Biology subjects

Comjean, A.

Publications and source records attributed to Comjean, A..

18 recordsLinked to original sources

bioRxiv2026

FlyPredictome: A structural atlas of predicted protein-protein interactions in Drosophila

Protein-protein interactions (PPIs) are fundamental to cellular function. Yet most Drosophila PPIs remain structurally uncharacterized despite the wealth of genetic and biochemical data available for this organism. Here we present FlyPredictome, a structural interactome based on 1.5 million pairwise AlphaFold-Multimer predictions. Using a local confidence metric performing robustly on interactions involving flexible and disordered proteins, we systematically assess experimentally reported Drosophila PPIs and predict direct binding interfaces at residue-level resolution. Testing their functional relevance, we find that phenotype-associated missense mutations are enriched at predicted interaction interfaces. Building on these predictions, we construct an evidence-supported PPI network, revealing modular organization from signaling pathways to individual protein complexes. We further predict higher-order complexes for nearly 400 of these modules with AlphaFold3, matching available cryo-EM structures and extending to unresolved assemblies. Fly-Predictome is available as an open, interactive database that maps interactions to residue-level binding surfaces, providing a structural foundation for interaction discovery in Drosophila.

bioinformatics↗

bioRxiv2026

DIOPT: the DRSC Integrative Ortholog Prediction Tool, 2026 update

Mapping orthologous proteins is a critical step for cross-species literature mining, data integration, experimental design, and more, making the ability to quickly predict orthologs across species a key tool for functional genomic studies. The DRSC Integrative Ortholog Prediction Tool (DIOPT) was initially developed in 2011 to provide a centralized portal for identifying inferred orthologs among major model organisms. By integrating results from multiple ortholog prediction algorithms, DIOPT allows users to compare predictions across methods and prioritize high-confidence ortholog relationships. Over the years, we regularly updated the underlying genome annotations and refreshed predictions from each integrated algorithm. In addition, both the number of supported species and the number of ortholog prediction algorithms incorporated into the platform have grown. The web portal has also been enhanced with new features designed to improve usability, facilitate data exploration, and support a broader range of research applications. We also developed a sister version of DIOPT tailored specifically for arthropod species; this enables researchers working with a diverse set of insects and related organisms to perform ortholog mapping and comparative analyses more effectively. Together, these developments ensure that DIOPT remains a robust and broadly useful resource for functional genomics research.

bioinformatics↗

bioRxiv2024

Enhanced Protein-Protein Interaction Discovery via AlphaFold-Multimer

Accurately mapping protein-protein interactions (PPIs) is critical for elucidating cellular functions and has significant implications for health and disease. Conventional experimental approaches, while foundational, often fall short in capturing direct, dynamic interactions, especially those with transient or small interfaces. Our study leverages AlphaFold-Multimer (AFM) to re-evaluate high-confidence PPI datasets from Drosophila and human. Our analysis uncovers a significant limitation of the AFM-derived interface pTM (ipTM) metric, which, while reflective of structural integrity, can miss physiologically relevant interactions at small interfaces or within flexible regions. To bridge this gap, we introduce the Local Interaction Score (LIS), derived from AFMs Predicted Aligned Error (PAE), focusing on areas with low PAE values, indicative of the high confidence in interaction predictions. The LIS method demonstrates enhanced sensitivity in detecting PPIs, particularly among those that involve flexible and small interfaces. By applying LIS to large-scale Drosophila datasets, we enhance the detection of direct interactions. Moreover, we present FlyPredictome, an online platform that integrates our AFM-based predictions with additional information such as gene expression correlations and subcellular localization predictions. This study not only improves upon AFMs utility in PPI prediction but also highlights the potential of computational methods to complement and enhance experimental approaches in the identification of PPI networks.

bioinformatics↗

bioRxiv2024

Transcriptional programs mediating neuronal toxicity and altered glial-neuronal signaling in a Drosophila knock-in tauopathy model

Missense mutations in the gene encoding the microtubule-associated protein tau cause autosomal dominant forms of frontotemporal dementia. Multiple models of frontotemporal dementia based on transgenic expression of human tau in experimental model organisms, including Drosophila, have been described. These models replicate key features of the human disease, but do not faithfully recreate the genetic context of the human disorder. Here we use CRISPR-Cas mediated gene editing to model frontotemporal dementia caused by the tau P301L mutation by creating the orthologous mutation, P251L, in the endogenous Drosophila tau gene. Flies heterozygous or homozygous for tau P251L display age-dependent neurodegeneration, metabolic defects and accumulate DNA damage in affected neurons. To understand the molecular events promoting neuronal dysfunction and death in knock-in flies we performed single-cell RNA sequencing on approximately 130,000 cells from brains of tau P251L mutant and control flies. We found that expression of disease-associated mutant tau altered gene expression cell autonomously in all neuronal cell types identified and non-cell autonomously in glial cells. Cell signaling pathways, including glial-neuronal signaling, were broadly dysregulated as were brain region and cell-type specific protein interaction networks and gene regulatory programs. In summary, we present here a genetic model of tauopathy, which faithfully recapitulates the genetic context and phenotypic features of the human disease and use the results of comprehensive single cell sequencing analysis to outline pathways of neurotoxicity and highlight the role of non-cell autonomous changes in glia.

neuroscience↗

bioRxiv2023

Epithelial Ca2+ waves triggered by enteric neurons heal the gut

A fundamental and unresolved question in regenerative biology is how tissues return to homeostasis after injury. Answering this question is essential for understanding the etiology of chronic disorders such as inflammatory bowel diseases and cancer. We used the Drosophila midgut to investigate this question and discovered that during regeneration a subpopulation of cholinergic enteric neurons triggers Ca2+ currents among enterocytes to promote return of the epithelium to homeostasis. Specifically, we found that down-regulation of the cholinergic enzyme Acetylcholinesterase in the epithelium enables acetylcholine from defined enteric neurons, referred as ARCENs, to activate nicotinic receptors in enterocytes found near ARCEN- innervations. This activation triggers high Ca2+ influx that spreads in the epithelium through Inx2/Inx7 gap junctions promoting enterocyte maturation followed by reduction of proliferation and inflammation. Disrupting this process causes chronic injury consisting of ion imbalance, Yki activation and increase of inflammatory cytokines together with hyperplasia, reminiscent of inflammatory bowel diseases. Altogether, we found that during gut regeneration the conserved cholinergic pathway facilitates epithelial Ca2+ waves that heal the intestinal epithelium. Our findings demonstrate nerve- and bioelectric-dependent intestinal regeneration which advance the current understanding of how a tissue returns to its homeostatic state after injury and could ultimately help existing therapeutics.

genetics↗

bioRxiv2023

Tumor Cytokine-Induced Hepatic Gluconeogenesis Contributes to Cancer Cachexia: Insights from Full Body Single Nuclei Sequencing

SummaryA primary cause of death in cancer patients is cachexia, a wasting syndrome attributed to tumor-induced metabolic dysregulation. Despite the major impact of cachexia on the treatment, quality of life, and survival of cancer patients, relatively little is known about the underlying pathogenic mechanisms. Hyperglycemia detected in glucose tolerance test is one of the earliest metabolic abnormalities observed in cancer patients; however, the pathogenesis by which tumors influence blood sugar levels remains poorly understood. Here, utilizing a Drosophila model, we demonstrate that the tumor secreted interleukin-like cytokine Upd3 induces fat body expression of Pepck1 and Pdk, two key regulatory enzymes of gluconeogenesis, contributing to hyperglycemia. Our data further indicate a conserved regulation of these genes by IL-6/JAK STAT signaling in mouse models. Importantly, in both fly and mouse cancer cachexia models, elevated gluconeogenesis gene levels are associated with poor prognosis. Altogether, our study uncovers a conserved role of Upd3/IL-6/JAK-STAT signaling in inducing tumor-associated hyperglycemia, which provides insights into the pathogenesis of IL-6 signaling in cancer cachexia. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=162 HEIGHT=200 SRC="FIGDIR/small/540823v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@508c1aorg.highwire.dtl.DTLVardef@1e1eecorg.highwire.dtl.DTLVardef@1802559org.highwire.dtl.DTLVardef@140e6c_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

bioRxiv2023

Mechanistic characterization of a Drosophila model of paraneoplastic nephrotic syndrome

Paraneoplastic syndromes occur in cancer patients and originate from dysfunction of organs at a distance from the tumor or its metastasis. A wide range of organs can be affected in paraneoplastic syndromes; however, the pathological mechanisms by which tumors influence host organs are poorly understood. Recent studies in the fly uncovered that tumor secreted factors target host organs, leading to pathological effects. In this study, using a Drosophila gut tumor model, we characterized a mechanism of tumor-induced kidney dysfunction. Specifically, we found that Pvf1, a PDGF/VEGF signaling ligand, secreted by gut tumors activates the PvR/JNK/Jra signaling pathway in the principal cells of the kidney, leading to mis-expression of renal genes and paraneoplastic renal syndrome-like phenotypes. Our study describes a novel mechanism by which gut tumors perturb the function of the kidney, which might be of clinical relevance for the treatment of paraneoplastic syndromes.

zoology↗

bioRxiv2023

PANGEA: A New Gene Set Enrichment Tool for Drosophila and Common Research Organisms

Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an omics study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualisations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species.

bioinformatics↗

bioRxiv2022

Paralog Explorer: a resource for mining information about paralogs in common research organisms

Paralogs are genes which arose via gene duplication, and when such paralogs retain overlapping or redundant function, this poses a challenge to functional genetics research. Recent technological advancements have made it possible to systematically probe gene function for redundant genes using dual or multiplex gene perturbation, and there is a need for a simple bioinformatic tool to identify putative paralogs of a gene(s) of interest. We have developed Paralog Explorer (https://www.flyrnai.org/tools/paralogs/), an online resource that allows researchers to quickly and accurately identify candidate paralogous genes in the genomes of the model organisms D. melanogaster, C. elegans, D. rerio, M. musculus, and H. sapiens. Paralog Explorer deploys an effective between-species ortholog prediction software, DIOPT, to analyze within-species paralogs. Paralog Explorer allows users to identify candidate paralogs, and to navigate relevant databases regarding gene co-expression, protein-protein and genetic interaction, as well as gene ontology and phenotype annotations. Altogether, this tool extends the value of current ortholog prediction resources by providing sophisticated features useful for identification and study of paralogous genes.

bioinformatics↗

bioRxiv2021

A cell atlas of the fly kidney

Like humans, insects rely on precise regulation of their internal environments to survive. The insect renal system consists of Malpighian tubules and nephrocytes that share similarities to the mammalian kidney. Studies of the Drosophila Malpighian tubules and nephrocytes have provided many insights into our understanding of the excretion of waste products, stem cell regeneration, protein reabsorption, and as human kidney disease models. Here, we analyzed single-nucleus RNA sequencing (snRNA-seq) data sets to characterize the cell types of the adult fly kidney. We identified 11 distinct clusters representing renal stem cells (RSCs), stellate cells (SCs), regionally specific principal cells (PCs), garland nephrocyte cells (GCs) and pericardial nephrocytes (PNs). Analyses of these clusters revealed many new interesting features. For example, we found a new, previously unrecognized cell cluster: lower segment PCs that express Esyt2. In addition, we find that the SC marker genes RhoGEF64c, Frq2, Prip and CG10939 regulate their unusual cell shape. Further, we identified transcription factors specific to each cluster and built a network of signaling pathways that are potentially involved in mediating cell-cell communication between Malpighian tubule cell types. Finally, cross-species analysis allowed us to match the fly kidney cell types to mouse kidney cell types and planarian protonephridia - knowledge that will help the generation of kidney disease models. To visualize this dataset, we provide a web-based resource for gene expression in single cells (https://www.flyrnai.org/scRNA/kidney/). Altogether, our study provides a comprehensive resource for addressing gene function in the fly kidney and future disease studies.

developmental biology↗

bioRxiv2021

FlyPhoneDB: An integrated web-based resource for cell-cell communication prediction in Drosophila

Multicellular organisms rely on cell-cell communication to exchange information necessary for developmental processes and metabolic homeostasis. Cell-cell communication pathways can be inferred from transcriptomic datasets based on ligand-receptor (L-R) expression. Recently, data generated from single cell RNA sequencing (scRNA-seq) have enabled L-R interaction predictions at an unprecedented resolution. While computational methods are available to infer cell-cell communication in vertebrates such a tool does not yet exist for Drosophila. Here, we generated a high confidence list of L-R pairs for the major fly signaling pathways and developed FlyPhoneDB, a quantification algorithm that calculates interaction scores to predict L-R interactions between cells. At the FlyPhoneDB user interface, results are presented in a variety of tabular and graphical formats to facilitate biological interpretation. To demonstrate that FlyPhoneDB can effectively identify active ligands and receptors to uncover cell-cell communication events, we applied FlyPhoneDB to Drosophila scRNA-seq data sets from adult midgut, abdomen, and blood, and demonstrate that FlyPhoneDB can readily identify previously characterized cell-cell communication pathways. Altogether, FlyPhoneDB is an easy-to-use framework that can be used to predict cell-cell communication between cell types from scRNA-seq data in Drosophila.

bioinformatics↗

bioRxiv2021

DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species

With the advent of single-cell RNA sequencing (scRNA-seq) technologies, there has been a spike in studies involving scRNA-seq of several tissues across diverse species including Drosophila. Although a few databases exist for users to query genes of interest within the scRNA-seq studies, search tools that enable users to find orthologous genes and their cell type-specific expression patterns across species are limited. Here, we built a new search database, called DRscDB (https://www.flyrnai.org/tools/single_cell/web/) to address this need. DRscDB serves as a comprehensive repository for published scRNA-seq datasets for Drosophila and the relevant datasets from human and other model organisms. DRscDB is based on manual curation of Drosophila scRNA-seq studies of various tissue types and their corresponding analogous tissues in vertebrates including zebrafish, mouse, and human. Of note, our search database provides most of the literature-derived marker genes, thus preserving the original analysis of the published scRNA-seq datasets. DRscDB serves as a web-based user interface that allows users to mine, utilize and compare gene expression data pertaining to scRNA-seq datasets from the published literature.

bioinformatics↗

bioRxiv2020

BioLitMine: advanced mining of biomedical and biological literature about human genes and genes from major model organisms

The accumulation of biological and biomedical literature outpaces the ability of most researchers and clinicians to stay abreast of their own immediate fields, let alone a broader range of topics. Although available search tools support identification of relevant literature, finding relevant and key publications is not always straightforward. For example, important publications might be missed in searches with an official gene name due to gene synonyms. Moreover, ambiguity of gene names can result in retrieval of a large number of irrelevant publications. To address these issues and help researchers and physicians quickly identify relevant publications, we developed BioLitMine, an advanced literature mining tool that takes advantage of the medical subject heading (MeSH) index and gene-to-publication annotations already available for PubMed literature. Using BioLitMine, a user can identify what MeSH terms are represented in the set of publications associated with a given gene of the interest, or start with a term and identify relevant publications. Users can also use the tool to find co-cited genes and a build a literature co-citation network. In addition, BioLitMine can help users build a gene list relevant to a MeSH terms, such as a list of genes relevant to "stem cells" or "breast neoplasms." Users can also start with a gene or pathway of interest and identify authors associated with that gene or pathway, a feature that makes it easier to identify experts who might serve as collaborators or reviewers. Altogether, BioLitMine extends the value of PubMed-indexed literature and its existing expert curation by providing a robust and gene-centric approach to retrieval of relevant information.

bioinformatics↗

bioRxiv2019

A single-cell survey of Drosophila blood

Drosophila blood cells, called hemocytes, are classified into plasmatocytes, crystal cells, and lamellocytes based on the expression of a few marker genes and cell morphologies, which are inadequate to classify the complete hemocyte repertoire. Here, we used single-cell RNA sequencing (scRNA-seq) to map hemocytes across different inflammatory conditions in larvae. We resolved plasmatocytes into different states based on the expression of genes involved in cell cycle, antimicrobial response, and metabolism together with the identification of intermediate states. Further, we discovered rare subsets within crystal cells and lamellocytes that express fibroblast growth factor (FGF) ligand branchless and receptor breathless, respectively. We demonstrate that these FGF components are required for mediating effective immune responses against parasitoid wasp eggs, highlighting a novel role for FGF signaling in inter-hemocyte crosstalk. Our scRNA-seq analysis reveals the diversity of hemocytes and provides a rich resource of gene expression profiles for a systems-level understanding of their functions. HighlightsO_LIscRNA-seq of Drosophila blood recovers plasmatocytes, crystal cells, and lamellocytes C_LIO_LIscRNA-seq identifies different plasmatocyte states based on the expression of genes involved in cell cycle regulation, antimicrobial response, and metabolism C_LIO_LIPseudotemporal ordering of single cells identifies crystal cell and lamellocyte intermediate states C_LIO_LIscRNA-seq uncovers a novel role for FGF signaling in inter-hemocyte crosstalk C_LI

immunology↗

bioRxiv2019

Large-scale transgenic Drosophila resource collections for loss- and gain-of-function studies

The Transgenic RNAi Project (TRiP), a Drosophila functional genomics platform at Harvard Medical School, was initiated in 2008 to generate and distribute a genome-scale collection of RNAi fly stocks. To date, the TRiP has generated >15,000 RNAi fly stocks. As this covers most Drosophila genes, we have largely transitioned to development of new resources based on CRISPR technology. Here, we present an update on our libraries of publicly available RNAi and CRISPR fly stocks, and focus on the TRiP-CRISPR overexpression (TRiP-OE) and TRiP-CRISPR knockout (TRiP-KO) collections. TRiP-OE stocks express sgRNAs targeting upstream of a gene transcription start site. Gene activation is triggered by co-expression of catalytically dead Cas9 (dCas9) fused to an activator domain, either VP64-p65-Rta (VPR) or Synergistic Activation Mediator (SAM). TRiP-KO stocks express one or two sgRNAs targeting the coding sequence of a gene or genes, allowing for generation of indels in both germline and somatic tissue. To date, we have generated more than 5,000 CRISPR-OE or -KO stocks for the community. These resources provide versatile, transformative tools for gene activation, gene repression, and genome engineering.

genetics↗

bioRxiv2018

A cell atlas of the adult Drosophila midgut

Studies of the adult Drosophila midgut have provided a number of insights on cell type diversity, stem cell regeneration, tissue homeostasis and cell fate decision. Advances in single-cell RNA sequencing (scRNA-seq) provide opportunities to identify new cell types and molecular features. We used inDrop to characterize the transcriptome of midgut epithelial cells and identified 12 distinct clusters representing intestinal stem cells (ISCs), enteroblasts (EBs), enteroendocrine cells (EEs), enterocytes (ECs) from different regions, and cardia. This unbiased approach recovered 90% of the known ISCs/EBs markers, highlighting the high quality of the dataset. Gene set enrichment analysis in conjunction with electron micrographs revealed that ISCs are enriched in free ribosomes and possess mitochondria with fewer cristae. We demonstrate that a subset of EEs in the middle region of the midgut expresses the progenitor marker esg and that individual EEs are capable of expressing up to 4 different gut hormone peptides. We also show that the transcription factor klumpfuss (klu) is expressed in EBs and functions to suppress EE formation. Lastly, we provide a web-based resource for visualization of gene expression in single cells. Altogether, our study provides a comprehensive resource for addressing novel functions of genes in the midgut epithelium.

genetics↗

bioRxiv2017

Zinc detoxification: a functional genomics and transcriptomics analysis in Drosophila melanogaster cultured cells

Cells require some metals, such as zinc and manganese, but excess levels of these metals can be toxic. As a result, cells have evolved complex mechanisms for maintaining metal homeostasis and surviving metal intoxication. Here, we present the results of a large-scale functional genomic screen in Drosophila cultured cells for modifiers of zinc chloride toxicity, together with transcriptomics data for wildtype or genetically zinc-sensitized cells challenged with mild zinc chloride supplementation. Altogether, we identified 47 genes for which knockdown conferred sensitivity or resistance to toxic zinc or manganese chloride treatment, and more than 1800 putative zinc-responsive genes. Analysis of the omics data points to the relevance of ion transporters, glutathione-related factors, and conserved disease-associated genes in zinc detoxification. Specific genes identified in the zinc screen include orthologs of human disease-associated genes CTNS, PTPRN (also known as IA-2), and ATP13A2 (also known as PARK9). We show that knockdown of red dog mine (rdog; CG11897), a candidate zinc detoxification gene encoding an ABCC-type transporter family protein related to yeast cadmium factor (YCF1), confers sensitivity to zinc intoxication in cultured cells and that rdog is transcriptionally up-regulated in response to zinc stress. As there are many links between the biology of zinc and other metals and human health, the omics datasets presented here provide a resource that will allow researchers to explore metal biology in the context of diverse health-relevant processes.

genetics↗

bioRxiv2017

Gene2Function: An Integrated Online Resource For Gene Function Discovery

One of the most powerful ways to develop hypotheses regarding biological functions of conserved genes in a given species, such as in humans, is to first look at what is known about function in another species. Model organism databases (MODs) and other resources are rich with functional information but difficult to mine. Gene2Function (G2F) addresses a broad need by integrating information about conserved genes in a single online resource.

bioinformatics↗