Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.

BACKGROUND: Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes.
RESULTS: We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of \'seed\' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.

Endometriosis is a gynecological disorder prevalent in women of reproductive age. The primary symptoms include dysmenorrhea, irregular menstruation, and infertility. However, the pathogenesis of endometriosis remains unclear. With the advent of high-throughput technologies, various omics experiments have been conducted to identify genes related to the pathophysiology of endometriosis. This review highlights the molecular mechanisms underlying endometriosis using omics. When genes identified in omics experiments were compared with endometriosis disease genes identified in independent studies, the number of overlapping genes was moderate. However, the characteristics of these genes were found to be equivalent when functional gene set enrichment analysis was performed using gene ontology and biological pathway information. These findings indicate that omics technology provides invaluable information regarding the pathophysiology of endometriosis. Moreover, the functional characteristics revealed using enrichment analysis provide important clues for discovering endometriosis disease genes in future research.

Current standards in clinical genetics recognize the need to establish the validity of gene-disease relationships as a first step in the interpretation of sequence variants. We describe our experience incorporating the ClinGen Gene-Disease Clinical Validity framework in our interpretation and reporting workflow for a clinical genome sequencing (cGS) test for individuals with rare and undiagnosed genetic diseases. This \"reactive\" gene curation is completed upon identification of candidate variants during active case analysis and within the test turn-around time by focusing on the most impactful evidence and taking advantage of the broad applicability of the framework to cover a wide range of disease areas. We demonstrate that reactive gene curation can be successfully implemented in support of cGS in a clinical laboratory environment, enabling robust clinical decision making and allowing all variants to be fully and appropriately considered and their clinical significance confidently interpreted.

Protein-protein interactions (PPI) play an essential role in the biological processes that occur in the cell. Therefore, the dissection of PPI networks becomes decisive to model functional coordination and predict pathological de-regulation. Cellular networks are dynamic and proteins display varying roles depending on the tissue-interactomic context. Thus, the use of centrality measures in individual proteins fall short to dissect the functional properties of the cell. For this reason, there is a need for more comprehensive, relational, and context-specific ways to analyze the multiple actions of proteins in different cells and identify specific functional assemblies within global biomolecular networks. Under this framework, we define Biological Interacting units (BioInt-U) as groups of proteins that interact physically and are enriched in a common Gene Ontology. A search strategy was applied on 33 tissue-specific (TS) PPI networks to generate BioInt libraries associated with each particular human tissue. The cross-tissue comparison showed that housekeeping assemblies incorporate different proteins and exhibit distinct network properties depending on the tissue. Furthermore, disease genes (DGs) of tissue-associated pathologies preferentially accumulate in units in the expected tissues, which in turn were more central in the TS networks. Overall, the study reveals a tissue-specific functional diversification based on the identification of specific protein units and suggests vulnerabilities specific of each tissue network, which can be applied to refine protein-disease association methods.

暂无摘要。

Mutation of the α-thalassemia/mental retardation syndrome X-linked protein, ATRX, causes intellectual disability and is associated with pleiotropic defects including ophthalmological abnormalities. We have previously demonstrated that Atrx deficiency in the mouse retina leads to the selective loss of inhibitory interneurons and inner retinal dysfunction. Onset of the amacrine cell neurodegenerative phenotype in Atrx-deficient retinas occurs postnatally after neuronal specification, and coincides with eye opening. Given this timing, we sought to interrogate the influence of light-dependent visual signaling on Atrx-mediated neuronal survival and function in the mouse retina. Retina-specific Atrx conditional knockout (cKO) mice were subjected to light deprivation using two different paradigms: (1) a dark-rearing regime, and (2) genetic deficiency of metabotropic glutamate receptor 6 (mGluR6) to block the ON retinal signaling pathway. Scotopic electroretinography was performed for adult dark-reared Atrx cKO mice and controls to measure retinal neuron function in vivo. Retinal immunohistochemistry and enumeration of amacrine cells were performed for both light deprivation paradigms. We observed milder normalized a-wave, b-wave and oscillatory potential (OP) deficits in electroretinograms of dark-reared Atrx cKO mice compared to light-exposed counterparts. In addition, amacrine cell loss was partially limited by genetic restriction of retinal signaling through the ON pathway. Our results suggest that the temporal features of the Atrx cKO phenotype are likely due to a combined effect of light exposure upon eye opening and coincident developmental processes impacting the retinal circuitry. In addition, this study reveals a novel activity-dependent role for Atrx in mediating post-replicative neuronal integrity in the CNS.

Diabetic retinopathy is a common complication of diabetes mellitus that causes pathogenic damage to the retina. Particularly, the proliferative diabetic retinopathy (PDR) state can cause abnormal angiogenesis in the retina tissues and trigger the retina destruction in advanced stage. In the clinic, the symptoms during the initiation and progression of PDR are relatively unrecognizable. Therefore, various studies have focused on the pathogenesis of PDR. According to published literature, genetic contributions play an irreplaceable role in the initiation and progression of PDR. Although many computational methods, such as shortest path- and random walk with restart-based methods, have been applied in screening the potential pathogenic factors of PDR, advanced computational methods, which may provide essential supplements for previous ones, are still widely needed. In this study, a novel computational method was presented to infer novel PDR-associated genes. Different from previous methods, the method used in this work employed a different network algorithm, that is, the Laplacian heat diffusion algorithm. This algorithm was applied on the protein-protein interaction network reported in the STRING database. Three screening tests were performed to filter the most likely inferred genes. A total of 26 genes were accessed using the proposed method. Compared with the two previous predictions, most of the identified genes were novel, and only one gene was shared. Several inferred genes, such as CSF3, COL18A1, CXCR2, CCR1, FGF23, CXCL11, and IL13, were related to the pathogenesis of PDR.

We describe unrelated individuals with ichthyosis, failure to thrive, thrombocytopenia, photophobia, and progressive hearing loss. Each have bi-allelic mutations in AP1B1, the gene encoding the β subunit of heterotetrameric adaptor protein 1 (AP-1) complexes, which mediate endomembrane polarization, sorting, and transport. In affected keratinocytes the AP-1 β subunit is lost, and the γ subunit is greatly reduced, demonstrating destabilization of the AP-1 complex. Affected cells and tissue contain an abundance of abnormal vesicles and show hyperproliferation, abnormal epidermal differentiation, and derangement of intercellular junction proteins. Transduction of affected cells with wild-type AP1B1 rescues the vesicular phenotype, conclusively establishing that loss of AP1B1 function causes this disorder.

Network biology and medicine provide unprecedented opportunities and challenges for deciphering disease mechanisms from integrative viewpoints. The disease genes and their products perform their dysfunctions via physical and biochemical interactions in the form of a molecular network. The topological parameters of these disease genes in the interactome are of prominent interest to the understanding of their functionality from a systematic perspective. In this work, we provide a systems biology analysis of the topological features of complex disease genes in an integrated biomolecular network. Firstly, we identify the characteristics of four network parameters in the ten most frequently studied disease genes and identify several specific patterns of their topologies. Then, we confirm our findings in the other disease genes of three complex disorders (i.e., Alzheimer\'s disease, diabetes mellitus, and hepatocellular carcinoma). The results reveal that the disease genes tend to have a higher betweenness centrality, a smaller average shortest path length, and a smaller clustering coefficient when compared to normal genes, whereas they have no significant degree prominence. The features highlight the importance of gene location in the integrated functional linkages.