Interactome Network Research Articles

Uncovering cell-type-specific immunomodulatory variants and molecular phenotypes in COVID-19 using structurally resolved protein networks

Immunomodulatory variants that lead to the loss or gain of specific protein interactions often manifest only as organismal phenotypes in infectious disease. Here, we propose a network-based approach to integrate genetic variation with a structurally resolved human protein interactome network to prioritize immunomodulatory variants in COVID-19. We find that, in addition to variants that pass genome-wide significance thresholds, variants at the interface of specific protein-protein interactions, even though they do not meet genome-wide thresholds, are equally immunomodulatory. The integration of these variants with single-cell epigenomic and transcriptomic data prioritizes myeloid and Tcell subsets as the most affected by these variants across both the peripheral blood and the lung compartments. Of particular interest is a common coding variant that disrupts the OAS1-PRMT6 interaction and affects downstream interferon signaling. Critically, our framework is generalizable across infectious disease contexts and can be used to implicate immunomodulatory variants that do not meet genome-wide significance thresholds.

A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations.

To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

Construction of an interactome network among circRNA-miRNA-mRNA reveals new biomarkers in hBMSCs osteogenic differentiation

Human bone marrow mesenchymal stem cells (hBMSCs) are adult stem cells residing in the bone marrow, characterized by their capacity for multi-directional differentiation, self-renewal, migration, and engraftment. Serving as seed cells, BMSCs play a pivotal role in the regeneration of bone defects. Hence, investigating the transcription factors and signaling pathways involved in the regulation of osteogenic differentiation in BMSCs holds significant importance. Recent research has unveiled that certain circular RNAs (circRNAs) can function as molecular sponges, influencing the osteogenic differentiation process of mesenchymal stem cells. However, many circRNAs remain undiscovered, and their precise mechanisms remain elusive. Therefore, the objective of this study is to construct an osteogenic differentiation-related circRNA-miRNA-mRNA network in hBMSCs. Subsequently, through bioinformatics analysis, we constructed a ceRNA network related to the osteogenic differentiation ability of hBMSCs, comprising 22 circRNAs, 17 miRNAs, and 15 mRNAs. The potential circRNA-miRNA-mRNA axes, including the role of hsa_circ_0001600 in promoting the osteogenic differentiation of hBMSCs through the targeted regulation of hsa-miR-542-3p, were validated through in vitro experiments.

A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson's disease.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. However, current treatments are directed at symptoms and lack ability to slow or prevent disease progression. Large-scale genome-wide association studies (GWAS) have identified numerous genomic loci associated with PD, which may guide the development of disease-modifying treatments. We presented a systems genetics approach to identify potential risk genes and repurposable drugs for PD. First, we leveraged non-coding GWAS loci effects on multiple human brain-specific quantitative trait loci (xQTLs) under the protein-protein interactome (PPI) network. We then prioritized a set of PD likely risk genes (pdRGs) by integrating five types of molecular xQTLs: expression (eQTLs), protein (pQTLs), splicing (sQTLs), methylation (meQTLs), and histone acetylation (haQTLs). We also integrated network proximity-based drug repurposing and patient electronic health record (EHR) data observations to propose potential drug candidates for PD treatments. We identified 175 pdRGs from QTL-regulated GWAS findings, such as SNCA , CTSB , LRRK2, DGKQ , CD38 and CD44 . Multi-omics data validation revealed that the identified pdRGs are likely to be druggable targets, differentially expressed in multiple cell types and impact both the parkin ubiquitin-proteasome and alpha-synuclein (a-syn) pathways. Based on the network proximity-based drug repurposing followed by EHR data validation, we identified usage of simvastatin as being significantly associated with reduced incidence of PD (fall outcome: hazard ratio (HR) = 0.91, 95% confidence interval (CI): 0.87-0.94; for dementia outcome: HR = 0.88, 95% CI: 0.86-0.89), after adjusting for 267 covariates. Our network-based systems genetics framework identifies potential risk genes and repurposable drugs for PD and other neurodegenerative diseases if broadly applied.

Evaluating deep neural networks in optimizing drug discovery and precision medicine: A review

Introduction/Background: Deep neural networks have shown great promise in advancing drug discovery and precision medicine. By leveraging large amounts of complex biomedical and chemical data, deep learning approaches can identify novel targets, predict drug-target and drug-drug interactions, generate new molecular structures, and assist in personalized treatment selection and development. However, fully utilizing deep learning techniques for optimization across the drug development pipeline remains an ongoing challenge. Materials and Methods: A comprehensive literature review was conducted using major bibliographic databases including PubMed, Web of Science, and Scopus. Search terms included combinations of "deep learning", "drug discovery", "precision medicine", "biomedical data", and "neural networks". Over 200 papers published between 2010-2023 related to deep learning applications in pharmacology and genomics were identified and reviewed. Results: Deep learning has been widely applied at various stages of the drug discovery process including target identification/prioritization, lead generation/optimization, and prediction of molecular properties. Convolutional neural networks are commonly used for the representation and classification of biological sequence and image data for tasks such as gene expression analysis and pathogen detection from microscopy images. Graph neural networks effectively model compound structures and interactome networks to predict molecular bindings and disease associations. Multi-modal neural networks integrate diverse data types for personalized treatment response prediction and biomarker discovery. Challenges remain around data and model interpretation, generalization to new targets/diseases, and integration across domains. Discussion: While deep learning has shown promise, rigorous benchmarking and validation on real-world clinical endpoints are still needed to establish usefulness in decision-making. Data and model transparency must be improved to enable scientific insights. Privacy and security risks accompanying "real world" biomedical big data will require ethical practices. Standardization and sharing of resources/protocols could accelerate progress by enabling comparison of techniques. Combining deep learning with other AI paradigms like causal inference may further improve utility in drug discovery and precision healthcare. Conclusion: Deep neural networks demonstrate potential for optimizing drug development and precision medicine applications. Continued advancement relies on addressing challenges around data, models, validation, and ethics. Multi-disciplinary collaborations integrating machine learning, molecular biology, medicine, and other domains are needed to fully realize benefits to patients.

Graph Attention Network-Based Prediction of Drug-Gene Interactions of Signal Transducer and Activator of Transcription (STAT) Receptor in Periodontal Regeneration.

Introduction The signal transducer and activator of transcription-1 (STAT-1) are tightly controlled signaling pathways, with induced genes acting as positive and negative regulators. Persistent activation of the signal transducer and activator of transcription (STATs), particularly signal transducer and activator of transcription-3 (STAT-3) and signal transducer and activator of transcription-5 (STAT-5), is common in human tumors and cell lines. STAT molecules act as transcription factors, regulated by ligands like interferon-α (IFN-α), interferon-γ (IFN-γ), epidermal growth factor (EGF), platelet-derived growth factor (PDGF), interleukin-6 (IL-6)and interleukin-27 (IL-27). STAT-1 mutations can cause infections like periodontitis, a chronic inflammatory disease affecting gum tissue and bone. STAT-1 drug-gene interactions are being studied for therapeutic applications. Our study aims to predict drug-gene interactions of STAT-1 receptors in periodontal inflammation using graph attention networks (GATs). Methodology The study used a dataset of 215 drug-gene interactions to train and test a GATmodel. The data was cleaned and normalized before being subjected to GATs using the Python library. Cytoscape and cytoHubba were used to visualize and analyze biological networks, including drug-gene interactome networks. The GAT model consisted of two graph attention layers, with the first layer producing eight features and the second layer aggregating outputs for binary classification. The model was trained using the Adam optimizer and CrossEntropyLoss function. Results The drug-gene interactome network, analyzed using Cytoscape, had 657 nodes, 1591 edges, and 4.755 neighbors. The predictive GAT model had low accuracy due to data availability and complexity. Conclusion The GAT model for drug-gene interactions in periodontal inflammation had low accuracy due to data limitations, complexity, and inability to capture all relevant features.

Zoledronic acid-treated rats show altered jaw histology and gene expression in non-exposed MRONJ

BackgroundDiagnosis and management of medication-related osteonecrosis of the jaws (MRONJ) prior to clinical exposure induced by trauma may lead to improved patient management. Currently, few studies have examined early histologic and molecular MRONJ-related changes in the jaws. PurposeThis study aimed to identify histological and gene expression changes in the maxilla and mandible of Sprague-Dawley (SD) rats after zoledronic acid (ZA) treatment. Study Design, Setting, and SampleThis was an in-vivo animal study. The experiments were conducted in the laboratory at the Stomatology Hospital of Tianjin Medical University. A total of 12 SD rats were included. Predictor variableThe predictor variable was ZA exposure. Twelve SD rats were divided into two groups: experimental (n = 6) and control (n = 6), and they were intraperitoneally injected with ZA and saline, respectively. Main outcome variableThe outcome variables were histological and molecular changes. The maxilla, mandible, and ilium bone tissue samples were examined using Masson's trichrome and hematoxylin-eosin staining. Gene expression changes were identified using transcriptome sequencing, Kyoto encyclopedia of genes and genomes (KEGG), and gene interactome network analysis. The key changes were validated using the quantitative real-time polymerase chain reaction and immunohistochemistry. CovariatesNone. AnalysesThe t-test, Chi-square test, and Fisher's exact probability method were used for statistical analyses using the Statistical package for the social sciences software (version 26.0). ResultsAll animals remained healthy during the experiments. Histological staining revealed that the percentage of empty bone lacunae in the maxilla and mandible was significantly higher than that in the ilium (P < 0.01). In total, 552 genes were screened using transcriptome sequencing. The sonic hedgehog (Shh) signaling pathway was highly enriched. The key gene for the Shh interaction was distal-less homeobox 5 (Dlx-5). The Shh, Dlx-5, and bone morphogenetic protein 2 genes and protein expression levels in the maxilla and mandible were higher in the experimental group than in the control group (P < 0.05). Conclusions and RelevanceMRONJ-induced osteonecrosis and gene expression changes precede trauma-induced clinical changes in the SD rat model. These findings may provide additional support for timely and clinically early diagnosis and intervention.

Proxitome profiling reveals a conserved SGT1-NSL1 signaling module that activates NLR-mediated immunity

Suppressor of G2 allele of skp1 (SGT1) is a highly conserved eukaryotic protein that plays a vital role in growth, development, and immunity in both animals and plants. Although some SGT1 interactors have been identified, the molecular regulatory network of SGT1 remains unclear. SGT1 serves as a co-chaperone to stabilize protein complexes such as the nucleotide-binding leucine-rich repeat (NLR) class of immune receptors, thereby positively regulating plant immunity. SGT1 has also been found to be associated with the SKP1-Cullin-F-box (SCF) E3 ubiquitin ligase complex. However, whether SGT1 targets immune repressors to coordinate plant immune activation remains elusive. In this study, we constructed a toolbox for TurboID- and split-TurboID-based proximity labeling (PL) assays in Nicotiana benthamiana and used the PL toolbox to explore the SGT1 interactome during pre- and post-immune activation. The comprehensive SGT1 interactome network we identified highlights a dynamic shift from proteins associated with plant development to those linked with plant immune responses. We found that SGT1 interacts with Necrotic Spotted Lesion 1 (NSL1), which negatively regulates salicylic acid-mediated defense by interfering with the nucleocytoplasmic trafficking of non-expressor of pathogenesis-related genes 1 (NPR1) during N NLR-mediated response to tobacco mosaic virus. SGT1 promotes the SCF-dependent degradation of NSL1 to facilitate immune activation, while salicylate-induced protein kinase-mediated phosphorylation of SGT1 further potentiates this process. Besides N NLR, NSL1 also functions in several other NLR-mediated immunity. Collectively, our study unveils the regulatory landscape of SGT1 and reveals a novel SGT1-NSL1 signaling module that orchestrates plant innate immunity.

Network Medicine: A Potential Approach for Virtual Drug Screening.

Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.

Structure-guided Evolutionary Analysis of Interactome Network Rewiring at Single Residue Resolution in Yeasts

Protein-protein interactions (PPIs) are known to rewire extensively during evolution leading to lineage-specific and species-specific changes in molecular processes. However, the detailed molecular evolutionary mechanisms underlying interactome network rewiring are not well-understood. Here, we combine high-confidence PPI data, high-resolution three-dimensional structures of protein complexes, and homology-based structural annotation transfer to construct structurally-resolved interactome networks for the two yeasts S. cerevisiae and S. pombe. We then classify PPIs according to whether they are preserved or different between the two yeast species and compare site-specific evolutionary rates of interfacial versus non-interfacial residues for these different categories of PPIs. We find that residues in PPI interfaces evolve significantly more slowly than non-interfacial residues when using lineage-specific measures of evolutionary rate, but not when using non-lineage-specific measures. Furthermore, both lineage-specific and non-lineage-specific evolutionary rate measures can distinguish interfacial residues from non-interfacial residues for preserved PPIs between the two yeasts, but only the lineage-specific measure is appropriate for rewired PPIs. Finally, both lineage-specific and non-lineage-specific evolutionary rate measures are appropriate for elucidating structural determinants of protein evolution for residues outside of PPI interfaces. Overall, our results demonstrate that unlike tertiary structures of single proteins, PPIs and PPI interfaces can be highly volatile in their evolution, thus requiring the use of lineage-specific measures when studying their evolution. These results yield insight into the evolutionary design principles of PPIs and the mechanisms by which interactions are preserved or rewired between species, improving our understanding of the molecular evolution of PPIs and PPI interfaces at the residue level.

Molecular regulation of calcium-sensing receptor (CaSR)-mediated signaling.

Calcium-sensing receptor (CaSR), a family C G-protein-coupled receptor, plays a crucial role in regulating calcium homeostasis by sensing small concentration changes of extracellular Ca2+, Mg2+, amino acids (e.g., L-Trp and L-Phe), small peptides, anions (e.g., HCO3 - and PO4 3-), and pH. CaSR-mediated intracellular Ca2+ signaling regulates a diverse set of cellular processes including gene transcription, cell proliferation, differentiation, apoptosis, muscle contraction, and neuronal transmission. Dysfunction of CaSR with mutations results in diseases such as autosomal dominant hypocalcemia, familial hypocalciuric hypercalcemia, and neonatal severe hyperparathyroidism. CaSR also influences calciotropic disorders, such as osteoporosis, and noncalciotropic disorders, such as cancer, Alzheimer's disease, and pulmonary arterial hypertension. This study first reviews recent advances in biochemical and structural determination of the framework of CaSR and its interaction sites with natural ligands, as well as exogenous positive allosteric modulators and negative allosteric modulators. The establishment of the first CaSR protein-protein interactome network revealed 94 novel players involved in protein processing in endoplasmic reticulum, trafficking, cell surface expression, endocytosis, degradation, and signaling pathways. The roles of these proteins in Ca2+-dependent cellular physiological processes and in CaSR-dependent cellular signaling provide new insights into the molecular basis of diseases caused by CaSR mutations and dysregulated CaSR activity caused by its protein interactors and facilitate the design of therapeutic agents that target CaSR and other family C G-protein-coupled receptors.

Extracellular Matrix Interactome in Modulating Vascular Homeostasis and Remodeling.

The ECM (extracellular matrix) is a major component of the vascular microenvironment that modulates vascular homeostasis. ECM proteins include collagens, elastin, noncollagen glycoproteins, and proteoglycans/glycosaminoglycans. ECM proteins form complex matrix structures, such as the basal lamina and collagen and elastin fibers, through direct interactions or lysyl oxidase-mediated cross-linking. Moreover, ECM proteins directly interact with cell surface receptors or extracellular secreted molecules, exerting matricellular and matricrine modulation, respectively. In addition, extracellular proteases degrade or cleave matrix proteins, thereby contributing to ECM turnover. These interactions constitute the ECM interactome network, which is essential for maintaining vascular homeostasis and preventing pathological vascular remodeling. The current review mainly focuses on endogenous matrix proteins in blood vessels and discusses the interaction of these matrix proteins with other ECM proteins, cell surface receptors, cytokines, complement and coagulation factors, and their potential roles in maintaining vascular homeostasis and preventing pathological remodeling.

Identification of a chromatin-bound ERRα interactome network in mouse liver

ObjectivesEstrogen-related-receptor α (ERRα) plays a critical role in the transcriptional regulation of cellular bioenergetics and metabolism, and perturbations in its activity have been associated with metabolic diseases. While several coactivators and corepressors of ERRα have been identified to date, a knowledge gap remains in understanding the extent to which ERRα cooperates with coregulators in the control of gene expression. Herein, we mapped the primary chromatin-bound ERRα interactome in mouse liver. MethodsRIME (Rapid Immuno-precipitation Mass spectrometry of Endogenous proteins) analysis using mouse liver samples from two circadian time points was used to catalog ERRα-interacting proteins on chromatin. The genomic crosstalk between ERRα and its identified cofactors in the transcriptional control of precise gene programs was explored through cross-examination of genome-wide binding profiles from chromatin immunoprecipitation-sequencing (ChIP-seq) studies. The dynamic interplay between ERRα and its newly uncovered cofactor Host cell factor C1 (HCFC1) was further investigated by loss-of-function studies in hepatocytes. ResultsCharacterization of the hepatic ERRα chromatin interactome led to the identification of 48 transcriptional interactors of which 42 were previously unknown including HCFC1. Interrogation of available ChIP-seq binding profiles highlighted oxidative phosphorylation (OXPHOS) under the control of a complex regulatory network between ERRα and multiple cofactors. While ERRα and HCFC1 were found to bind to a large set of common genes, only a small fraction showed their colocalization, found predominately near the transcriptional start sites of genes particularly enriched for components of the mitochondrial respiratory chain. Knockdown studies demonstrated inverse regulatory actions of ERRα and HCFC1 on OXPHOS gene expression ultimately dictating the impact of their loss-of-function on mitochondrial respiration. ConclusionsOur work unveils a repertoire of previously unknown transcriptional partners of ERRα comprised of chromatin modifiers and transcription factors thus advancing our knowledge of how ERRα regulates metabolic transcriptional programs.

Abstract 858: A functional atlas of cancer mutation-rewired protein interactome networks

Abstract Somatic mutations in cancer can exert effects on protein-protein interactions (PPIs), leaving some wild-type (WT) interactions perturbed while others unperturbed. The type of PPI perturbations can be used to link the mutation to its phenotypic effects. Here, we apply a technique known as functional variomics to characterize PPI profile changes for around 1,000 cancer missense mutations. Remarkably, our results reveal 5 distinct classes (edgotypes) of mutations: mutations losing PPIs compared to WT (edgetic), gaining PPIs (gain), gaining &amp; losing PPIs (gain/loss), losing all PPIs (quasi-null), and those that do not perturb PPIs (quasi-WT). Notably, gastroenterological cancers have a greater proportion of edgetic mutations, whereas reproductive cancers have a greater proportion of gain edgotype mutations, demonstrating the link between edgotype and cancer type. We further show that more severe cancer stages are associated with interaction-perturbing mutation edgotypes, and the gain/loss edgotype is the most deleterious of all. In addition, pathway enrichment analysis suggests that the edgotypes are predictive of biological effects. For example, mutation edgotypes with loss of interactions are involved in apoptotic process and DNA damage repair, consistent with the expected loss of such functions in cancer. Meanwhile, gain edgotype is linked with cell division and G2/M transition. To our knowledge, this represents the first attempt to perform systematic interactome profiling for cancer mutations. As our results can reveal specific phenotypic attributes and biological functions that are altered for different PPI profiles, our functional platform is important for cancer precision medicine. Our study strengthens the link between genotypes and phenotypes while illustrating the significance of edgotypes in complex disease modeling. Citation Format: Anjali Agrawal, Daniel McGrail, Nidhi Sahni, S. Stephen Yi. A functional atlas of cancer mutation-rewired protein interactome networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 858.

Deciphering Breast Cancer Metastasis Cascade: A Systems Biology Approach Integrating Transcriptome and Interactome Insights for Target Discovery.

Breast cancer is the lead cause of cancer-related deaths among women globally. Breast cancer metastasis is a complex and still inadequately understood process and a key dimension of mortality attendant to breast cancer. This study reports dysregulated genes across metastatic stages and tissues, shedding light on their molecular interplay in disease pathogenesis and new possibilities for drug discovery. Comprehensive analyses of gene expression data from primary breast tumor, circulating tumor cells, and distant metastatic sites in the brain, lung, liver, and bone were conducted. Genes dysregulated across multiple stages and tissues were identified as metastatic cascade genes, and are further classified based on functional associations with metastasis-related mechanisms. Their interactions with HUB genes in interactome networks were scrutinized, followed by pathway enrichment analysis. Validation for their potential as targets included assessments for survival, druggability, prognostic marker status, secretome annotation, protein expression, and cell type marker association. Results displayed critical genes in the metastatic cascade and those specific to metastatic sites, revealing the involvement of the collagen degradation and assembly of collagen fibrils and other multimeric structure pathways in driving metastasis. Notably, pivotal cascade genes FABP4, CXCL12, APOD, and IGF1 emerged with high metastatic potential, linked to significant druggability and survival scores, establishing them as potential molecular targets. The significance of this research lies in its potential to uncover novel biomarkers for early detection, therapeutic targets, and a deeper understanding of the molecular mechanisms underpinning the metastatic cascade in breast cancer, and with an eye to precision/personalized medicine.

An atlas of cell-type-specific interactome networks across 44 human tumor types

BackgroundBiological processes are controlled by groups of genes acting in concert. Investigating gene–gene interactions within different cell types can help researchers understand the regulatory mechanisms behind human complex diseases, such as tumors.MethodsWe collected extensive single-cell RNA-seq data from tumors, involving 563 patients with 44 different tumor types. Through our analysis, we identified various cell types in tumors and created an atlas of different immune cell subsets across different tumor types. Using the SCINET method, we reconstructed interactome networks specific to different cell types. Diverse functional data was then integrated to gain biological insights into the networks, including somatic mutation patterns and gene functional annotation. Additionally, genes with prognostic relevance within the networks were also identified. We also examined cell–cell communications to investigate how gene interactions modulate cell–cell interactions.ResultsWe developed a data portal called CellNetdb for researchers to study cell-type-specific interactome networks. Our findings indicate that these networks can be used to identify genes with topological specificity in different cell types. We also found that prognostic genes can deconvolved into cell types through analyzing network connectivity. Additionally, we identified commonalities and differences in cell-type-specific networks across different tumor types. Our results suggest that these networks can be used to prioritize risk genes.ConclusionsThis study presented CellNetdb, a comprehensive repository featuring an atlas of cell-type-specific interactome networks across 44 human tumor types. The findings underscore the utility of these networks in delineating the intricacies of tumor microenvironments and advancing the understanding of molecular mechanisms underpinning human tumors.

Multiscale topology in interactomic network: from transcriptome to antiaddiction drug repurposing.

The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery. We initiated our approach by conducting differential gene expression analysis on addiction-related transcriptomic data to identify key genes. We propose a novel topological differentiation to identify key genes from a protein-protein interaction network derived from DEGs. This method utilizes persistent Laplacians to accurately single out pivotal nodes within the network, conducting this analysis in a multiscale manner to ensure high reliability. Through rigorous literature validation, pathway analysis and data-availability scrutiny, we identified three pivotal molecular targets, mTOR, mGluR5 and NMDAR, for drug repurposing from DrugBank. We crafted machine learning models employing two natural language processing (NLP)-based embeddings and a traditional 2D fingerprint, which demonstrated robust predictive ability in gauging binding affinities of DrugBank compounds to selected targets. Furthermore, we elucidated the interactions of promising drugs with the targets and evaluated their drug-likeness. This study delineates a multi-faceted and comprehensive analytical framework, amalgamating bioinformatics, topological data analysis and machine learning, for drug repurposing in addiction treatment, setting the stage for subsequent experimental validation. The versatility of the methods we developed allows for applications across a range of diseases and transcriptomic datasets.

Mapping adipocyte interactome networks by HaloTag-enrichment-mass spectrometry.

Mapping protein interaction complexes in their natural state in vivo is arguably the Holy Grail of protein network analysis. Detection of protein interaction stoichiometry has been an important technical challenge, as few studies have focused on this. This may, however, be solved by artificial intelligence (AI) and proteomics. Here, we describe the development of HaloTag-based affinity purification mass spectrometry (HaloMS), a high-throughput HaloMS assay for protein interaction discovery. The approach enables the rapid capture of newly expressed proteins, eliminating tedious conventional one-by-one assays. As a proof-of-principle, we used HaloMS to evaluate the protein complex interactions of 17 regulatory proteins in human adipocytes. The adipocyte interactome network was validated using an in vitro pull-down assay and AI-based prediction tools. Applying HaloMS to probe adipocyte differentiation facilitated the identification of previously unknown transcription factor (TF)-protein complexes, revealing proteome-wide human adipocyte TF networks and shedding light on how different pathways are integrated.

The intricate cellular ecosystem of human peripheral veins as revealed by single-cell transcriptomic analysis.

The venous system has been historically understudied despite its critical roles in blood distribution, heart function, and systemic immunity. This study dissects the microanatomy of upper arm veins at the single cell level, and how it relates to wall structure, remodeling processes, and inflammatory responses to injury. We applied single-cell RNA sequencing to 4 non-diseased human veins (3 basilic, 1 cephalic) obtained from organ donors, followed by bioinformatic and histological analyses. Unsupervised clustering of 20,006 cells revealed a complex ecosystem of endothelial cell (EC) types, smooth muscle cell (SMCs) and pericytes, various types of fibroblasts, and immune cell populations. The venous endothelium showed significant upregulation of cell adhesion genes, with arteriovenous zonation EC phenotypes highlighting the heterogeneity of vasa vasorum (VV) microvessels. Venous SMCs had atypical contractile phenotypes and showed widespread localization in the intima and media. MYH11+DESlo SMCs were transcriptionally associated with negative regulation of contraction and pro-inflammatory gene expression. MYH11+DEShi SMCs showed significant upregulation of extracellular matrix genes and pro-migratory mediators. Venous fibroblasts ranging from secretory to myofibroblastic phenotypes were 4X more abundant than SMCs and widely distributed throughout the wall. Fibroblast-derived angiopoietin-like factors were identified as versatile signaling hubs to regulate angiogenesis and SMC proliferation. An abundant monocyte/macrophage population was detected and confirmed by histology, including pro-inflammatory and homeostatic phenotypes, with cell counts positively correlated with age. Ligand-receptor interactome networks identified the venous endothelium in the main lumen and the VV as a niche for monocyte recruitment and infiltration. This study underscores the transcriptional uniqueness of venous cells and their relevance for vascular inflammation and remodeling processes. Findings from this study may be relevant for molecular investigations of upper arm veins used for vascular access creation, where single-cell analyses of cell composition and phenotypes are currently lacking.

Network Analysis of Osteoarthritis Progression Using a Steiner Minimal Tree Algorithm.

To provide a comprehensive analysis of associated genes with osteoarthritis (OA). Here, we reported a network analysis of OA progression by using a Steiner minimal tree algorithm. We collected the OA-related genes through screening the publications in MEDLINE. We performed functional analysis to analyze the associated biochemical pathways of the OA-related genes. Pathway crosstalk analysis was constructed to explore interactions of the enriched pathways. Steiner minimal tree algorithm was used to analyze molecular pathway networks. The average clustering coefficient was compared with the corresponding values of the Osteoarthritis-specific network. The new finding RNA was compared with former single-cell RNA-seq analysis results. A gene set with 177 members reported to be significantly associated with Osteoarthritis was collected from 187 studies. Functional enrichment analysis revealed a specific related-OA gene including skeletal system development, cytokine-mediated signaling pathway, inflammatory response, cartilage development, and extracellular matrix organization. We performed a pathway crosstalk analysis among the 72 significantly enriched pathways. A total of 151 of the 177 genes in the Osteoarthritis gene set were included in the human interactome network. There were 31 genes in the former single-cell RNA-seq analysis results. The CLU, ENO1, SRRM1, UBC, HMGB1, NR3C1, NOTCH2NL, and CBX5 have significantly increased expression in seven molecularly defined populations of OA cartilage. The Steiner tree-based approach finds new biological molecules associated with OA genes.