Physical Interaction Networks Research Articles

Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease.

Background: We hypothesize that the poor survival outcomes of end-stage kidney disease (ESKD) patients undergoing hemodialysis are associated with a low filtering efficiency and selectivity. The current gold standard criteria using single or several markers show an inability to predict or disclose the treatment effect and disease progression accurately. Methods: We performed an integrated mass spectrometry-based metabolomic and proteomic workflow capable of detecting and quantifying circulating small molecules and proteins in the serum of ESKD patients. Markers linked to cardiovascular disease (CVD) were validated on human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. Results: We identified dozens of elevated molecules in the serum of patients compared with healthy controls. Surprisingly, many metabolites, including lipids, remained at an elevated blood concentration despite dialysis. These molecules and their associated physical interaction networks are correlated with clinical complications in chronic kidney disease. This study confirmed two uremic toxins associated with CVD, a major risk for patients with ESKD. Conclusion: The retained molecules and metabolite-protein interaction network address a knowledge gap of candidate uremic toxins associated with clinical complications in patients undergoing dialysis, providing mechanistic insights and potential drug discovery strategies for ESKD.

A Study on Graph Centrality Measures of Different Diseases Due to DNA Sequencing

Rare genetic diseases are often caused by single-gene defects that affect various biological processes across different scales. However, it is challenging to identify the causal genes and understand the molecular mechanisms of these diseases. In this paper, we present a multiplex network approach to study the relationship between human diseases and genes. We construct a human disease network (HDN) and a human genome network (HGN) based on genotype–phenotype associations and gene interactions, respectively. We analyze 3771 rare diseases and find distinct phenotypic modules within each dimension that reflect the functional effects of gene mutations. These modules can also be used to predict novel gene candidates for unsolved rare diseases and to explore the cross-scale impact of gene perturbations. We compute various centrality measures for both networks and compare them. Our main finding is that diseases are weakly connected in the HDN, while genes are strongly connected in the HGN. This implies that diseases are relatively isolated from each other, while genes are involved in multiple biological processes. This result has implications for understanding the transmission of infectious diseases and the development of therapeutic interventions. We also show that not all diseases have the same potential to spread infections to other parts of the body, depending on their centrality in the HDN. Our results show that the phenotypic module formalism can capture the complexity of rare diseases beyond simple physical interaction networks and can be applied to study diseases arising from DNA (Deoxyribonucleic Acid) sequencing errors. This study provides a novel network-based framework for integrating multi-scale data and advancing the understanding and diagnosis of rare genetic diseases.

Impact of opinion dynamics on the public health damage inflicted by COVID-19 in the presence of societal heterogeneities.

Certain behavioral practices, such as wearing masks, practicing social distancing, and accepting vaccines, play a crucial role in impeding the spread of COVID-19 and reducing the severity of symptoms. Opinions regarding whether to observe such behavioral practices evolve over time through interactions via networks that overlap with but are not identical to the physical interaction networks over which the disease progresses. This necessitates the joint study of the dynamics of COVID-19 and opinion evolution. We develop a mathematical model that can be easily adapted to a wide range of behavioral practices and captures in a computationally tractable manner the joint evolution of the disease and relevant opinions in populations of large sizes. Populations of large sizes are typically heterogeneous in that they comprise individuals of different age groups, genders, races, and underlying health conditions. Such groups have different propensities to imbibe severe forms of the disease, different physical contact, and social interaction patterns and rates. These lead to different disease and opinion dynamics in them. Our model is designed to effectively capture such diversities. Computations using our model reveal that opinion dynamics have a strong impact on fatality and hospitalization counts and the number of man-days lost due to symptoms both in the regular form of the disease and the extended forms, more commonly known as long COVID. We show that opinion dynamics in certain groups have a disproportionate impact on the overall public health attributes because they have high physical interaction rates, even when they have the lowest propensity to imbibe severe forms of the disease. This identifies a social vulnerability that malactors can utilize to inflict heavy public health damages through opinion campaigns targeting specific segments. Once such vulnerabilities are identified, which we accomplish, adequate precautions may be designed to enhance resilience to such targeted attacks and better protect public health. By recognizing and understanding the vulnerabilities, appropriate precautions can be developed to enhance resilience against targeted attacks and safeguard public health. Our study underscores the importance of considering opinion evolution alongside disease dynamics, providing insights into the interplay between behavioral practices, opinions, and disease outcomes. We believe that our model is a valuable tool for understanding the joint dynamics of COVID-19 and opinions. We hope that our findings will help to inform public health policy and facilitate evidence-based decision-making for public health interventions.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

Hematological and gene co-expression network analyses of high-risk beef cattle defines immunological mechanisms and biological complexes involved in bovine respiratory disease and weight gain.

Bovine respiratory disease (BRD), the leading disease complex in beef cattle production systems, remains highly elusive regarding diagnostics and disease prediction. Previous research has employed cellular and molecular techniques to describe hematological and gene expression variation that coincides with BRD development. Here, we utilized weighted gene co-expression network analysis (WGCNA) to leverage total gene expression patterns from cattle at arrival and generate hematological and clinical trait associations to describe mechanisms that may predict BRD development. Gene expression counts of previously published RNA-Seq data from 23 cattle (2017; n = 11 Healthy, n = 12 BRD) were used to construct gene co-expression modules and correlation patterns with complete blood count (CBC) and clinical datasets. Modules were further evaluated for cross-populational preservation of expression with RNA-Seq data from 24 cattle in an independent population (2019; n = 12 Healthy, n = 12 BRD). Genes within well-preserved modules were subject to functional enrichment analysis for significant Gene Ontology terms and pathways. Genes which possessed high module membership and association with BRD development, regardless of module preservation ("hub genes"), were utilized for protein-protein physical interaction network and clustering analyses. Five well-preserved modules of co-expressed genes were identified. One module ("steelblue"), involved in alpha-beta T-cell complexes and Th2-type immunity, possessed significant correlation with increased erythrocytes, platelets, and BRD development. One module ("purple"), involved in mitochondrial metabolism and rRNA maturation, possessed significant correlation with increased eosinophils, fecal egg count per gram, and weight gain over time. Fifty-two interacting hub genes, stratified into 11 clusters, may possess transient function involved in BRD development not previously described in literature. This study identifies co-expressed genes and coordinated mechanisms associated with BRD, which necessitates further investigation in BRD-prediction research.

The physical interactome between Peregrinus maidis proteins and the maize mosaic virus glycoprotein provides insights into the cellular biology of a rhabdovirus in the insect vector.

Rhabdovirus glycoproteins (G) serve multifunctional roles in virus entry, assembly, and exit from animal cells. We hypothesize that maize mosaic virus (MMV) G is required for invasion, infection, and spread in Peregrinus maidis, the planthopper vector. Using a membrane-based yeast two-hybrid assay, we identified 107 P. maidis proteins that physically interacted with MMV G, of which approximately 53% matched proteins with known functions including endocytosis, vesicle-mediated transport, protein synthesis and turnover, nuclear export, metabolism and host defense. Physical interaction networks among conserved proteins indicated a possible cellular coordination of processes associated with MMV G translation, protein folding and trafficking. Non-annotated proteins contained predicted functional sites, including a diverse array of ligand binding sites. Cyclophilin A and apolipophorin III co-immunoprecipitated with MMV G, and each showed different patterns of localization with G in insect cells. This study describes the first protein interactome for a rhabdovirus spike protein and insect vector.

Scalable multiplex co-fractionation/mass spectrometry platform for accelerated protein interactome discovery

Co-fractionation/mass spectrometry (CF/MS) enables the mapping of endogenous macromolecular networks on a proteome scale, but current methods are experimentally laborious, resource intensive and afford lesser quantitative accuracy. Here, we present a technically efficient, cost-effective and reproducible multiplex CF/MS (mCF/MS) platform for measuring and comparing, simultaneously, multi-protein assemblies across different experimental samples at a rate that is up to an order of magnitude faster than previous approaches. We apply mCF/MS to map the protein interaction landscape of non-transformed mammary epithelia versus breast cancer cells in parallel, revealing large-scale differences in protein-protein interactions and the relative abundance of associated macromolecules connected with cancer-related pathways and altered cellular processes. The integration of multiplexing capability within an optimized workflow renders mCF/MS as a powerful tool for systematically exploring physical interaction networks in a comparative manner.

Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq.

Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell–cell interactions and relevant ligand–receptor signaling from the undissociated cell fractions in massively parallel single cell sequencing data. Neighbor-seq accurately identifies microanatomical features of diverse tissue types such as the small intestinal epithelium, terminal respiratory tract, and splenic white pulp. It also captures the differing topologies of cancer-immune-stromal cell communications in pancreatic and skin tumors, which are consistent with the patterns observed in spatial transcriptomic data. Neighbor-seq is fast and scalable. It draws inferences from routine single-cell data and does not require prior knowledge about sample cell-types or multiplets. Neighbor-seq provides a framework to study the organ-level cellular interactome in health and disease, bridging the gap between single-cell and spatial transcriptomics.

Conservation of Ilp4, Pdk1 and HDAC4 Across Drosophila Species

The degree of gene conservation is influenced by many factors including relative expression levels and number of interactions of the gene and its protein product. In collaboration with the Genomics Education Project, the conservation of several insulin signaling pathway genes and their protein products within Drosophila has been investigated. Insulin‐like peptide 4 (Ilp4) functions in the insulin signaling pathway by binding to the insulin receptor, thus triggering the phosphatidylinositol 3‐kinase pathway, which involves Phosphoinositide‐dependent kinase 1 (Pdk1). Another player in the insulin‐signaling pathway investigated in this work is Histone deacetylase 4 (HDAC4), which catalyzes the removal of acetyl functional groups from histone proteins, ultimately decreasing the expression of associated genes. Predicting the relative conservation of these genes is not trivial. For example, Ilp4 is at the beginning of the pathway and has one contact according to the Flybase physical interaction network. This might indicate the potential for greater evolutionary change over time, but contradicting this hypothesis is this insulin‐like peptide’s importance as a ligand in the pathway. Pdk1 also shows one contact on Flybase, but is located towards the beginning of the pathway, suggesting a relatively higher evolutionary rate. HDAC4 plays a role in many other cellular processes and so should be well conserved. To evaluate conservation, we first used BLAST to identify orthologs of these three genes across several Drosophilaspecies. We then evaluated conservation in terms of amino acid identity, exon length, number of exons, and degree of synteny using genomic sequences, BLAST, and evidence such as gene prediction tracks and RNA expression data available on the UCSC Genome Browser. Thus far, Pdk1 has demonstrated the greatest similarity over multiple species, with less conservation being observed in Ilp4 and HDAC4. This suggests the complexity in determining the interplay between number of protein contacts and pathway position on evolutionary rate.

A natural genetic variation screen identifies insulin signaling, neuronal communication, and innate immunity as modifiers of hyperglycemia in the absence of Sirt1.

Variation in the onset, progression, and severity of symptoms associated with metabolic disorders such as diabetes impairs the diagnosis and treatment of at-risk patients. Diabetes symptoms, and patient variation in these symptoms, are attributed to a combination of genetic and environmental factors, but identifying the genes and pathways that modify diabetes in humans has proven difficult. A greater understanding of genetic modifiers and the ways in which they interact with metabolic pathways could improve the ability to predict a patient’s risk for severe symptoms, as well as enhance the development of individualized therapeutic approaches. In this study, we use the Drosophila Genetic Reference Panel to identify genetic variation influencing hyperglycemia associated with loss of Sirt1 function. Through analysis of individual candidate functions, physical interaction networks, and gene set enrichment analysis, we identify not only modifiers involved in canonical glucose metabolism and insulin signaling, but also genes important for neuronal signaling and the innate immune response. Furthermore, reducing the expression of several of these candidates suppressed hyperglycemia, making them potential candidate therapeutic targets. These analyses showcase the diverse processes contributing to glucose homeostasis and open up several avenues of future investigation.

Fracture behaviors of double network elastomers with dynamic non-covalent linkages: A molecular dynamics study

Hybrid double network (HDN) elastomers, containing the physical interaction (PI) network formed by non-covalent interaction and the chemically cross-linked (C-CL) network, attract enormous interest due to the excellent toughness. Herein, the fracture process of HDN elastomers is elucidated via coarse-grained molecular dynamics simulation methods, which is essential to improve the toughness. The evolution of microstructures, including voids, and clusters serving as cross-linked points in the PI network, is analyzed to understand the fracture behavior. Secondly, the influence of PI strength on toughness is investigated. As the PI strength increases, the dissociation of clusters dissipates more energy. In addition, more bond scissions in the C-CL network also contribute to the promotion of toughness. In summary, this work provides the molecular perspective of the fracture and contributes to preparing HDN elastomers with high toughness.

Estrogen Receptor 1Inhibition ofWnt/β-Catenin SignalingContributes toSex Differences in Hepatocarcinogenesis.

BackgroundHepatocellular Carcinoma (HCC) is a sexually dimorphic cancer, with female sex being independently protective against HCC incidence and progression. The aim of our study was to understand the mechanism of estrogen receptor signaling in driving sex differences in hepatocarcinogenesis.MethodsWe integrated 1,268 HCC patient sample profiles from publicly available gene expression data to identify the most differentially expressed genes (DEGs). We mapped DEGs into a physical protein interaction network and performed network topology analysis to identify the most important proteins. Experimental validation was performed in vitro on HCC cell lines, in and in vivo, using HCC mouse model.ResultsWe showed that the most central protein, ESR1, is HCC prognostic, as increased ESR1 expression was protective for overall survival, with HR=0.45 (95%CI 0.32-0.64, p=4.4E-06), and was more pronounced in women. Transfection of HCC cell lines with ESR1 and exposure to estradiol affected expression of genes involved in the Wnt/β-catenin signaling pathway. ER-α (protein product of ESR1) agonist treatment in a mouse model of HCC resulted in significantly longer survival and decreased tumor burden (p<0.0001), with inhibition of Wnt/β-Catenin signaling. In vitro experiments confirmed colocalization of β-catenin with ER-α, leading to inhibition of β-catenin-mediated transcription of target genes c-Myc and Cyclin D1.ConclusionCombined, the centrality of ESR1 and its inhibition of the Wnt/β-catenin signaling axis provide a biological rationale for protection against HCC incidence and progression in women.

Network analysis reveals rare disease signatures across multiple levels of biological organization

Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype. A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration.

HumanNet v3: an improved database of human gene networks for disease research.

Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein–protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene–phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein–protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.

An atlas of protein-protein interactions across mouse tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape invivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

Global Single Clustering of Phenotype-Associated Human Aging Genes in the Co-Expression and Physical Interaction Networks: An OMIM-Based Investigative Review.

While a large wealth of literature on aging pertains to in silico, experimental, and predicted genes, many of those genes do not have validated phenotypic consequences in human. Online Mendelian Inheritance in Man (OMIM) provides an exceptional compendium of authoritative, validated aging genes and phenotypes, the interactions among which may enhance the overall perspective of aging mechanisms in human. Here, we reviewed and investigated the global clustering pattern of the OMIM-indexed aging genes (until April 2021) in the gene co-expression and physical interaction networks, using the two keywords "aging" and "ageing". To allow for validity check, we randomly selected six sets of genes from the human genome as control genes, each set consisting of a similar number of genes obtained from the OMIM search. STRING was implemented in the weighted setting and using the edge betweenness parameter, to construct the integrated and tissue-specific networks of the age-related and control genes. 286 aging (ageing) genes and a wide spectrum of 96 associated phenotypes were detected, including late-onset neurodegenerative disorders, cancers, osteoarthritis, and longevity. Despite the general terms used and the vast range of age-related phenotypes, we detected single clustering of the OMIM-extracted aging (ageing) genes in each of the integrated weighted co-expression and physical interaction networks (p<0.0005), as opposed to multiple clustering of the control genes (p≥0.04). TP53 was the overlapping hub gene in each of the networks. Three genes, TP53, APP, and SIRT1 were the consistent hub genes co-expressed across eleven selected human tissues frequently affected by age-related phenotypes. We propose predominant single clustering of the human phenotype-associated aging genes in the co-expression and physical interaction networks, and list the top pathways and genes involved.

A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study.

This study describes two complementary methods that use network-based and sequence similarity tools to identify drug repurposing opportunities predicted to modulate viral proteins. This approach could be rapidly adapted to new and emerging viruses. The first method built and studied a virus-host-physical interaction network; a three-layer multimodal network of drug target proteins, human protein-protein interactions, and viral-host protein-protein interactions. The second method evaluated sequence similarity between viral proteins and other proteins, visualized by constructing a virus-host-similarity interaction network. Methods were validated on the human immunodeficiency virus, hepatitis B, hepatitis C, and human papillomavirus, then deployed on SARS-CoV-2. Comparison of virus-host-physical interaction predictions to known antiviral drugs had AUCs of 0.69, 0.59, 0.78, and 0.67, respectively, reflecting that the scores are predictive of effective drugs. For SARS-CoV-2, 569 candidate drugs were predicted, of which 37 had been included in clinical trials for SARS-CoV-2 (AUC = 0.75, P-value 3.21 × 10-3). As further validation, top-ranked candidate antiviral drugs were analyzed for binding to protein targets in silico; binding scores generated by BindScope indicated a 70% success rate.

An Atlas of Protein-Protein Interactions Across Mouse Tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the complete interactome, has therefore been a central objective of high-throughput biology. However, interaction dynamics across physiological contexts remain poorly understood. Here, we combine protein correlation profiling with stable isotope labelling of mammals to map the interactomes of seven mouse tissues. The resulting maps provide the first proteome-scale survey of interactome dynamics across mammalian tissues, revealing over 125,000 unique interactions with an accuracy comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewiring of protein interactions across tissues is widespread, and rewired proteins are tightly regulated by multiple cellular mechanisms and implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

Protein interaction network and drug design of stomach cancer and associated disease: a bioinformatics approach

In this research, seven diseases OB (Obesity), SC (Stomach Cancer), CC (Colorectal Cancer), PC (Pancreatic Cancer), PRC (Prostate Cancer), LK (Leukemia), MD (Metabolic Disorder) has been encountered to analyze the network. When one or few diseases among them affects human there will be a possibility for them to be affected by the other diseases. Previous studies indicate that there are large numbers of similar biological and genetic characteristics among the seven disease. Common gene network models among these diseases have been explored for this reason. The genes obtained were compared and number of the gene has been decreased by the preprocessing and filtering process. After the filtering process, 9 common genes among seven related disease has been extracted. Topological properties, protein interactions network, enrichment analysis, co-expression network analysis, gene regulatory network, and physical interaction network are analyzed in this research. The substantial hub proteins based on biological, genetic and biochemical relationships among common genes has been identified by this analysis. Drug signature suggestion implies the 9 hub proteins which has been identified by PPI, GRN, gene interaction protein network. Finally, Protein chemical interaction and gene-disease interaction network indicated 6 significant genes and 9 significant genes consecutively for drug design. Network Enrichment shows the 9 common hub proteins interaction with other related genes. This analysis helps us to identify drug design pathway with chemical interactions. By applying further examining and studies those gene structures, it may be able to notify us to take precautionary steps to reduce the risk of Stomach Cancer and Associated disease.

Bud23 promotes the final disassembly of the small subunit Processome in Saccharomyces cerevisiae.

The first metastable assembly intermediate of the eukaryotic ribosomal small subunit (SSU) is the SSU Processome, a large complex of RNA and protein factors that is thought to represent an early checkpoint in the assembly pathway. Transition of the SSU Processome towards continued maturation requires the removal of the U3 snoRNA and biogenesis factors as well as ribosomal RNA processing. While the factors that drive these events are largely known, how they do so is not. The methyltransferase Bud23 has a role during this transition, but its function, beyond the nonessential methylation of ribosomal RNA, is not characterized. Here, we have carried out a comprehensive genetic screen to understand Bud23 function. We identified 67 unique extragenic bud23Δ-suppressing mutations that mapped to genes encoding the SSU Processome factors DHR1, IMP4, UTP2 (NOP14), BMS1 and the SSU protein RPS28A. These factors form a physical interaction network that links the binding site of Bud23 to the U3 snoRNA and many of the amino acid substitutions weaken protein-protein and protein-RNA interactions. Importantly, this network links Bud23 to the essential GTPase Bms1, which acts late in the disassembly pathway, and the RNA helicase Dhr1, which catalyzes U3 snoRNA removal. Moreover, particles isolated from cells lacking Bud23 accumulated late SSU Processome factors and ribosomal RNA processing defects. We propose a model in which Bud23 dissociates factors surrounding its binding site to promote SSU Processome progression.