Analysis Of Biological Networks Research Articles

GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search

Biological network similarity search plays a crucial role in the analysis of biological networks for human disease research and drug discovery. A biological network similarity search aims to efficiently identify novel networks biologically homologous to the query networks. Great progress has been achieved in biological network similarity searches. However, it remains a challenge to mine the biological network information fully to improve the accuracy of query results without increasing time overheads. In this study, we propose a biological network similarity search method based on graph neural networks named GBNSS, which combines topological and biological information (GO annotations) of biological networks into graph neural networks to find topologically and biologically similar biological networks in the database. Additionally, GBNSS is a topology-free biological network similarity search method with an arbitrary network structure. The experimental results on four benchmark datasets show that GBNSS outperforms the existing methods in terms of computational efficiency and search accuracy. Case studies further demonstrate that GBNSS is capable of searching similar networks in real-world biological networks.

Decoding the genetic comorbidity network of Alzheimer's disease

Alzheimer's disease (AD) has emerged as the most prevalent and complex neurodegenerative disorder among the elderly population. However, the genetic comorbidity etiology for AD remains poorly understood. In this study, we conducted pleiotropic analysis for 41 AD phenotypic comorbidities, identifying ten genetic comorbidities with 16 pleiotropy genes associated with AD. Through biological functional and network analysis, we elucidated the molecular and functional landscape of AD genetic comorbidities. Furthermore, leveraging the pleiotropic genes and reported biomarkers for AD genetic comorbidities, we identified 50 potential biomarkers for AD diagnosis. Our findings deepen the understanding of the occurrence of AD genetic comorbidities and provide new insights for the search for AD diagnostic markers. Graphical Study pipeline.

Overlapping Structure Detection in Protein-Protein Interaction Networks Using a Modified Version of Particle Swarm Optimization

In today's world, the science of bioinformatics is developing rapidly, especially with regard to the analysis and study of biological networks. Scientists have used various nature-inspired algorithms to find protein complexes in protein-protein interaction (PPI) networks. These networks help scientists guess the molecular function of unknown proteins and show how cells work regularly. It is very common in PPI networks for a protein to participate in multiple functions and belong to many complexes, and as a result, complexes may overlap in the PPI networks. However, developing an efficient and reliable method to address the problem of detecting overlapping protein complexes remains a challenge since it is considered a complex and hard optimization problem. One of the main difficulties in identifying overlapping protein complexes is the accuracy of the partitioning results. In order to accurately identify the overlapping structure of protein complexes, this paper has proposed an overlapping complex detection algorithm termed OCDPSO-Net, which is based on PSO-Net (a well-known modified version of the particle swarm optimization algorithm). The framework of the OCDPSO-Net method consists of three main steps, including an initialization strategy, a movement strategy for each particle, and enhancing search ability in order to expand the solution space. The proposed algorithm has employed the partition density concept for measuring the partitioning quality in PPI network complexes and tried to optimize the value of this quantity by applying the line graph concept of the original graph representing the protein interaction network. The OCDPSO-Net algorithm is applied to a Collins PPI network and the obtained results are compared with different state-of-the-art algorithms in terms of precision ( ), recall ( ), and F-measure ( ). Experimental results confirm that the proposed algorithm has good clustering performance and has outperformed most of the existing recent overlapping algorithms. .

ITraNet: A Web-Based Platform for integrated Trans-Omics Network Visualization and Analysis

Abstract Motivation Visualization and analysis of biological networks play crucial roles in understanding living systems. Biological networks include diverse types, from gene regulatory networks and protein–protein interactions to metabolic networks. Metabolic networks include substrates, products, and enzymes, which are regulated by allosteric mechanisms and gene expression. However, the analysis of these diverse omics types is challenging due to the diversity of databases and the complexity of network analysis. Results We developed iTraNet, a web application that visualizes and analyzes trans-omics networks involving four types of networks: gene regulatory networks; protein–protein interactions; metabolic networks; and metabolite exchange networks. Using iTraNet, we found that in wild-type mice, hub molecules within the network tended to respond to glucose administration, whereas in ob/ob mice, this tendency disappeared. With its ability to facilitate network analysis, we anticipate that iTraNet will help researchers gain insights into living systems. Availability iTraNet is available at https://itranet.streamlit.app/. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Exploring the Genetic and Molecular Connection between Autism and Huntington's Disease Via Transcriptomics and Biological Interaction Networks Analysis

Exploring the Genetic and Molecular Connection between Autism and Huntington's Disease Via Transcriptomics and Biological Interaction Networks Analysis

Finding conserved low‐diameter subgraphs in social and biological networks

AbstractThe analysis of social and biological networks often involves modeling clusters of interest as cliques or their graph‐theoretic generalizations. The ‐club model, which relaxes the requirement of pairwise adjacency in a clique to length‐bounded paths inside the cluster, has been used to model cohesive subgroups in social networks and functional modules or complexes in biological networks. However, if the graphs are time‐varying, or if they change under different conditions, we may be interested in clusters that preserve their property over time or under changes in conditions. To model such clusters that are conserved in a collection of graphs, we consider a cross‐graph ‐club model, a subset of nodes that forms a ‐club in every graph in the collection. In this article, we consider the canonical optimization problem of finding a cross‐graph ‐club of maximum cardinality in a graph collection. We develop integer programming approaches to solve this problem. Specifically, we introduce strengthened formulations, valid inequalities, and branch‐and‐cut algorithms based on delayed constraint generation. The results of our computational study indicate the significant benefits of using the approaches we introduce.

Cell surface protein–protein interaction profiling for biological network analysis and novel target discovery

Cell surface protein–protein interaction profiling for biological network analysis and novel target discovery

Identification of Drought Stress-Responsive Genes in Rice by Random Walk with Multi-Restart Probability on MultiPlex Biological Networks.

Exploring drought stress-responsive genes in rice is essential for breeding drought-resistant varieties. Rice drought resistance is controlled by multiple genes, and mining drought stress-responsive genes solely based on single omics data lacks stability and accuracy. Multi-omics correlation analysis and biological molecular network analysis provide robust solutions. This study proposed a random walk with a multi-restart probability (RWMRP) algorithm, based on the Restarted Random Walk (RWR) algorithm, to operate on rice MultiPlex biological networks. It explores the interactions between biological molecules across various levels and ranks potential genes. RWMRP uses eigenvector centrality to evaluate node importance in the network and adjusts the restart probabilities accordingly, diverging from the uniform restart probability employed in RWR. In the random walk process, it can be better to consider the global relationships in the network. Firstly, we constructed a MultiPlex biological network by integrating the rice protein-protein interaction, gene pathway, and gene co-expression network. Then, we employed RWMRP to predict the potential genes associated with rice tolerance to drought stress. Enrichment and correlation analyses resulted in the identification of 12 drought-related genes. We further conducted quantitative real-time polymerase chain reaction (qRT-PCR) analysis on these 12 genes, ultimately identifying 10 genes responsive to drought stress.

A novel hierarchical network-based approach to unveil the complexity of functional microbial genome

Biological networks serve a crucial role in elucidating intricate biological processes. While interspecies environmental interactions have been extensively studied, the exploration of gene interactions within species, particularly among individual microorganisms, is less developed. The increasing amount of microbiome genomic data necessitates a more nuanced analysis of microbial genome structures and functions. In this context, we introduce a complex structure using higher-order network theory, “Solid Motif Structures (SMS)”, via a hierarchical biological network analysis of genomes within the same genus, effectively linking microbial genome structure with its function. Leveraging 162 high-quality genomes of Microcystis, a key freshwater cyanobacterium within microbial ecosystems, we established a genome structure network. Employing deep learning techniques, such as adaptive graph encoder, we uncovered 27 critical functional subnetworks and their associated SMSs. Incorporating metagenomic data from seven geographically distinct lakes, we conducted an investigation into Microcystis’ functional stability under varying environmental conditions, unveiling unique functional interaction models for each lake. Our work compiles these insights into an extensive resource repository, providing novel perspectives on the functional dynamics within Microcystis. This research offers a hierarchical network analysis framework for understanding interactions between microbial genome structures and functions within the same genus.

Mass spectrum oriented metabolomics for evaluating the efficacy and discovering the mechanism of Shaofuzhuyu Decoction for endometriosis of cold coagulation and blood stasis

Shaofuzhuyu Decoction (SFZYD) is a classical formula for treating endometriosis of cold coagulation and blood stasis (ECB). The clinical efficacy is definite, but the potential mechanisms require further exploration. The study aimed to reveal the metabolic mechanisms of SFZYD for treating ECB using mass spectrum oriented metabolomics. Firstly, the study has used metabolomics data to identify biomarkers and to investigate metabolic pathways. Then, the targets of SFZYD for treating ECB were dug by building and analyzing a biological network of biomarkers. Finally, the obtained targets were validated by molecular docking. This study found that SFZYD could significantly improve the biochemical indicators and metabolic abnormalities of ECB. A total of 18 ECB-related biomarkers in 7 pathways were identified. SFZYD was able to regulate the levels of 14 biomarkers that were involved in 5 metabolic pathways. Furthermore, the study yielded 119 SFZYD active ingredients, 1119 target proteins associated with endometriosis, 610 targets associated with biomarkers, 727 GO functions, and 159 KEGG pathways. Biological network analysis constructed a network diagram of herbs-ingredients-targets-biomarkers, and found 6 key active ingredients and 9 core targets. Molecular docking showed high affinities between key ingredients and core targets. This study elucidated that SFZYD plays a role in treating ECB through multi-component, multi-target, and multi-pathway.

Biological regulatory network analysis for targeting the mitochondrial calcium uniporter (MCU) mediated calcium (Ca2+) transport in neurodegenerative disorders

AbstractCalcium (Ca2+) has been observed as the most important ion involved in a series of cellular processes and its homeostasis is critical for normal cellular functions. Mitochondrial calcium uniporter (MCU) complex has been recognized as the most important calcium‐specific channel located in the inner mitochondrial membrane and is one of the major players in maintaining the Ca2+ homeostasis by transporting Ca2+ across the mitochondrial membrane. Furthermore, dysregulation of the mitochondrial Ca2+ homeostasis has been orchestrated to neurodegenerative response. This necessitates quantitative evaluation of the MCU‐dependent mROS production and subsequent cellular responses for more specific therapeutic interventions against neurodegenerative disorders. Towards this goal, here we present a biological regulatory network of MCU to dynamically simulate the MCU‐mediated ROS production and its response in neurodegeneration. Previously, ruthenium complex RuRed and its derivatives have been reported to show low nM to high µM potency against MCU to maintain cytosolic Ca2+ (cCa2+) homeostasis by modulating mitochondrial Ca2+ (mCa2+) uptake. Therefore, structural modeling and dynamic simulation of MCU pore‐forming subunit is performed to probe the interaction profiling of previously reported Ru265 and its derivatives compounds with MCU. The current study highlighted MCU as a potential drug target in neurodegenerative disorders. Furthermore, ASP261 and GLU264 amino acid residues in DIME motif of MCU pore‐forming subunits are identified as crucial for modulating the activity of MCU in neurodegenerative disorders.

Mechanistic insights into xanthomicrol as the active anti-HCC ingredient of Phytolacca acinosa Roxb.: A network pharmacology analysis and transcriptomics integrated experimental verification

Ethnopharmacological relevancePhytolacca acinosa Roxb. (PAR) is a Traditional Chinese Medicinal (TCM) plant with a broad global distribution encompassing 35 species, four of which are found in the People's Republic of China. It occupies a significant role in both Oriental and American traditional medicine, employed in treating a range of conditions such as edema, inflammation, dermatitis, and rheumatism. PAR is also used as a molluscicide and for addressing tumors and bronchitis. The plant is documented in the Chinese Pharmacopoeia and has a longstanding history in TCM, particularly for its diuretic properties and in treating ailments such as edema, swelling, and ulcers. Notably, PAR has demonstrated potent inhibitory effects against the A549 human lung cancer cell line, underscoring its potential in contributing to the development of novel cancer therapeutics. Aim of the studyThe research aims to elucidate the active components of PAR and their mechanisms in treating hepatocellular carcinoma (HCC). Materials and methodsEmploying network pharmacology, this study predicted the principal active compounds and key targets of PAR. A holistic methodology incorporating biological network analysis, transcriptomics sequencing, molecular docking, and molecular dynamics (MD) simulations was utilized to forecast the effects of PAR on HCC, with empirical evidence supporting these findings. ResultsNetwork pharmacology identified xanthomicrol as the foremost active compound in PAR. The tumor-suppressive functions of PAR, as indicated by KEGG pathway analysis and transcriptomics sequencing, predominantly occur via the PI3K/AKT pathway. Molecular docking and dynamics simulations demonstrated the high affinity of xanthomicrol towards TNF, MMP9, PPARG, KDR, and MMP2. In vivo experiments verified the efficacy of xanthomicrol in curtailing HCC tumor growth, while in vitro assessments revealed its substantial impact on the proliferation and apoptosis of HepG2 and HCCLM3 cells. Moreover, the study indicates that xanthomicrol may modulate the expression of TNF, MMP9, PPARG, KDR, and MMP2 in HCC cells and inhibit the activation of the PI3K/AKT pathway. ConclusionsXanthomicrol, a principal active component of PAR, has been identified to impede the growth of HCC by targeting the PI3K/Akt/MMP9 pathway. This insight could enhance therapeutic approaches for HCC.

Genome-wide DNA methylation profiles and breast cancer among World Trade Center survivors.

Increased incidence of cancer has been reported among World Trade Center (WTC)-exposed persons. Aberrant DNA methylation is a hallmark of cancer development. To date, only a few small studies have investigated the relationship between WTC exposure and DNA methylation. The main objective of this study was to assess the DNA methylation profiles of WTC-exposed community members who remained cancer free and those who developed breast cancer. WTC-exposed women were selected from the WTC Environmental Health Center clinic, with peripheral blood collected during routine clinical monitoring visits. The reference group was selected from the NYU Women's Health Study, a prospective cohort study with blood samples collected before 9 November 2001. The Infinium MethylationEPIC array was used for global DNA methylation profiling, with adjustments for cell type composition and other confounders. Annotated probes were used for biological pathway and network analysis. A total of 64 WTC-exposed (32 cancer free and 32 with breast cancer) and 32 WTC-unexposed (16 cancer free and 16 with prediagnostic breast cancer) participants were included. Hypermethylated cytosine-phosphate-guanine probe sites (defined as β > 0.8) were more common among WTC-exposed versus unexposed participants (14.3% vs. 4.5%, respectively, among the top 5000 cytosine-phosphate-guanine sites). Cancer-related pathways (e.g., human papillomavirus infection, cGMP-PKG) were overrepresented in WTC-exposed groups (breast cancer patients and cancer-free subjects). Compared to the unexposed breast cancer patients, 47 epigenetically dysregulated genes were identified among WTC-exposed breast cancers. These genes formed a network, including Wnt/β-catenin signaling genes WNT4 and TCF7L2, and dysregulation of these genes contributes to cancer immune evasion. WTC exposure likely impacts DNA methylation and may predispose exposed individuals toward cancer development, possibly through an immune-mediated mechanism.

Transcriptomic Analysis of Lipid Metabolism Genes in Alzheimer's Disease: Highlighting Pathological Outcomes and Compartmentalized Immune Status.

The dysregulation of lipid metabolism has been strongly associated with Alzheimer's disease (AD) and has intricate connections with various aspects of disease progression, such as amyloidogenesis, bioenergetic deficit, oxidative stress, neuroinflammation, and myelin degeneration. Here, a comprehensive bioinformatic assessment was conducted on lipid metabolism genes in the brains and peripheral blood of AD-derived transcriptome datasets, characterizing the correlation betweendifferentially expressed genes (DEGs) of lipid metabolism and disease pathologies, as well as immune cell preferences. Through the application of weighted geneco-expression network analysis (WGCNA),modules eigengenes related to lipid metabolism were pinpointed, and the examination oftheir molecular functions within biological processes, molecular pathways, andtheir associations with pathological phenotypes and molecular networkshas been characterized. Analysis of biological networks indicates notable discrepancies in the expression patterns of the DEGs between neuronal and immune cells, as well as variations in cell type enrichments within both brain tissue and peripheral blood. Additionally, drugs targeting the DEGs from central and peripheral and a diagnostic model for hub genes from the blood were retrieved and assessed, some of which were shown to be useful for therapeutic and diagnostic.These results revealed the distinctive pattern of transcriptionally abnormal lipid metabolism in central, peripheral, and immune cell activation, providing valuable insight into lipid metabolism for diagnosing and guiding more effectivetreatment forAD.

System-level analysis of genes mutated in muscular dystrophies reveals a functional pattern associated with muscle weakness distribution

Muscular dystrophies (MDs) are inherited genetic diseases causing weakness and degeneration of muscles. The distribution of muscle weakness differs between MDs, involving distal muscles or proximal muscles. While the mutations in most of the MD-associated genes lead to either distal or proximal onset, there are also genes whose mutations can cause both types of onsets. We hypothesized that the genes associated with different MD onsets code proteins with distinct cellular functions. To investigate this, we collected the MD-associated genes and assigned them to three onset groups: genes mutated only in distal onset dystrophies, genes mutated only in proximal onset dystrophies, and genes mutated in both types of onsets. We then systematically evaluated the cellular functions of these gene sets with computational strategies based on functional enrichment analysis and biological network analysis. Our analyses demonstrate that genes mutated in either distal or proximal onset MDs code proteins linked with two distinct sets of cellular processes. Interestingly, these two sets of cellular processes are relevant for the genes that are associated with both onsets. Moreover, the genes associated with both onsets display high centrality and connectivity in the network of muscular dystrophy genes. Our findings support the hypothesis that the proteins associated with distal or proximal onsets have distinct functional characteristics, whereas the proteins associated with both onsets are multifunctional.

Computational Analysis and NGS for Human LDHC Complexed with NAD+ and Ethylamino Acetic Acid (Lung Cancer)

Abstract: This study presents a multidimensional exploration of the structural and functional characteristics of the human Lactate Dehydrogenase C (LDHC) complexed with NAD+ and ethylamino acetic acid (EAA), focusing on its implications in lung cancer. Leveraging data from the Protein Data Bank (PDB) and Molecular Modeling Database (MMDB), a comprehensive analysis was conducted using various computational tools and resources. Structural insights were gained through PDBsum and RasMol, elucidating molecular interactions and identifying potential drug-target sites. Sequence analysis via BLAST and Multiple Sequence Alignment provided further understanding of conserved sequences and structural motifs. Visualization and manipulation of molecular structures were facilitated by PyMOL, enabling the identification of active drug-target sites. Proteinligand docking studies using the CB-Dock2 server assessed the efficiency of LDHC complexation with NAD+. Structural validation through the SAVES server ensured the reliability of the complex structure. Additionally, biological network analysis using the STRING database revealed associations with gene ontology and structural classification of the protein, offering insights into targeted interventions in the ancestry of the protein. This comprehensive approach provides valuable insights into LDHC's role in cancer biology and offers promising avenues for therapeutic development in lung cancer.

Exploring the Molecular Terrain: A Survey of Analytical Methods for Biological Network Analysis

Biological systems, characterized by their complex interplay of symmetry and asymmetry, operate through intricate networks of interacting molecules, weaving the elaborate tapestry of life. The exploration of these networks, aptly termed the “molecular terrain”, is pivotal for unlocking the mysteries of biological processes and spearheading the development of innovative therapeutic strategies. This review embarks on a comprehensive survey of the analytical methods employed in biological network analysis, focusing on elucidating the roles of symmetry and asymmetry within these networks. By highlighting their strengths, limitations, and potential applications, we delve into methods for network reconstruction, topological analysis with an emphasis on symmetry detection, and the examination of network dynamics, which together reveal the nuanced balance between stable, symmetrical configurations and the dynamic, asymmetrical shifts that underpin biological functionality. This review equips researchers with a multifaceted toolbox designed to navigate and decipher biological networks’ intricate, balanced landscape, thereby advancing our understanding and manipulation of complex biological systems. Through this detailed exploration, we aim to foster significant advancements in biological network analysis, paving the way for novel therapeutic interventions and a deeper comprehension of the molecular underpinnings of life.

Shared genetic aetiology of Alzheimer’s disease and age-related macular degeneration by APOC1 and APOE genes

BackgroundAlzheimer’s disease (AD) and age-related macular degeneration (AMD) share similar pathological features, suggesting common genetic aetiologies between the two. Investigating gene associations between AD and AMD may provide useful insights...

Integrative analysis of RNA-sequencing and microarray for the identification of adverse effects of UVB exposure on human skin.

Ultraviolet B (UVB) from sunlight represents a major environmental factor that causes toxic effects resulting in structural and functional cutaneous abnormalities in most living organisms. Although numerous studies have indicated the biological mechanisms linking UVB exposure and cutaneous manifestations, they have typically originated from a single study performed under limited conditions. We accessed all publicly accessible expression data of various skin cell types exposed to UVB, including skin biopsies, keratinocytes, and fibroblasts. We performed biological network analysis to identify the molecular mechanisms and identify genetic biomarkers. We interpreted the inflammatory response and carcinogenesis as major UVB-induced signaling alternations and identified three candidate biomarkers (IL1B, CCL2, and LIF). Moreover, we confirmed that these three biomarkers contribute to the survival probability of patients with cutaneous melanoma, the most aggressive and lethal form of skin cancer. Our findings will aid the understanding of UVB-induced cutaneous toxicity and the accompanying molecular mechanisms. In addition, the three candidate biomarkers that change molecular signals due to UVB exposure of skin might be related to the survival rate of patients with cutaneous melanoma.

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