validate our algorithm on 13 simulated datasets with both synthetic and curated networks and demonstrate its efficacy on 4 experimental data sets. Author summaryUnderstanding how genes interact to regulate cellular functions is crucial for advancing our knowledge of biology and disease. We present OTVelo, a method that uses single-cell gene expression data collected at different time points to infer gene regulatory networks. OTVelo offers a dynamic view of how gene interactions change over time, providing deeper insights into cellular processes. Unlike traditional methods, OTVelo captures temporal information through ancestor-descendant transitions without assuming a specific underlying regulatory model. We validate our approach using both simulated and real-world data, demonstrating its effectiveness in revealing complex gene regulation patterns. This method could lead to new discoveries in understanding biological systems and developing disease treatments.

bioinformatics↗

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

scNODE: Generative Model for Temporal Single Cell Transcriptomic Data Prediction

AO_SCPLOWBSTRACTC_SCPLOWMeasurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely-sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder (VAE) with neural ordinary differential equations (ODEs) to predict gene expression using a continuous and non-linear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust against distribution shifts when predicting single-cell gene expression at unobserved timepoints. Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods. We further demonstrate that scNODEs predictions help cell trajectory inference under the missing timepoint paradigm and the learned latent space is useful for in silico perturbation analysis of relevant genes along a developmental cell path. The data and code are publicly available at https://github.com/rsinghlab/scNODE.

bioinformatics↗

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↗

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↗

bioRxiv2020

Integrating long-range regulatory interactions to predict gene expression using graph convolutional neural networks

Long-range spatial interactions among genomic regions are critical for regulating gene expression, and their disruption has been associated with a host of diseases. However, when modeling the effects of regulatory factors, most deep learning models either neglect long-range interactions or fail to capture the inherent 3D structure of the underlying genomic organization. To address these limitations, we present GC-MERGE, a Graph Convolutional Model for Epigenetic Regulation of Gene Expression. Using a graph-based framework, the model incorporates important information about long-range interactions via a natural encoding of spatial interactions into the graph representation. It integrates measurements of both the spatial genomic organization and local regulatory factors, specifically histone modifications, to not only predict the expression of a given gene of interest but also quantify the importance of its regulatory factors. We apply GC-MERGE to datasets for three cell lines - GM12878 (lymphoblastoid), K562 (myelogenous leukemia), and HUVEC (human umbilical vein endothelial) - and demonstrate its state-of-the-art predictive performance. Crucially, we show that our model is interpretable in terms of the observed biological regulatory factors, high-lighting both the histone modifications and the interacting genomic regions contributing to a genes predicted expression. We provide model explanations for multiple exemplar genes and validate them with evidence from the literature. Our model presents a novel setup for predicting gene expression by integrating multimodal datasets in a graph convolutional framework. More importantly, it enables interpretation of the biological mechanisms driving the models predictions. Available at: https://github.com/rsinghlab/GC-MERGE.

bioinformatics↗

bioRxiv2020

The zinc finger protein CLAMP promotes long-range chromatin interactions that mediate dosage compensation of the Drosophila male X-chromosome

Drosophila dosage compensation is an important model system for defining how active chromatin domains are formed. The Male-specific lethal dosage compensation complex (MSLc) increases transcript levels of genes along the length of the single male X-chromosome to equalize with that on the two female X-chromosomes. The strongest binding sites for MSLc cluster together in three-dimensional space independent of MSLc because clustering occurs in both sexes. CLAMP, a non-sex specific, ubiquitous zinc finger protein, binds synergistically with MSLc to enrich the occupancy of both factors on the male X-chromosome. Here, we demonstrate that CLAMP promotes the observed clustering of MSLc bindings sites. Genome-wide, CLAMP promotes interactions between active chromatin regions. Moreover, the X-enriched CLAMP protein more strongly promotes longer-range interactions on the X-chromosome than autosomes. Genome-wide, CLAMP promotes interactions between active chromatin regions together with other insulator proteins. Overall, we define how long-range interactions which are modulated by a locally enriched ubiquitous transcription factor promote hyper-activation of the X-chromosome to mediate dosage compensation.

genomics↗

bioRxiv2020

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

Uncovering how transcription factors (TFs) regulate their targets at the DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) in normal and diseased states. RNA-seq has become a standard method to measure gene regulation using an established set of analysis steps. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods which integrate ordered RNA-seq data with transcription factor binding data are urgently needed. Here, we present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to predict causal regulatory mechanism networks between TFs from time series multi-omics data. We used TIMEOR to identify a new link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git.

bioinformatics↗

bioRxiv2020

CLAMP and Zelda function together as pioneer transcription factors to promote Drosophila zygotic genome activation

During the essential and conserved process of zygotic genome activation (ZGA), chromatin accessibility must increase to promote transcription. Drosophila is a well-established model for defining mechanisms that drive ZGA. Zelda (ZLD) is a key pioneer transcription factor (TF) that promotes ZGA in the Drosophila embryo. However, many genomic loci that contain GA-rich motifs become accessible during ZGA independent of ZLD. Therefore, we hypothesized that other early TFs that function with ZLD have not yet been identified, especially those that are capable of binding to GA-rich motifs such as CLAMP. Here, we demonstrate that Drosophila embryonic development requires maternal CLAMP to: 1) activate zygotic transcription; 2) increase chromatin accessibility at promoters of specific genes that often encode other essential TFs; 3) enhance chromatin accessibility to facilitate ZLD occupancy at a subset of key embryonic promoters. Thus, maternal CLAMP functions with ZLD in a pioneer-like role to drive zygotic genome activation.

genomics↗