Biology subjects

Kovacs, I. A.

Publications and source records attributed to Kovacs, I. A..

9 recordsLinked to original sources

bioRxiv2024

Nematode Extracellular Protein Interactome Expands Connections between Signaling Pathways

Multicellularity was accompanied by the emergence of new classes of cell surface and secreted proteins. The nematode C. elegans is a favorable model to study cell surface interactomes, given its well-defined and stereotyped cell types and intercellular contacts. Here we report our C. elegans extracellular interactome dataset, the largest yet for an invertebrate. Most of these interactions were unknown, despite recent datasets for flies and humans, as our collection contains a larger selection of protein families. We uncover new interactions for all four major axon guidance pathways, including ectodomain interactions between three of the pathways. We demonstrate that a protein family known to maintain axon locations are secreted receptors for insulins. We reveal novel interactions of cystine-knot proteins with putative signaling receptors, which may extend the study of neurotrophins and growth-factor-mediated functions to nematodes. Finally, our dataset provides insights into human disease mechanisms and how extracellular interactions may help establish connectomes.

biochemistry↗

bioRxiv2023

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BackgroundIneffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. MethodsHere, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. ResultsscDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohns disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohns disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. ConclusionWe propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrios potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

immunology↗

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↗

bioRxiv2021

Assessment of community efforts to advance computational prediction of protein-protein interactions

Comprehensive insights from the human protein-protein interaction (PPI) network, known as the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of new PPIs. Many such approaches have been proposed. However, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 24 representative network-based methods to predict PPIs across five different interactomes, including a synthetic interactome generated by the duplication-mutation-complementation model, and the interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. We selected the top-seven methods through a computational validation on the human interactome. We next experimentally validated their top-500 predicted PPIs (in total 3,276 predicted PPIs) using the yeast two-hybrid assay, finding 1,177 new human PPIs (involving 633 proteins). Our results indicate that task-tailored similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods. Through experimental validation, we confirmed that the top-ranking methods show promising performance externally. For example, from the top 500 PPIs predicted by an advanced similarity-base method [MPS(B&T)], 430 were successfully tested by Y2H with 376 testing positive, yielding a precision of 87.4%. These results establish advanced similarity-based methods as powerful tools for the prediction of human PPIs.

systems biology↗

bioRxiv2021

Binary Interactome Models of Inner- Versus Outer-Complexome Organization

Hundreds of different protein complexes that perform important functions across all cellular processes, collectively comprising the "complexome" of an organism, have been identified1. However, less is known about the fraction of the interactome that exists outside the complexome, in the "outer-complexome". To investigate features of "inner"- versus outer-complexome organisation in yeast, we generated a high-quality atlas of binary protein-protein interactions (PPIs), combining three previous maps2-4 and a new reference all-by-all binary interactome map. A greater proportion of interactions in our map are in the outer-complexome, in comparison to those found by affinity purification followed by mass spectrometry5-7 or in literature curated datasets8-11. In addition, recent advances in deep learning predictions of PPI structures12 mirror the existing experimentally resolved structures in being largely focused on the inner complexome and missing most interactions in the outer-complexome. Our new PPI network suggests that the outer-complexome contains considerably more PPIs than the inner-complexome, and integration with functional similarity networks13-15 reveals that interactions in the inner-complexome are highly detectable and correspond to pairs of proteins with high functional similarity, while proteins connected by more transient, harder-to-detect interactions in the outer-complexome, exhibit higher functional heterogeneity.

systems biology↗

bioRxiv2020

Proteomic Analysis Uncovers Measles Virus Protein C Interaction with p65/iASPP/p53 Protein Complex

Viruses manipulate central machineries of host cells to their advantage. They prevent host cell antiviral responses to create a favorable environment for their survival and propagation. Measles virus (MV) encodes two non-structural proteins MV-V and MV-C known to counteract the host interferon response and to regulate cell death pathways. Several molecular mechanisms underlining MV-V regulation of innate immunity and cell death pathways have been proposed, whereas MV-C host protein partners are less studied. We suggest that some cellular factors that are controlled by MV-C protein during viral replication could be components of innate immunity and the cell death pathways. To determine which host factors are targeted by MV-C, we captured both direct and indirect host protein partners of MV-C protein. For this, we used a strategy based on recombinant viruses expressing tagged viral proteins followed by affinity purification and a bottom-up mass spectrometry analysis. From the list of host proteins specifically interacting with MV-C protein in different cell lines we selected the host targets that belong to immunity and cell death pathways for further validation. Direct protein partners of MV-C were determined by applying protein complementation assay (PCA) and the bioluminescence resonance energy transfer (BRET) approach. As a result, we found that MV-C protein specifically interacts with p65/iASPP/p53 protein complex that controls both cell death and innate immunity pathways.

microbiology↗

bioRxiv2020

Uncovering the genetic blueprint of the C. elegans nervous system

Despite rapid advances in connectome mapping and neuronal genetics, we lack theoretical and computational tools to unveil, in an experimentally testable fashion, the genetic mechanisms that govern neuronal wiring. Here we introduce a computational framework to link the adjacency matrix of a connectome to the expression patterns of its neurons, helping us uncover a set of genetic rules that govern the interactions between adjacent neurons. The method incorporates the biological realities of the system, accounting for noise from data collection limitations, as well as spatial restrictions. The resulting methodology allows us to infer a network of 19 innexin interactions that govern the formation of gap junctions in C. elegans, five of which are already supported by experimental data. As advances in single-cell gene expression profiling increase the accuracy and the coverage of the data, the developed framework will allow researchers to systematically infer experimentally testable connection rules, offering mechanistic predictions for synapse and gap junction formation.

biophysics↗

bioRxiv2019

A reference map of the human protein interactome

Global insights into cellular organization and function require comprehensive understanding of interactome networks. Similar to how a reference genome sequence revolutionized human genetics, a reference map of the human interactome network is critical to fully understand genotype-phenotype relationships. Here we present the first human \"all-by-all\" binary reference interactome map, or \"HuRI\". With ~53,000 high-quality protein-protein interactions (PPIs), HuRI is approximately four times larger than the information curated from small-scale studies available in the literature. Integrating HuRI with genome, transcriptome and proteome data enables the study of cellular function within essentially any physiological or pathological cellular context. We demonstrate the use of HuRI in identifying specific subcellular roles of PPIs and protein function modulation via splicing during brain development. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI thus represents an unprecedented, systematic reference linking genomic variation to phenotypic outcomes.

systems biology↗

bioRxiv2018

Network-based prediction of protein interactions

As biological function emerges through interactions between a cells molecular constituents, understanding cellular mechanisms requires us to catalogue all physical interactions between proteins [1-4]. Despite spectacular advances in high-throughput mapping, the number of missing human protein-protein interactions (PPIs) continues to exceed the experimentally documented interactions [5, 6]. Computational tools that exploit structural, sequence or network topology information are increasingly used to fill in the gap, using the patterns of the already known interactome to predict undetected, yet biologically relevant interactions [7-9]. Such network-based link prediction tools rely on the Triadic Closure Principle (TCP) [10-12], stating that two proteins likely interact if they share multiple interaction partners. TCP is rooted in social network analysis, namely the observation that the more common friends two individuals have, the more likely that they know each other [13, 14]. Here, we offer direct empirical evidence across multiple datasets and organisms that, despite its dominant use in biological link prediction, TCP is not valid for most protein pairs. We show that this failure is fundamental - TCP violates both structural constraints and evolutionary processes. This understanding allows us to propose a link prediction principle, consistent with both structural and evo-lutionary arguments, that predicts yet uncovered protein interactions based on paths of length three (L3). A systematic computational cross-validation shows that the L3 principle significantly outperforms existing link prediction methods. To experimentally test the L3 predictions, we perform both large-scale high-throughput and pairwise tests, finding that the predicted links test positively at the same rate as previously known interactions, suggesting that most (if not all) predicted interactions are real. Combining L3 predictions with experimen-tal tests provided new interaction partners of FAM161A, a protein linked to retinitis pigmentosa, offering novel insights into the molecular mechanisms that lead to the disease. Because L3 is rooted in a fundamental biological principle, we expect it to have a broad applicability, enabling us to better understand the emergence of biological function under both healthy and pathological conditions.\n\nSummaryWe unveil a fundamental organizing principle of biological networks and demonstrate its predictive power for uncovering novel protein interactions.

systems biology↗