Chapter 5: Network biology approach to complex diseases

Homologous mapping yielded a comprehensive predicted protein-protein interaction network for peanut (Arachis hypogaea L.)

BACKGROUND

Protein-protein interactions are the primary means through which proteins carry out their functions. These interactions thus have crucial roles in life activities. The wide availability of fully sequenced animal and plant genomes has facilitated establishment of relatively complete global protein interaction networks for some model species. The genomes of cultivated and wild peanut (Arachis hypogaea L.) have also been sequenced, but the functions of most of the encoded proteins remain unclear.

RESULTS

We here used homologous mapping of validated protein interaction data from model species to generate complete peanut protein interaction networks for A. hypogaea cv. 'Tifrunner' (282,619 pairs), A. hypogaea cv. 'Shitouqi' (256,441 pairs), A. monticola (440,470 pairs), A. duranensis (136,363 pairs), and A. ipaensis (172,813 pairs). A detailed analysis was conducted for a putative disease-resistance subnetwork in the Tifrunner network to identify candidate genes and validate functional interactions. The network suggested that DX2UEH and its interacting partners may participate in peanut resistance to bacterial wilt; this was preliminarily validated with overexpression experiments in peanut.

CONCLUSION

Our results provide valuable new information for future analyses of gene and protein functions and regulatory networks in peanut.

pyGROMODS: a Python package for the generation of input files for molecular dynamic simulation with GROMACS

The pyGROMODS, an easy-to-use cross-platform python-based package, with a graphical user interface, for the generation of molecular dynamic (MD) input files and running MD simulation (MDS) of proteins, peptides, and protein-ligand complex using GROMACS, is here presented. Four routes, with underlining Python scripts, are implemented in pyGROMODS for the generation of MD input files. They are 'RLmulti' for processing multi-ligand protein complex, 'RLmany' for processing multiple ligands against a single protein target, 'RLsingle' for processing multiple pairs of proteins and ligands, and 'PPmore' for processing peptides or proteins without ligands or non-standard residues. In addition, using the package, the generated input files or appropriate input files from other sources can be uploaded to run MDS with GROMACS. The pyGROMODS is implemented with a unique ability to search the host machine systems for the installation of the required software, update and/or install required Python packages, allow the user to pre-define working directory, and generate unique workflow organization with well-defined folders and files in a well-organized manner. The pyGROMODS, which is released under the MIT License, is freely available for download via the GitHub (https://github.com/Dankem/pyGROMODS) and Zenodo (https://doi.org/10.5281/zenodo.7912747) repositories. The precompiled executables can also be downloaded from Zenodo (https://doi.org/10.5281/zenodo.8087090), and a video tutorial can be downloaded from https://youtu.be/I4OKc6uVx1M.Communicated by Ramaswamy H. Sarma.

Transcriptomic Module Discovery of Diarrhea-Predominant Irritable Bowel Syndrome: A Causal Network Inference Approach

Irritable bowel syndrome with diarrhea (IBS-D) is the most prevalent subtype of IBS, characterized by chronic gastrointestinal symptoms in the absence of identifiable pathological findings. This study aims to investigate the molecular mechanisms underlying IBS-D using transcriptomic data. By employing causal network inference methods, we identify key transcriptomic modules associated with IBS-D. Utilizing data from public databases and applying advanced computational techniques, we uncover potential biomarkers and therapeutic targets. Our analysis reveals significant molecular alterations that affect cellular functions, offering new insights into the complex pathophysiology of IBS-D. These findings enhance our understanding of the disease and may foster the development of more effective treatments.

Phenotypic drug discovery: a case for thymosin alpha-1

Phenotypic drug discovery (PDD) involves screening compounds for their effects on cells, tissues, or whole organisms without necessarily understanding the underlying molecular targets. PDD differs from target-based strategies as it does not require knowledge of a specific drug target or its role in the disease. This approach can lead to the discovery of drugs with unexpected therapeutic effects or applications and allows for the identification of drugs based on their functional effects, rather than through a predefined target-based approach. Ultimately, disease definitions are mostly symptom-based rather than mechanism-based, and the therapeutics should be likewise. In recent years, there has been a renewed interest in PDD due to its potential to address the complexity of human diseases, including the holistic picture of multiple metabolites engaging with multiple targets constituting the central hub of the metabolic host-microbe interactions. Although PDD presents challenges such as hit validation and target deconvolution, significant achievements have been reached in the era of big data. This article explores the experiences of researchers testing the effect of a thymic peptide hormone, thymosin alpha-1, in preclinical and clinical settings and discuss how its therapeutic utility in the precision medicine era can be accommodated within the PDD framework.

phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets

Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.

Prioritizing drug targets by perturbing biological network response functions

Therapeutic interventions are designed to perturb the function of a biological system. However, there are many types of proteins that cannot be targeted with conventional small molecule drugs. Accordingly, many identified gene-regulatory drivers and downstream effectors are currently undruggable. Drivers and effectors are often connected by druggable signaling and regulatory intermediates. Methods to identify druggable intermediates therefore have general value in expanding the set of targets available for hypothesis-driven validation. Here we identify and prioritize potential druggable intermediates by developing a network perturbation theory, termed NetPert, for response functions of biological networks. Dynamics are defined by a network structure in which vertices represent genes and proteins, and edges represent gene-regulatory interactions and protein-protein interactions. Perturbation theory for network dynamics prioritizes targets that interfere with signaling from driver to response genes. Applications to organoid models for metastatic breast cancer demonstrate the ability of this mathematical framework to identify and prioritize druggable intermediates. While the short-time limit of the perturbation theory resembles betweenness centrality, NetPert is superior in generating target rankings that correlate with previous wet-lab assays and are more robust to incomplete or noisy network data. NetPert also performs better than a related graph diffusion approach. Wet-lab assays demonstrate that drugs for targets identified by NetPert, including targets that are not themselves differentially expressed, are active in suppressing additional metastatic phenotypes.

An across breed, diet and tissue analysis reveals the transcription factor NR1H3 as a key mediator of residual feed intake in beef cattle

BACKGROUND

Provision of feed is a major determinant of overall profitability in beef production systems, accounting for up to 75% of the variable costs. Thus, improving cattle feed efficiency, by way of determining the underlying genomic control and subsequently selecting for feed efficient cattle, provides a method through which feed input costs may be reduced. The objective of this study was to undertake gene co-expression network analysis using RNA-Sequence data generated from Longissimus dorsi and liver tissue samples collected from steers of two contrasting breeds (Charolais and Holstein-Friesian) divergent for residual feed intake (RFI), across two consecutive distinct dietary phases (zero-grazed grass and high-concentrate). Categories including differentially expressed genes (DEGs) based on the contrasts of RFI phenotype, breed and dietary source, as well as key transcription factors and proteins secreted in plasma were utilised as nodes of the gene co-expression network.

RESULTS

Of the 2,929 DEGs within the network analysis, 1,604 were reported to have statistically significant correlations (≥ 0.80), resulting in a total of 43,876 significant connections between genes. Pathway analysis of clusters of co-expressed genes revealed enrichment of processes related to lipid metabolism (fatty acid biosynthesis, fatty acid β-oxidation, cholesterol biosynthesis), immune function, (complement cascade, coagulation system, acute phase response signalling), and energy production (oxidative phosphorylation, mitochondrial L-carnitine shuttle pathway) based on genes related to RFI, breed and dietary source contrasts.

CONCLUSIONS

Although similar biological processes were evident across the three factors examined, no one gene node was evident across RFI, breed and diet contrasts in both liver and muscle tissues. However within the liver tissue, the IRX4, NR1H3, HOXA13 and ZNF648 gene nodes, which all encode transcription factors displayed significant connections across the RFI, diet and breed comparisons, indicating a role for these transcription factors towards the RFI phenotype irrespective of diet and breed. Moreover, the NR1H3 gene encodes a protein secreted into plasma from the hepatocytes of the liver, highlighting the potential for this gene to be explored as a robust biomarker for the RFI trait in beef cattle.

From genome to clinic: The power of translational bioinformatics in improving human health

Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes.

Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

BACKGROUND

Modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features. Deep-learning offers a potential solution, as it can model complex interactions and higher-order latent features, although does not provide interpretable models and latent features.

RESULTS

We propose a novel autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor) for modeling, and a metric, explained relative variance (ERV), for interpretation of GRNs. We evaluate SupirFactor with ERV in a wide set of contexts. Compared to current state-of-the-art GRN inference methods, SupirFactor performs favorably. We evaluate latent feature activity as an estimate of TFA and biological function in S. cerevisiae as well as in peripheral blood mononuclear cells (PBMC).

CONCLUSION

Here we present a framework for structure-primed inference and interpretation of GRNs, SupirFactor, demonstrating interpretability using ERV in multiple biological and experimental settings. SupirFactor enables TFA estimation and pathway analysis using latent factor activity, demonstrated here on two large-scale single-cell datasets, modeling S. cerevisiae and PBMC. We find that the SupirFactor model facilitates biological analysis acquiring novel functional and regulatory insight.

Small networks of expressed genes in the whole blood and relationships to profiles in circulating metabolites provide insights in inter-individual variability of feed efficiency in growing pigs

BACKGROUND

Feed efficiency is a research priority to support a sustainable meat production. It is recognized as a complex trait that integrates multiple biological pathways orchestrated in and by various tissues. This study aims to determine networks between biological entities to explain inter-individual variation of feed efficiency in growing pigs.

RESULTS

The feed conversion ratio (FCR), a measure of feed efficiency, and its two component traits, average daily gain and average daily feed intake, were obtained from 47 growing pigs from a divergent selection for residual feed intake and fed high-starch or high-fat high-fiber diets during 58 days. Datasets of transcriptomics (60 k porcine microarray) in the whole blood and metabolomics (1H-NMR analysis and target gas chromatography) in plasma were available for all pigs at the end of the trial. A weighted gene co-expression network was built from the transcriptomics dataset, resulting in 33 modules of co-expressed molecular probes. The eigengenes of eight of these modules were significantly ([Formula: see text]) or tended to be ([Formula: see text]) correlated to FCR. Great homogeneity in the enriched biological pathways was observed in these modules, suggesting co-expressed and co-regulated constitutive genes. They were mainly enriched in genes participating to immune and defense-related processes, and to a lesser extent, to translation, cell development or learning. They were also generally associated with growth rate and percentage of lean mass. In the whole network, only one module composed of genes participating to the response to substances, was significantly associated with daily feed intake and body adiposity. The plasma profiles in circulating metabolites and in fatty acids were summarized by weighted linear combinations using a dimensionality reduction method. Close association was thus found between a module composed of co-expressed genes participating to T cell receptor signaling and cell development process in the whole blood and related to FCR, and the circulating concentrations of polyunsaturated fatty acids in plasma.

CONCLUSION

These systemic approaches have highlighted networks of entities driving key biological processes involved in the phenotypic difference in feed efficiency between animals. Connecting transcriptomics and metabolic levels together had some additional benefits.

Uncovering Protein Networks in Cardiovascular Proteomics

Biological networks have been widely used in many different diseases to identify potential biomarkers and design drug targets. In the present review, we describe the main computational techniques for reconstructing and analyzing different types of protein networks and summarize the previous applications of such techniques in cardiovascular diseases. Existing tools are critically compared, discussing when each method is preferred such as the use of co-expression networks for functional annotation of protein clusters and the use of directed networks for inferring regulatory associations. Finally, we are presenting examples of reconstructing protein networks of different types (regulatory, co-expression, and protein-protein interaction networks). We demonstrate the necessity to reconstruct networks separately for each cardiovascular tissue type and disease entity and provide illustrative examples of the importance of taking into consideration relevant post-translational modifications. Finally, we demonstrate and discuss how the findings of protein networks could be interpreted using single-cell RNA-sequencing data.

Assessing complexity and dynamics in epidemics: geographical barriers and facilitators of foot-and-mouth disease dissemination

Introduction

Physical and non-physical processes that occur in nature may influence biological processes, such as dissemination of infectious diseases. However, such processes may be hard to detect when they are complex systems. Because complexity is a dynamic and non-linear interaction among numerous elements and structural levels in which specific effects are not necessarily linked to any one specific element, cause-effect connections are rarely or poorly observed.

Methods

To test this hypothesis, the complex and dynamic properties of geo-biological data were explored with high-resolution epidemiological data collected in the 2001 Uruguayan foot-and-mouth disease (FMD) epizootic that mainly affected cattle. County-level data on cases, farm density, road density, river density, and the ratio of road (or river) length/county perimeter were analyzed with an open-ended procedure that identified geographical clustering in the first 11 epidemic weeks. Two questions were asked: (i) do geo-referenced epidemiologic data display complex properties? and (ii) can such properties facilitate or prevent disease dissemination?

Results

Emergent patterns were detected when complex data structures were analyzed, which were not observed when variables were assessed individually. Complex properties-including data circularity-were demonstrated. The emergent patterns helped identify 11 counties as 'disseminators' or 'facilitators' (F) and 264 counties as 'barriers' (B) of epidemic spread. In the early epidemic phase, F and B counties differed in terms of road density and FMD case density. Focusing on non-biological, geographical data, a second analysis indicated that complex relationships may identify B-like counties even before epidemics occur.

Discussion

Geographical barriers and/or promoters of disease dispersal may precede the introduction of emerging pathogens. If corroborated, the analysis of geo-referenced complexity may support anticipatory epidemiological policies.

Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

The modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of regulatory features in genome-wide screens. Most GRN inference methods are therefore forced to model relationships between regulatory genes and their targets with expression as a proxy for the upstream independent features, complicating validation and predictions produced by modeling frameworks. Separating covariance and regulatory influence requires aggregation of independent and complementary sets of evidence, such as transcription factor (TF) binding and target gene expression. However, the complete regulatory state of the system, e.g. TF activity (TFA) is unknown due to a lack of experimental feasibility, making regulatory relations difficult to infer. Some methods attempt to account for this by modeling TFA as a latent feature, but these models often use linear frameworks that are unable to account for non-linearities such as saturation, TF-TF interactions, and other higher order features. Deep learning frameworks may offer a solution, as they are capable of modeling complex interactions and capturing higher-order latent features. However, these methods often discard central concepts in biological systems modeling, such as sparsity and latent feature interpretability, in favor of increased model complexity. We propose a novel deep learning autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor), that scales to single cell genomic data and maintains interpretability to perform GRN inference and estimate TFA as a latent feature. We demonstrate that SupirFactor outperforms current leading GRN inference methods, predicts biologically relevant TFA and elucidates functional regulatory pathways through aggregation of TFs.

Gene expression networks and functionally enriched pathways involved in the response of domestic chicken to acute heat stress

Heat stress in poultry houses, especially in warm areas, is one of the main environmental factors that restrict the growth of broilers or laying performance of layers, suppresses the immune system, and deteriorates egg quality and feed conversion ratio. The molecular mechanisms underlying the response of chicken to acute heat stress (AHS) have not been comprehensively elucidated. Therefore, the main object of the current work was to investigate the liver gene expression profile of chickens under AHS in comparison with their corresponding control groups, using four RNA-seq datasets. The meta-analysis, GO and KEGG pathway enrichment, WGCNA, machine-learning, and eGWAS analyses were performed. The results revealed 77 meta-genes that were mainly related to protein biosynthesis, protein folding, and protein transport between cellular organelles. In other words, under AHS, the expression of genes involving in the structure of rough reticulum membrane and in the process of protein folding was adversely influenced. In addition, genes related to biological processes such as "response to unfolded proteins," "response to reticulum stress" and "ERAD pathway" were differentially regulated. We introduce here a couple of genes such as HSPA5, SSR1, SDF2L1, and SEC23B, as the most significantly differentiated under AHS, which could be used as bio-signatures of AHS. Besides the mentioned genes, the main findings of the current work may shed light to the identification of the effects of AHS on gene expression profiling of domestic chicken as well as the adaptive response of chicken to environmental stresses.

Identifying Network Biomarkers for Alzheimer's Disease Using Single-Cell RNA Sequencing Data

System-level network-based approaches are an emerging field in the biomedical domain since biological networks can be used to analyze complicated biological processes and complex human disorders more efficiently. Network biomarkers are groups of interconnected molecular components causing perturbations in the entire network topology that can be used as indicators of pathogenic biological processes when studying a given disease. Although in the last years computational systems-based approaches have gained ground on the path to discovering new network biomarkers, in complex diseases like Alzheimer's disease (AD), this approach has still much to offer. Especially the adoption of single-cell RNA sequencing (scRNA-seq) has now become the dominant technology for the study of stochastic gene expression. Toward this orientation, we propose an R workflow that extracts disease-perturbed subpathways within a pathway network. We construct a gene-gene interaction network integrated with scRNA-seq expression profiles, and after network processing and pruning, the most active subnetworks are isolated from the entire network topology. The proposed methodology was applied on a real AD-based scRNA-seq data, providing already existing and new potential AD biomarkers in gene network context.

Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review

BACKGROUND

Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques.

OBJECTIVE

This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways.

METHODS

The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria.

RESULTS

We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

RNA biomarkers for alcohol use disorder

Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.

Conflict Dynamics in Scale-Free Networks with Degree Correlations and Hierarchical Structure

We present a study of the dynamic interactions between actors located on complex networks with scale-free and hierarchical scale-free topologies with assortative mixing, that is, correlations between the degree distributions of the actors. The actor's state evolves according to a model that considers its previous state, the inertia to change, and the influence of its neighborhood. We show that the time evolution of the system depends on the percentage of cooperative or competitive interactions. For scale-free networks, we find that the dispersion between actors is higher when all interactions are either cooperative or competitive, while a balanced presence of interactions leads to a lower separation. Moreover, positive assortative mixing leads to greater divergence between the states, while negative assortative mixing reduces this dispersion. We also find that hierarchical scale-free networks have both similarities and differences when compared with scale-free networks. Hierarchical scale-free networks, like scale-free networks, show the least divergence for an equal mix of cooperative and competitive interactions between actors. On the other hand, hierarchical scale-free networks, unlike scale-free networks, show much greater divergence when dominated by cooperative rather than competitive actors, and while the formation of a rich club (adding links between hubs) with cooperative interactions leads to greater divergence, the divergence is much less when they are fully competitive. Our findings highlight the importance of the topology where the interaction dynamics take place, and the fact that a balanced presence of cooperators and competitors makes the system more cohesive, compared to the case where one strategy dominates.

On the limits of graph neural networks for the early diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disease whose molecular mechanisms are activated several years before cognitive symptoms appear. Genotype-based prediction of the phenotype is thus a key challenge for the early diagnosis of AD. Machine learning techniques that have been proposed to address this challenge do not consider known biological interactions between the genes used as input features, thus neglecting important information about the disease mechanisms at play. To mitigate this, we first extracted AD subnetworks from several protein-protein interaction (PPI) databases and labeled these with genotype information (number of missense variants) to make them patient-specific. Next, we trained Graph Neural Networks (GNNs) on the patient-specific networks for phenotype prediction. We tested different PPI databases and compared the performance of the GNN models to baseline models using classical machine learning techniques, as well as randomized networks and input datasets. The overall results showed that GNNs could not outperform a baseline predictor only using the APOE gene, suggesting that missense variants are not sufficient to explain disease risk beyond the APOE status. Nevertheless, our results show that GNNs outperformed other machine learning techniques and that protein-protein interactions lead to superior results compared to randomized networks. These findings highlight that gene interactions are a valuable source of information in predicting disease status.

Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro

Differentially expressed genes can serve as drug targets and are used to predict drug response and disease progression. In silico drug analysis based on the expression of these genetic biomarkers allows the detection of putative therapeutic agents, which could be used to reverse a pathological gene expression signature. Indeed, a set of bioinformatics tools can increase the accuracy of drug discovery, helping in biomarker identification. Once a drug target is identified, in vitro cell line models of disease are used to evaluate and validate the therapeutic potential of putative drugs and novel natural molecules. This study describes the development of efficacious PCR primers that can be used to identify gene expression of specific genetic pathways, which can lead to the identification of natural molecules as therapeutic agents in specific molecular pathways. For this study, genes involved in health conditions and processes were considered. In particular, the expression of genes involved in obesity, xenobiotics metabolism, endocannabinoid pathway, leukotriene B4 metabolism and signaling, inflammation, endocytosis, hypoxia, lifespan, and neurotrophins were evaluated. Exploiting the expression of specific genes in different cell lines can be useful in in vitro to evaluate the therapeutic effects of small natural molecules.

Investigation of Anti-Liver Cancer Activity of the Herbal Drug FDY003 Using Network Pharmacology

Globally, liver cancer (LC) is the sixth-most frequently occurring and the second-most fatal malignancy, responsible for 0.83 million deaths annually. Although the application of herbal drugs in cancer therapies has increased, their anti-LC activity and relevant mechanisms have not been fully studied from a systems perspective. To address these issues, we conducted a system-perspective network pharmacological investigation into the activity and mechanisms underlying the action of the herbal drug. FDY003 reduced the viability of human LC treatment. FDY003 reduced the viability of human LC cells and elevated their chemosensitivity. There were a total of 16 potential bioactive chemical components in FDY003 and they had 91 corresponding targets responsible for the pathological processes in LC. These FDY003 targets were functionally involved in regulating the survival, proliferation, apoptosis, and cell cycle of LC cells. Additionally, we found that FDY003 may target key signaling cascades connected to diverse LC pathological mechanisms, namely, PI3K-Akt, focal adhesion, IL-17, FoxO, MAPK, and TNF pathways. Overall, this study contributed to integrative mechanistic insights into the anti-LC potential of FDY003.

PhenoGeneRanker: Gene and Phenotype Prioritization Using Multiplex Heterogeneous Networks

Uncovering genotype-phenotype relationships is a fundamental challenge in genomics. Gene prioritization is an important step for this endeavor to make a short manageable list from a list of thousands of genes coming from high-throughput studies. Network propagation methods are promising and state of the art methods for gene prioritization based on the premise that functionally related genes tend to be close to each other in the biological networks. Recently, we introduced PhenoGeneRanker, a network-propagation algorithm for multiplex heterogeneous networks. PhenoGeneRanker allows multi-layer gene and phenotype networks. It also calculates empirical p values of gene and phenotype ranks using random stratified sampling of seeds of genes and phenotypes based on their connectivity degree in the network. In this study, we introduce the PhenoGeneRanker Bioconductor package and its application to multi-omics rat genome datasets to rank hypertension disease-related genes and strains. We showed that PhenoGeneRanker performed better to rank hypertension disease-related genes using multiplex gene networks than aggregated gene networks. We also showed that PhenoGeneRanker performed better to rank hypertension disease-related strains using multiplex phenotype network than single or aggregated phenotype networks. We performed a rigorous hyperparameter analysis and, finally showed that Gene Ontology (GO) enrichment of statistically significant top-ranked genes resulted in hypertension disease-related GO terms.

A Study on the Mechanism of Herbal Drug FDY003 for Colorectal Cancer Treatment by Employing Network Pharmacology

Colorectal cancer (CRC) originates from the uncontrolled growth of epithelial cells in the colon or rectum. Annually, 1.9 million new CRC cases are being reported, causing 0.9 million deaths worldwide. The suppressive effects of the herbal prescription FDY003, a mixture of Cordyceps militaris, Lonicera japonica Thunberg, and Artemisia capillaris Thunberg, against CRC have previously been reported. Nonetheless, the multiple compound-multiple target mechanisms of FDY003 in CRC cells have not been fully elucidated. In this study, we used network pharmacology (NP) to analyze the polypharmacological mechanisms of action of FDY003 in CRC treatment. FDY003 promoted the suppression of viability of CRC cells and strengthened their sensitivity to anticancer drugs. The NP study enabled the investigation of 17 pharmaceutical compounds and 90 CRC-related genes that were targets of the compounds. The gene ontology terms enriched with the CRC-related target genes of FDY003 were those involved in the control of a variety of phenotypes of CRC cells, for instance, the decision of apoptosis and survival, growth, stress response, and chemical response of cells. In addition, the targeted genes of FDY003 were further enriched in various Kyoto Encyclopedia of Genes and Genomes pathways that coordinate crucial pathological processes of CRC; these are ErbB, focal adhesion, HIF-1, IL-17, MAPK, PD-L1/PD-1, PI3K-Akt, Ras, TNF, and VEGF pathways. The overall analysis results obtained from the NP methodology support the multiple-compound-multiple-target-multiple-pathway pharmacological features of FDY003 as a potential agent for CRC treatment.

Unveiling the Mechanism of the Traditional Korean Medicinal Formula FDY003 on Glioblastoma Through a Computational Network Pharmacology Approach

Glioblastoma (GBM) is the most common type of primary malignant tumor that develops in the brain, with 0.21 million new cases per year globally and a median survival period of less than 2 years after diagnosis. Traditional Korean medicines have been increasingly suggested as effective and safe therapeutic strategies for GBM. However, their pharmacological effects and mechanistic characteristics remain to be studied. In this study, we employed a computational network pharmacological approach to determine the effects and mechanisms of the traditional Korean medicinal formula FDY003 on GBM. We found that FDY003 treatment decreased the viability of human GBM cells and increased their response to chemotherapeutics. We identified 10 potential active pharmacological compounds of FDY003 and 67 potential GBM-related target genes and proteins. The GBM-related targets of FDY003 were signaling components of various crucial GBM-associated pathways, such as PI3K-Akt, focal adhesion, MAPK, HIF-1, FoxO, Ras, and TNF. These pathways are functional regulators for the determination of cell growth and proliferation, survival and death, and cell division cycle of GBM cells. Together, the overall analyses contribute to the pharmacological basis for the anti-GBM roles of FDY003 and its systematic mechanisms.

Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View

Network and systemic approaches to studying human pathologies are helping us to gain insight into the molecular mechanisms of and potential therapeutic interventions for human diseases, especially for complex diseases where large numbers of genes are involved. The complex human pathological landscape is traditionally partitioned into discrete “diseases”; however, that partition is sometimes problematic, as diseases are highly heterogeneous and can differ greatly from one patient to another. Moreover, for many pathological states, the set of symptoms (phenotypes) manifested by the patient is not enough to diagnose a particular disease. On the contrary, phenotypes, by definition, are directly observable and can be closer to the molecular basis of the pathology. These clinical phenotypes are also important for personalised medicine, as they can help stratify patients and design personalised interventions. For these reasons, network and systemic approaches to pathologies are gradually incorporating phenotypic information. This review covers the current landscape of phenotype-centred network approaches to study different aspects of human diseases.

A Network Pharmacological Elucidation of the Systematic Treatment Activities and Mechanisms of the Herbal Drug FDY003 Against Esophageal Cancer

Despite accumulating evidence for the value of herbal drugs for cancer treatment, the mechanisms underlying their effects have not been fully elucidated in a systematic manner. In this study, we performed a network pharmacological analysis to elucidate the anti-esophageal cancer (EC) properties of the herbal drug FDY003, a mixture of Artemisia capillaris Thunberg (AcT), Cordyceps militaris (Linnaeus) Link (Cm), and Lonicera japonica Thunberg (LjT). FDY003 reduced human EC cell viability and increased the pharmacological effects of chemotherapeutic drugs. There were 15 active pharmacological chemicals targeting 61 EC-associated genes and proteins in FDY003. The FDY003 targets were key regulators of major oncogenic EC-associated signaling pathways, such as phosphoinositide 3-kinase (PI3K)-Akt, hypoxia-inducible factor (HIF)-1, mitogen-activated protein kinase (MAPK), tumor necrosis factor (TNF), p53, Janus kinase (JAK)-signal transducer and activator of transcription (STAT), erythroblastic leukemia viral oncogene homolog (ErbB), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kappa B), and vascular endothelial growth factor (VEGF) cascades. These EC-associated genes, proteins, and pathways targeted by FDY003 determine the malignant behaviors of EC cells, including cell death, survival, division, proliferation, and growth. This network pharmacological analysis provides an integrative view of the mechanisms by which FDY003 contributes to EC treatment.

Loss of CHGA Protein as a Potential Biomarker for Colon Cancer Diagnosis: A Study on Biomarker Discovery by Machine Learning and Confirmation by Immunohistochemistry in Colorectal Cancer Tissue Microarrays

Simple Summary

The identification of effective novel biomarkers is emergently needed in colon cancer patients. In the present study, firstly we predicted that CHGA could be a biomarker for colon cancer based on the protein–protein interaction network of all the reported biomarkers that were collected from our colorectal cancer biomarker database (CBD). Then we verified our results using a diagnostic test in gene expression data and an immunohistochemistry test. The results of this study suggest that a loss of CHGA expression from the normal colon and adjacent mucosa to colon cancer may be used as a valuable biomarker for early diagnosis of colon cancer patients.

Abstract

Background. The incidence of colorectal cancers has been constantly increasing. Although the mortality has slightly decreased, it is far from satisfaction. Precise early diagnosis for colorectal cancer has been a great challenge in order to improve patient survival. Patients and Methods. We started with searching for protein biomarkers based on our colorectal cancer biomarker database (CBD), finding differential expressed genes (GEGs) and non-DEGs from RNA sequencing (RNA-seq) data, and further predicted new biomarkers of protein–protein interaction (PPI) networks by machine learning (ML) methods. The best-selected biomarker was further verified by a receiver operating characteristic (ROC) test from microarray and RNA-seq data, biological network, and functional analysis, and immunohistochemistry in the tissue arrays from 198 specimens. Results. There were twelve proteins (MYO5A, CHGA, MAPK13, VDAC1, CCNA2, YWHAZ, CDK5, GNB3, CAMK2G, MAPK10, SDC2, and ADCY5) which were predicted by ML as colon cancer candidate diagnosis biomarkers. These predicted biomarkers showed close relationships with reported biomarkers of the PPI network and shared some pathways. An ROC test showed the CHGA protein with the best diagnostic accuracy (AUC = 0.9 in microarray data and 0.995 in RNA-seq data) among these candidate protein biomarkers. Furthermore, immunohistochemistry examination on our colon cancer tissue microarray samples further confirmed our bioinformatical prediction, indicating that CHGA may be used as a potential biomarker for early diagnosis of colon cancer patients. Conclusions. CHGA could be a potential candidate biomarker for diagnosing earlier colon cancer in the patients.

Genomic integration to identify molecular biomarkers associated with indicator traits of gastrointestinal nematode resistance in sheep

This study aimed to integrate GWAS and structural variants to propose possible molecular biomarkers related to gastrointestinal nematode resistance traits in Santa Inês sheep. The phenotypic records FAMACHA, haematocrit, white blood cell count, red blood cell count, haemoglobin, platelets and egg counts per gram of faeces were collected from 700 naturally infected animals, belonging to four Brazilian flocks. A total of 576 animals were genotyped using the Ovine SNP12k BeadChip and were imputed using a reference population with Ovine SNP50 BeadChip. The GWAS approaches were based on SNPs, haplotypes, CNVs and ROH. The overlapping between the significant genomic regions detected from all approaches was investigated, and the results were integrated using a network analysis. Genes related to the immune system were found, such as ABCB1, IL6, WNT5A and IRF5. Genomic regions containing candidate genes and metabolic pathways involved in immune responses, inflammatory processes and immune cells affecting parasite resistance traits were identified. The genomic regions, biological processes and candidate genes uncovered could lead to biomarkers for selecting more resilient sheep and improving herd welfare and productivity. The results obtained are the start point to identify molecular biomarkers related to indicator traits of gastrointestinal nematode resistance in Santa Inês sheep.

Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data

Untargeted metabolomics is a global molecular profiling technology that can be used to screen for inborn errors of metabolism (IEMs). Metabolite perturbations are evaluated based on current knowledge of specific metabolic pathway deficiencies, a manual diagnostic process that is qualitative, has limited scalability, and is not equipped to learn from accumulating clinical data. Our purpose was to improve upon manual diagnosis of IEMs in the clinic by developing novel computational methods for analyzing untargeted metabolomics data. We employed CTD, an automated computational diagnostic method that "connects the dots" between metabolite perturbations observed in individual metabolomics profiling data and modules identified in disease-specific metabolite co-perturbation networks learned from prior profiling data. We also extended CTD to calculate distances between any two individuals (CTDncd) and between an individual and a disease state (CTDdm), to provide additional network-quantified predictors for use in diagnosis. We show that across 539 plasma samples, CTD-based network-quantified measures can reproduce accurate diagnosis of 16 different IEMs, including adenylosuccinase deficiency, argininemia, argininosuccinic aciduria, aromatic L-amino acid decarboxylase deficiency, cerebral creatine deficiency syndrome type 2, citrullinemia, cobalamin biosynthesis defect, GABA-transaminase deficiency, glutaric acidemia type 1, maple syrup urine disease, methylmalonic aciduria, ornithine transcarbamylase deficiency, phenylketonuria, propionic acidemia, rhizomelic chondrodysplasia punctata, and the Zellweger spectrum disorders. Our approach can be used to supplement information from biochemical pathways and has the potential to significantly enhance the interpretation of variants of uncertain significance uncovered by exome sequencing. CTD, CTDdm, and CTDncd can serve as an essential toolset for biological interpretation of untargeted metabolomics data that overcomes limitations associated with manual diagnosis to assist diagnosticians in clinical decision-making. By automating and quantifying the interpretation of perturbation patterns, CTD can improve the speed and confidence by which clinical laboratory directors make diagnostic and treatment decisions, while automatically improving performance with new case data.

Clinical Phenotypes of Cardiovascular and Heart Failure Diseases Can Be Reversed? The Holistic Principle of Systems Biology in Multifaceted Heart Diseases

Recent advances in cardiology and biological sciences have improved quality of life in patients with complex cardiovascular diseases (CVDs) or heart failure (HF). Regardless of medical progress, complex cardiac diseases continue to have a prolonged clinical course with high morbidity and mortality. Interventional coronary techniques together with drug therapy improve quality and future prospects of life, but do not reverse the course of the atherosclerotic process that remains relentlessly progressive. The probability of CVDs and HF phenotypes to reverse can be supported by the advances made on the medical holistic principle of systems biology (SB) and on artificial intelligence (AI). Studies on clinical phenotypes reversal should be based on the research performed in large populations of patients following gathering and analyzing large amounts of relative data that embrace the concept of complexity. To decipher the complexity conundrum, a multiomics approach is needed with network analysis of the biological data. Only by understanding the complexity of chronic heart diseases and explaining the interrelationship between different interconnected biological networks can the probability for clinical phenotypes reversal be increased.

A Rice Protein Interaction Network Reveals High Centrality Nodes and Candidate Pathogen Effector Targets

Network science identifies key players in diverse biological systems including host-pathogen interactions. We demonstrated a scale-free network property for a comprehensive rice protein–protein interactome (RicePPInets) that exhibits nodes with increased centrality indices. While weighted k-shell decomposition was shown efficacious to predict pathogen effector targets in Arabidopsis, we improved its computational code for a broader implementation on large-scale networks including RicePPInets. We determined that nodes residing within the internal layers of RicePPInets are poised to be the most influential, central, and effective information spreaders. To identify central players and modules through network topology analyses, we integrated RicePPInets and co-expression networks representing susceptible and resistant responses to strains of the bacterial pathogens Xanthomonas oryzae pv. oryzae and X. oryzae pv. oryzicola (Xoc) and generated a RIce-Xanthomonas INteractome (RIXIN). This revealed that previously identified candidate targets of pathogen transcription activator-like (TAL) effectors are enriched in nodes with enhanced connectivity, bottlenecks, and information spreaders that are located in the inner layers of the network, and these nodes are involved in several important biological processes. Overall, our integrative multi-omics network-based platform provides a potentially useful approach to prioritizing candidate pathogen effector targets for functional validation, suggesting that this computational framework can be broadly translatable to other complex pathosystems.

Network Theoretical Approach to Explore Factors Affecting Signal Propagation and Stability in Dementia’s Protein-Protein Interaction Network

Dementia—a syndrome affecting human cognition—is a major public health concern given to its rising prevalence worldwide. Though multiple research studies have analyzed disorders such as Alzheimer’s disease and Frontotemporal dementia using a systems biology approach, a similar approach to dementia syndrome as a whole is required. In this study, we try to find the high-impact core regulating processes and factors involved in dementia’s protein–protein interaction network. We also explore various aspects related to its stability and signal propagation. Using gene interaction databases such as STRING and GeneMANIA, a principal dementia network (PDN) consisting of 881 genes and 59,085 interactions was achieved. It was assortative in nature with hierarchical, scale-free topology enriched in various gene ontology (GO) categories and KEGG pathways, such as negative and positive regulation of apoptotic processes, macroautophagy, aging, response to drug, protein binding, etc. Using a clustering algorithm (Louvain method of modularity maximization) iteratively, we found a number of communities at different levels of hierarchy in PDN consisting of 95 “motif-localized hubs”, out of which, 7 were present at deepest level and hence were key regulators (KRs) of PDN (HSP90AA1, HSP90AB1, EGFR, FYN, JUN, CELF2 and CTNNA3). In order to explore aspects of network’s resilience, a knockout (of motif-localized hubs) experiment was carried out. It changed the network’s topology from a hierarchal scale-free topology to scale-free, where independent clusters exhibited greater control. Additionally, network experiments on interaction of druggable genome and motif-localized hubs were carried out where UBC, EGFR, APP, CTNNB1, NTRK1, FN1, HSP90AA1, MDM2, VCP, CTNNA1 and GRB2 were identified as hubs in the resultant network (RN). We finally concluded that stability and resilience of PDN highly relies on motif-localized hubs (especially those present at deeper levels), making them important therapeutic intervention candidates. HSP90AA1, involved in heat shock response (and its master regulator, i.e., HSF1), and EGFR are most important genes in pathology of dementia apart from KRs, given their presence as KRs as well as hubs in RN.

A Network Pharmacology Study to Uncover the Mechanism of FDY003 for Ovarian Cancer Treatment

Ovarian cancer (OC) is one of the deadliest gynecological tumors responsible for 0.21 million deaths per year worldwide. Despite the increasing interest in the use of herbal drugs for cancer treatment, their pharmacological effects in OC treatment are not understood from a systems perspective. Using network pharmacology, we determined the anti-OC potential of FDY003 from a comprehensive systems view. We observed that FDY003 suppressed the viability of human OC cells and further chemosensitized them to cytotoxic chemotherapy. Through network pharmacological and pharmacokinetic approaches, we identified 16 active ingredients in FDY003 and their 108 targets associated with OC mechanisms. Functional enrichment investigation revealed that the targets may coordinate diverse cellular behaviors of OC cells, including their growth, proliferation, survival, death, and cell cycle regulation. Furthermore, the FDY003 targets are important constituents of diverse signaling pathways implicated in OC mechanisms (eg, phosphoinositide 3-kinase [PI3K]-Akt, mitogen-activated protein kinase [MAPK], focal adhesion, hypoxia-inducible factor [HIF]-1, estrogen, tumor necrosis factor [TNF], erythroblastic leukemia viral oncogene homolog [ErbB], Janus kinase [JAK]-signal transducer and activator of transcription [STAT], and p53 signaling). In summary, our data present a comprehensive understanding of the anti-OC effects and mechanisms of action of FDY003.

Deep graph representations embed network information for robust disease marker identification

MOTIVATION

Accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state.

RESULTS

By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.

AVAILABILITY AND IMPLEMENTATION

https://github.com/omarmaddouri/GCNCC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Network Pharmacology Perspective Investigation of the Pharmacological Mechanisms of the Herbal Drug FDY003 in Gastric Cancer

Gastric cancer (GC) is one of the most common and deadly malignant tumors worldwide. While the application of herbal drugs for GC treatment is increasing, the multicompound–multitarget pharmacological mechanisms involved are yet to be elucidated. By adopting a network pharmacology strategy, we investigated the properties of the anticancer herbal drug FDY003 against GC. We found that FDY003 reduced the viability of human GC cells and enhanced their chemosensitivity. We also identified 8 active phytochemical compounds in FDY003 that target 70 GC-associated genes and proteins. Gene ontology (GO) enrichment analysis suggested that the targets of FDY003 are involved in various cellular processes, such as cellular proliferation, survival, and death. We further identified various major FDY003 target GC-associated pathways, including PIK3-Akt, MAPK, Ras, HIF-1, ErbB, and p53 pathways. Taken together, the overall analysis presents insight at the systems level into the pharmacological activity of FDY003 against GC.

A study of the therapeutic mechanism of Jakyakgamcho-Tang about functional dyspepsia through network pharmacology research

Herbal medicines have traditionally been used as an effective digestive medicine. However, compared to the effectiveness of Herbal medicines, the treatment mechanism has not been fully identified. To solve this problem, a system-level treatment mechanism of Jakyakgamcho-Tang (JGT), which is used for the treatment of functional dyspepsia (FD), was identified through a network pharmacology study. The two components, paeoniae radix alba and licorice constituting JGT were analyzed based on broad information on chemical and pharmacological properties, confirming 84 active chemical compounds and 84 FD-related targets. The JGT target confirmed the relationship with the regulation of various biological movements as follows: cellular behaviors of muscle and cytokine, calcium ion concentration and homeostasis, calcium- and cytokine-mediated signalings, drug, inflammatory response, neuronal cells, oxidative stress and response to chemical. And the target is enriched in variety FD-related signaling as follows: MAPK, Toll-like receptor, NOD-like receptor, PI3K-Akt, Apoptosis and TNF signaling pathway. These data give a new approach to identifying the molecular mechanisms underlying the digestive effect of JGT.

Network-Driven Drug Discovery

We describe an approach to early stage drug discovery that explicitly engages with the complexities of human biology. The combined computational and experimental approach is formulated on a conceptual framework in which network biology is used to bridge between individual molecular entities and the cellular phenotype that emerges when those entities interact in a network. Multiple aspects of early stage discovery are addressed including the data-driven elucidation of biological processes implicated in disease, target identification and validation, phenotypic discovery of active molecules and their mechanism of action, and extraction of genetic target support from human population genetics data. Validation is described via summary of a number of discovery projects and details from a project aimed at COVID-19 disease.

Smell Detection Agent Optimisation Framework and Systems Biology Approach to Detect Dys-Regulated Subnetwork in Cancer Data

Network biology has become a key tool in unravelling the mechanisms of complex diseases. Detecting dys-regulated subnetworks from molecular networks is a task that needs efficient computational methods. In this work, we constructed an integrated network using gene interaction data as well as protein–protein interaction data of differentially expressed genes derived from the microarray gene expression data. We considered the level of differential expression as well as the topological weight of proteins in interaction network to quantify dys-regulation. Then, a nature-inspired Smell Detection Agent (SDA) optimisation algorithm is designed with multiple agents traversing through various paths in the network. Finally, the algorithm provides a maximum weighted module as the optimum dys-regulated subnetwork. The analysis is performed for samples of triple-negative breast cancer as well as colorectal cancer. Biological significance analysis of module genes is also done to validate the results. The breast cancer subnetwork is found to contain (i) valid biomarkers including PIK3CA, PTEN, BRCA1, AR and EGFR; (ii) validated drug targets TOP2A, CDK4, HDAC1, IL6, BRCA1, HSP90AA1 and AR; (iii) synergistic drug targets EGFR and BIRC5. Moreover, based on the weight values assigned to nodes in the subnetwork, PLK1, CTNNB1, IGF1, AURKA, PCNA, HSPA4 and GAPDH are proposed as drug targets for further studies. For colorectal cancer module, the analysis revealed the occurrence of approved drug targets TYMS, TOP1, BRAF and EGFR. Considering the higher weight values, HSP90AA1, CCNB1, AKT1 and CXCL8 are proposed as drug targets for experimentation. The derived subnetworks possess cancer-related pathways as well. The SDA-derived breast cancer subnetwork is compared with that of tools such as MCODE and Minimum Spanning Tree, and observed a higher enrichment (75%) of significant elements. Thus, the proposed nature-inspired algorithm is a novel approach to derive the optimum dys-regulated subnetwork from huge molecular network.

Network biology bridges the gaps between quantitative genetics and multi-omics to map complex diseases

With advances in high-throughput sequencing technologies, quantitative genetics approaches have provided insights into genetic basis of many complex diseases. Emerging in-depth multi-omics profiling technologies have created exciting opportunities for systematically investigating intricate interaction networks with different layers of biological molecules underlying disease etiology. Herein, we summarized two main categories of biological networks: evidence-based and statistically inferred. These different types of molecular networks complement each other at both bulk and single-cell levels. We also review three main strategies to incorporate quantitative genetics results with multi-omics data by network analysis: (a) network propagation, (b) functional module-based methods, (c) comparative/dynamic networks. These strategies not only aid in elucidating molecular mechanisms of complex diseases but can guide the search for therapeutic targets.

Heterogeneity in the Response of Different Subtypes of Drosophila melanogaster Midgut Cells to Viral Infections

Single-cell RNA sequencing (scRNA-seq) offers the possibility to monitor both host and pathogens transcriptomes at the cellular level. Here, public scRNA-seq datasets from Drosophila melanogaster midgut cells were used to compare the differences in replication strategy and cellular responses between two fly picorna-like viruses, Thika virus (TV) and D. melanogaster Nora virus (DMelNV). TV exhibited lower levels of viral RNA accumulation but infected a higher number of cells compared to DMelNV. In both cases, viral RNA accumulation varied according to cell subtype. The cellular heat shock response to TV and DMelNV infection was cell-subtype- and virus-specific. Disruption of bottleneck genes at later stages of infection in the systemic response, as well as of translation-related genes in the cellular response to DMelNV in two cell subtypes, may affect the virus replication.

Network Pharmacological Dissection of the Mechanisms of Eucommiae Cortex-Achyranthis Radix Combination for Intervertebral Disc Herniation Treatment

Eucommiae cortex (EC) and Achyranthis radix (AR) are herbal medicines widely used in combination for the treatment of intervertebral disc herniation (IDH). The mechanisms of action of the herbal combination have not been understood from integrative and comprehensive points of view. By adopting network pharmacological methodology, we aimed to investigate the pharmacological properties of the EC-AR combination as a therapeutic agent for IDH at a systematic molecular level. Using the pharmacokinetic information for the chemical ingredients of the EC-AR combination obtained from the comprehensive herbal drug-associated databases, we determined its 31 bioactive ingredients and 68 IDH-related therapeutic targets. By analyzing their enrichment for biological functions, we observed that the targets of the EC-AR combination were associated with the regulation of angiogenesis; cytokine and chemokine activity; oxidative and inflammatory stress responses; extracellular matrix organization; immune response; and cellular processes such as proliferation, apoptosis, autophagy, differentiation, migration, and activation. Pathway enrichment investigation revealed that the EC-AR combination may target IDH-pathology-associated signaling pathways, such as those of cellular senescence and chemokine, neurotrophin, TNF, MAPK, toll-like receptor, and VEGF signaling, to exhibit its therapeutic effects. Collectively, these data provide mechanistic insights into the pharmacological activity of herbal medicines for the treatment of musculoskeletal diseases such as IDH.

A Network Pharmacology Analysis of the Systems-Perspective Anticancer Mechanisms of the Herbal Drug FDY2004 for Breast Cancer

Breast cancer is a malignant tumor with high incidence, prevalence, and mortality rates in women. In recent years, herbal drugs have been assessed as anticancer therapy against breast cancer, owing to their promising therapeutic effects and reduced toxicity. However, their pharmacological mechanisms have not been fully explored at the systemic level. Here, we conducted a network pharmacology analysis of the systems-perspective molecular mechanisms of FDY2004, an anticancer herbal formula that consists of Moutan Radicis Cortex, Persicae Semen , and Rhei Radix et Rhizoma, against breast cancer. We determined that FDY2004 may contain 28 active compounds that exert pharmacological effects by targeting 113 breast cancer-related human genes/proteins. Based on the gene ontology terms, the FDY2004 targets were involved in modulating biological processes such as cell growth, cell proliferation, and apoptosis. Pathway enrichment analysis identified various breast cancer-associated pathways that may mediate the anticancer activity of FDY2004, including the PI3K-Akt, MAPK, TNF, HIF-1, focal adhesion, estrogen, ErbB, NF-kappa B, p53, and VEGF signaling pathways. Thus, our analysis offers novel insights into the anticancer properties of herbal drugs for breast cancer treatment from a systemic perspective.

Network Pharmacology-Based Dissection of the Comprehensive Molecular Mechanisms of the Herbal Prescription FDY003 Against Estrogen Receptor-Positive Breast Cancer

Estrogen receptor-positive breast cancer (ERPBC) is the commonest subtype of breast cancer, with a high prevalence, incidence, and mortality. Herbal drugs are increasingly being used to treat ERPBC, although their mechanisms of action are not fully understood. Therefore, in this study, we aimed to analyze the therapeutic properties of FDY003, a herbal anti-ERPBC prescription, using a network pharmacology approach. FDY003 decreased the viability of human ERPBC cells and sensitized them to tamoxifen, an endocrine drug that is widely used in the treatment of ERPBC. The network pharmacology analysis revealed 18 pharmacologically active components in FDY003 that may interact with and regulate 66 therapeutic targets. The enriched gene ontology terms for the FDY003 targets were associated with the modulation of cell survival and death, cell proliferation and growth arrest, and estrogen-associated cellular processes. Analysis of the pathway enrichment of the targets showed that FDY003 may target a variety of ERPBC-associated pathways, including the PIK3-Akt, focal adhesion, MAPK, and estrogen pathways. Overall, these data provide a comprehensive mechanistic insight into the anti-ERPBC activity of FDY003.

Variations of root-associated bacterial cooccurrence relationships in paddy soils under chlorantraniliprole (CAP) stress

Root-associated microbiomes are beneficial for plant development and health. However, the assembly of root-associated bacterial communities and their feedback under chlorantraniliprole (CAP) stress are unclear. This study investigated the response of root-associated bacterial microbiota to CAP dosage during the two developmental phases of rice. The results showed that CAP application had little effect on the bacterial diversity of bulk and rhizosphere soils, whereas that of the endosphere samples demonstrated a large variability. Moreover, the CAP stress exhibited less influence than the plant compartment and developmental stage contributing to microbiome variation. The core bacterial co-occurrence relationships also changed with the CAP application, especially, in the endosphere of the roots. These results further elucidate the impacts of CAP application on root-associated bacterial communities in intensive agricultural ecosystems and provide new insights for CAP ecological risk assessments.

Optimized Molecular Interaction Networks for the Study of Skeletal Muscle

BACKGROUND

Molecular interaction networks (MINs) aim to capture the complex relationships between interacting molecules within a biological system. MINs can be constructed from existing knowledge of molecular functional associations, such as protein-protein binding interactions (PPI) or gene co-expression, and these different sources may be combined into a single MIN. A given MIN may be more or less optimal in its representation of the important functional relationships of molecules in a tissue.

OBJECTIVE

The aim of this study was to establish whether a combined MIN derived from different types of functional association could better capture muscle-relevant biology compared to its constituent single-source MINs.

METHODS

MINs were constructed from functional association databases for both protein-binding and gene co-expression. The networks were then compared based on the capture of muscle-relevant genes and gene ontology (GO) terms, tested in two different ways using established biological network clustering algorithms. The top performing MINs were combined to test whether an optimal MIN for skeletal muscle could be constructed.

RESULTS

The STRING PPI network was the best performing single-source MIN among those tested. Combining STRING with interactions from either the MyoMiner or CoXPRESSdb gene co-expression sources resulted in a combined network with improved performance relative to its constituent networks.

CONCLUSION

MINs constructed from multiple types of functional association can better represent the functional relationships of molecules in a given tissue. Such networks may be used to improve the analysis and interpretation of functional genomics data in the study of skeletal muscle and neuromuscular diseases. Networks and clusters described by this study, including the combinations of STRING with MyoMiner or with CoXPRESSdb, are available for download from https://www.sys-myo.com/myominer/download.php.

Graphery: interactive tutorials for biological network algorithms

Networks have been an excellent framework for modeling complex biological information, but the methodological details of network-based tools are often described for a technical audience. We have developed Graphery, an interactive tutorial webserver that illustrates foundational graph concepts frequently used in network-based methods. Each tutorial describes a graph concept along with executable Python code that can be interactively run on a graph. Users navigate each tutorial using their choice of real-world biological networks that highlight the diverse applications of network algorithms. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Graphery is available at https://graphery.reedcompbio.org/.

Protein residues determining interaction specificity in paralogous families

MOTIVATION

Predicting the residues controlling a protein's interaction specificity is important not only to better understand its interactions but also to design mutations aimed at fine-tuning or swapping them as well.

RESULTS

In this work, we present a methodology that combines sequence information (in the form of multiple sequence alignments) with interactome information to detect that kind of residues in paralogous families of proteins. The interactome is used to define pairwise similarities of interaction contexts for the proteins in the alignment. The method looks for alignment positions with patterns of amino-acid changes reflecting the similarities/differences in the interaction neighborhoods of the corresponding proteins. We tested this new methodology in a large set of human paralogous families with structurally characterized interactions, and discuss in detail the results for the RasH family. We show that this approach is a better predictor of interfacial residues than both, sequence conservation and an equivalent 'unsupervised' method that does not use interactome information.

AVAILABILITY AND IMPLEMENTATION

http://csbg.cnb.csic.es/pazos/Xdet/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Systems biology approach highlights mechanistic differences between Crohn's disease and ulcerative colitis

The molecular mechanisms of IBD have been the subject of intensive exploration. We, therefore, assembled the available information into a suite of causal biological network models, which offer comprehensive visualization of the processes underlying IBD. Scientific text was curated by using Biological Expression Language (BEL) and compiled with OpenBEL 3.0.0. Network properties were analysed by Cytoscape. Network perturbation amplitudes were computed to score the network models with transcriptomic data from public data repositories. The IBD network model suite consists of three independent models that represent signalling pathways that contribute to IBD. In the “intestinal permeability” model, programmed cell death factors were downregulated in CD and upregulated in UC. In the “inflammation” model, PPARG, IL6, and IFN-associated pathways were prominent regulatory factors in both diseases. In the “wound healing” model, factors promoting wound healing were upregulated in CD and downregulated in UC. Scoring of publicly available transcriptomic datasets onto these network models demonstrated that the IBD models capture the perturbation in each dataset accurately. The IBD network model suite can provide better mechanistic insights of the transcriptional changes in IBD and constitutes a valuable tool in personalized medicine to further understand individual drug responses in IBD.

Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease

The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.

A Comprehensive Understanding of the Anticancer Mechanisms of FDY2004 Against Cervical Cancer Based on Network Pharmacology

Herbal drugs are continuously being developed and used as effective therapeutics for various cancers, such as cervical cancer (CC); however, their mechanisms of action at a systemic level have not been explored fully. To study such mechanisms, we conducted a network pharmacological investigation of the anti-CC mechanisms of FDY2004, an herbal drug consisting of Moutan Radicis Cortex, Persicae Semen , and Rhei Radix et Rhizoma. We found that FDY2004 inhibited the viability of human CC cells. By performing pharmacokinetic evaluation and network analysis of the phytochemical components of FDY2004, we identified 29 bioactive components and their 116 CC-associated pharmacological targets. Gene ontology enrichment analysis showed that the modulation of cellular functions, such as apoptosis, growth, proliferation, and survival, might be mediated through the FDY2004 targets. The therapeutic targets were also key components of CC-associated oncogenic and tumor-suppressive pathways, including PI3K-Akt, human papillomavirus infection, IL-17, MAPK, TNF, focal adhesion, and viral carcinogenesis pathways. In conclusion, our data present a comprehensive insight for the mechanisms of the anti-CC properties of FDY2004.