Multi-omic biomarkers associated with multiple sclerosis: from Mendelian randomization to drug predictionYang, Liu, Li
et alSci Rep (2025) 15 (1), 9421
Abstract: Currently, the treatment and prevention of multiple sclerosis (MS) continue to encounter significant challenges. Mendelian randomization (MR) analysis has emerged as a crucial research method in the pursuit of new therapeutic strategies. Accordingly, we hypothesize that there exists a causal association between genetic variants of specific plasma proteins and MS through MR mechanisms, and that key therapeutic targets can be precisely identified by integrating multi-omics analytical approaches. In this study, we developed a comprehensive analytical framework aimed at identifying and validating potential therapeutic targets for MS. The framework commenced with a two-sample Mendelian randomization (MR) study utilizing two large plasma protein quantitative trait locus (pQTL) datasets. Building on this foundation, we performed Bayesian co-localization analysis of coding genes, followed by a full phenotype-wide association study (PheWAS) on the co-positive genes identified through both analytical methods. This approach allowed us to explore the functions of key genes and the mechanisms of co-morbidity associated with the disease. Subsequently, we integrated protein-protein interaction (PPI) network analysis, gene ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to facilitate drug prediction and molecular docking studies. This study conducted a systematic analysis between two large plasma pQTLs datasets and MS. In the MR analysis, the MR analysis of Icelandic plasma pQTLs and MS identified 88 positive plasma proteins, while the MR analysis of the UK Biobank database pQTLs and MS identified 122 positive plasma proteins. By comparison, uroporphyrinogen III synthase (UROS) and glutathione S-transferase theta 2B (GSTT2B) were found to be the positive proteins shared by the two datasets. After false discovery rate (FDR) correction, signal transducer and activator of transcription 3 (STAT3) was a significantly positive protein in the analysis of Icelandic plasma pQTLs. In the analysis of the UK Biobank database pQTLs, advanced glycosylation end product-specific receptor (AGER), allograft inflammatory factor 1 (AIF1), butyrophilin subfamily 1 member A1 (BTN1A1), cluster of differentiation 58 (CD58), desmoglein 4 (DSG4), ecotropic viral integration site 5 (EVI5), tumor necrosis factor (TNF), and tumor necrosis factor receptor superfamily member 14 (TNFRSF14) were significantly positive proteins. After Bonferroni correction, AGER, CD58, EVI5, and TNF remained significantly positive proteins in the analysis of the UK Biobank database pQTLs. In the Bayesian colocalization analysis, EVI5 (PPH4 = 0.9800), O-GlcNAcase (OGA) (PPH4 = 0.8569), and TNFRSF14 (PPH4 = 0.8904) were the common positive genes in the two analysis methods. In conclusion, EVI5, OGA, and TNFRSF14 may be potential therapeutic targets for MS. Through the comprehensive application of MR analysis and Bayesian colocalization analysis, we have successfully identified that EVI5, OGA, and TNFRSF14 may be key therapeutic targets for MS. These findings may provide a scientific basis for the development of novel immunotherapies, combination treatment regimens, or targeted intervention strategies.© 2025. The Author(s).
Genomic Tools for Medicinal Properties of Goat Milk for Cosmetic and Health Benefits: A Narrative ReviewNcube, Modiba, Mpofu
et alInt J Mol Sci (2025) 26 (3)
Abstract: Goat milk has gained recognition for its medicinal, cosmetic, and health benefits, particularly its potential to improve human skin conditions. Its therapeutic properties are attributed to bioactive compounds influenced by genes such as lactoferrin (LTF), lysozyme (LYZ), and β-casein (CSN2), known for their antimicrobial, immunomodulatory, and anti-inflammatory effects. Genetic factors are hypothesized to shape goat milk's composition and its effectiveness in managing dermatological conditions like eczema and psoriasis. Understanding these genetic determinants is critical to optimizing the use of goat milk in skin health applications. This review aims to explore the application of genomic tools to elucidate the medicinal properties of goat milk and its implications for skin care. By identifying the specific genes and molecular mechanisms underpinning its therapeutic effects, genomic studies have provided insights into the bioactive constituents of goat milk, such as peptides, proteins, and lipids, which contribute to its dermatological efficacy. Candidate genes, including growth hormone receptor (GHR), butyrophilin (BTN1A1), and lactoglobulin (LGB), have been identified as critical for enhancing milk quality and functionality. Future research should integrate genomic data with functional studies to further investigate goat milk's immunomodulatory, antimicrobial, and antioxidant activities. Such insights could advance targeted breeding strategies and innovative formulations for managing inflammatory skin conditions and promoting skin health.
Genetic and Plasma Proteomic Approaches to Identify Therapeutic Targets for Graves' Disease and Graves' OphthalmopathyKe, Yu, Li
et alImmunotargets Ther (2025) 14, 87-98
Abstract: The blood proteome is a major source of biomarkers and therapeutic targets. We aimed to identify the causal proteins and potential targets for Graves' disease (GD) and Graves' ophthalmopathy (GO) via systematic genetic analyses.Genome-wide association studies (GWASs) on the UK Biobank- Pharma Proteomics Project (UKB-PPP) collected 2923 Olink proteins from 54,219 participants. We conducted a proteome-wide Mendelian randomization (MR) study with cis-pQTLs to identify candidate proteins for GD and GO risk. Colocalization analysis and the Heidi test were used to examine whether the identified proteins and diseases shared the same variant. More proteins with potential causal associations were identified in Summary-data-based MR (SMR) analyses using trans-pQTLs. Then, downstream analyses were performed to detect protein interactions, gene function, cell type-specific expression and druggable information.This study genetically predicted levels of 62 plasma proteins were associated with GD risk. Four proteins (CD40, TINAGL1, GMPR and CXCL10) were prioritized with the evidence of sharing the same variants with GD. Specifically, some proteins had potential associations with GD with trans-pQTLs mapping in CD40. The four prioritized protein-coding genes were mainly enriched in the regulation of apoptotic and death processes. In addition, GMPR was associated with both GO and GD in a consistent direction. BTN1A1 and FCRL1 were prioritized as the causal proteins for GO onset and were not associated with GD.By synthesizing proteomic and genetic data, we identified several protein biomarkers for GD, with one linked to both GD and GO and two other protein biomarkers specific to GO onset, which provides valuable insights into the etiology and potential therapeutic targets for the two diseases.© 2025 Ke et al.
Genome-wide copy number variation regions in indigenous (Bos indicus) cattle breeds of Tamil Nadu, IndiaVani, Balasubramanyam, Tirumurugaan
et alAnim Biosci (2025) 38 (3), 395-407
Abstract: Identification of large scale structural polymorphisms (copy number variations [CNVs]) of more than 50 bp between the individuals of a species would help in knowing genetic diversity, phenotypic variability, adaptability to tropical environment and disease resistance.Read depth-based method implemented in CNVnator was used for calling copy number variant regions on sequenced data obtained from whole-genome sequencing from 15 pooled samples belonging to five draught cattle breeds of Tamil Nadu.A total of 11,605 CNV regions (CNVRs) were observed covering a genome size of 18.63 percent. Among these, 11,459 were restricted to autosomes, consisting of 11,013 deletions, 353 duplications and 93 complex events. These CNVRs were annotated to 4,989 candidate genes. A total of 8,291 numbers of CNVRs were shared among the five cattle breeds as also supported by principal component analysis and STRUCTURE analyses and 1,172 CNVRs were breed-specific. Four out of five selected breed-specific CNVRs were validated using real-time polymerase chain reaction. Genes with CNVRs are related to milk production (BTN1A1, ABCA1, and LAP3), disease resistance (TLR4 and DNAH8), adaptability (SOD1, CAST, and SMARCAL1), growth (EGFR, NKAIN3), reproduction (BRWD1 and PDE6D), meat and carcass traits (MAP3K5 and NCAM1) and exterior (ATRN and MITF) traits. Gene enrichment analysis based on the gene list retrieved from the CNVRs disclosed over-represented terms (p<0.01) associated with milk fat production. NETWORK analysis had identified 13 putative candidate genes involved in milk fat percentage, milk fat yield, lactation persistency, milk yield, heat tolerance, calving ease, growth and conformation traits.The genome-wide CNVRs identified in the present study produced genomewide partial CNV map in indigenous cattle breeds of Tamil Nadu.
膜杰作
Star Staining
