Interactome Network Research Articles

Machine-learning analysis of opioid use disorder informed by MOR, DOR, KOR, NOR and ZOR-based interactome networks

Opioid use disorder (OUD) continuously poses major public health challenges and social implications worldwide with dramatic rise of opioid dependence leading to potential abuse. Despite that a few pharmacological agents have been approved for OUD treatment, the efficacy of said agents for OUD requires further improvement in order to provide safer and more effective pharmacological and psychosocial treatments. Proteins including mu, delta, kappa, nociceptin, and zeta opioid receptors are the direct targets of opioids and play critical roles in therapeutic treatments. The protein–protein interaction (PPI) networks of the these receptors increase the complexity in the drug development process for an effective opioid addiction treatment. The report below presents a PPI-network informed machine-learning study of OUD. We have examined more than 500 proteins in the five opioid receptor networks and subsequently collected 74 inhibitor datasets. Machine learning models were constructed by pairing gradient boosting decision tree (GBDT) algorithm with two advanced natural language processing (NLP)-based autoencoder and Transformer fingerprints for molecules. With these models, we systematically carried out evaluations of screening and repurposing potential of more than 120,000 drug candidates for four opioid receptors. In addition, absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were also considered in the screening of potential drug candidates. Our machine-learning tools determined a few inhibitor compounds with desired potency and ADMET properties for nociceptin opioid receptors. Our approach offers a valuable and promising tool for the pharmacological development of OUD treatments.

Deciphering peroxisomal reactive species interactome and redox signalling networks.

Plant peroxisomes are highly dynamic organelles with regard to metabolic pathways, number and morphology and participate in different metabolic processes and cell responses to their environment. Peroxisomes from animal and plant cells house a complex system of reactive oxygen species (ROS) production associated to different metabolic pathways which are under control of an important set of enzymatic and non enzymatic antioxidative defenses. Nitric oxide (NO) and its derivate reactive nitrogen species (RNS) are also produced in these organelles. Peroxisomes can regulate ROS and NO/RNS levels to allow their role as signalling molecules. The metabolism of other reactive species such as carbonyl reactive species (CRS) and sulfur reactive species (SRS) in peroxisomes and their relationship with ROS and NO have not been explored in depth. In this review, we define a peroxisomal reactive species interactome (PRSI), including all reactive species ROS, RNS, CRS and SRS, their interaction and effect on target molecules contributing to the dynamic redox/ROS homeostasis and plasticity of peroxisomes, enabling fine-tuned regulation of signalling networks associated with peroxisome-dependent H2O2. Particular attention will be paid to update the information available on H2O2-dependent peroxisomal retrograde signalling and to discuss a specific peroxisomal footprint.

Adaptor protein complex interaction map in Arabidopsis identifies P34 as a common stability regulator.

Adaptor protein (AP) complexes are evolutionarily conserved vesicle transport regulators that recruit coat proteins, membrane cargoes and coated vesicle accessory proteins. As in plants endocytic and post-Golgi trafficking intersect at the trans-Golgi network, unique mechanisms for sorting cargoes of overlapping vesicular routes are anticipated. The plant AP complexes are part of the sorting machinery, but despite some functional information, their cargoes, accessory proteins and regulation remain largely unknown. Here, by means of various proteomics approaches, we generated the overall interactome of the five AP and the TPLATE complexes in Arabidopsis thaliana. The interactome converged on a number of hub proteins, including the thus far unknown adaptin binding-like protein, designated P34. P34 interacted with the clathrin-associated AP complexes, controlled their stability and, subsequently, influenced clathrin-mediated endocytosis and various post-Golgi trafficking routes. Altogether, the AP interactome network offers substantial resources for further discoveries of unknown endomembrane trafficking regulators in plant cells.

Coregulatory mechanism and interactome network of miRNA, lncRNA, and mRNA involved in Human Diseases

Coregulatory mechanism and interactome network of miRNA, lncRNA, and mRNA involved in Human Diseases

The Alzheimer’s Cell Atlas (TACA): A single‐cell type transcriptomics and network pathobiology map for target identification and drug repurposing in Alzheimer’s disease and related dementia

AbstractBackgroundRecent advances in massive single‐cell/nucleus (sc/sn) transcriptomic data have great potential for identification of cell‐type specific Alzheimer’s disease (AD) pathobiology, as well as target discovery for drug repurposing.MethodWe designed and implemented a multimodal omics analytic framework for the processing of sc/sn transcriptomic datasets, analyses of gene expression, pathobiology, cell‐cell communications with results presentation, interpretation, and translation. We performed an exhaustive search of cell type‐specific gene/protein network modules and cell‐cell interaction networks using our recently compiled ligand‐receptor and the human protein‐protein interactome networks. Furthermore, we developed a web portal with comprehensive visualization tools, such as cell and gene expression viewers, volcano plot and protein‐protein interaction network for differentially expressed genes (DEGs), ligand‐receptor interaction network for cell‐cell interactions, and heatmap for drug perturbation profiles cell type‐specific DEGs.ResultWe created The Alzheimer’s Cell Atlas (TACA, available at https://taca.lerner.ccf.org). In this endeavor, we compiled an AD brain cell atlas consisting of more than one million cells/nuclei from 20 datasets, covering major brain regions (cortex, hippocampus, cerebellum, etc.) and cell types (astrocytes, microglia, neurons, oligodendrocytes, etc.). We also conducted over 1,000 DE comparisons of these datasets to reveal cell‐type specific gene expression alterations. Major comparison types are (i) case vs healthy control; (ii) sex‐specific differential expression, (iii) genotype‐driven DE (i.e., APOE4/4 vs. APOE3/3; TREM2 R47H vs. common variants) analysis; and (iv) others. In addition, each comparison was followed by human protein‐protein interactome network module analysis, pathway enrichment analysis, and gene‐set enrichment analysis. For drug screening, we conducted gene set enrichment analysis for all comparisons with over 700,000 drug perturbation profiles connecting more than 12,000 human genes and 13,000 drugs/compounds. A total of over 300 analyses of cell‐cell interactions against 6,000 experimentally validated ligand‐receptor interactions were also conducted. We then generated summaries for the genes (for target identification) and drugs (for drug repurposing) from all analyses in sex‐specific and cell type‐specific manners.ConclusionWe envision that TACA will be a highly valuable resource for both basic and translational research in AD, as it provides abundant information for AD pathobiology and actionable systems biology tools for therapeutic discovery.

Transcriptome - wide bioinformatics analysis of the binding sites of RNA - binding proteins and their putative role in mendelian diseases

Background: Post-transcriptional regulation is the control of gene expression at the RNA level. After produced, the stability and distribution of the different transcripts are regulated by means of RNA-binding proteins (RBPs). Mutations in RNA-binding proteins can cause Mendelian diseases - prominently neuromuscular disorders and cancers. This study determines the interaction between RBPs and target-RNA complexes from public data of the ENCODE project and identifies mutations associated with Mendelian diseases that could disrupt the RBP-RNA interactions. Materials and methods: we performed a transcriptome - wide bioinformatics prediction of the binding sites of RBPs in the human transcriptome from public data of the ENCODE project. Results: The majority (54%) of pathogenic mutation putatively affecting the binding sites of RBPs are located in protein - coding genes and are mainly classified as loss - of - function mutations. Mutations located in the binding sites of RBPs related to RNA processing. For 13 diseases, Familial hypercholesterolemia is the most significant disease with about 40% of mutations in ClinVar database located into the binding sites of RBPs (p=2.3e-65), but congenital hypogonadotropic hypogonadism is the disease with the highest percentage of mutations affecting the binding sites of RBPs (98%, p=2.7e-25). The RBPs most involved in human Mendelian diseases by binding sites-disrupting mutations are YBX3, AQR and PRPF8. Conclusions: A large number of Mendelian diseases are potentially mediated by disease - causing variants that potentially disrupt the binding sites of RBPs. This will provide insight sharper on post - transcriptional mechanisms. Besides, it is useful to know the role of protein - RNA interactome networks in pathologies, thereby serving the treatment of diseases. Key words: bioinformatics analysis, ENCODE project, ClinVar, RNA-binding proteins, Mendelian diseases

A Comprehensive Study of De Novo Mutations on the Protein-Protein Interaction Interfaces Provides New Insights into Developmental Delay.

Mutations, especially those at the protein-protein interaction (PPI) interface, have been associated with various diseases. Meanwhile, though de novo mutations (DNMs) have been proven important in neuropsychiatric disorders, such as developmental delay (DD), the relationship between PPI interface DNMs and DD has not been well studied. Here we curated developmental delay DNM datasets from the PsyMuKB database and showed that DD patients showed a higher rate and deleteriousness in DNM missense on the PPI interface than sibling control. Next, we identified 302 DD-related PsychiPPIs, defined as PPIs harboring a statistically significant number of DNM missenses at their interface, and 42 DD candidate genes from PsychiPPI. We observed that PsychiPPIs preferentially affected the human protein interactome network hub proteins. When analyzing DD candidate genes using gene ontology and gene spatio-expression, we found that PsychiPPI genes carrying PPI interface mutations, such as FGFR3 and ALOX5, were enriched in development-related pathways and the development of the neocortex, and cerebellar cortex, suggesting their potential involvement in the etiology of DD. Our results demonstrated that DD patients carried an excess burden of PPI-truncating DNM, which could be used to efficiently search for disease-related genes and mutations in large-scale sequencing studies. In conclusion, our comprehensive study indicated the significant role of PPI interface DNMs in developmental delay pathogenicity.

Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease.

Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p<0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets.

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Alzheimer’s disease: insights from a network medicine perspective

Alzheimer’s disease (AD) is the most common neurodegenerative disease that currently lacks available effective therapy. Thus, identifying novel molecular biomarkers for diagnosis and treatment of AD is urgently demanded. In this study, we exploited tools and concepts of the emerging research area of Network Medicine to unveil a novel putative disease gene signature associated with AD. We proposed a new pipeline, which combines the strengths of two consolidated algorithms of the Network Medicine: DIseAse MOdule Detection (DIAMOnD), designed to predict new disease-associated genes within the human interactome network; and SWItch Miner (SWIM), designed to predict important (switch) genes within the co-expression network. Our integrated computational analysis allowed us to enlarge the set of the known disease genes associated to AD with additional 14 genes that may be proposed as new potential diagnostic biomarkers and therapeutic targets for AD phenotype.

ALDH3A2, ODF2, QSOX2, and MicroRNA-503-5p Expression to Forecast Recurrence in TMPRSS2-ERG-Positive Prostate Cancer.

Following radical surgery, patients may suffer a relapse. It is important to identify such patients so that therapy tactics can be modified appropriately. Existing stratification schemes do not display the probability of recurrence with enough precision since locally advanced prostate cancer (PCa) is classified as high-risk but is not ranked in greater detail. Between 40 and 50% of PCa cases belong to the TMPRSS2-ERG subtype that is a sufficiently homogeneous group for high-precision prognostic marker search to be possible. This study includes two independent cohorts and is based on high throughput sequencing and qPCR data. As a result, we have been able to suggest a perspective-trained model involving a deep neural network based on both qPCR data for mRNA and miRNA and clinicopathological criteria that can be used for recurrence risk forecasts in patients with TMPRSS2-ERG-positive, locally advanced PCa (the model uses ALDH3A2 + ODF2 + QSOX2 + hsa-miR-503-5p + ISUP + pT, with an AUC = 0.944). In addition to the prognostic model’s use of identified differentially expressed genes and miRNAs, miRNA–target pairs were found that correlate with the prognosis and can be presented as an interactome network.

An Integrative Transcriptional Network Revealed Spatial Molecular Interplay Underlying Alantolactone and Inulin Biosynthesis in Inula racemosa Hook f.

Inula racemosa Hook. f. (Pushkarmula), a perennial Himalayan herb known for its aromatic and phytopharmaceutical attributes, is not yet explored at genomic/transcriptomic scale. In this study, efforts were made to unveil the global transcriptional atlas underlying organ-specific specialized metabolite biosynthesis by integrating RNA-Seq analysis of 433 million sequenced reads with the phytochemical analysis of leaf, stem, and root tissues. Overall, 7242 of 83,772 assembled nonredundant unigenes were identified exhibiting spatial expression in leaf (3761), root (2748), and stem (733). Subsequently, integration of the predicted transcriptional interactome network of 2541 unigenes (71,841 edges) with gene ontology and KEGG pathway enrichment analysis revealed isoprenoid, terpenoid, diterpenoid, and gibberellin biosynthesis with antimicrobial activities in root tissue. Interestingly, the root-specific expression of germacrene-mediated alantolactone biosynthesis (GAS, GAO, G8H, IPP, DMAP, and KAO) and antimicrobial activities (BZR1, DEFL, LTP) well-supported with both quantitative expression profiling and phytochemical accumulation of alantolactones (726.08 μg/10 mg) and isoalantolactones (988.59 μg/10 mg), which suggests “roots” as the site of alantolactone biosynthesis. A significant interaction of leaf-specific carbohydrate metabolism with root-specific inulin biosynthesis indicates source (leaf) to sink (root) regulation of inulin. Our findings comprehensively demonstrate the source-sink transcriptional regulation of alantolactone and inulin biosynthesis, which can be further extended for upscaling the targeted specialized metabolites. Nevertheless, the genomic resource created in this study can also be utilized for development of genome-wide functionally relevant molecular markers to expedite the breeding strategies for genetic improvement of I. racemosa.

Urinary Microbial and Metabolomic Profiles in Kidney Stone Disease.

BackgroundKidney stones or nephrolithiasis is a chronic metabolic disease characterized by renal colic and hematuria. Currently, a pathogenetic mechanism resulting in kidney stone formation remains elusive. We performed a multi-omic study investigating urinary microbial compositions and metabolic alterations during nephrolithiasis.MethodUrine samples from healthy and individuals with nephrolithiasis were collected for 16S rRNA gene sequencing and liquid chromatography-mass spectroscopy. Microbiome and metabolome profiles were analyzed individually and combined to construct interactome networks by bioinformatic analysis.ResultsDistinct urinary microbiome profiles were determined in nephrolithiasis patients compared with controls. Thirty-nine differentially abundant taxa between controls and nephrolithiasis patients were identified, and Streptococcus showed the most significant enrichment in nephrolithiasis patients. We also observed significantly different microbial compositions between female and male nephrolithiasis patients. The metabolomic analysis identified 112 metabolites that were differentially expressed. Two significantly enriched metabolic pathways, including biosynthesis of unsaturated fatty acids and tryptophan metabolism, were also identified in nephrolithiasis patients. Four potentially diagnostic metabolites were also identified, including trans-3-hydroxycotinine, pyroglutamic acid, O-desmethylnaproxen, and FAHFA (16:0/18:2), and could function as biomarkers for the early diagnosis of nephrolithiasis. We also identified three metabolites that contributed to kidney stone size. Finally, our integrative analysis of the urinary tract microbiome and metabolome identified distinctly different network characteristics between the two groups.ConclusionsOur study has characterized important profiles and correlations among urinary tract microbiomes and metabolomes in nephrolithiasis patients for the first time. These results shed new light on the pathogenesis of nephrolithiasis and could provide early clinical biomarkers for diagnosing the disease.

Comparative transcriptomics of tropical woody plants supports fast and furious strategy along the leaf economics spectrum in lianas.

ABSTRACTLianas, climbing woody plants, influence the structure and function of tropical forests. Climbing traits have evolved multiple times, including ancestral groups such as gymnosperms and pteridophytes, but the genetic basis of the liana strategy is largely unknown. Here, we use a comparative transcriptomic approach for 47 tropical plant species, including ten lianas of diverse taxonomic origins, to identify genes that are consistently expressed or downregulated only in lianas. Our comparative analysis of full-length transcripts enabled the identification of a core interactomic network common to lianas. Sets of transcripts identified from our analysis reveal features related to functional traits pertinent to leaf economics spectrum in lianas, include upregulation of genes controlling epidermal cuticular properties, cell wall remodeling, carbon concentrating mechanism, cell cycle progression, DNA repair and a large suit of downregulated transcription factors and enzymes involved in ABA-mediated stress response as well as lignin and suberin synthesis. All together, these genes are known to be significant in shaping plant morphologies through responses such as gravitropism, phyllotaxy and shade avoidance.

Interacting partners of Brassica juncea regulator of G-protein signaling protein suggest its role in cell wall metabolism and cellular signaling.

Heterotrimeric G-proteins interact with various upstream and downstream effectors to regulate various aspects of plant growth and development. G-protein effectors have been recently reported in Arabidopsis thaliana; however, less information is available from polyploid crop species having complex networks of G-protein components. Regulator of G-protein signaling (RGS) is a well-characterized GTPase accelerating protein, which plays an important role in the regulation of the G-protein cycle in plants. In the present study, four homologs encoding RGS proteins were isolated from the allotetraploid Brassica juncea, a globally important oilseed, vegetable, and condiment crop. The B. juncea RGS proteins were grouped into distinct BjuRGS1 and BjuRGS2 orthologous clades, and the expression of BjuRGS1 homologs was predominantly higher than BjuRGS2 homologs across the tested tissue types of B. juncea. Utilizing B. juncea Y2H library screening, a total of 30 nonredundant interacting proteins with the RGS-domain of the highly expressed BjuA.RGS1 was identified. Gene ontology analysis indicated that these effectors exerted various molecular, cellular, and physiological functions. Many of them were known to regulate cell wall metabolism (BjuEXP6, Bju-α-MAN, BjuPGU4, BjuRMS3) and phosphorylation-mediated cell signaling (BjuMEK4, BjuDGK3, and BjuKinase). Furthermore, transcript analysis indicated that the identified interacting proteins have a coexpression pattern with the BjuRGS homologs. These findings increase our knowledge about the novel targets of G-protein components from a globally cultivated Brassica crop and provide an important resource for developing a plant G-protein interactome network.

Computational insights into missense mutations in HTT gene causing Huntington's disease and its interactome networks.

Huntington's disease is a rare neurodegenerative illness of the central nervous system that is inherited in an autosomal dominant pattern. Mutant huntingtin proteinis produced as a result of enlargement of CAG repeat in the N-terminal of the polyglutamine tract. Herein, we aim to investigate the mutations and their effects on the HTT gene and its genetic variants. Additionally, the protein-protein interaction of HTT with other proteins and receptor-ligand interaction with the three-dimensional structure of huntingtin proteinwere identified. A comprehensive analysis of the HTT interactome and protein-ligand interaction has been carried out to provide a global picture of structure-function analysis of huntingtin protein. Mutations were analyzed and mutation verification tools were used to check the effect of mutation on protein function. The results showed, mutations in a single gene are not only responsible for causing a particular disease but may also cause other hereditary disorders as well. Moreover, the modification at the nucleotide level also cause the change in the specific amino acid which may disrupt the function of HTT and its interacting proteins contributing in disease pathogenesis. Furthermore, the interaction between MECP2 and BDNF lowers the rate of transcriptional activity. Molecular docking further confirmed the strong interaction between MECP2 and BDNF with highest affinity. Amino acid residues of the HTT protein, involved in the interaction with tetrabenazine were N912, Y890, G2385, and V2320. These findings proved, tetrabenazine as one of the potential therapeutic agent for treatment of Huntington's disease. These results give further insights into the genetics of Huntington's disease for a better understanding of disease models which will be beneficial for the future therapeutic studies.

A functional reference map of the RNF8 interactome in cancer

BackgroundRNF8 is an E3 ligase identified as a critical DNA damage-responsive protein. Recently, multiple reports have shown that RNF8 could be used as an important therapeutic target for cancer chemo/radiotherapy. However, the understanding of RNF8 remains limited due to the lack of its interactome reference map and comprehensive analysis of RNF8 in diverse cancers, which underscores the need to map the interactome of RNF8 via high-throughput methods.ResultsA two-way identification method based on LC–MS was designed for the identification of the RNF8 interactome with high-specificity. By in silico analysis and in vitro validation, we identified a new reference map of the RNF8 interactome network containing many new targets, such as YBX1, DNMT1, and HDCA1, new biological functions and the gene-disease associations of RNF8. Our results revealed a close relationship between RNF8 and neurodegenerative diseases or tumor-infiltrating immune cells using bulk RNA-seq and scRNA-seq datasets. As a proof of concept of our interactome map, we validated the direct binding between RNF8 and YBX1 and showed that RNF8 catalyzed the ubiquitination of YBX1. These results demonstrated that RNF8 might be a crucial regulator of YBX1.ConclusionsOur work provides a unique framework for researchers and clinicians who seek to better explore or understand RNF8-regulated biological functions in cancers. This study will hopefully facilitate the rational design and further development of anti-RNF8 therapy in cancers.Graphical abstract

The close interaction between hypoxia-related proteins and metastasis in pancarcinomas

Many primary-tumor subregions exhibit low levels of molecular oxygen and restricted access to nutrients due to poor vascularization in the tissue, phenomenon known as hypoxia. Hypoxic tumors are able to regulate the expression of certain genes and signaling molecules in the microenvironment that shift it towards a more aggressive phenotype. The transcriptional landscape of the tumor favors malignant transformation of neighboring cells and their migration to distant sites. Herein, we focused on identifying key proteins that participate in the signaling crossroads between hypoxic environment and metastasis progression that remain poorly defined. To shed light on these mechanisms, we performed an integrated multi-omics analysis encompassing genomic/transcriptomic alterations of hypoxia-related genes and Buffa hypoxia scores across 17 pancarcinomas taken from the PanCancer Atlas project from The Cancer Genome Atlas consortium, protein–protein interactome network, shortest paths from hypoxia-related proteins to metastatic and angiogenic phenotypes, and drugs involved in current clinical trials to treat the metastatic disease. As results, we identified 30 hypoxia-related proteins highly involved in metastasis and angiogenesis. This set of proteins, validated with the MSK-MET Project, could represent key targets for developing therapies. The upregulation of mRNA was the most prevalent alteration in all cancer types. The highest frequencies of genomic/transcriptomic alterations and hypoxia score belonged to tumor stage 4 and positive metastatic status in all pancarcinomas. The most significantly associated signaling pathways were HIF-1, PI3K-Akt, thyroid hormone, ErbB, FoxO, mTOR, insulin, MAPK, Ras, AMPK, and VEGF. The interactome network revealed high-confidence interactions among hypoxic and metastatic proteins. The analysis of shortest paths revealed several ways to spread metastasis and angiogenesis from hypoxic proteins. Lastly, we identified 23 drugs enrolled in clinical trials focused on metastatic disease treatment. Six of them were involved in advanced-stage clinical trials: aflibercept, bevacizumab, cetuximab, erlotinib, ipatasertib, and panitumumab.

Diabetes mellitus susceptibility with varied diseased phenotypes and its comparison with phenome interactome networks.

An emerging area of interest in understanding disease phenotypes is systems genomics. Complex diseases such as diabetes have played an important role towards understanding the susceptible genes and mutations. A wide number of methods have been employed and strategies such as polygenic risk score and allele frequencies have been useful, but understanding the candidate genes harboring those mutations is an unmet goal. In this perspective, using systems genomic approaches, we highlight the application of phenome-interactome networks in diabetes and provide deep insights. LINC01128, which we previously described as candidate for diabetes, is shown as an example to discuss the approach.

Abstract 998: Extracellular proximity labeling (ePL) as a tool to identify protein-protein interactions in the tumor microenvironment

Abstract The extracellular matrix (ECM) is a dynamic niche that is extensively reshaped in the development of the tumor microenvironment (TME). Our current understanding of ECM function and dynamics is largely informed by identification of protein-protein interactions (PPIs) using co-immunoprecipitation (co-IP) techniques that may miss transient and weak/unstable interactions. Recent advances in proximity labeling techniques have greatly expanded the interactome networks of numerous intracellular proteins, however these tools have yet to be extended to study PPIs in the ECM. We have recently optimized a systematic approach to identify PPIs in the ECM using fusion constructs of the biotinylating enzymes, BioID2 and TurboID, with the widely expressed matrix regulator TIMP2. BioID2 and TurboID offer differing reaction kinetics that may provide complimentary information on extracellular PPIs (ePPIs). Matrix metalloproteinases (MMPs) and their endogenous inhibitors, tissue inhibitors of metalloproteinases (TIMPs) are crucial regulators of ECM structure and composition. TIMPs are widely expressed multifunctional proteins that serve to promote ECM homeostasis that is often perturbed in many cancers and chronic disorders. Although biochemical data suggests that TIMPs are promiscuous proteins, the TIMP interactome is poorly defined. We have optimized a protocol for the identification of ePPIs for the TIMP family of proteins. Fusion constructs equipped with a promiscuous biotin ligase (BioID2/TurboID) fused to the N- or C-terminal of full length TIMP2 were packaged into retroviral vectors for cellular delivery. Cells were exposed to the extracellular proximity labelling (ePL) fusion proteins and processed in our optimized analysis pipeline. ePPIs were identified via streptavidin pulldown and proteomic techniques. We present our optimized ePL pipeline and show that this technique is an effective tool for the identification of novel ePPIs for multiple extracellular targets. Citation Format: Sadeechya Gurung. Extracellular proximity labeling (ePL) as a tool to identify protein-protein interactions in the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 998.