A tissue-specific atlas of protein-protein associations enables prioritization of candidate disease genes
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens (human) Rattus norvegicus (brown rat)
SUBMITTER:
PROVIDER: S-BSST1423 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:Proteins that interact together participate in the same cellular process and influence the same organismal traits. Despite the progress in mapping protein interactions we lack knowledge of how these change across tissues. Due to coordinated (post)transcriptional control, protein complex members have highly correlated abundances that are predictive of functional association. Here, we have compiled 7840 proteomic samples measuring protein levels in 11 human tissues and use these to define an atlas of tissue specific protein associations. This method recapitulates known protein complexes and the larger structural organization of the cell. Stable protein complexes are well preserved across tissues with signaling and metabolic interactions showing larger variation. Less than 20% of interaction changes across tissues are estimated to be due to protein expression differences with cell-type specific cellular structures, such as synaptic vesicles, being a second significant driver of differences. We further supported the brain protein association network through co-fraction experiments in synaptosomes, curation of brain derived pull-down data and AlphaFold2 models and illustrate how this network can functionally prioritize candidate genes within loci linked to brain disorders
2024-05-17 | PXD049084 | Pride
Project description:MotivationIn attempts to determine the genetic causes of human disease, researchers are often faced with a large number of candidate genes. Linkage studies can point to a genomic region containing hundreds of genes, while the high-throughput sequencing approach will often identify a great number of non-synonymous genetic variants. Since systematic experimental verification of each such candidate gene is not feasible, a method is needed to decide which genes are worth investigating further. Computational gene prioritization presents itself as a solution to this problem, systematically analyzing and sorting each gene from the most to least likely to be the disease-causing gene, in a fraction of the time it would take a researcher to perform such queries manually.ResultsHere, we present Gene TIssue Expression Ranker (GeneTIER), a new web-based application for candidate gene prioritization. GeneTIER replaces knowledge-based inference traditionally used in candidate disease gene prioritization applications with experimental data from tissue-specific gene expression datasets and thus largely overcomes the bias toward the better characterized genes/diseases that commonly afflict other methods. We show that our approach is capable of accurate candidate gene prioritization and illustrate its strengths and weaknesses using case study examples.Availability and implementationFreely available on the web at http://dna.leeds.ac.uk/GeneTIER/.Contactumaan@leeds.ac.ukSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4528628 | biostudies-literature
Project description:The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.
| S-EPMC3459874 | biostudies-literature
Project description:BACKGROUND: Identification of gene-phenotype relationships is a fundamental challenge in human health clinic. Based on the observation that genes causing the same or similar phenotypes tend to correlate with each other in the protein-protein interaction network, a lot of network-based approaches were proposed based on different underlying models. A recent comparative study showed that diffusion-based methods achieve the state-of-the-art predictive performance. RESULTS: In this paper, a new diffusion-based method was proposed to prioritize candidate disease genes. Diffusion profile of a disease was defined as the stationary distribution of candidate genes given a random walk with restart where similarities between phenotypes are incorporated. Then, candidate disease genes are prioritized by comparing their diffusion profiles with that of the disease. Finally, the effectiveness of our method was demonstrated through the leave-one-out cross-validation against control genes from artificial linkage intervals and randomly chosen genes. Comparative study showed that our method achieves improved performance compared to some classical diffusion-based methods. To further illustrate our method, we used our algorithm to predict new causing genes of 16 multifactorial diseases including Prostate cancer and Alzheimer's disease, and the top predictions were in good consistent with literature reports. CONCLUSIONS: Our study indicates that integration of multiple information sources, especially the phenotype similarity profile data, and introduction of global similarity measure between disease and gene diffusion profiles are helpful for prioritizing candidate disease genes. AVAILABILITY: Programs and data are available upon request.
| S-EPMC3622672 | biostudies-literature
Project description:The identification of genes associated with hereditary disorders has contributed to improving medical care and to a better understanding of gene functions, interactions, and pathways. However, there are well over 1500 Mendelian disorders whose molecular basis remains unknown. At present, methods such as linkage analysis can identify the chromosomal region in which unknown disease genes are located, but the regions could contain up to hundreds of candidate genes. In this work, we present a method for prioritization of candidate genes by use of a global network distance measure, random walk analysis, for definition of similarities in protein-protein interaction networks. We tested our method on 110 disease-gene families with a total of 783 genes and achieved an area under the ROC curve of up to 98% on simulated linkage intervals of 100 genes surrounding the disease gene, significantly outperforming previous methods based on local distance measures. Our results not only provide an improved tool for positional-cloning projects but also add weight to the assumption that phenotypically similar diseases are associated with disturbances of subnetworks within the larger protein interactome that extend beyond the disease proteins themselves.
| S-EPMC2427257 | biostudies-literature
Project description:Autism spectrum disorder (ASD) is a neurodevelopmental condition that results in behavioral, social and communication impairments. ASD has a substantial genetic component, with 88-95% trait concordance among monozygotic twins. Efforts to elucidate the causes of ASD have uncovered hundreds of susceptibility loci and candidate genes. However, owing to its polygenic nature and clinical heterogeneity, only a few of these markers represent clear targets for further analyses. In the present study, we used the linkage structure associated with published genetic markers of ASD to simultaneously improve candidate gene detection while providing a means of prioritizing markers of common genetic variation in ASD. We first mined the literature for linkage and association studies of single-nucleotide polymorphisms, copy-number variations and multi-allelic markers in Autism Genetic Resource Exchange (AGRE) families. From markers that reached genome-wide significance, we calculated male-specific genetic distances, in light of the observed strong male bias in ASD. Four of 67 autism-implicated regions, 3p26.1, 3p26.3, 3q25-27 and 5p15, were enriched with differentially expressed genes in blood and brain from individuals with ASD. Of 30 genes differentially expressed across multiple expression data sets, 21 were within 10 cM of an autism-implicated locus. Among them, CNTN4, CADPS2, SUMF1, SLC9A9, NTRK3 have been previously implicated in autism, whereas others have been implicated in neurological disorders comorbid with ASD. This work leverages the rich multimodal genomic information collected on AGRE families to present an efficient integrative strategy for prioritizing autism candidates and improving our understanding of the relationships among the vast collection of past genetic studies.
| S-EPMC3669925 | biostudies-literature
Project description:BackgroundAlthough most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN) analyses.ResultsFor the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds"), and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings - for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method - the three methods achieved a comparable AUC value, suggesting a similar performance.ConclusionEven though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.
| S-EPMC2657789 | biostudies-literature
Project description:The majority of common diseases are multi-factorial and modified by genetically and mechanistically complex polygenic interactions and environmental factors. High-throughput genome-wide studies like linkage analysis and gene expression profiling, tend to be most useful for classification and characterization but do not provide sufficient information to identify or prioritize specific disease causal genes.Extending on an earlier hypothesis that the majority of genes that impact or cause disease share membership in any of several functional relationships we, for the first time, show the utility of mouse phenotype data in human disease gene prioritization. We study the effect of different data integration methods, and based on the validation studies, we show that our approach, ToppGene http://toppgene.cchmc.org, outperforms two of the existing candidate gene prioritization methods, SUSPECTS and ENDEAVOUR.The incorporation of phenotype information for mouse orthologs of human genes greatly improves the human disease candidate gene analysis and prioritization.
| S-EPMC2194797 | biostudies-other
Project description:Although most tissues in an organism are genetically identical, the biochemistry of each is optimized to fulfill its unique physiological roles, with important consequences for human health and disease. Each tissue's unique physiology requires tightly regulated gene and protein expression coordinated by specialized, phosphorylation-dependent intracellular signaling. To better understand the role of phosphorylation in maintenance of physiological differences among tissues, we performed proteomic and phosphoproteomic characterizations of nine mouse tissues. We identified 12,039 proteins, including 6296 phosphoproteins harboring nearly 36,000 phosphorylation sites. Comparing protein abundances and phosphorylation levels revealed specialized, interconnected phosphorylation networks within each tissue while suggesting that many proteins are regulated by phosphorylation independently of their expression. Our data suggest that the "typical" phosphoprotein is widely expressed yet displays variable, often tissue-specific phosphorylation that tunes protein activity to the specific needs of each tissue. We offer this dataset as an online resource for the biological research community.
| S-EPMC3035969 | biostudies-literature
Project description:Epilepsy is a severe neurological disorder affecting a large number of individuals, yet the underlying genetic risk factors for epilepsy remain unclear. Recent studies have revealed several recurrent copy number variations (CNVs) that are more likely to be associated with epilepsy. The responsible gene(s) within these regions have yet to be definitively linked to the disorder, and the implications of their interactions are not fully understood. Identification of these genes may contribute to a better pathological understanding of epilepsy, and serve to implicate novel therapeutic targets for further research.In this study, we examined genes within heterozygous deletion regions identified in a recent large-scale study, encompassing a diverse spectrum of epileptic syndromes. By integrating additional protein-protein interaction data, we constructed subnetworks for these CNV-region genes and also those previously studied for epilepsy. We observed 20 genes common to both networks, primarily concentrated within a small molecular network populated by GABA receptor, BDNF/MAPK signaling, and estrogen receptor genes. From among the hundreds of genes in the initial networks, these were designated by convergent evidence for their likely association with epilepsy. Importantly, the identified molecular network was found to contain complex interrelationships, providing further insight into epilepsy's underlying pathology. We further performed pathway enrichment and crosstalk analysis and revealed a functional map which indicates the significant enrichment of closely related neurological, immune, and kinase regulatory pathways.The convergent framework we proposed here provides a unique and powerful approach to screening and identifying promising disease genes out of typically hundreds to thousands of genes in disease-related CNV-regions. Our network and pathway analysis provides important implications for the underlying molecular mechanisms for epilepsy. The strategy can be applied for the study of other complex diseases.
| S-EPMC3044734 | biostudies-literature