Comprehensive Interaction Network Research Articles

A Comprehensive Interaction Network Constructed Using miRNAs and mRNAs Provides New Insights into Potato Tuberization under High Temperatures.

High temperatures delay tuberization and decrease potato (Solanum tuberosum L.) yields. However, the molecular mechanisms and regulatory networks underlying tuberization under high temperatures remain largely unknown. Here, we performed the mRNA and miRNA sequencing of leaves and stems to identify genes and regulatory networks involved in tuberization under high temperatures. A total of 2804 and 5001 differentially expressed genes (DEGs) under high-temperature stress were identified in leaves and stems, respectively. These genes were significantly enriched in gene ontology terms regarding meristem development, the sucrose biosynthetic process, and response to heat. Meanwhile, 101 and 75 differentially expressed miRNAs (DEmiRNAs) were identified in leaves and stems, respectively. We constructed an interaction network between DEmiRNAs and DEGs, identifying 118 and 150 DEmiRNA-DEG pairs in leaves and stems, respectively. We found three miRNA-mRNA candidate modules involved in tuberization under high temperatures, including stu-miR8030-5p/StCPY714, stu-miR7981f-p5/StAGL8a, and stu-miR10532A/StAGL8b. Our study constructed an interaction network between miRNAs and target genes and proposes candidate miRNA-gene modules that regulate tuber formation under high temperatures. Our study provides new insights for revealing the regulatory mechanism of the high-temperature inhibition of tuberization and also provides gene resources for improving the heat tolerance in potatoes.

Abstract 4490: Targeting NQO1 with Episesaminol: A new therapeutic option identified for colorectal cancer using integrated systems biology and structural biology-based approach

Abstract Introduction: Colon and/or rectal cancer together constitute colorectal cancer (CRC), the 2nd most lethal and the 3rd common cancer worldwide. Drug resistance to modern chemotherapies results in decreased therapeutic efficacy and CRC treatment failure. Herein, multi-layered strategies are used for a better identification of the newer therapeutics. Methodology: Different publicly available datasets were used for the identification of the differentially expressed genes (DEGs) between normal and CRC tissues. miRNAs, lncRNAs and small molecules interacting with those DEGs were mined from various databases. A complex network of interacting mRNA-miRNAs-lncRNAs-small molecules was created using the Cytoscape. Hub RNAs, significant modules and cliques were identified using Cytohubba and MClique plugins. The mRNAs from the top 30 cliques were analyzed by expression, oncoprint, survival and gene ontology. Polyphenols from the PhenolExplorer database were also assessed for their druggability and toxicity properties to finally obtain druggable polyphenols (DPs). A two-step molecular docking was done using PyRx for mRNAs from top 30 cliques and DPs and the top ten complexes were docked again using AutoDock. Molecular dynamics simulation (MDS) is performed for the best fit protein-proposed DP vs protein-known drug using GROMACS. Results: 1915 DEGs, 2609 miRNAs, 1411 lncRNAs and 8790 small molecules (other than selected DPs) interactions were considered for generating a comprehensive interaction network. The 27 mRNAs from the top 30 cliques were subjected to an array of analyses. NCAPD2, CDK4 and FASN were found to be involved in different stages of CRC. IRF2BPL, CMPK1, CCNF and UST were found as survival biomarkers, whereas FAT4 was found to be the only genetically altered mRNA. Functional enrichment supported the analysis revealing significant CRC-associated gene ontology as well as pathways. 56 DPs when docked with the DEGs, the two-step docking revealed NQO1-Episesaminol to be the best target-drug complex. Results of docking were comparable when MDS was performed for NOQ1•Episesaminol Vs NQO1•Doxorubicin (approved drug). Conclusion: In the current interaction network and MDS-based integrated analysis, significant druggable CRC biomarkers and their newer therapeutic solutions were identified. MDS reveals NQO1•Episesaminol as a potential target•drug combination for CRC. Citation Format: Sohini Chakraborty, Gaurav Sharma, Sricheta Karmakar, Prarthana Chatterjee, Saptarshi Sinha, Pradipta Ghosh, Satarupa Banerjee. Targeting NQO1 with Episesaminol: A new therapeutic option identified for colorectal cancer using integrated systems biology and structural biology-based approach [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 4490.

Abstract 4373: Molecular interactions of human receptor tyrosine kinase oncogenic fusions

Abstract Oncogenic fusions (OFs) play a critical role in cancer development. Despite the exponential increase in identifications, only a small number have been extensively characterized functionally. In this study, we are focused on receptor tyrosine kinase OFs (RTK-OFs) prevalent in aggressive cancers. Our primary objective is to elucidate the underlying molecular mechanisms of 182 in-frame RTK-OFs identified in The Cancer Genome Atlas. An automated high-throughput PCR technique will be used to clone individual gene fusions using the sequences obtained from RNA-Seq data sourced from the TCGA dataset. These clones will be used to establish an RTK-OF-expressing isogenic cell line library. Multiple Approaches Combined (MAC)-tag workflow combining Affinity Purification (AP) and proximity biotin-dependent (BioID) purification within a single construct will be used. Subsequent high-throughput EvoSep One Liquid Chromatography coupled to ultra-fast TimsTOF Pro 2 Mass Spectrometry sample processing enables sensitive and robust analysis. These methods enable the capture of stable protein complexes and transient interactors associated with RTK-OFs. In our proof of concept study, we have identified four in-frame variants of FGFR3::TACC3 oncogenic fusions.FGFR3, part of the fibroblast growth factor (FGF) RTK family, activates cellular proliferation and growth through FGF binding and receptor dimerization. In FGFR3-TACC3 OFs, diverse isoforms are observed, with prevalent rearrangements linking FGFR3 exon 17 to various TACC3 exons. TACC3 is vital for mitotic microtubule organization and stabilizes kinetochore fibers and the mitotic spindle through its conserved C-terminal coiled-coil domain (TACC domain). TACC3 contributes to the fusion with varying sizes of the C-terminal coiled-coil domain which all constitutively activate the FGFR3 tyrosine kinase. In our studies, we show that FGFR3::TACC3 variants have a different localization to their wild-type counterparts. While FGFR3 and TACC3 are found in the cytoplasm all the OF variants strongly localize around the nucleus. Our identified high-confidence interactors represent a comprehensive FGFR3::TACC3 interaction network and reveal central pathways and key interactors related to microtubule assembly and possible deregulation of the cell cycle. The resultant data from these advanced technologies enable the construction of comprehensive interaction networks, shedding light on critical signaling pathways and subnetworks relevant to RTK-OFs. Further in-depth analysis of 182 RTK-OFs may uncover actionable pathways and druggable interactors within these networks. Our multi-faceted research strategy holds significant promise for advancing targeted drug discovery, ultimately contributing to improved cancer treatment strategies. Citation Format: Giovanna Dashi, Markku Varjosalo. Molecular interactions of human receptor tyrosine kinase oncogenic fusions [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 4373.

Deciphering principles of nucleosome interactions and impact of cancer-associated mutations from comprehensive interaction network analysis.

Nucleosomes represent hubs in chromatin organization and gene regulation and interact with a plethora of chromatin factors through different modes. In addition, alterations in histone proteins such as cancer mutations and post-translational modifications have profound effects on histone/nucleosome interactions. To elucidate the principles of histone interactions and the effects of those alterations, we developed histone interactomes for comprehensive mapping of histone-histone interactions (HHIs), histone-DNA interactions (HDIs), histone-partner interactions (HPIs) and DNA-partner interactions (DPIs) of 37 organisms, which contains a total of 3808 HPIs from 2544 binding proteins and 339 HHIs, 100 HDIs and 142 DPIs across 110 histone variants. With the developed networks, we explored histone interactions at different levels of granularities (protein-, domain- and residue-level) and performed systematic analysis on histone interactions at a large scale. Our analyses have characterized the preferred binding hotspots on both nucleosomal/linker DNA and histone octamer and unraveled diverse binding modes between nucleosome and different classes of binding partners. Last, to understand the impact of histone cancer-associated mutations on histone/nucleosome interactions, we complied one comprehensive cancer mutation dataset including 7940 cancer-associated histone mutations and further mapped those mutations onto 419,125 histone interactions at the residue level. Our quantitative analyses point to histone cancer-associated mutations' strongly disruptive effects on HHIs, HDIs and HPIs. We have further predicted 57 recurrent histone cancer mutations that have large effects on histone/nucleosome interactions and may have driver status in oncogenesis.

Integrating Multi-Omics Analysis for Enhanced Diagnosis and Treatment of Glioblastoma: A Comprehensive Data-Driven Approach

The most aggressive primary malignant brain tumor in adults is glioblastoma (GBM), which has poor overall survival (OS). There is a high relapse rate among patients with GBM despite maximally safe surgery, radiation therapy, temozolomide (TMZ), and aggressive treatment. Hence, there is an urgent and unmet clinical need for new approaches to managing GBM. The current study identified modules (MYC, EGFR, PIK3CA, SUZ12, and SPRK2) involved in GBM disease through the NeDRex plugin. Furthermore, hub genes were identified in a comprehensive interaction network containing 7560 proteins related to GBM disease and 3860 proteins associated with signaling pathways involved in GBM. By integrating the results of the analyses mentioned above and again performing centrality analysis, eleven key genes involved in GBM disease were identified. ProteomicsDB and Gliovis databases were used for determining the gene expression in normal and tumor brain tissue. The NetworkAnalyst and the mGWAS-Explorer tools identified miRNAs, SNPs, and metabolites associated with these 11 genes. Moreover, a literature review of recent studies revealed other lists of metabolites related to GBM disease. The enrichment analysis of identified genes, miRNAs, and metabolites associated with GBM disease was performed using ExpressAnalyst, miEAA, and MetaboAnalyst tools. Further investigation of metabolite roles in GBM was performed using pathway, joint pathway, and network analyses. The results of this study allowed us to identify 11 genes (UBC, HDAC1, CTNNB1, TRIM28, CSNK2A1, RBBP4, TP53, APP, DAB1, PINK1, and RELN), five miRNAs (hsa-mir-221-3p, hsa-mir-30a-5p, hsa-mir-15a-5p, hsa-mir-130a-3p, and hsa-let-7b-5p), six metabolites (HDL, N6-acetyl-L-lysine, cholesterol, formate, N, N-dimethylglycine/xylose, and X2. piperidinone) and 15 distinct signaling pathways that play an indispensable role in GBM disease development. The identified top genes, miRNAs, and metabolite signatures can be targeted to establish early diagnostic methods and plan personalized GBM treatment strategies.

Hi-TrAC reveals division of labor of transcription factors in organizing chromatin loops

The three-dimensional genomic structure plays a critical role in gene expression, cellular differentiation, and pathological conditions. It is pivotal to elucidate fine-scale chromatin architectures, especially interactions of regulatory elements, to understand the temporospatial regulation of gene expression. In this study, we report Hi-TrAC as a proximity ligation-free, robust, and sensitive technique to profile genome-wide chromatin interactions at high-resolution among regulatory elements. Hi-TrAC detects chromatin looping among accessible regions at single nucleosome resolution. With almost half-million identified loops, we reveal a comprehensive interaction network of regulatory elements across the genome. After integrating chromatin binding profiles of transcription factors, we discover that cohesin complex and CTCF are responsible for organizing long-range chromatin loops, related to domain formation; whereas ZNF143 and HCFC1 are involved in structuring short-range chromatin loops between regulatory elements, which directly regulate gene expression. Thus, we introduce a methodology to identify a delicate and comprehensive network of cis-regulatory elements, revealing the complexity and a division of labor of transcription factors in organizing chromatin loops for genome organization and gene expression.

A circulating miRNA-based AI prediction system to identify multiple types of cancer.

3050 Background: Cancer is a major problem for human health. Development of an early diagnostic tool can increase the survival of cancer patients. Liquid biopsies have many advantages over traditional tumor tissue biopsies. Circulating microRNAs (miRNAs) are one type of liquid biopsies in part because they regulate the expression and thus functions of their target genes. Circulating miRNAs are stable, non-invasive and changes in their expression are detectable in the early stage of cancer progression, often before clear evidence of tissue biopsy/image tests. To date, there are few liquid biopsy-based tools for multiple-cancer diagnosis and their performance is unsatisfactory. The development of a non-invasive, effective early detection system for cancers is urgently needed. Methods: We integrated and investigated circulating miRNA expression data of 5046 non-cancer samples along with 3856 cancer samples of 6 major cancer types downloaded from publicly available databases. We used these expression data along with gender to establish a multiple cancer type AI prediction system. Furthermore, we built comprehensive interaction networks (miRNA-drug, miRNA-target gene) and performed functional enrichment analysis. Results: We constructed high-performance AI prediction model that can detect and differentiate 6 cancer groups from one non-cancer group. A median of sensitivity of 93.84% in test data was achieved for the multiple cancer classification task. A panel of gender and 15 most important circulating miRNAs was further shown to achieve excellent performance (sensitivity = 90.44%), with just a bit of decrease in the sensitivity of using the full set (gender and 2565 miRNAs). The 15 key circulating miRNAs worked well for the early stage (stage 1: sensitivity = 88%), much better than other liquid-biopsy results reported in the literature. This is important because these miRNAs and the AI system can be used to significantly decrease clinical cost and increase efficiency of early diagnosis, not to mention it is non-invasive. Finally, we constructed comprehensive interaction networks (drug/ target gene) for these key miRNAs to explore potential therapeutic strategies and understand the underlying biological mechanisms. Conclusions: In the study, we constructed the multiple-cancer prediction AI system to classify groups of normal individuals and cancer patients of multiple types, while finding key circulating miRNAs. As several key circulating miRNAs were shown to be potential drug targets or serve as diagnosis biomarkers to fulfill the aim of cancer precision medicine, this work represents a significant step toward achieving the goal of developing a non-invasive tool for early diagnosis of cancers.

A High-Resolution Map of SBP1 Interactomes in Plasmodium falciparum-infected Erythrocytes.

SummaryThe pathogenesis of malaria parasites depends on host erythrocyte modifications that are facilitated by parasite proteins exported to the host cytoplasm. These exported proteins form a trafficking complex in the host cytoplasm that transports virulence determinants to the erythrocyte surface; this complex is thus essential for malaria virulence. Here, we report a comprehensive interaction network map of this complex. We developed authentic, unbiased, highly sensitive proteomic approaches to determine the proteins that interact with a core component of the complex, SBP1 (skeleton-binding protein 1). SBP1 interactomes revealed numerous exported proteins and potential interactors associated with SBP1 intracellular trafficking. We identified several host-parasite protein interactions and linked the exported protein MAL8P1.4 to Plasmodium falciparum virulence in infected erythrocytes. Our study highlights the complicated interplay between parasite and host proteins in the host cytoplasm and provides an interaction dataset connecting dozens of exported proteins required for P. falciparum virulence.

Predicting the binding affinities of compound–protein interactions by random forest using network topology features

A predictor is proposed for the binding affinity of compound–protein interactions by constructing a comprehensive interaction network incorporating topology features.

Genomewide landscape of gene–metabolome associations in Escherichia coli

Metabolism is one of the best‐understood cellular processes whose network topology of enzymatic reactions is determined by an organism's genome. The influence of genes on metabolite levels, however, remains largely unknown, particularly for the many genes encoding non‐enzymatic proteins. Serendipitously, genomewide association studies explore the relationship between genetic variants and metabolite levels, but a comprehensive interaction network has remained elusive even for the simplest single‐celled organisms. Here, we systematically mapped the association between > 3,800 single‐gene deletions in the bacterium Escherichia coli and relative concentrations of > 7,000 intracellular metabolite ions. Beyond expected metabolic changes in the proximity to abolished enzyme activities, the association map reveals a largely unknown landscape of gene–metabolite interactions that are not represented in metabolic models. Therefore, the map provides a unique resource for assessing the genetic basis of metabolic changes and conversely hypothesizing metabolic consequences of genetic alterations. We illustrate this by predicting metabolism‐related functions of 72 so far not annotated genes and by identifying key genes mediating the cellular response to environmental perturbations.

PheNetic: network-based interpretation of unstructured gene lists in E. coli

At the present time, omics experiments are commonly used in wet lab practice to identify leads involved in interesting phenotypes. These omics experiments often result in unstructured gene lists, the interpretation of which in terms of pathways or the mode of action is challenging. To aid in the interpretation of such gene lists, we developed PheNetic, a decision theoretic method that exploits publicly available information, captured in a comprehensive interaction network to obtain a mechanistic view of the listed genes. PheNetic selects from an interaction network the sub-networks highlighted by these gene lists. We applied PheNetic to an Escherichia coli interaction network to reanalyse a previously published KO compendium, assessing gene expression of 27 E. coli knock-out mutants under mild acidic conditions. Being able to unveil previously described mechanisms involved in acid resistance demonstrated both the performance of our method and the added value of our integrated E. coli network. PheNetic is available at .

Development and application of a DNA microarray-based yeast two-hybrid system

The yeast two-hybrid (Y2H) system is the most widely applied methodology for systematic protein–protein interaction (PPI) screening and the generation of comprehensive interaction networks. We developed a novel Y2H interaction screening procedure using DNA microarrays for high-throughput quantitative PPI detection. Applying a global pooling and selection scheme to a large collection of human open reading frames, proof-of-principle Y2H interaction screens were performed for the human neurodegenerative disease proteins huntingtin and ataxin-1. Using systematic controls for unspecific Y2H results and quantitative benchmarking, we identified and scored a large number of known and novel partner proteins for both huntingtin and ataxin-1. Moreover, we show that this parallelized screening procedure and the global inspection of Y2H interaction data are uniquely suited to define specific PPI patterns and their alteration by disease-causing mutations in huntingtin and ataxin-1. This approach takes advantage of the specificity and flexibility of DNA microarrays and of the existence of solid-related statistical methods for the analysis of DNA microarray data, and allows a quantitative approach toward interaction screens in human and in model organisms.

B03 Systematic interactome mapping: understanding the molecular principles of neurodegenerative diseases

Neurodegenerative diseases (NDs) such as Alzheimer9s disease (AD), Parkinson9s disease (PD), amyotrophic lateral sclerosis, Huntington9s disease (HD) or spinal cerebellar ataxias are progressive brain disorders characterised by the accumulation of insoluble protein aggregates in neurons and the extracellular space. To elucidate potential common pathological mechanisms in NDs, we created comprehensive interaction networks for various known neurodegenerative disease proteins (NDPs). Using an automated yeast two-hybrid system, we identified 16 353 protein-protein interactions for 449 bioinformatically selected target proteins, connecting 80 known NDPs. This information was used to generate networks for individual NDs such as AD or PD and to predict proteins that are connected to multiple NDs. We found, for example, that the diseases AD, PD, amyotrophic lateral sclerosis, HD, and spinal cerebellar ataxia 1 are connected via four proteins that link known NDPs such as huntingtin, TDP43, parkin, τ, ataxin-1 and SOD1. Analysis of gene expression data predicted that these four proteins are abnormally altered in brains of HD, PD, and AD patients, supporting their importance for disease pathogenesis. Moreover, utilising computational methods as well as cell-based assays we have identified 21 human proteins that influence polyQ-induced ataxin-1 misfolding and proteotoxicity in various disease model systems. By analysing the protein sequences of these modifiers, we discovered a recurrent presence of coiled-coil domains in ataxin-1 toxicity enhancers, while such domains were not present in suppressors. This suggests that coiled-coil domains contribute to the aggregation- and toxicity-promoting effects of modifiers in mammalian cells. Our studies contribute to a deeper understanding of protein misfolding and aggregation in different NDs and highlight potential therapeutic targets for further investigations.

Ontology-based Brucella vaccine literature indexing and systematic analysis of gene-vaccine association network

BackgroundVaccine literature indexing is poorly performed in PubMed due to limited hierarchy of Medical Subject Headings (MeSH) annotation in the vaccine field. Vaccine Ontology (VO) is a community-based biomedical ontology that represents various vaccines and their relations. SciMiner is an in-house literature mining system that supports literature indexing and gene name tagging. We hypothesize that application of VO in SciMiner will aid vaccine literature indexing and mining of vaccine-gene interaction networks. As a test case, we have examined vaccines for Brucella, the causative agent of brucellosis in humans and animals.ResultsThe VO-based SciMiner (VO-SciMiner) was developed to incorporate a total of 67 Brucella vaccine terms. A set of rules for term expansion of VO terms were learned from training data, consisting of 90 biomedical articles related to Brucella vaccine terms. VO-SciMiner demonstrated high recall (91%) and precision (99%) from testing a separate set of 100 manually selected biomedical articles. VO-SciMiner indexing exhibited superior performance in retrieving Brucella vaccine-related papers over that obtained with MeSH-based PubMed literature search. For example, a VO-SciMiner search of "live attenuated Brucella vaccine" returned 922 hits as of April 20, 2011, while a PubMed search of the same query resulted in only 74 hits. Using the abstracts of 14,947 Brucella-related papers, VO-SciMiner identified 140 Brucella genes associated with Brucella vaccines. These genes included known protective antigens, virulence factors, and genes closely related to Brucella vaccines. These VO-interacting Brucella genes were significantly over-represented in biological functional categories, including metabolite transport and metabolism, replication and repair, cell wall biogenesis, intracellular trafficking and secretion, posttranslational modification, and chaperones. Furthermore, a comprehensive interaction network of Brucella vaccines and genes were identified. The asserted and inferred VO hierarchies provide semantic support for inferring novel knowledge of association of vaccines and genes from the retrieved data. New hypotheses were generated based on this analysis approach.ConclusionVO-SciMiner can be used to improve the efficiency for PubMed searching in the vaccine domain.

Systematic Identification of Methyllysine-Driven Interactions for Histone and Nonhistone Targets

An important issue in epigenetic research is to understand how the numerous methylation marks associated with histone and certain nonhistone proteins are recognized and interpreted by the hundreds of chromatin-binding modules (CBMs) in a cell to control chromatin state, gene expression, and other cellular functions. We have assembled a peptide chip that represents known and putative lysine methylation marks on histones and p53 and probed the chip for binding to a group of CBMs to obtain a comprehensive interaction network mediated by lysine methylation. Interactions revealed by the peptide array screening were validated by in-solution binding assays. This study not only recapitulated known interactions but also uncovered new ones. A novel heterochromatin protein 1 beta (HP1β) chromodomain-binding site on histone H3, H3K23me, was discovered from the peptide array screen and subsequently verified by mass spectrometry. Data from peptide pull-down and colocalization in cells suggest that, besides the H3K9me mark, H3K23me may play a role in facilitating the recruitment of HP1β to the heterochromatin. Extending the peptide array and mass spectrometric approach presented here to more histone marks and CBMs would eventually afford a comprehensive specificity and interaction map to aid epigenetic studies.

Combined RNA-expression and 2D-PAGE-screening identifies comprehensive interaction networks affected after bortezomib or enzastaurin exposure of mantle cell lymphoma

Despite recent advances in treatment, mantle cell lymphoma (MCL) still represents a disease with dismal prognosis due to its progressive clinical course, high rate of therapy refractory cases and frequent relapses. During recent years, the proteasome inhibitor bortezomib and enzastaurin, an inhibitor of protein kinase c have been explored in MCL. In relapsed disease enzastaurin achieved disease stabilization in a subset of patients. Bortezomib in relapsed and refractory MCL achieves response rates of 30–40%. To identify signal pathways and manifold interactions regulating cellular response to molecular targeted approaches several high throughput screening methods were applied. A combined network analysis of the identified target molecules based on both RNA array expression data and a survey of cellular protein levels resulted in a unified interaction network more comprehensive (bortezomib: 394 and enzastaurin: 174 molecules) than the networks of the individual screening techniques (329/44 and 117/36 molecules respectively). Interestingly, although none of the target molecules were matched in both RNA-expression and protein level analysis they were mapped nonetheless to common pathways. Additionally, the ranking of identified pathways allowed an improved characterization of the observed induction of cell apoptosis.