Biology subjects

Rodiger, J.

Publications and source records attributed to Rodiger, J..

11 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↗

bioRxiv2025

TF2TG: an online resource mining the potential gene targets of transcription factors in Drosophila

Sequence-specific transcription factors (TFs) are key regulators of many biological processes, controlling the expression of their target genes through binding to the cis-regulatory regions such as promoters and enhancers. Each TF has unique DNA binding site motifs, and large-scale experiments have been conducted to characterize TF-DNA binding preferences. However, no comprehensive resource currently integrates these datasets for Drosophila. To address this need, we developed TF2TG ("transcription factor to target gene"), a comprehensive resource that combines both in vitro and in vivo datasets to link transcription factors (TFs) to their target genes based on TF-DNA binding preferences along with the protein-protein interaction data, tissue-specific transcriptomic data, and chromatin accessibility data. Although the genome offers numerous potential binding sites for each TF, only a subset is actually bound in vivo, and of these, only a fraction is functionally relevant. For instance, some TFs bind to their specific sites due to synergistic interactions with other factors nearby. This integration provides users with a comprehensive list of potential candidates as well as aids users in ranking candidate genes and determining condition-specific TF binding for studying transcriptional regulation in Drosophila.

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↗

bioRxiv2022

Next-generation large-scale binary protein interaction network for Drosophila

Generating reference maps of the interactome networks underlying most cellular functions can greatly illuminate genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. Here, we applied state-of-the-art experimental and bioinformatics methods to identify high-confidence binary protein-protein interactions (PPIs) for Drosophila melanogaster. We performed four all-by-all yeast two-hybrid (Y2H) screens of >10,000 Drosophila proteins, resulting in the FlyBi dataset of 8,723 PPIs among 2,939 proteins. As part of this effort, we tested subsets of our data and data from previous PPI datasets using an orthogonal assay, which allowed us to normalize data quality across datasets. Next, we integrated our FlyBi data with previous PPI data, resulting in an expanded, high-confidence binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6,511 proteins. These data are accessible through the Molecular Interaction Search Tool (MIST) and other databases. To assess the utility of the PPI resource, we used novel interactions from the FlyBi dataset to generate an autophagy interaction network that we validated in vivo using two different autophagy-related assays. We found that deformed wings (dwg) encodes a protein that is both a regulator and a target of autophagy. Altogether, the resources generated in this project provide a strong foundation for building high-confidence new hypotheses regarding protein networks and function.

cell biology↗

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

REPTOR/CREBRF encode key regulators of muscle energy metabolism

Metabolic flexibility of muscle tissue describes the capacity to use glucose or lipids as energy substrates and its disruption is associated with metabolic dysfunction. Cancer-induced cachexia is a metabolic syndrome linked with muscle wasting, changes in muscle energy metabolism and lower life expectancy in cancer patients. The molecular mechanisms driving metabolic changes in muscle, however, are poorly characterized. Here, using a Drosophila model of systemic metabolic dysfunction triggered by yorkie-induced gut tumors, we identify the transcription factor REPTOR as a key regulator of energy metabolism in muscle. We show that REPTOR is upregulated in muscles of adult flies with gut yorkie-tumors, where it is necessary to modulate glucose metabolism. REPTOR expression in muscles is induced by ImpL2, a tumor-derived insulin binding protein that reduces systemic insulin signaling, or by nutritional restriction. Further, in vitro and in vivo studies indicate that high activity of REPTOR is sufficient to increase glucose content, transcriptionally repress phosphofructokinase and increase mitochondrial respiration. Consistent with the fly studies, higher levels of CREBRF, the mammalian ortholog of REPTOR, reduce glycolysis in mouse myotubes while promoting an oxidative phenotype. Altogether, our results implicate REPTOR/CREBRF as key regulators of muscle metabolism and metabolic flexibility that share a conserved function as repressors of glycolysis and promoters of oxidative phosphorylation.

physiology↗

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

Bioinformatic and cell-based tools for pooled CRISPR knockout screening in mosquitos

Mosquito-borne diseases present a worldwide public health burden. Genome-scale screening tools that could inform our understanding of mosquitos and their control are lacking. Here, we adapt a recombination-mediated cassette exchange system for delivery of CRISPR sgRNA libraries into cell lines from several mosquito species and perform pooled CRISPR screens in an Anopheles cell line. To implement this method, we engineered modified mosquito cell lines, validated promoters and developed bioinformatics tools for multiple mosquito species.

genetics↗

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

SNP-CRISPR: a web tool for SNP-specific genome editing

CRISPR-Cas9 is a powerful genome editing technology in which a single guide RNA (sgRNA) confers target site specificity to achieve Cas9-mediated genome editing. Numerous sgRNA design tools have been developed based on reference genomes for humans and model organisms. However, existing resources are not optimal as genetic mutations or single nucleotide polymorphisms (SNPs) within the targeting region affect the efficiency of CRISPR-based approaches by interfering with guide-target complementarity. To facilitate identification of sgRNAs (1) in non-reference genomes, (2) across varying genetic backgrounds, or (3) for specific targeting of SNP-containing alleles, for example, disease relevant mutations, we developed a web tool, SNP-CRISPR (https://www.flyrnai.org/tools/snp_crispr/). SNP-CRISPR can be used to design sgRNAs based on public variant data sets or user-identified variants. In addition, the tool computes efficiency and specificity scores for sgRNA designs targeting both the variant and the reference. Moreover, SNP-CRISPR provides the option to upload multiple SNPs and target single or multiple nearby base changes simultaneously with a single sgRNA design. Given these capabilities, SNP-CRISPR has a wide range of potential research applications in model systems and for design of sgRNAs for disease-associated variant correction.

bioinformatics↗