Protein-protein Interaction Data Research Articles

I'm Walking into Spiderwebs: Making Sense of Protein-Protein Interaction Data.

Protein-protein interactions (PPIs) are at the heart of the molecular landscape permeating life. Proteomics studies can explore this protein interaction landscape using mass spectrometry (MS). Thanks to their high sensitivity, mass spectrometers can easily identify thousands of proteins within a single sample, but that same sensitivity generates tangled spiderwebs of data that hide biologically relevant findings. So, what does a researcher do when she finds herself walking into spiderwebs? In a field focused on discovery, MS data require rigor in their analysis, experimental validation, or a combination of both. In this Review, we provide a brief primer on MS-based experimental methods to identify PPIs. We discuss approaches to analyze the resulting data and remove the proteomic background. We consider the advantages between comprehensive and targeted studies. We also discuss how scoring might be improved through AI-based protein structure information. Women have been essential to the development of proteomics, so we will specifically highlight work by women that has made this field thrive in recent years.

Molecular mechanisms underlying the therapeutic effects of Linggui Zhugan decoction in stroke: Insights from network pharmacology and single-cell transcriptomics analysis.

Linggui Zhugan decoction (LZD), a traditional Chinese medicine formula, has demonstrated significant therapeutic effects in managing poststroke cognitive impairment and hemiplegia. However, the precise molecular mechanisms underlying its efficacy remain incompletely elucidated. The active ingredients and target proteins of LZD were retrieved from the traditional Chinese medicine systems pharmacology database and analysis platform database, which is specifically designed for traditional Chinese medicine research. The stroke-related genes were obtained from publicly available databases. Protein-protein interaction, enrichment analysis, and single-cell data analysis were conducted to identify key cells, targets, and pathways. Molecular docking was employed to assess the binding affinity between key components and targets. Network pharmacology analysis identified 190 active ingredients and 248 targets in LZD. These targets were significantly enriched in processes and pathways such as cellular response to lipid, orexin receptor pathway, and were significantly associated with Cerebral infarction and Middle Cerebral Artery Occlusion. Intersection analysis with 2035 stroke-related genes revealed 144 potential targets, which exhibited 2870 interactions and were significantly enriched in signaling pathways such as PI3K-AKT single pathway, MAPK single pathway, and tumor necrosis factor single pathway. Gene set variation analysis showed that the targets of LZD exhibited higher enrichment scores in microglia, M2 macrophages, endothelial cells, and neutrophils, while lower enrichment scores were observed in oligodendrocytes. Furthermore, molecular docking demonstrated a strong binding affinity between key active ingredients and targets. Network pharmacology and single-cell sequencing analysis elucidated the key cells, pathways, targets, and components involved in the therapeutic mechanism of LZD for the treatment of stroke.

Abstract 1525: Integration of high-plex tumor-Immune phenotyping and checkpoint interactions for deeper spatial characterization of human cancer tissues

Abstract Background: Immune evasion is a key cancer hallmark, and one of the primary mechanisms is the PD-1/PD-L1 checkpoint axis. While immunotherapies that target these interactions have reshaped cancer treatment, the variable efficacy in patients is not fully understood. Due to the weak correlation between PD-1/PD-L1 expression measured by single-plex assays and observed clinical outcomes, there is a need to understand protein interactions at the cellular and subcellular level. Proximity Ligation Assays, detecting spatial interactions between ligand-receptor targets, provide insights into the activation of signaling pathways. Coupled with high-plex spatial phenotyping with tumor and immune profiling biomarkers, the integration of cellular and functional information will be beneficial for a deeper characterization of the tumor microenvironment (TME), understanding immune responses, and identifying spatial signatures for patient stratification and targeted treatment strategies. Methods: In this study, we phenotyped biopsies from head and neck cancer patients enrolled in immune checkpoint inhibitor therapies. We integrated Naveni® PD-1/PD-L1 proximity ligation assays and a customizable high-plex PhenoCode™ Signature Panel (CD3ε/CD8/CD20/CD68/PanCK) for high-throughput profiling of the TME utilizing the PhenoImager® HT 2.0 platform. Comprehensive bioinformatic analyses were conducted for whole-slide segmentation, identifying cellular phenotypes, spatial neighborhoods, functional interactions, and distinct spatial signatures via the open-source image analysis software QuPath. Results: Our analyses revealed key differences in the localization of the PD-1/PD-L1 interactions within the TME of head and neck cancers. Areas of PD-1/PD-L1 interactions are associated with immune cell types on the periphery of the tumor and some within the tumor-infiltrating lymphocytes. The PhenoCode Signature panels helped resolve the localization of PD-1/PD-L1 interactions to immune cell types within the TME and provided a dual workflow for spatial co-localization of interacting receptor/ligand interactions and cell phenotypes within the TME. Conclusions: By combining spatial immune profiling with Akoya’s PhenoCode Signature panels and protein-protein interaction data from Navinci’s Naveni® PD-1/PD-L1 assay, deep correlative insights of the TME can be applied to improve predictions of clinical outcomes. This innovative approach enriches our comprehension of underlying mechanisms and stands as a promising tool for refining patient selection and optimizing treatment outcomes in immunotherapy. Citation Format: Sara Bodbin, Ning Ma, Aditya Pratapa, Nadya Nikulina, James Monkman, Niyati Jhaveri, Hampus Elofsson, Subham Basu, Agata Zieba-Wicher, Arutha Kulasinghe. Integration of high-plex tumor-Immune phenotyping and checkpoint interactions for deeper spatial characterization of human cancer tissues [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 1525.

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits.

The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

Dynamic Model Inference of Gene Regulatory Network based on Hybrid Parallel Genetic Algorithm and Threshold Qualification Method

Gene regulation is the process by which various substances in cells regulate the behaviour of gene expression, thereby controlling almost all cellular activities. Therefore, studying gene regulation not only helps to uncover the internal laws governing life processes but also plays a crucial role in predicting, diagnosing, treating, and designing drugs for genetic diseases. By utilizing multi-source biological information such as gene expression profiles, transcription factor information, and protein interaction data, a network model can be developed to depict the regulatory relationships between genes, facilitating further research. To address the limitations of traditional gene regulatory network construction methods, a novel dynamic model has been created by combining hybrid genetics and threshold restriction. This model comprises two parts: solution space reduction and parameter fitting. During solution space reduction, singular value decomposition is employed to define a mathematically feasible gene regulatory network, reducing unnecessary calculations. Subsequently, the control genes of each gene are constrained within a certain range using threshold limitation, enhancing computational efficiency while adhering to bioinformatics principles. In the parameter fitting phase, parallel genetic algorithms are utilized to expediently optimize the entire solution space. The mountain climbing method is then applied to solve problems meticulously within a limited scope, improving calculation accuracy. In this study, this approach was applied to establish genetic regulatory systems for complex skin melanoma and type 2 diabetes. Through comparison with actual networks, the validity of the method was confirmed. Compared to traditional genetic and particle swarm optimization methods, the effectiveness of the proposed method was verified. This paper models the intricate mechanism of gene regulation and elucidates the regulatory process involving genes, proteins, and small biological molecules in greater detail than other models, aligning more closely with intracellular dynamics laws.

SpecLoop predicts cell type-specific chromatin loop via transcription factor cooperation

Cell-type-Specific Chromatin Loops (CSCLs) are crucial for gene regulation and cell fate determination. However, the mechanisms governing their establishment remain elusive. Here, we present SpecLoop, a network regularization-based machine learning framework, to investigate the role of transcription factors (TFs) cooperation in CSCL formation. SpecLoop integrates multi-omics data, including gene expression, chromatin accessibility, sequence, protein-protein interaction, and TF binding motif data, to predict CSCLs and identify TF cooperations. Using high resolution Hi-C data as the gold standard, SpecLoop accurately predicts CSCL in GM12878, IMR90, HeLa–S3, K562, HUVEC, HMEC, and NHEK seven cell types, with the AUROC values ranging from 0.8645 to 0.9852 and AUPR values ranging from 0.8654 to 0.9734. Notably SpecLoop demonstrates improved accuracy in predicting long-distance CSCLs and identifies TF complexes with strong predictive ability. Our study systematically explores the TFs and TF pairs associated with CSCL through effective integration of diverse omics data. SpecLoop is freely available at https://github.com/AMSSwanglab/SpecLoop.

Binding affinity between coronavirus spike protein and human ACE2 receptor

Coronaviruses (CoVs) pose a major risk to global public health due to their ability to infect diverse animal species and potential for emergence in humans. The CoV spike protein mediates viral entry into the cell and plays a crucial role in determining the binding affinity to host cell receptors. With particular emphasis on α- and β-coronaviruses that infect humans and domestic animals, current research on CoV receptor use suggests that the exploitation of the angiotensin-converting enzyme 2 (ACE2) receptor poses a significant threat for viral emergence with pandemic potential. This review summarizes the approaches used to study binding interactions between CoV spike proteins and the human ACE2 (hACE2) receptor. Solid-phase enzyme immunoassays and cell binding assays allow qualitative assessment of binding but lack quantitative evaluation of affinity. Surface plasmon resonance, Bio-layer interferometry, and Microscale Thermophoresis on the other hand, provide accurate affinity measurement through equilibrium dissociation constants (KD). In silico modeling predicts affinity through binding structure modeling, protein-protein docking simulations, and binding energy calculations but reveals inconsistent results due to the lack of a standardized approach. Machine learning and deep learning models utilize simulated and experimental protein-protein interaction data to elucidate the critical residues associated with CoV binding affinity to hACE2. Further optimization and standardization of existing approaches for studying binding affinity could aid pandemic preparedness. Specifically, prioritizing surveillance of CoVs that can bind to human receptors stands to mitigate the risk of zoonotic spillover.

NetVA: an R package for network vulnerability and influence analysis

In biological network analysis, identifying key molecules plays a decisive role in the development of potential diagnostic and therapeutic candidates. Among various approaches of network analysis, network vulnerability analysis is quite important, as it assesses significant associations between topological properties and the functional essentiality of a network. Similarly, some node centralities are also used to screen out key molecules. Among these node centralities, escape velocity centrality (EVC), and its extended version (EVC+) outperform others, viz., Degree, Betweenness, and Clustering coefficient. Keeping this in mind, we aimed to develop a first-of-its-kind R package named NetVA, which analyzes networks to identify key molecular players (individual proteins and protein pairs/triplets) through network vulnerability and EVC+-based approaches. To demonstrate the application and relevance of our package in network analysis, previously published and publicly available protein–protein interactions (PPIs) data of human breast cancer were analyzed. This resulted in identifying some most important proteins. These included essential proteins, non-essential proteins, hubs, and bottlenecks, which play vital roles in breast cancer development. Thus, the NetVA package, available at https://github.com/kr-swapnil/NetVA with a detailed tutorial to download and use, assists in predicting potential candidates for therapeutic and diagnostic purposes by exploring various topological features of a disease-specific PPIs network. Communicated by Ramaswamy H. Sarma

A systems biology approach to understand retinal ciliopathies

Aims/Purpose: Many of the described ciliopathies display multi‐organ developmental phenotypes, including retina. A variety of proteins encoded by genes associated with retinal ciliopathies are present in the connecting cilium of the retina, which is essential for photoreceptor function. Owing to the complicated genetic networks of ciliopathies, it is more difficult to characterize ciliopathies than other rare diseases, and only a handful of exact genotype–phenotype correlations have been established. The scientific aim of this study was to unravel the protein‐level mechanisms of retinal ciliopathies using system and molecular biology techniques.Methods: Due to the network of protein interactions, the effect of a ciliopathy mutation is not limited to a single gene product. Instead, it impacts the interacting partners and, as a result, can have an effect on the activity of a whole subnetwork. To analyse this, we selected 46 genes that have been previously associated with non‐syndromic and multi‐syndromic retinal ciliopathies and applied proximity labeling‐based method (UltraID) coupled with mass spectrometry (MS) approach for systematic analysis of retinal ciliopathy proteomes.Results: Bioinformatic analysis of the comprehensive dataset reveals 3772 high confidence interactions and among them 179 interactions (4.7%) were previously reported for the retinal ciliopathy protein. We further conducted a subcellular localization analysis on the interacting proteins. There are 232 unique prey proteins (13.6%) with annotated subcellular localization or tissue distribution in photoreceptor, cilium, retina, or eye, indicating our PPI data are retinal ciliopathy context specific.Conclusions: Protein–protein interaction (PPI)‐based analyses, rather than focusing on specific genes or loci implicated in retinal ciliopathies, highlight connections that are most affected by the disease state, thereby facilitating the discovery of higher‐order relationships within the genetic architecture of ciliopathies.

Identification of differences in CD4+ T-cell gene expression between people with asthma and healthy controls

Functional enrichment analysis of genome-wide association study (GWAS)-summary statistics has suggested that CD4+ T-cells play an important role in asthma pathogenesis. Despite this, CD4+ T-cells are under-represented in asthma transcriptome studies. To fill the gap, 3'-RNA-Seq was used to generate gene expression data on CD4+ T-cells (isolated within 2 h from collection) from peripheral blood from participants with well-controlled asthma (n = 32) and healthy controls (n = 11). Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify sets of co-expressed genes (modules) associated with the asthma phenotype. We identified three modules associated with asthma, which are strongly enriched for GWAS-identified asthma genes, antigen processing/presentation and immune response to viral infections. Through integration of publicly available eQTL and GWAS summary statistics (colocalisation), and protein–protein interaction (PPI) data, we identified PTPRC, a potential druggable target, as a putative master regulator of the asthma gene-expression profiles. Using a co-expression network approach, with integration of external genetic and PPI data, we showed that CD4+ T-cells from peripheral blood from asthmatics have different expression profiles, albeit small in magnitude, compared to healthy controls, for sets of genes involved in immune response to viral infections (upregulated) and antigen processing/presentation (downregulated).

Transcriptional changes are tightly coupled to chromatin reorganization during cellular aging.

Human life expectancy is constantly increasing and aging has become a major risk factor for many diseases, although the underlying gene regulatory mechanisms are still unclear. Using transcriptomic and chromosomal conformation capture (Hi-C) data from human skin fibroblasts from individuals across different age groups, we identified a tight coupling between the changes in co-regulation and co-localization of genes. We obtained transcription factors, cofactors, and chromatin regulators that could drive the cellular aging process by developing a time-course prize-collecting Steiner tree algorithm. In particular, by combining RNA-Seq data from different age groups and protein-protein interaction data we determined the key transcription regulators and gene regulatory changes at different life stage transitions. We then mapped these transcription regulators to the 3D reorganization of chromatin in young and old skin fibroblasts. Collectively, we identified key transcription regulators whose target genes are spatially rearranged and correlate with changes in their expression, thereby providing potential targets for reverting cellular aging.

A seed expansion-based method to identify essential proteins by integrating protein–protein interaction sub-networks and multiple biological characteristics

BackgroundThe identification of essential proteins is of great significance in biology and pathology. However, protein–protein interaction (PPI) data obtained through high-throughput technology include a high number of false positives. To overcome this limitation, numerous computational algorithms based on biological characteristics and topological features have been proposed to identify essential proteins.ResultsIn this paper, we propose a novel method named SESN for identifying essential proteins. It is a seed expansion method based on PPI sub-networks and multiple biological characteristics. Firstly, SESN utilizes gene expression data to construct PPI sub-networks. Secondly, seed expansion is performed simultaneously in each sub-network, and the expansion process is based on the topological features of predicted essential proteins. Thirdly, the error correction mechanism is based on multiple biological characteristics and the entire PPI network. Finally, SESN analyzes the impact of each biological characteristic, including protein complex, gene expression data, GO annotations, and subcellular localization, and adopts the biological data with the best experimental results. The output of SESN is a set of predicted essential proteins.ConclusionsThe analysis of each component of SESN indicates the effectiveness of all components. We conduct comparison experiments using three datasets from two species, and the experimental results demonstrate that SESN achieves superior performance compared to other methods.

Decoding the Epigenetic Drivers of Menin-MLL Inhibitor Resistance in KMT2A-Rearranged Acute Myeloid Leukemia

Menin inhibitors that disrupt Menin-MLL interaction are candidate therapeutic agents for several acute myeloid leukemia (AML) subtypes such as KMT2A rearrangement and NPM1 mutation. Several Menin inhibitors are currently being assessed in clinical trials and have demonstrated promising initial results. However, the development of both innate and acquired resistance to Menin inhibitors could limit their therapeutic potential. While studies have found that around 40% of these resistance cases can be traced back to mutations in the Menin gene, the Menin gene remains unaltered in other majorities, suggesting the presence of other mechanisms driving resistance. Here, we developed a unique chromatin-focused CRISPR knockout library, named Advanced Catalogue of Epigenetic Regulators (ACER). The ACER library contains approximately 800 traditional and newly-predicted epigenetic regulators using epigenetic network analyses that integrated publicly available gene codependency (DepMap) and protein-protein interaction data (BioPlex) (Figure A). To understand the chromatin mechanisms responsible for Menin-MLL inhibition resistance, we employed the ACER library for CRISPR screen in the context of Menin inhibition. Notably, our screen identified non-canonical polycomb repressive complex PRC1.1 (PCGF1, BCOR, and RYBP) and PRC2.2 (EZH2, SUZ12, and EED) among the top hits that conferred resistance to MLL-Menin inhibition upon gene depletion (Figure B). Consistent with our screen results, the depletion of PRC1.1 components led to a markedly increase in IC50 values when treated with the Menin inhibitor VTP50469 in both human and murine KMT2A-rearranged cell models. While treatment with Menin inhibitors typically induces cellular differentiation in leukemia cells, depletion of PRC1.1 genes resulted in an almost complete blockage in this differentiation process. Menin inhibitors evict Menin from chromatin and repress the expression levels of Menin-MLL target genes like MEIS1, PBX3, and MEF2C. Strikingly, our ChIP-seq and RNA-seq data indicatethat the loss of PRC1.1 components doesn't impact Menin's displacement from chromatin or the repression of KMT2A target genes by the Menin inhibitor. This is consistent with recent data from phase I clinical trial of revumenib (SNDX-5613) and preclinical patient derived xenograft models, wherein the Menin inhibitor persistently inhibitedkey KMT2A targets, even in resistant samples. These findings highlightthe possibility that resistance to Menin inhibition arising from mechanisms independent of canonical KMT2A target genes. Importantly, our unbiased transcriptomic analysis revealed that MYC signatures as the most affected pathway in PRC1.1-depleted cells following Menin inhibition. Furthermore, we show that both the transcriptionally active Menin-MLL and the repressive PRC1.1 complexes coexist at the MYC promoter, and the interplay between these two complexes determines MYC transcription output in AML. Moreover, enforced expression of MYC leads to resistance against Menin inhibition, while genetic and pharmacologic targeting MYC in PRC1.1 deficient cells rescued the resistance to Menin inhibition. Finally, in our RNA-seq datasets, we observed a diminished monocyte differentiation signature after PRC1.1 depletion. Considering AML cells with monocytic signatures, typically seen in KMT2A-rearranged AML, often exhibit resistance to BCL-2 inhibitor venetoclax, we hypothesized that AML cells lacking PRC1.1 could be more susceptible to BCL2 inhibition. This was confirmed in our ACER library screen with venetoclax treatment, where the PRC1.1 components PCGF1 and BCOR stood out as top epigenetic factors rendering drug sensitivity. Our subsequent studies using murine AML models and human primary samples further validated that PRC1.1-deficient AML cells are markedly sensitive to venetoclax. Furthermore, we show that venetoclax can be employed to overcome the resistance to Menin-MLL inhibition. In summary, our study identifiesthe non-canonical PRC1.1 as a key epigenetic driver of Menin-MLL resistance through a KMT2A target gene-independent mechanism which involves aberrant activation of MYC. We have further provided evidence that AML cells with loss of PRC1.1 are hypersensitive to BCL-2 inhibitor Venetoclax, opening a new therapeutic avenue for tackling Menin-resistant AMLs driven by polycomb inactivation.

Protein-Protein Interactions: Bimolecular Fluorescence Complementation and Cytology Two Hybrid.

Identifying protein-protein interactions between machine components of bacterial secretion systems and their cognate substrates is central to delineating how the machines operate to translocate their substrates. Further, establishing which among the machine components and their substrates interact with each other facilitates (i) advancement in our understanding of the architecture and assembly of the machines, (ii) understanding the substrates' translocation routes and mechanisms, and (iii) how the machines and the substrates talk to each other. Currently, though diverse biochemical methods exist in identifying direct and indirect protein-protein interactions, they primarily remain in vitro and can be quite labor intensive. They also may capture/exhibit false-positive interactions because of barrier breakdowns as part of methodology. Thus, adopting novel genetic approaches to help visualize the same in vivo can yield quick, advantageous, reliable, and informative protein-protein interactions data. Here, we describe the easily adoptable bimolecular fluorescence complementation and cytology-based two-hybrid assays to understand the bacterial secretions systems.

Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding.

The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark" members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing.

Drug and Protein Interaction Network Construction for Drug Repurposing in Alzheimer’s Disease

Alzheimer’s disease is one of the leading causes of death globally, significantly impacting countless families and communities. In parallel, recent advancements in molecular biology and network approaches, guided by the Network Medicine perspective, offer promising outcomes for Alzheimer’s disease research and treatment. In this study, we aim to discover candidate therapies for AD through drug repurposing. We combined a protein-protein interaction (PPI) network with drug-target interactions. Experimentally validated PPI data were collected from the PICKLE meta-database, while drugs and their protein targets were sourced from the DrugBank database. Then, based on RNA-Seq data, we first assigned weights to edges to indicate co-expression, and secondly, estimated differential gene expression to select a subset of genes potentially related to the disease. Finally, small subgraphs (modules) were extracted from the graph, centered on the genes of interest. The analysis revealed that even if there is no drug targeting several genes of interest directly, an existing drug might target a neighboring node, thus indirectly affecting the aforementioned genes. Our approach offers a promising method for treating various diseases by repurposing existing drugs, thereby reducing the cost and time of experimental procedures and paving the way for more precise Network Medicine strategies.

To Break or Not to Break: The Role of TOP2B in Transcription.

Transcription and its regulation pose challenges related to DNA torsion and supercoiling of the DNA template. RNA polymerase tracking the helical groove of the DNA introduces positive helical torsion and supercoiling upstream and negative torsion and supercoiling behind its direction of travel. This can inhibit transcriptional elongation and other processes essential to transcription. In addition, chromatin remodeling associated with gene activation can generate or be hindered by excess DNA torsional stress in gene regulatory regions. These topological challenges are solved by DNA topoisomerases via a strand-passage reaction which involves transiently breaking and re-joining of one (type I topoisomerases) or both (type II topoisomerases) strands of the phosphodiester backbone. This review will focus on one of the two mammalian type II DNA topoisomerase enzymes, DNA topoisomerase II beta (TOP2B), that have been implicated in correct execution of developmental transcriptional programs and in signal-induced transcription, including transcriptional activation by nuclear hormone ligands. Surprisingly, several lines of evidence indicate that TOP2B-mediated protein-free DNA double-strand breaks are involved in signal-induced transcription. We discuss the possible significance and origins of these DSBs along with a network of protein interaction data supporting a variety of roles for TOP2B in transcriptional regulation.

Unveiling the potential mechanisms of Amomi fructus against gastric ulcers via integrating network pharmacology and in vivo experiments

Ethnopharmacological relevanceAs a well-known traditional Chinese medicine, Amomi fructus (A. fructus) (Sharen) has been used therapeutically to treat gastrointestinal illnesses, including gastric ulcer (GU). The mechanism underlying this impact is still not fully known, though. Aim of the studyTo investigate the hidden mechanism by which A. fructus influences the pathogenesis of GU, we employed network pharmacology approaches and in vivo validated studies. Materials and methodsMultiple public databases were used to compile information on bioactive compounds, potential targets of A. fructus, and associated genes of GU. Then, the STRING database's protein-protein interaction (PPI) data of the drug-disease overlapping gene targets was obtained, and the core targets for A. fructus against GU were discovered. Additionally, molecular docking was done to examine the binding capabilities of the active substances and core targets. Then, the pathways of A. fructus that target GU were examined using the Annotation, Visualization and Integrated Discovery (DAVID)'s Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway studies. In a mouse model of acute stomach mucosal damage brought on by absolute ethanol, the findings of network pharmacology were finally validated. ResultsIn total, 610 targets derived from the 196 bioactive compounds in A. fructus, were discovered, and along with 115 A. fructus target genes for therapy of GU. Then, ten core targets associated with apoptosis and inflammation were determined based on node degree, and ALB, AKT1, TNF, EGFR, MAPK3, CASP3, MMP9, STAT3, SRC, and HRAS were identified as promising therapeutic targets of A. fructus against GU. The results of molecular docking also revealed that 65 active compounds had strong binding activity with the core targets, with volatile chemicals being the most significant active ingredients. So, for following in vivo tests, A. fructus volatile oils (AVO) were used. The KEGG analysis showed that the phosphoinositide-3-kinase/protein kinase B (PI3K/AKT) signaling pathway may be crucial for the therapeutic mechanism of GU. In experiments that were validated in vivo, AVO considerably decreased the ulcer area and enhanced the histological appearance of the gastric tissues. In addition, compared to the model group, up-regulated the expression of IGF-1, p-PI3K, and p-AKT and down-regulated the protein levels of TNF-α and Caspase 3 in the stomach tissues. ConclusionAccording to preliminary findings from this work, A. fructus may influence inflammatory response and apoptosis via regulating the PI3K/AKT signaling pathway and associated gene targets. Importantly, our research might offer a theoretical foundation for future research into the intricate anti-GU mechanism of A. fructus.

NN-RNALoc: Neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profiles.

The localization of messenger RNAs (mRNAs) is a frequently observed phenomenon and a crucial aspect of gene expression regulation. It is also a mechanism for targeting proteins to a specific cellular region. Moreover, prior research and studies have shown the significance of intracellular RNA positioning during embryonic and neural dendrite formation. Incorrect RNA localization, which can be caused by a variety of factors, such as mutations in trans-regulatory elements, has been linked to the development of certain neuromuscular diseases and cancer. In this study, we introduced NN-RNALoc, a neural network-based method for predicting the cellular location of mRNA using novel features extracted from mRNA sequence data and protein interaction patterns. In fact, we developed a distance-based subsequence profile for RNA sequence representation that is more memory and time-efficient than well-known k-mer sequence representation. Combining protein-protein interaction data, which is essential for numerous biological processes, with our novel distance-based subsequence profiles of mRNA sequences produces more accurate features. On two benchmark datasets, CeFra-Seq and RNALocate, the performance of NN-RNALoc is compared to powerful predictive models proposed in previous works (mRNALoc, RNATracker, mLoc-mRNA, DM3Loc, iLoc-mRNA, and EL-RMLocNet), and a ground neural (DNN5-mer) network. Compared to the previous methods, NN-RNALoc significantly reduces computation time and also outperforms them in terms of accuracy. This study's source code and datasets are freely accessible at https://github.com/NeginBabaiha/NN-RNALoc.

Unraveling the Potential Anti-Severe Acute Respiratory Syndrome Coronavirus 2 Mechanisms of Ginger by Computational Target Fishing.

Ginger (Zingiber officinale) is one of the most commonly consumed botanical foods. Owing to its anti-inflammatory and antiviral properties, ginger has been widely used as a homemade remedy during the corona virus disease 2019 (COVID-19) pandemic; however, the mechanisms of its therapeutic activities against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain obscure. In this study, we used a drug-likeness approach to screen the active compounds of ginger. Next, we identified candidate targets of active compounds responsible for the anti-SARS-CoV-2 effects of ginger using chemical similarity searching and SARS-CoV-2-human protein-protein interaction (PPI) data. Finally, we analyzed PPIs, Gene Ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment of the candidate proteins using different bioinformatics tools. A network comprising ginger compounds, human proteins, and SARS-CoV-2 proteins was built through Cytoscape 3.3. The results indicate that the anti-SARS-CoV-2 activity of ginger involves 20 active compounds, 18 potential human targets, and 12 SARS-CoV-2 proteins. These form a pharmacological network in which sigma nonopioid intracellular receptor 1 (SIGMAR1) and histone deacetylase 2 may be druggable hub proteins. In addition, molecular docking showed that 8-gingerdione and dihydrocapsaicin may preferentially interact with SIGMAR1, which was confirmed by further molecular dynamics simulation (150 ns) experiments. In conclusion, ginger targets multiple human proteins and affects multiple SARS-CoV-2 proteins to exert anti-COVID-19 effects. Although further experimental verification is needed, this study provides a quick visual overview of the anti-SARS-CoV-2 action of ginger.