Male infertility affects approximately 17% of all men and represents a complex disorder in which not only semen parameters such as sperm motility, morphology, and number of sperm are highly variable, but also testicular phenotypes range from normal spermatogenesis to complete absence of germ cells. Genetic factors significantly contribute to the disease but chromosomal aberrations, mostly Klinefelter syndrome, and microdeletions of the Y-chromosome have remained the only diagnostically and clinically considered genetic causes. Monogenic causes remain understudied and, thus, often unidentified, leaving the majority of the male factor couple infertility pathomechanistically unexplained. This has been changing mostly because of the introduction of exome sequencing that allows the analysis of multiple genes in large patient cohorts. As a result, pathogenic variants in single genes have been associated with non-syndromic forms of all aetiologic sub-categories in the last decade. This review highlights the contribution of exome sequencing to the identification of novel disease genes for isolated (non-syndromic) male infertility by presenting the results of a comprehensive literature search. Both, reduced sperm count in azoospermic and oligozoospermic patients, and impaired sperm motility and/or morphology, in asthenozoospermic and/or teratozoospermic patients are highly heterogeneous diseases with well over 100 different candidate genes described for each entity. Applying the standardized evaluation criteria of the ClinGen gene curation working group, 70 genes with at least moderate evidence to contribute to the disease are highlighted. The implementation of these valid disease genes in clinical exome sequencing is important to increase the diagnostic yield in male infertility and, thus, improve clinical decision-making and appropriate genetic counseling. Future advances in androgenetics will continue to depend on large-scale exome and genome sequencing studies of comprehensive international patient cohorts, which are the most promising approaches to identify additional disease genes and provide reliable data on the gene-disease relationship.

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.

Allergic rhinitis is a common allergic disease with a complex pathogenesis and many unresolved issues. Studies have shown that the incidence of allergic rhinitis is closely related to genetic factors, and research on the related genes could help further understand its pathogenesis and develop new treatment methods. In this study, 446 allergic rhinitis-related genes were obtained on the basis of the DisGeNET database. The protein-protein interaction network was searched using the random-walk-with-restart algorithm with these 446 genes as seed nodes to assess the linkages between other genes and allergic rhinitis. Then, this result was further examined by three screening tests, including permutation, interaction, and enrichment tests, which aimed to pick up genes that have strong and special associations with allergic rhinitis. 52 novel genes were finally obtained. The functional enrichment test confirmed their relationships to the biological processes and pathways related to allergic rhinitis. Furthermore, some genes were extensively analyzed to uncover their special or latent associations to allergic rhinitis, including IRAK2 and MAPK, which are involved in the pathogenesis of allergic rhinitis and the inhibition of allergic inflammation via the p38-MAPK pathway, respectively. The new found genes may help the following investigations for understanding the underlying molecular mechanisms of allergic rhinitis and developing effective treatments.

Diseases are caused by genetic and/or environmental factors. It is important to understand the pathomechanism of monogenic diseases that are caused only by genetic factors, especially prenatal- or childhood-onset diseases for pediatricians. Identifying \"novel\" disease genes and elucidating how genomic changes lead to human phenotypes would develop new therapeutic approaches for rare diseases for which no fundamental cure has yet been established. Genomic analysis has evolved along with the development of analytical techniques, from Sanger sequencing (first-generation sequencing) to techniques such as comparative genomic hybridization, massive parallel short-read sequencing (using a next-generation sequencer or second-generation sequencer) and long-read sequencing (using a next-next generation sequencer or third-generation sequencer). I have been researching human genetics using conventional and new technologies, together with my mentors and numerous collaborators, and have identified genes responsible for more than 60 diseases. Here, an overview of genomic analyses of monogenic diseases that aims to identify novel disease genes, and several examples using different approaches depending on the disease characteristics are presented.

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.

Routine exome sequencing (ES) in individuals with neurodevelopmental disorders (NDD) remains inconclusive in >50% of the cases. Research analysis of unsolved cases can identify novel candidate genes but is time-consuming, subjective, and hard to compare between labs. The field, therefore, requires automated and standardized assessment methods to prioritize candidates for matchmaking. We developed AutoCaSc (https://autocasc.uni-leipzig.de) based on our candidate scoring scheme. We validated our approach using synthetic trios and real in-house trio ES data. AutoCaSc consistently (94.5% of all cases) scored the relevant variants in valid novel NDD genes in the top three ranks. In 93 real trio exomes, AutoCaSc identified most (97.5%) previously manually scored variants while evaluating additional high-scoring variants missed in manual evaluation. It identified candidate variants in previously undescribed NDD candidate genes (CNTN2, DLGAP1, SMURF1, NRXN3, and PRICKLE1). AutoCaSc enables anybody to quickly screen a variant for its plausibility in NDD. After contributing >40 descriptions of NDD-associated genes, we provide usage recommendations based on our extensive experience. Our implementation is capable of pipeline integration and therefore allows the screening of large cohorts for candidate genes. AutoCaSc empowers even small labs to a standardized matchmaking collaboration and to contribute to the ongoing identification of novel NDD entities.

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.

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