Human Genetics最新文献

The role of plasma-derived exosomal miRNA in premature ovarian failure (POF) remains unclear. This study aimed to investigate the epigenetic pathogenesis of POF through exosomal miRNA sequencing. Exosomes were isolated and characterized from six POF patients and four healthy individuals using nanoparticle tracking analysis, transmission electron microscopy and western blot analysis. Exosomal miRNA sequencing was performed to identify differentially expressed miRNAs with |fold change| greater than 1.5 and p value less than 0.05. Bioinformatics analysis in GSE39501 dataset and our sequencing data was conducted to investigate underlying mechanisms of POF. The functional role of hsa-miR-19b-3p was assessed using CCK8, western blot, flow cytometry and fluorescence staining. The regulatory effect of hsa-miR-19b-3p on BMPR2 was investigated through miRNA transfection, qPCR analysis, and luciferase reporter assay. Statistical significance was determined using t-tests and one-way ANOVA (p < 0.05). Exosomal miRNA sequencing revealed 18 dysregulated miRNAs in POF patients compared to healthy controls. Functional enrichment analysis demonstrated their involvement in cell growth, oocyte meiosis and PI3K-Akt signaling pathways. Moreover, the constructed miRNA-mRNA network unveiled potential regulatory mechanisms underlying POF, particularly implicating hsa-miR-19b-3p in the regulation of BMPR2. In vitro assays conducted on KGN cells confirmed that hsa-miR-19b-3p promoted apoptosis, as evidenced by reduced cell viability, decayed mitochondrial membrane potential and increased apoptotic rate, thereby supporting its role in POF. Notably, hsa-miR-19b-3p was found to significantly downregulate BMPR2 expression via targeting its 3'UTR, while co-expression analysis revealed strong associations between BMPR2 and POF-related processes. This study sheds light on the epigenetic pathogenesis of POF by investigating exosomal miRNA profiles. Particularly, hsa-miR-19b-3p emerged as a potential regulator of BMPR2 and demonstrated its functional significance in POF through modulation of apoptosis.

Endometriosis is characterized by the ectopic proliferation of endometrial cells, posing considerable diagnostic and therapeutic challenges. Our study investigates AGPAT4's involvement in endometriosis pathogenesis, aiming to unveil new therapeutic targets. Our investigation by analyzing eQTL data from GWAS for preliminary screening. Subsequently, within the GEO dataset, we utilized four machine learning algorithms to precisely identify risk-associated genes. Gene validity was confirmed through five Mendelian Randomization methods. AGPAT4 expression was measured by Single-Cell Analysis, ELISA and immunohistochemistry. We investigated AGPAT4's effect on endometrial stromal cells using RNA interference, assessing cell proliferation, invasion, and migration with CCK8, wound-healing, and transwell assays. Protein expression was analyzed by western blot, and AGPAT4 interactions were explored using AutoDock. Our investigation identified 11 genes associated with endometriosis risk, with AGPAT4 and COMT emerging as pivotal biomarkers through machine learning analysis. AGPAT4 exhibited significant upregulation in both ectopic tissues and serum samples from patients with endometriosis. Reduced expression of AGPAT4 was observed to detrimentally impact the proliferation, invasion, and migration capabilities of endometrial stromal cells, concomitant with diminished expression of key signaling molecules such as Wnt3a, β-Catenin, MMP-9, and SNAI2. Molecular docking analyses further underscored a substantive interaction between AGPAT4 and Wnt3a.Our study highlights AGPAT4's key role in endometriosis, influencing endometrial stromal cell behavior, and identifies AGPAT4 pathways as promising therapeutic targets for this condition.

It remains challenging to translate the findings from genome-wide association studies (GWAS) of autoimmune diseases (AIDs) into interventional targets, presumably due to the lack of knowledge on how the GWAS risk variants contribute to AIDs. In addition, current immunomodulatory drugs for AIDs are broad in action rather than disease-specific. We performed a comprehensive protein-centric omics integration analysis to identify AIDs-associated plasma proteins through integrating protein quantitative trait loci datasets of plasma protein (1348 proteins and 7213 individuals) and totally ten large-scale GWAS summary statistics of AIDs under a cutting-edge systematic analytic framework. Specifically, we initially screened out the protein-AID associations using proteome-wide association study (PWAS), followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we performed both Mendelian randomization (MR) and colocalization analyses to further identify protein-AID pairs with putatively causal relationships. We finally prioritized the potential drug targets for AIDs. A total of 174 protein-AID associations were identified by PWAS. AIDs-associated plasma proteins were significantly enriched in immune-related biological process and pathways, such as inflammatory response (P = 3.96 × 10^-10). MR analysis further identified 97 protein-AID pairs with potential causal relationships, among which 21 pairs were highly supported by colocalization analysis (PP.H4 > 0.75), 10 of 21 were the newly discovered pairs and not reported in previous GWAS analyses. Further explorations showed that four proteins (TLR3, FCGR2A, IL23R, TCN1) have corresponding drugs, and 17 proteins have druggability. These findings will help us to further understand the biological mechanism of AIDs and highlight the potential of these proteins to develop as therapeutic targets for AIDs.

Preimplantation embryonic arrest is an important pathogenesis of female infertility, but little is known about the genetic factors behind this phenotype. MEI4 is an essential protein for DNA double-strand break formation during meiosis, and Mei4 knock-out female mice are viable but sterile, indicating that MEI4 plays a crucial role in reproduction. To date, MEI4 has not been found to be associated with any human reproductive diseases. Here, we identified six compound heterozygous and homozygous MEI4 variants-namely, c.293C > T, p.(Ser98Leu), c.401C > G, p.(Pro134Arg), c.391C > G, p.(Pro131Ala), c.914A > T, p.(Tyr305Phe), c.908C > G, p.(Ala303Gly), and c.899A > T, p.(Gln300Leu)-in four independent families that were responsible for female infertility mainly characterized by preimplantation embryonic arrest. In vitro, we found that these variants reduced the interaction between MEI4 and DNA. In vivo, we generated a knock-in mouse model and demonstrated that female mice were infertile and were characterized by developmental defects during oogenesis. Our findings reveal the important roles of MEI4 in human reproduction and provide a new diagnostic marker for genetic counseling of clinical infertility patients.

Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short read scRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.

Aims Many studies indicated use of diabetes medications can influence the electrocardiogram (ECG), which remains the simplest and fastest tool for assessing cardiac functions. However, few studies have explored the role of genetic factors in determining the relationship between the use of diabetes medications and ECG trace characteristics (ETC). Methods Genome-wide association studies (GWAS) were performed for 168 ETCs extracted from the 12-lead ECGs of 42,340 Europeans in the UK Biobank. The genetic correlations, causal relationships, and phenotypic relationships of these ETCs with medication usage, as well as the risk of cardiovascular diseases (CVDs), were estimated by linkage disequilibrium score regression (LDSC), Mendelian randomization (MR), and regression model, respectively. Results The GWAS identified 124 independent single nucleotide polymorphisms (SNPs) that were study-wise and genome-wide significantly associated with at least one ETC. Regression model and LDSC identified significant phenotypic and genetic correlations of T-wave area in lead aVR (aVR_T-area) with usage of diabetes medications (ATC code: A10 drugs, and metformin), and the risks of ischemic heart disease (IHD) and coronary atherosclerosis (CA). MR analyses support a putative causal effect of the use of diabetes medications on decreasing aVR_T-area, and on increasing risk of IHD and CA. ConclusionPatients taking diabetes medications are prone to have decreased aVR_T-area and an increased risk of IHD and CA. The aVR_T-area is therefore a potential ECG marker for pre-clinical prediction of IHD and CA in patients taking diabetes medications.

Hypospadias refers to the abnormal position of the male urethral orifice, which not only leads to urination disorder but also causes sexual dysfunction in adulthood. However, the complex and diverse pathogenic factors of hypospadias are still unclear. To study the pathogenesis and prognosis of hypospadias, we counted the serological indexes of children with hypospadias, and found that sSBP, TC and LDL increased in children with mild, moderate and severe hypospadias. Subsequently, we used quantitative proteomics to find differential proteins in mild, moderate and severe hypospadias. After bioinformatics analysis and biochemical experiments on the screened DEPs, we found that the expression of proteins related to immune inflammation, coagulation, blood pressure and inflammation, and blood lipid were differential expressed in the prepuce tissue of children with hypospadias. We further confirmed that the proteins FGB, FGG, SERPINA1, and AGT involved in the angiotensin system, cholesterol metabolism, and coagulation were significantly up-regulated by biochemical experiments. In particular, the AGT protein of the angiotensin system involved in blood pressure regulation, we have shown that it increases with the severity of hypospadias. This study suggests that children with hypospadias are more likely to suffer from hyperlipidemia and cardiovascular disease (CVD). Our findings provide a theoretical basis for early monitoring of blood lipids and blood pressure to prevent CVD in children with hypospadias.

Mendelian randomization is a powerful method for inferring causal relationships. However, obtaining suitable genetic instrumental variables is often challenging due to gene interaction, linkage, and pleiotropy. We propose Bayesian network-based Mendelian randomization (BNMR), a Bayesian causal learning and inference framework using individual-level data. BNMR employs the random graph forest, an ensemble Bayesian network structural learning process, to prioritize candidate genetic variants and select appropriate instrumental variables, and then obtains a pleiotropy-robust estimate by incorporating a shrinkage prior in the Bayesian framework. Simulations demonstrate BNMR can efficiently reduce the false-positive discoveries in variant selection, and outperforms existing MR methods in terms of accuracy and statistical power in effect estimation. With application to the UK Biobank, BNMR exhibits its capacity in handling modern genomic data, and reveals the causal relationships from hematological traits to blood pressures and psychiatric disorders. Its effectiveness in handling complex genetic structures and modern genomic data highlights the potential to facilitate real-world evidence studies, making it a promising tool for advancing our understanding of causal mechanisms.

Cisplatin-induced acute kidney injury (CP-AKI) is a common complication in cancer patients. Although ferroptosis is believed to contribute to the progression of CP-AKI, its mechanisms remain incompletely understood. In this study, after initially processed individual omics datasets, we integrated multi-omics data to construct a ferroptosis network in the kidney, resulting in the identification of the key driver TACSTD2. In vitro and in vivo results showed that TACSTD2 was notably upregulated in cisplatin-treated kidneys and BUMPT cells. Overexpression of TACSTD2 accelerated ferroptosis, while its gene disruption decelerated ferroptosis, likely mediated by its potential downstream targets HMGB1, IRF6, and LCN2. Drug prediction and molecular docking were further used to propose that drugs targeting TACSTD2 may have therapeutic potential in CP-AKI, such as parthenolide, progesterone, premarin, estradiol and rosiglitazone. Our findings suggest a significant association between ferroptosis and the development of CP-AKI, with TACSTD2 playing a crucial role in modulating ferroptosis, which provides novel perspectives on the pathogenesis and treatment of CP-AKI.

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with poor prognosis and high mortality. Although a large number of studies have explored its potential prognostic markers using traditional RNA sequencing (RNA-Seq) data, they have not achieved good prediction effect. In order to explore the possible prognostic signaling pathways leading to the difference in prognosis, we identified differentially expressed genes from one scRNA-seq cohort and four GEO cohorts, respectively. Then Cox and Lasso regression analysis showed that 12 genes were independent prognostic factors for PDAC. AUC and calibration curve analysis showed that the prognostic model had good discrimination and calibration. Compared with the low-risk group, the high-risk group had a higher proportion of gene mutations than the low-risk group. Immune infiltration analysis revealed differences in macrophages and monocytes between the two groups. Prognosis related genes were mainly distributed in fibroblasts, macrophages and type 2 ducts. The results of cell communication analysis showed that there was a strong communication between cancer-associated fibroblasts (CAF) and type 2 ductal cells, and collagen formation was the main interaction pathway.