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PubMedDate not supplied

Methods to map protein interactions in mammalian cells: different tools to address different questions.

In the post-genome era, functional annotation of the predicted gene-sets will be one of the most important upcoming challenges. So-called interactome analysis positions a protein in its subcellular environment by mapping its interaction partners. Such interaction maps are essential for an accurate insight into protein function since many cellular processes are organised to operate in protein complexes. These assemblies have dynamic structures and can interact with each other, two properties which are often controlled by regulated protein expression and modification. Various methods exist to unravel protein interaction circuitries, which can be roughly divided into biochemical and genetic strategies. In this review we focus on the different strategies to study protein-protein interactions in living mammalian cells. Recently developed analytical and screening methods are also addressed.

Animals↗
PubMed2002

Protein interaction mapping for target validation: the need for an integrated combinatory process involving complementary approaches.

Identification, selection, validation and prioritization of targets for therapeutic intervention requires understanding of the biological role of individual proteins in cellular pathways. Unraveling the ways in which proteins interact with each other appears to be crucial in achieving that goal. A number of recently described high-throughput approaches for analyzing cellular protein-protein interactions and previously proposed prediction procedures are compared in this review. The relative advantages of each method are discussed in relation to reproducibility, comprehensiveness and biological significance. It is concluded that only a combination of complementary biochemical technologies supported by reliable algorithms, will provide exhaustive maps of protein interactions for a cellular interactome.

Animals↗
PubMed2004

Cytokine-like activities of some aminoacyl-tRNA synthetases and auxiliary p43 cofactor of aminoacylation reaction and their role in oncogenesis.

Multifunctionality of proteins is among mechanisms accounting for the complexity of interactome networks in higher eukaryotes. During oncogenesis and other pathologic conditions many proteins perform additional functions without changes in three dimensional structures. One family of these moonlighting proteins is represented by enzymes and cofactors of aminoacylation reactions, by means of which tRNAs are attached to their cognate amino acids. Tyrosyl-tRNA synthetase (TyrRS), tryptophanyl-tRNA synthetases (TrpRS) and auxiliary factor of mammalian multi-aminoacyl-tRNA synthetases, p43 (precusor of endothelial monocyte activating polypeptide II - EMAP II) upon their release in intracellular environment become proinflammatory cytokines with multiple activities during apoptosis, angiogenesis and inflammation. In addition, these proteins play important role in cancer progression, modulating tumor angiogenesis and its escape from surveillance by immune system.

Amino Acyl-tRNA Synthetases↗
PubMed2025

Network-driven discovery of repurposable drugs targeting hallmarks of aging.

Despite the thousands of genes implicated in age-related phenotypes, effective interventions for aging remain elusive, a lack of advance rooted in the multifactorial nature of longevity and the functional interconnectedness of the molecular components implicated in aging. Here, we introduce a network medicine framework that integrates 2,358 longevity-associated genes onto the human interactome to identify existing drugs that can modulate aging processes. We find that genes associated with each hallmark of aging form a connected subgraph, or hallmark module, a discovery enabling us to measure the proximity of 6,442 clinically approved or experimental compounds to each hallmark. We then introduce a transcription-based metric, pAGE, which evaluates whether the drug-induced expression shifts reinforce or counteract known age-related expression changes. By integrating network proximity and pAGE, we identify multiple drug repurposing candidate that not only target specific hallmarks but act to reverse their aging-associated transcriptional changes. Our findings are interpretable, revealing for each drug the molecular mechanisms through which it modulates the hallmark, offering an experimentally falsifiable framework to leverage genomic discoveries to accelerate drug repurposing for longevity.

Journal Article↗
PubMed2025

ERCnet: Phylogenomic Prediction of Interaction Networks in the Presence of Gene Duplication.

Assigning gene function from genome sequences is a rate-limiting step in molecular biology research. A protein's position within an interaction network can potentially provide insights into its molecular mechanisms. Phylogenetic analysis of evolutionary rate covariation (ERC) in protein sequence has been shown to be effective for large-scale prediction of functional relationships and interactions. However, gene duplication, gene loss, and other sources of phylogenetic incongruence are barriers for analyzing ERC on a genome-wide basis. Here, we developed ERCnet, a bioinformatic program designed to overcome these challenges, facilitating efficient all-versus-all ERC analyses for large protein sequence datasets. We simulated proteome datasets and found that ERCnet achieves combined false positive and negative error rates well below 10% and that our novel "branch-by-branch" length measurements outperforms "root-to-tip" approaches in most cases, offering a valuable new strategy for performing ERC. We also compiled a sample set of 35 angiosperm genomes to test the performance of ERCnet on empirical data, including its sensitivity to user-defined analysis parameters such as input dataset size and branch-length measurement strategy. We investigated the overlap between ERCnet runs with different species samples to understand how species number and composition affect predicted interactions and to identify the protein sets that consistently exhibit ERC across angiosperms. Our systematic exploration of the performance of ERCnet provides a roadmap for design of future ERC analyses to predict functional interactions in a wide array of genomic datasets. ERCnet code is freely available at https://github.com/EvanForsythe/ERCnet.

Gene Duplication↗
PubMed2026

Quantitative analysis of DNA-GATA1 binding alterations linked to hematopoietic disorders.

GATA1 is a crucial transcription factor involved in hematopoiesis and mutations in this gene are linked to severe hematological disorders, including anemia, thrombocytopenia, Down syndrome-related transient abnormal myelopoiesis (DS-TAM), and myeloid leukemia of Down syndrome (ML-DS). Despite significant clinical interest in the molecular level characterization of GATA1 mutations, a comprehensive understanding of their impact on DNA binding is limited. Efforts to conduct detailed studies on full-length recombinant GATA1 have faced significant technical challenges, while alternative approaches are limited by low throughput or qualitative nature. Here, we introduce a native holdup (nHU) assay designed to systematically quantify DNA-protein interactions and is suitable for studying the impact of transcription factor mutations on DNA binding affinity. First, using the erythroid-specific ATP2B4 promoter as a model, we demonstrate that nHU can capture sequence-specific interactions and detect even subtle differences in DNA binding affinities. Then, we quantitatively characterize the impact of pathological mutations on DNA binding affinities in the context of full-length human GATA1. Our findings reveal that the GATA1s isoform, lacking the N-terminal transactivation domain (N-TAD), binds to DNA with increased affinity, while the R307C mutation reduces binding to the ATP2B4 erythroid promoter. In harmony with these observations, GATA1s exhibits increased functional activity, while the R307C mutation results in decreased activity. This study demonstrates the power of the nHU assay for studying DNA interactions of transcription factor variants and providing insight into the molecular mechanism of related diseases.

GATA1 Transcription Factor↗