Disease networks. Uncovering disease-disease relationships through the incomplete interactome

Heterogeneous network approaches to protein pathway prediction

Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.

Network-based artificial intelligence approaches for advancing personalized psychiatry

Psychiatric disorders have a complex biological underpinning likely involving an interplay of genetic and environmental risk contributions. Substantial efforts are being made to use artificial intelligence approaches to integrate features within and across data types to increase our etiological understanding and advance personalized psychiatry. Network science offers a conceptual framework for exploring the often complex relationships across different levels of biological organization, from cellular mechanistic to brain-functional and phenotypic networks. Utilizing such network information effectively as part of artificial intelligence approaches is a promising route toward a more in-depth understanding of illness biology, the deciphering of patient heterogeneity, and the identification of signatures that may be sufficiently predictive to be clinically useful. Here, we present examples of how network information has been used as part of artificial intelligence within psychiatry and beyond and outline future perspectives on how personalized psychiatry approaches may profit from a closer integration of psychiatric research, artificial intelligence development, and network science.

A weighted prior tensor train decomposition method for community detection in multi-layer networks

Community detection in multi-layer networks stands as a prominent subject within network analysis research. However, the majority of existing techniques for identifying communities encounter two primary constraints: they lack suitability for high-dimensional data within multi-layer networks and fail to fully leverage additional auxiliary information among communities to enhance detection accuracy. To address these limitations, a novel approach named weighted prior tensor training decomposition (WPTTD) is proposed for multi-layer network community detection. Specifically, the WPTTD method harnesses the tensor feature optimization techniques to effectively manage high-dimensional data in multi-layer networks. Additionally, it employs a weighted flattened network to construct prior information for each dimension of the multi-layer network, thereby continuously exploring inter-community connections. To preserve the cohesive structure of communities and to harness comprehensive information within the multi-layer network for more effective community detection, the common community manifold learning (CCML) is integrated into the WPTTD framework for enhancing the performance. Experimental evaluations conducted on both artificial and real-world networks have verified that this algorithm outperforms several mainstream multi-layer network community detection algorithms.

Intrinsic structural disorder on proteins is involved in the interactome evolution

New mathematical tools help understand cell functions, adaptability, and evolvability to discover hidden variables to predict phenotypes that could be tested in the future in wet labs. Different models have been successfully used to discover the properties of the protein-protein interaction networks or interactomes. I found that in the hyperbolic Popularity-Similarity model, cellular proteins with the highest contents of structural intrinsic disorder cluster together in many different eukaryotic interactomes and this is not the case for the prokaryotic E. coli, where proteins with high degree of intrinsic disorder are scarce. I also found that the normalized theta variable from the Popularity-Similarity model for orthologues proteins correlate to the complexity of the organisms in analysis.

Ras, RhoA, and vascular pharmacology in neurodevelopment and aging

Small GTPases Ras, Rac, and RhoA are crucial regulators of cellular functions. They also act in dysregulated cell proliferation and transformation. Multiple publications have focused on illuminating their roles and mechanisms, including in immune system pathologies. Their functions in neurology-related diseases, neurodegeneration and neurodevelopment, are also emerging, as well as their potential as pharmacological targets in both pathologies. Observations increasingly suggest that these pathologies may relate to activation (or suppression) of signaling by members of the Ras superfamily, especially Ras, Rho, and Rac isoforms, and components of their signaling pathways. Germline (or embryonic) mutations that they harbor are responsible for neurodevelopmental disorders, such as RASopathies, autism spectrum disorder, and dilated cardiomyopathy. In aging, they promote neurodegenerative diseases, with Rho GTPase featuring in their pharmacology, as in the case of Alzheimer's disease (AD). Significantly, drugs with observed anti-AD activity, particularly those involved in cardiovascular systems, are associated with the RhoA signaling, as well as cerebral vasculature in brain development and aging. This leads us to suggest that anti-AD drugs could inform neurodevelopmental disorders, including pediatric low-grade gliomas pharmacology. Neurodevelopmental disorders associated with RhoA, like autism, are also connected with vascular systems, thus could be targets of vascular system-connected drugs.

Leveraging pleiotropy for the improved treatment of psychiatric disorders

Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and one billion US dollars to develop a single successful drug. Drug development is more challenging for psychiatric disorders, where disease comorbidity and complex symptom profiles obscure the identification of causal mechanisms for therapeutic intervention. One promising approach for determining more suitable drug candidates in clinical trials is integrating human genetic data into the selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. In this article, we argue that leveraging pleiotropic effects will provide opportunities to discover novel drug targets and identify more effective treatments for psychiatric disorders by targeting a common mechanism rather than treating each disease separately.

Drug-target prediction through self supervised learning with dual task ensemble approach

Drug-Target interaction (DTI) prediction, a transformative approach in pharmaceutical research, seeks novel therapeutic applications for computational method based virtual screening, existing drugs to address untreated diseases and discovery of existing drugs side effects. The proposed model predict DTI through Heterogeneous biological network by combining drug, genes and disease related knowledge. For the purpose of embedding extraction Self-supervised learning (SSL) has been used which, trains models through pretext tasks, eliminating the need for manual annotations. The pretext tasks are related to either structural based information or similarity based information. To mitigate GNN vulnerability to non-robustness, ensemble learning can be incorporated into GNNs, harnessing multiple models to enhance robustness. This paper introduces a Graph neural network based architecture consisting of task based module and ensemble module for link prediction of DTI. The ensemble module of dual task combinations, both in cold start and warm start scenarios achieve very good performance as it provide 0.960 in cold start and 0.970 in warm start mean AUCROC score with less deviation.

Exploring novel natural compound-based therapies for Duchenne muscular dystrophy management: insights from network pharmacology, QSAR modeling, molecular dynamics, and free energy calculations

Muscular dystrophies encompass a heterogeneous group of rare neuromuscular diseases characterized by progressive muscle degeneration and weakness. Among these, Duchenne muscular dystrophy (DMD) stands out as one of the most severe forms. The present study employs an integrative approach combining network pharmacology, quantitative structure-activity relationship (QSAR) modeling, molecular dynamics (MD) simulations, and free energy calculations to identify potential therapeutic targets and natural compounds for DMD. Upon analyzing the GSE38417 dataset, it was found that individuals with DMD exhibited 290 upregulated differentially expressed genes (DEGs) compared to healthy controls. By utilizing gene ontology (GO) and protein-protein interaction (PPI) network analysis, this study provides insights into the functional roles of the identified DEGs, identifying ten hub genes that play a critical role in the pathology of DMD. These key genes include DMD, TTN, PLEC, DTNA, PKP2, SLC24A, FBXO32, SNTA1, SMAD3, and NOS1. Furthermore, through the use of ligand-based pharmacophore modeling and virtual screening, three natural compounds were identified as potential inhibitors. Among these, compounds 3874518 and 12314417 have demonstrated significant promise as an inhibitor of the SMAD3 protein, a crucial factor in the fibrotic and inflammatory mechanisms associated with DMD. The therapeutic potential of the compounds was further supported by molecular dynamics simulation and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analysis. These findings suggest that the compounds are viable candidates for experimental validation against DMD.

Drug repurposing to tackle parainfluenza 3 based on multi-similarities and network proximity analysis

Given that there is currently no clinically approved drug or vaccine for parainfluenza 3 (PIV3), we applied a drug repurposing method based on disease similarity and chemical similarity to screen 2,585 clinically approved chemical drugs using PIV3 potential drugs BCX-2798 and zanamivir as our controls. Twelve candidate drugs were obtained after being screened with good disease similarity and chemical similarity (S > 0.50, T > 0.56). When docking them with the PIV3 target protein, hemagglutinin-neuraminidase (HN), only oseltamivir was docked with a better score than BCX-2798, which indicates that oseltamivir has an inhibitory effect on PIV3. After the distance (Z d c ) between the drug target of 14 drugs and the PIV3 disease target was measured by the network proximity method based on the PIV3 disease module, it was found that theZ d c values of amikacin, oseltamivir, ribavirin, and streptomycin were less than those of the control. Thus, oseltamivir is the best potential drug because it met all the above screening requirements. Additionally, to explore whether oseltamivir binds to HN stably, molecular dynamics simulation of the binding of oseltamivir to HN was carried out, and the results showed that the RMSD value of the complex tended to be stable within 100 ns, and the binding free energy of the complex was low (-10.60 kcal/mol). It was proved that oseltamivir screened by our drug repurposing method had the potential feasibility of treating PIV3.

A Network-Based Framework to Discover Treatment-Response-Predicting Biomarkers for Complex Diseases

The potential of precision medicine to transform complex autoimmune disease treatment is often challenged by limited data availability and inadequate sample size when compared with the number of molecular features found in high-throughput multi-omics data sets. To address this issue, the novel framework PRoBeNet (Predictive Response Biomarkers using Network medicine) was developed. PRoBeNet operates under the hypothesis that the therapeutic effect of a drug propagates through a protein-protein interaction network to reverse disease states. PRoBeNet prioritizes biomarkers by considering i) therapy-targeted proteins, ii) disease-specific molecular signatures, and iii) an underlying network of interactions among cellular components (the human interactome). PRoBeNet helped discover biomarkers predicting patient responses to both an established autoimmune therapy (infliximab) and an investigational compound (a mitogen-activated protein kinase 3/1 inhibitor). The predictive power of PRoBeNet biomarkers was validated with retrospective gene-expression data from patients with ulcerative colitis and rheumatoid arthritis and prospective data from tissues from patients with ulcerative colitis and Crohn disease. Machine-learning models using PRoBeNet biomarkers significantly outperformed models using either all genes or randomly selected genes, especially when data were limited. These results illustrate the value of PRoBeNet in reducing features and for constructing robust machine-learning models when data are limited. PRoBeNet may be used to develop companion and complementary diagnostic assays, which may help stratify suitable patient subgroups in clinical trials and improve patient outcomes.

Genetics and precision genomics approaches to pulmonary hypertension

Considerable progress has been made in the genomics of pulmonary arterial hypertension (PAH) since the 6th World Symposium on Pulmonary Hypertension, with the identification of rare variants in several novel genes, as well as common variants that confer a modest increase in PAH risk. Gene and variant curation by an expert panel now provides a robust framework for knowing which genes to test and how to interpret variants in clinical practice. We recommend that genetic testing be offered to specific subgroups of symptomatic patients with PAH, and to children with certain types of group 3 pulmonary hypertension (PH). Testing of asymptomatic family members and the use of genetics in reproductive decision-making require the involvement of genetics experts. Large cohorts of PAH patients with biospecimens now exist and extension to non-group 1 PH has begun. However, these cohorts are largely of European origin; greater diversity will be essential to characterise the full extent of genomic variation contributing to PH risk and treatment responses. Other types of omics data are also being incorporated. Furthermore, to advance gene- and pathway-specific care and targeted therapies, gene-specific registries will be essential to support patients and their families and to lay the foundation for genetically informed clinical trials. This will require international outreach and collaboration between patients/families, clinicians and researchers. Ultimately, harmonisation of patient-derived biospecimens, clinical and omic information, and analytic approaches will advance the field.

A foundation model for clinician-centered drug repurposing

Drug repurposing-identifying new therapeutic uses for approved drugs-is often a serendipitous and opportunistic endeavour to expand the use of drugs for new diseases. The clinical utility of drug-repurposing artificial intelligence (AI) models remains limited because these models focus narrowly on diseases for which some drugs already exist. Here we introduce TxGNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs. Trained on a medical knowledge graph, TxGNN uses a graph neural network and metric learning module to rank drugs as potential indications and contraindications for 17,080 diseases. When benchmarked against 8 methods, TxGNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, TxGNN's Explainer module offers transparent insights into multi-hop medical knowledge paths that form TxGNN's predictive rationales. Human evaluation of TxGNN's Explainer showed that TxGNN's predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of TxGNN's new predictions align well with off-label prescriptions that clinicians previously made in a large healthcare system. TxGNN's drug-repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.

Endotype-driven Co-module mechanisms of danhong injection in the Co-treatment of cardiovascular and cerebrovascular diseases: A modular-based drug and disease integrated analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Cardiovascular and cerebrovascular diseases are the leading causes of death worldwide and interact closely with each other. Danhong Injection (DHI) is a widely used preparation for the co-treatment of brain and heart diseases (CTBH). However, the underlying molecular endotype mechanisms of DHI in the CTBH remain unclear.

AIM OF THIS STUDY

To elucidate the underlying endotype mechanisms of DHI in the CTBH.

MATERIALS AND METHODS

In this study, we proposed a modular-based disease and drug-integrated analysis (MDDIA) strategy for elucidating the systematic CTBH mechanisms of DHI using high-throughput transcriptome-wide sequencing datasets of DHI in the treatment of patients with stable angina pectoris (SAP) and cerebral infarction (CI). First, we identified drug-targeted modules of DHI and disease modules of SAP and CI based on the gene co-expression networks of DHI therapy and the protein-protein interaction networks of diseases. Moreover, module proximity-based topological analyses were applied to screen CTBH co-module pairs and driver genes of DHI. At the same time, the representative driver genes were validated via in vitro experiments on hypoxia/reoxygenation-related cardiomyocytes and neuronal cell lines of H9C2 and HT22.

RESULTS

Seven drug-targeted modules of DHI and three disease modules of SAP and CI were identified by co-expression networks. Five modes of modular relationships between the drug and disease modules were distinguished by module proximity-based topological analyses. Moreover, 13 targeted module pairs and 17 driver genes associated with DHI in the CTBH were also screened. Finally, the representative driver genes AKT1, EDN1, and RHO were validated by in vitro experiments.

CONCLUSIONS

This study, based on clinical sequencing data and modular topological analyses, integrated diseases and drug targets. The CTBH mechanism of DHI may involve the altered expression of certain driver genes (SRC, STAT3, EDN1, CYP1A1, RHO, RELA) through various enriched pathways, including the Wnt signaling pathway.

Drug repurposing for Parkinson's disease by biological pathway based edge-weighted network proximity analysis

Parkinson's disease is the second most frequent neurodegenerative disease, and its severity is increasing with extended life expectancy. Most of current treatments provide symptomatic relief; however, disease progression is not inhibited. There are multiple trials for treatments that target the causes of the disease but they were flawed. The mechanisms underlying neurodegenerative diseases are intricate, and understanding the interplay among the biological elements involved is crucial. These relationships can be effectively analyzed through biological networks, and the application of network-based analyses in the context of neurodegenerative disease treatment has gained considerable attention. Moreover, considering the significance differences in interactions between biological elements within the network is important. Therefore, we introduce a novel biological pathway based edge-weighted network construction method for drug repurposing in Parkinson's disease. The interaction found in multiple Parkinson's disease-related pathways is more significant than other interactions, and this significance is reflected in the network edge weights. Using the edge-weighted network construction method, we found a significant difference in the efficacy between known and unknown Parkinson's disease drugs. The method predicts drug-disease interactions more accurately than approaches that do not consider the significance differences among interactions, and the paths between the drug and disease within the network correspond to the drug's mechanism of action. In summary, we propose a network-based drug repurposing method using the biological pathway based edge-weighted network. Using this methodology, researchers can find novel drug candidates for the parkinson's disease and their mechanism of actions.

Network medicine and systems pharmacology approaches to predicting adverse drug effects

Identifying and understanding the relationships between drug intake and adverse effects that can occur due to inadvertent molecular interactions between drugs and targets is a difficult task, especially considering the numerous variables that can influence the onset of such events. The ability to predict these side effects in advance would help physicians develop strategies to avoid or counteract them. In this article, we review the main computational methods for predicting side effects caused by drug molecules, highlighting their performance, limitations and application cases. Furthermore, we provide an overall view of resources, such as databases and tools, useful for building side effect prediction analyses.

The Future Exploring of Gut Microbiome-Immunity Interactions: From In Vivo/Vitro Models to In Silico Innovations

Investigating the complex interactions between microbiota and immunity is crucial for a fruitful understanding progress of human health and disease. This review assesses animal models, next-generation in vitro models, and in silico approaches that are used to decipher the microbiome-immunity axis, evaluating their strengths and limitations. While animal models provide a comprehensive biological context, they also raise ethical and practical concerns. Conversely, modern in vitro models reduce animal involvement but require specific costs and materials. When considering the environmental impact of these models, in silico approaches emerge as promising for resource reduction, but they require robust experimental validation and ongoing refinement. Their potential is significant, paving the way for a more sustainable and ethical future in microbiome-immunity research.

DNA copy number profiles and systems biology connect chromatin remodeling and DNA repair in high-risk neuroblastoma

Neuroblastoma (NB) is a solid tumor that accounts for 15% of all pediatric oncological deaths, and much is due to the low response to therapy in relapsed tumors. High-risk NB may present deletions in chromosome 11q, which may be associated with other chromosomal alterations and a poor response to therapy, but this association is still poorly understood. Using a systems biology network approach, we studied three patients with high-risk NB with deleted 11q stage 4 to highlight the connections between treatment resistance and copy number alterations in distinct cases. We built different protein-protein interaction networks for each patient based on protein-coding genes mapped at the cytobands pre- and post-chemotherapy from distinct copy number alterations data. In the post-chemotherapy networks, we identified five common regulatory nodes corresponding to the gained region located in ch17q:BIRC5, BRCA1, PRKCA, SUMO2, andGPS1. A crosslink between DNA damage and chromatin remodeling proteins was also found - a connection still poorly understood in NB. We identified a potential connection between XPB gain and chemoresistance of NB. The findings help elucidate the molecular profiles of high-risk NB with 11q deletion in pre- and post-chemotherapy tumor samples, which may reflect unique profiles in poor response to treatment.

Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories

The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.

Network medicine analysis for dissecting the therapeutic mechanism of consensus TCM formulae in treating hepatocellular carcinoma with different TCM syndromes

Introduction

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality worldwide. Traditional Chinese Medicine (TCM) is widely utilized as an adjunct therapy, improving patient survival and quality of life. TCM categorizes HCC into five distinct syndromes, each treated with specific herbal formulae. However, the molecular mechanisms underlying these treatments remain unclear.

Methods

We employed a network medicine approach to explore the therapeutic mechanisms of TCM in HCC. By constructing a protein-protein interaction (PPI) network, we integrated genes associated with TCM syndromes and their corresponding herbal formulae. This allowed for a quantitative analysis of the topological and functional relationships between TCM syndromes, HCC, and the specific formulae used for treatment.

Results

Our findings revealed that genes related to the five TCM syndromes were closely associated with HCC-related genes within the PPI network. The gene sets corresponding to the five TCM formulae exhibited significant proximity to HCC and its related syndromes, suggesting the efficacy of TCM syndrome differentiation and treatment. Additionally, through a random walk algorithm applied to a heterogeneous network, we prioritized active herbal ingredients, with results confirmed by literature.

Discussion

The identification of these key compounds underscores the potential of network medicine to unravel the complex pharmacological actions of TCM. This study provides a molecular basis for TCM's therapeutic strategies in HCC and highlights specific herbal ingredients as potential leads for drug development and precision medicine.

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

Not applicable.

A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological networks

Network controllability is unifying the traditional control theory with the structural network information rooted in many large-scale biological systems of interest, from intracellular networks in molecular biology to brain neuronal networks. In controllability approaches, the set of minimum driver nodes is not unique, and critical nodes are the most important control elements because they appear in all possible solution sets. On the other hand, a common but largely unexplored feature in network control approaches is the probabilistic failure of edges or the uncertainty in the determination of interactions between molecules. This is particularly true when directed probabilistic interactions are considered. Until now, no efficient algorithm existed to determine critical nodes in probabilistic directed networks. Here we present a probabilistic control model based on a minimum dominating set framework that integrates the probabilistic nature of directed edges between molecules and determines the critical control nodes that drive the entire network functionality. The proposed algorithm, combined with the developed mathematical tools, offers practical efficiency in determining critical control nodes in large probabilistic networks. The method is then applied to the human intracellular signal transduction network revealing that critical control nodes are associated with important biological features and perturbed sets of genes in human diseases, including SARS-CoV-2 target proteins and rare disorders. We believe that the proposed methodology can be useful to investigate multiple biological systems in which directed edges are probabilistic in nature, both in natural systems or when determined with large uncertainties in-silico.

A foundation model for clinician-centered drug repurposing

Drug repurposing - identifying new therapeutic uses for approved drugs - is often serendipitous and opportunistic, expanding the use of drugs for new diseases. The clinical utility of drug repurposing AI models remains limited because the models focus narrowly on diseases for which some drugs already exist. Here, we introduce TXGNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs. Trained on a medical knowledge graph, TXGNN utilizes a graph neural network and metric-learning module to rank drugs as potential indications and contraindications across 17,080 diseases. When benchmarked against eight methods, TXGNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, TXGNN's Explainer module offers transparent insights into multi-hop medical knowledge paths that form TXGNN's predictive rationales. Human evaluation of TXGNN's Explainer showed that TXGNN's predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of TxGNN's novel predictions align with off-label prescriptions clinicians make in a large healthcare system. TXGNN's drug repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.

Disease gene prioritization with quantum walks

MOTIVATION

Disease gene prioritization methods assign scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. This scoring can be used to find new biologically relevant genes or proteins for many diseases. Although methods based on classical random walks have proven to yield competitive results, quantum walk methods have not been explored to this end.

RESULTS

We propose a new algorithm for disease gene prioritization based on continuous-time quantum walks using the adjacency matrix of a protein-protein interaction (PPI) network. We demonstrate the success of our proposed quantum walk method by comparing it to several well-known gene prioritization methods on three disease sets, across seven different PPI networks. In order to compare these methods, we use cross-validation and examine the mean reciprocal ranks of recall and average precision values. We further validate our method by performing an enrichment analysis of the predicted genes for coronary artery disease.

AVAILABILITY AND IMPLEMENTATION

The data and code for the methods can be accessed at https://github.com/markgolds/qdgp.

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multiorgan single-cell atlas, PINNACLE learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PINNACLE's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. PINNACLE outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases and pinpoints cell type contexts with higher predictive capability than context-free models. PINNACLE's ability to adjust its outputs on the basis of the context in which it operates paves the way for large-scale context-specific predictions in biology.

Integrative Multi-omics Analysis to Characterize Herpes Virus Infection Increases the Risk of Alzheimer's Disease

Evidence suggests that herpes virus infection is associated with an increased risk of Alzheimer's disease (AD), and innate and adaptive immunity plays an important role in the association. Although there have been many studies, the mechanism of the association is still unclear. This study aims to reveal the underlying molecular and immune regulatory network through multi-omics data and provide support for the study of the mechanism of infection and AD in the future. Here, we found that the herpes virus infection significantly increased the risk of AD. Genes associated with the occurrence and development of AD and genetically regulated by herpes virus infection are mainly enrichment in immune-related pathways. The 22 key regulatory genes identified by machine learning are mainly immune genes. They are also significantly related to the infiltration changes of 3 immune cell in AD. Furthermore, many of these genes have previously been reported to be linked, or potentially linked, to the pathological mechanisms of both herpes virus infection and AD. In conclusion, this study contributes to the study of the mechanisms related to herpes virus infection and AD, and indicates that the regulation of innate and adaptive immunity may be an effective strategy for preventing and treating herpes virus infection and AD. Additionally, the identified key regulatory genes, whether previously studied or newly discovered, may serve as valuable targets for prevention and treatment strategies.

Decoding the Foodome: Molecular Networks Connecting Diet and Health

Diet, a modifiable risk factor, plays a pivotal role in most diseases, from cardiovascular disease to type 2 diabetes mellitus, cancer, and obesity. However, our understanding of the mechanistic role of the chemical compounds found in food remains incomplete. In this review, we explore the "dark matter" of nutrition, going beyond the macro- and micronutrients documented by national databases to unveil the exceptional chemical diversity of food composition. We also discuss the need to explore the impact of each compound in the presence of associated chemicals and relevant food sources and describe the tools that will allow us to do so. Finally, we discuss the role of network medicine in understanding the mechanism of action of each food molecule. Overall, we illustrate the important role of network science and artificial intelligence in our ability to reveal nutrition's multifaceted role in health and disease.

Alzheimer's Disease: Combination Therapies and Clinical Trials for Combination Therapy Development

Alzheimer's disease (AD) is a complex multifaceted disease. Recently approved anti-amyloid monoclonal antibodies slow disease progression by approximately 30%, and combination therapy appears necessary to prevent the onset of AD or produce greater slowing of cognitive and functional decline. Combination therapies may address core features, non-specific co-pathology commonly occurring in patients with AD (e.g., inflammation), or non-AD pathologies that may co-occur with AD (e.g., α-synuclein). Combination therapies may be advanced through co-development of more than one new molecular entity or through add-on strategies including an approved agent plus a new molecular entity. Addressing add-on combination therapy is currently urgent since patients on anti-amyloid monoclonal antibodies may be included in clinical trials for experimental agents. Phase 1 information must be generated for each agent in combination drug development. Phase 2 and Phase 3 of add-on therapies may contrast the new molecular entity, the approved agent as standard of care, and the combination. More complex development programs including standard or modified combinatorial designs are required for co-development of two or more new molecular entities. Biomarkers are markedly affected by anti-amyloid monoclonal antibodies, and these effects must be anticipated in add-on trials. Examining target engagement biomarkers and comparing the magnitude and sequence of biomarker changes in those receiving more than one therapy, compared with those on monotherapy, may be informative. Using network-based medicine approaches, computational strategies may identify rational combinations using disease and drug effect network mapping.

Guiding questions to avoid data leakage in biological machine learning applications

Machine learning methods for extracting patterns from high-dimensional data are very important in the biological sciences. However, in certain cases, real-world applications cannot confirm the reported prediction performance. One of the main reasons for this is data leakage, which can be seen as the illicit sharing of information between the training data and the test data, resulting in performance estimates that are far better than the performance observed in the intended application scenario. Data leakage can be difficult to detect in biological datasets due to their complex dependencies. With this in mind, we present seven questions that should be asked to prevent data leakage when constructing machine learning models in biological domains. We illustrate the usefulness of our questions by applying them to nontrivial examples. Our goal is to raise awareness of potential data leakage problems and to promote robust and reproducible machine learning-based research in biology.

Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives

Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.

A network-based trans-omics approach for predicting synergistic drug combinations

BACKGROUND

Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses.

METHODS

The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML).

RESULTS

Here we show that SyndrumNET outperforms the previous methods in terms of high accuracy. We perform in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibits synergistic anticancer effects. Our mode-of-action analysis also reveals that the drug synergy of the top predicted combination of capsaicin and mitoxantrone is due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway.

CONCLUSIONS

The proposed method is expected to be useful for discovering synergistic drug combinations for various complex diseases.

Non-Coding RNAs Extended Omnigenic Module of Cancers

The emergence of cancers involves numerous coding and non-coding genes. Understanding the contribution of non-coding RNAs (ncRNAs) to the cancer neighborhood is crucial for interpreting the interaction between molecular markers of cancer. However, there is a lack of systematic studies on the involvement of ncRNAs in the cancer neighborhood. In this paper, we construct an interaction network which encompasses multiple genes. We focus on the fundamental topological indicator, namely connectivity, and evaluate its performance when applied to cancer-affected genes using statistical indices. Our findings reveal that ncRNAs significantly enhance the connectivity of affected genes and mediate the inclusion of more genes in the cancer module. To further explore the role of ncRNAs in the network, we propose a connectivity-based method which leverages the bridging function of ncRNAs across cancer-affected genes and reveals the non-coding RNAs extended omnigenic module (NeOModule). Topologically, this module promotes the formation of cancer patterns involving ncRNAs. Biologically, it is enriched with cancer pathways and treatment targets, providing valuable insights into disease relationships.

Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study

Herbal medicines, particularly traditional Chinese medicines (TCMs), are a rich source of natural products with significant therapeutic potential. However, understanding their mechanisms of action is challenging due to the complexity of their multi-ingredient compositions. We introduced Herb-CMap, a multimodal fusion framework leveraging protein-protein interactions and herb-perturbed gene expression signatures. Utilizing a network-based heat diffusion algorithm, Herb-CMap creates a connectivity map linking herb perturbations to their therapeutic targets, thereby facilitating the prioritization of active ingredients. As a case study, we applied Herb-CMap to Suhuang antitussive capsule (Suhuang), a TCM formula used for treating cough variant asthma (CVA). Using in vivo rat models, our analysis established the transcriptomic signatures of Suhuang and identified its key compounds, such as quercetin and luteolin, and their target genes, including IL17A, PIK3CB, PIK3CD, AKT1, and TNF. These drug-target interactions inhibit the IL-17 signaling pathway and deactivate PI3K, AKT, and NF-κB, effectively reducing lung inflammation and alleviating CVA. The study demonstrates the efficacy of Herb-CMap in elucidating the molecular mechanisms of herbal medicines, offering valuable insights for advancing drug discovery in TCM.

Predicting drug synergy using a network propagation inspired machine learning framework

Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number of possible drug pairs and diseases. At present, machine learning techniques have been widely applied to predict drug combinations, but most studies rely on the response of drug combinations to specific cell lines and are not entirely satisfactory in terms of mechanism interpretability and model scalability. Here, we proposed a novel network propagation-based machine learning framework to predict synergistic drug combinations. Based on the topological information of a comprehensive drug-drug association network, we innovatively introduced an affinity score between drug pairs as one of the features to train machine learning models. We applied network-based strategy to evaluate their therapeutic potential to different cancer types. Finally, we identified 17 specific-, 21 general- and 40 broad-spectrum antitumor drug combinations, in which 69% drug combinations were validated by vitro cellular experiments, 83% drug combinations were validated by literature reports and 100% drug combinations were validated by biological function analyses. By quantifying the network relationships between drug targets and cancer-related driver genes in the human protein-protein interactome, we show the existence of four distinct patterns of drug-drug-disease relationships. We also revealed that 32 biological pathways were correlated with the synergistic mechanism of broad-spectrum antitumor drug combinations. Overall, our model offers a powerful scalable screening framework for cancer treatments.

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection

Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.

Adaptation of turnip mosaic virus to Arabidopsis thaliana involves rewiring of VPg-host proteome interactions

The outcome of a viral infection depends on a complex interplay between the host physiology and the virus, mediated through numerous protein-protein interactions. In a previous study, we used high-throughput yeast two-hybrid (HT-Y2H) to identify proteins in Arabidopsis thaliana that bind to the proteins encoded by the turnip mosaic virus (TuMV) genome. Furthermore, after experimental evolution of TuMV lineages in plants with mutations in defense-related or proviral genes, most mutations observed in the evolved viruses affected the VPg cistron. Among these mutations, D113G was a convergent mutation selected in many lineages across different plant genotypes, including cpr5-2 with constitutive expression of systemic acquired resistance. In contrast, mutation R118H specifically emerged in the jin1 mutant with affected jasmonate signaling. Using the HT-Y2H system, we analyzed the impact of these two mutations on VPg's interaction with plant proteins. Interestingly, both mutations severely compromised the interaction of VPg with the translation initiation factor eIF(iso)4E, a crucial interactor for potyvirus infection. Moreover, mutation D113G, but not R118H, adversely affected the interaction with RHD1, a zinc-finger homeodomain transcription factor involved in regulating DNA demethylation. Our results suggest that RHD1 enhances plant tolerance to TuMV infection. We also discuss our findings in a broad virus evolution context.

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce Pinnacle, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, Pinnacle learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. Pinnacle's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. Pinnacle outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. Pinnacle's ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.

Broadcasters, receivers, functional groups of metabolites, and the link to heart failure by revealing metabolomic network connectivity

BACKGROUND AND OBJECTIVE

Blood-based small molecule metabolites offer easy accessibility and hold significant potential for insights into health processes, the impact of lifestyle, and genetic variation on disease, enabling precise risk prevention. In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline.

METHODS

We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites.

RESULTS

We identified metabolites associated with higher and lower risk of HF incidence, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. These associations were not confounded by the other metabolites due to uncovering the connectivity among metabolites and adjusting each association for the confounding metabolites. Examples of our findings include the direct influence of asparagine on glycine, both of which were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids, which are not synthesized in the human body and are obtained directly from the diet.

CONCLUSION

Metabolites may play a critical role in linking genetic background and lifestyle factors to HF incidence. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates studying complex conditions like HF.

D'or: deep orienter of protein-protein interaction networks

MOTIVATION

Protein-protein interactions (PPIs) provide the skeleton for signal transduction in the cell. Current PPI measurement techniques do not provide information on their directionality which is critical for elucidating signaling pathways. To date, there are hundreds of thousands of known PPIs in public databases, yet only a small fraction of them have an assigned direction. This information gap calls for computational approaches for inferring the directionality of PPIs, aka network orientation.

RESULTS

In this work, we propose a novel deep learning approach for PPI network orientation. Our method first generates a set of proximity scores between a protein interaction and sets of cause and effect proteins using a network propagation procedure. Each of these score sets is fed, one at a time, to a deep set encoder whose outputs are used as features for predicting the interaction's orientation. On a comprehensive dataset of oriented PPIs taken from five different sources, we achieve an area under the precision-recall curve of 0.89-0.92, outperforming previous methods. We further demonstrate the utility of the oriented network in prioritizing cancer driver genes and disease genes.

AVAILABILITY AND IMPLEMENTATION

D'or is implemented in Python and is publicly available at https://github.com/pirakd/DeepOrienter.

Preclinical side effect prediction through pathway engineering of protein interaction network models

Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein-protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein-protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein-protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.

Multicentric Assessment of a Multimorbidity-Adjusted Disability Score to Stratify Depression-Related Risks Using Temporal Disease Maps: Instrument Validation Study

BACKGROUND

Comprehensive management of multimorbidity can significantly benefit from advanced health risk assessment tools that facilitate value-based interventions, allowing for the assessment and prediction of disease progression. Our study proposes a novel methodology, the Multimorbidity-Adjusted Disability Score (MADS), which integrates disease trajectory methodologies with advanced techniques for assessing interdependencies among concurrent diseases. This approach is designed to better assess the clinical burden of clusters of interrelated diseases and enhance our ability to anticipate disease progression, thereby potentially informing targeted preventive care interventions.

OBJECTIVE

This study aims to evaluate the effectiveness of the MADS in stratifying patients into clinically relevant risk groups based on their multimorbidity profiles, which accurately reflect their clinical complexity and the probabilities of developing new associated disease conditions.

METHODS

In a retrospective multicentric cohort study, we developed the MADS by analyzing disease trajectories and applying Bayesian statistics to determine disease-disease probabilities combined with well-established disability weights. We used major depressive disorder (MDD) as a primary case study for this evaluation. We stratified patients into different risk levels corresponding to different percentiles of MADS distribution. We statistically assessed the association of MADS risk strata with mortality, health care resource use, and disease progression across 1 million individuals from Spain, the United Kingdom, and Finland.

RESULTS

The results revealed significantly different distributions of the assessed outcomes across the MADS risk tiers, including mortality rates; primary care visits; specialized care outpatient consultations; visits in mental health specialized centers; emergency room visits; hospitalizations; pharmacological and nonpharmacological expenditures; and dispensation of antipsychotics, anxiolytics, sedatives, and antidepressants (P<.001 in all cases). Moreover, the results of the pairwise comparisons between adjacent risk tiers illustrate a substantial and gradual pattern of increased mortality rate, heightened health care use, increased health care expenditures, and a raised pharmacological burden as individuals progress from lower MADS risk tiers to higher-risk tiers. The analysis also revealed an augmented risk of multimorbidity progression within the high-risk groups, aligned with a higher incidence of new onsets of MDD-related diseases.

CONCLUSIONS

The MADS seems to be a promising approach for predicting health risks associated with multimorbidity. It might complement current risk assessment state-of-the-art tools by providing valuable insights for tailored epidemiological impact analyses of clusters of interrelated diseases and by accurately assessing multimorbidity progression risks. This study paves the way for innovative digital developments to support advanced health risk assessment strategies. Further validation is required to generalize its use beyond the initial case study of MDD.

Network proximity analysis as a theoretical model for identifying potential novel therapies in primary sclerosing cholangitis

Primary Sclerosing Cholangitis (PSC) is a progressive cholestatic liver disease with no licensed therapies. Previous Genome Wide Association Studies (GWAS) have identified genes that correlate significantly with PSC, and these were identified by systematic review. Here we use novel Network Proximity Analysis (NPA) methods to identify already licensed candidate drugs that may have an effect on the genetically coded aspects of PSC pathophysiology.Over 2000 agents were identified as significantly linked to genes implicated in PSC by this method. The most significant results include previously researched agents such as metronidazole, as well as biological agents such as basiliximab, abatacept and belatacept. This in silico analysis could potentially serve as a basis for developing novel clinical trials in this rare disease.

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.

Mind shift I: Fructus Aurantii - Rhizoma Chuanxiong synergistically anchors stress-induced depression-like behaviours and gastrointestinal dysmotility cluster by regulating psycho-immune-neuroendocrine network

BACKGROUND

Researchers have not studied the integrity, orderly correlation, and dynamic openness of complex organisms and explored the laws of systems from a global perspective. In the context of reductionism, antidepressant development formerly focused on advanced technology and molecular details, clear targets and mechanisms, but the clinical results were often unsatisfactory.

PURPOSE

MDD represents an aggregate of different and highly diverse disease subtypes. The co-occurrence of stress-induced nonrandom multimorbidity is widespread, whereas only a fraction of the potential clusters are well known, such as the MDD-FGID cluster. Mapping these clusters, and determining which are nonrandom, is vital for discovering new mechanisms, developing treatments, and reconfiguring services to better meet patient needs.

STUDY DESIGN

Acute stress 15-minute forced swimming (AFS) or CUMS protocols can induce the nonrandom MDD-FGID cluster. Multiple biological processes of rats with depression-like behaviours and gastrointestinal dysmobility will be captured under conditions of stress, and the Fructus Aurantii-Rhizoma Chuanxiong (ZQCX) decoction will be utilized to dock the MDD-FGID cluster.

METHODS/RESULTS

Here, Rhizoma Chuanxiong, one of the seven components of Chaihu-shugan-San, elicited the best antidepressant effect on CUMS rats, followed by Fructus Aurantii. ZQCX reversed AFS-induced depression-like behaviours and gastrointestinal dysmobility by regulating the glutamatergic system, AMPAR/BDNF/mTOR/synapsin I pathway, ghrelin signalling and gastrointestinal nitric oxide synthase. Based on the bioethnopharmacological analysis strategy, the determined meranzin hydrate (MH) and senkyunolide I (SI) by UPLC-PDA, simultaneously absorbed by the jejunum and hippocampus of rats, have been considered major absorbed bioactive compounds acting on behalf of ZQCX. Cotreatment with MH and SI at an equivalent dose in ZQCX synergistically replicated over 50.33 % efficacy of the parent formula in terms of antidepressant and prokinetic actions by modulating neuroinflammation and ghrelin signalling.

CONCLUSION

Brain-centric mind shifts require the integration of multiple central and peripheral systems and the elucidation of the underlying neurobiological mechanisms that ultimately contribute to novel therapeutic options. Ghrelin signalling and the immune system may partially underlie multimorbidity vulnerability, and ZQCX anchors stress-induced MDD-FGID clusters by docking them. Combining the results of micro details with the laws of the macro world may be more effective in finding treatments for MDD.

Network-based approach for drug repurposing against mpox

The current outbreak of mpox presents a significant threat to the global community. However, the lack of mpox-specific drugs necessitates the identification of additional candidates for clinical trials. In this study, a network medicine framework was used to investigate poxviruses-human interactions to identify potential drugs effective against the mpox virus (MPXV). The results indicated that poxviruses preferentially target hubs on the human interactome, and that these virally-targeted proteins (VTPs) tend to aggregate together within specific modules. Comorbidity analysis revealed that mpox is closely related to immune system diseases. Based on predicted drug-target interactions, 268 drugs were identified using the network proximity approach, among which 23 drugs displaying the least side-effects and significant proximity to MPXV were selected as the final candidates. Lastly, specific drugs were explored based on VTPs, differentially expressed proteins, and intermediate nodes, corresponding to different categories. These findings provide novel insights that can contribute to a deeper understanding of the pathogenesis of MPXV and development of ready-to-use treatment strategies based on drug repurposing.

Predicting gene disease associations with knowledge graph embeddings for diseases with curtailed information

Knowledge graph embeddings (KGE) are a powerful technique used in the biomedical domain to represent biological knowledge in a low dimensional space. However, a deep understanding of these methods is still missing, and, in particular, regarding their applications to prioritize genes associated with complex diseases with reduced genetic information. In this contribution, we built a knowledge graph (KG) by integrating heterogeneous biomedical data and generated KGE by implementing state-of-the-art methods, and two novel algorithms: Dlemb and BioKG2vec. Extensive testing of the embeddings with unsupervised clustering and supervised methods showed that KGE can be successfully implemented to predict genes associated with diseases and that our novel approaches outperform most existing algorithms in both scenarios. Our findings underscore the significance of data quality, preprocessing, and integration in achieving accurate predictions. Additionally, we applied KGE to predict genes linked to Intervertebral Disc Degeneration (IDD) and illustrated that functions pertinent to the disease are enriched within the prioritized gene set.

FSGT capsule inhibits IL-1β-induced inflammation in chondrocytes and ameliorates osteoarthritis by upregulating LncRNA PACER and downregulating COX2/PGE2

OBJECTIVE

To explore the efficacy and potential mechanism of Fengshi Gutong capsule (FSGTC) in osteoarthritis (OA) inflammation.

METHODS

The impact of FSGTC on laboratory indicators of OA patients was explored using data mining technology and association rule analysis. Then, the OA cell model was constructed by inducing chondrocytes (CHs) with interleukin-1β (IL-1β). In the presence of FSGTC intervention, the regulatory mechanism of PACER/COX2/PGE2 in OA-CH viability and inflammatory responses was evaluated.

RESULTS

Retrospective data mining showed that FSGTC effectively reduced inflammation indexes (ESR, HCRP) of OA patients. Cell experiments showed that LncRNA PACER (PACER) silencing inhibited the proliferation activity of OA-CHs, increased the level of COX2 protein, elevated the levels of PGE2, TNF-α, and IL-1β, and decreased the levels of IL-4 and IL-10 (p < .01). On the contrary, FSGTC-containing serum reversed the effect of PACER silencing on OA-CHs (p < .01). After the addition of COX2 pathway inhibitor, the proliferation activity of OA-CHs was enhanced; the levels of PGE2, TNF-α, and IL-1β were decreased while the levels of IL-4 and IL-10 were increased (p < .01).

CONCLUSION

FSGTC inhibits IL-1β-induced inflammation in CHs and ameliorates OA by upregulating PACER and downregulating COX2/PGE2.

Matrine exhibits antiviral activities against PEDV by directly targeting Spike protein of the virus and inducing apoptosis via the MAPK signaling pathway

Porcine Epidemic Diarrhea Virus (PEDV) is a highly contagious virus that causes Porcine Epidemic Diarrhea (PED). This enteric disease results in high mortality rates in piglets, leading to significant financial losses in the pig industry. However, vaccines cannot provide sufficient protection against epidemic strains. Spike (S) protein exposed on the surface of virion mediates PEDV entry into cells. Our findings imply that matrine (MT), a naturally occurring alkaloid, inhibits PEDV infection targeting S protein of virions and biological process of cells. The GLY434 residue in the autodocking site of the S protein and MT conserved based on sequence comparison. This study provides a comprehensive analysis of viral attachment, entry, and virucidal effects to investigate how that MT inhibits virus replication. MT inhibits PEDV attachment and entry by targeting S protein. MT was added to cells before, during, or after infection, it exhibits anti-PEDV activities and viricidal effects. Network pharmacology focuses on addressing causal mechanisms rather than just treating symptoms. We identified the key genes and screened the cell apoptosis involved in the inhibition of MT on PEDV infection in network pharmacology. MT significantly promotes cell apoptosis in PEDV-infected cells to inhibit PEDV infection by activating the MAPK signaling pathway. Collectively, we provide the biological foundations for the development of single components of traditional Chinese medicine to inhibit PEDV infection and spread.

Predicting the Prognosis of Bladder Cancer Patients Through Integrated Multi-omics Exploration of Chemotherapy-Related Hypoxia Genes

Bladder cancer is a prevalent malignancy with high mortality rates worldwide. Hypoxia is a critical factor in the development and progression of cancers. However, whether and how hypoxia-related genes (HRGs) could affect the development and the chemotherapy response of bladder cancer is still largely unexplored. This study comprehensively explored the complex molecular landscape associated with hypoxia in bladder cancer by analyzing 260 hypoxia genes based on transcriptomic and genomic data in 411 samples. Employing the 109 dysregulated hypoxia genes for consensus clustering, we delineated two distinct bladder cancer clusters characterized by disparate survival outcomes and distinct oncogenic roles. We defined a HPscore that was correlated with a variety of clinical features, including TNM stages and pathologic grades. Tumor immune landscape analysis identified three immune clusters and close interactions between hypoxia genes and the various immune cells. Utilizing a network-based method, we defined 129 HRGs exerting influence on apoptotic processes and critical signaling pathways in cancer. Further analysis of chemotherapy drug sensitivity identified potential drug-target HRGs. We developed a Risk Score model that was related to the overall survival of bladder cancer patients based on doxorubicin-target HRGs: ACTG2, MYC, PDGFRB, DHRS2, and KLRD1. This study not only enhanced our understanding of bladder cancer at the molecular level but also provided promising avenues for the development of targeted therapies, representing a significant step toward the identification of effective treatments and addressing the urgent need for advancements in bladder cancer management.

Distinct Network Patterns Emerge from Cartesian and XOR Epistasis Models: A Comparative Network Science Analysis

Background

Epistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, can significantly contribute to the observed phenotypic variance of complex traits. To date, it has been generally assumed that genetic interactions can be detected using a Cartesian, or multiplicative, interaction model commonly utilized in standard regression approaches. However, a recent study investigating epistasis in obesity-related traits in rats and mice has identified potential limitations of the Cartesian model, revealing that it only detects some of the genetic interactions occurring in these systems. By applying an alternative approach, the exclusive-or (XOR) model, the researchers detected a greater number of epistatic interactions and identified more biologically relevant ontological terms associated with the interacting loci. This suggests that the XOR model may provide a more comprehensive understanding of epistasis in these species and phenotypes. To further explore these findings and determine if different interaction models also make up distinct epistatic networks, we leverage network science to provide a more comprehensive view into the genetic interactions underlying BMI in this system.

Results

Our comparative analysis of networks derived from Cartesian and XOR interaction models in rats (Rattus norvegicus) uncovers distinct topological characteristics for each model-derived network. Notably, we discover that networks based on the XOR model exhibit an enhanced sensitivity to epistatic interactions. This sensitivity enables the identification of network communities, revealing novel trait-related biological functions through enrichment analysis. Furthermore, we identify triangle network motifs in the XOR epistatic network, suggestive of higher-order epistasis, based on the topology of lower-order epistasis.

Conclusions

These findings highlight the XOR model's ability to uncover meaningful biological associations as well as higher-order epistasis from lower-order epistatic networks. Additionally, our results demonstrate that network approaches not only enhance epistasis detection capabilities but also provide more nuanced understandings of genetic architectures underlying complex traits. The identification of community structures and motifs within these distinct networks, especially in XOR, points to the potential for network science to aid in the discovery of novel genetic pathways and regulatory networks. Such insights are important for advancing our understanding of phenotype-genotype relationships.

Comorbidity and multimorbidity in patients with cirrhosis, hospitalised in an internal medicine ward: a monocentric, cross-sectional study

OBJECTIVES

There are no data regarding the prevalence of comorbidity (ie, additional conditions in reference to an index disease) and multimorbidity (ie, co-occurrence of multiple diseases in which no one holds priority) in patients with liver cirrhosis. We sought to determine the rate and differences between comorbidity and multimorbidity depending on the aetiology of cirrhosis.

DESIGN

This is a subanalysis of the San MAtteo Complexity (SMAC) study. We have analysed demographic, clinical characteristics and rate of comorbidity/multimorbidity of patients with liver cirrhosis depending on the aetiology-alcoholic, infectious and non-alcoholic fatty liver disease (NAFLD). A multivariable analysis for factors associated with multimorbidity was fitted.

SETTING

Single-centre, cross-sectional study conducted in a tertiary referral, academic, internal medicine ward in northern Italy (November 2017-November 2019).

PARTICIPANTS

Data from 1433 patients previously enrolled in the SMAC study were assessed; only those with liver cirrhosis were eventually included.

RESULTS

Of the 1433 patients, 172 (median age 79 years, IQR 67-84; 83 females) had liver cirrhosis. Patients with cirrhosis displayed higher median Cumulative Illness Rating Scale (CIRS) comorbidity (4, IQR 3-5; p=0.01) and severity (1.85, IQR 16.-2.0; p<0.001) indexes and lower educational level (103, 59.9%; p=0.003). Patients with alcohol cirrhosis were significantly younger (median 65 years, IQR 56-79) than patients with cirrhosis of other aetiologies (p<0.001) and more commonly males (25, 75.8%). Comorbidity was more prevalent in patients with alcohol cirrhosis (13, 39.4%) and multimorbidity was more prevalent in viral (64, 81.0%) and NAFLD (52, 86.7%) cirrhosis (p=0.015). In a multivariable model for factors associated with multimorbidity, a CIRS comorbidity index >3 (OR 2.81, 95% CI 1.14 to 6.93, p=0.024) and admission related to cirrhosis (OR 0.19, 95% CI 0.07 to 0.54, p=0.002) were the only significant associations.

CONCLUSIONS

Comorbidity is more common in alcohol cirrhosis compared with other aetiologies in a hospital, internal medicine setting.