Biomedical subjects

Tao Guo

Publications and source records attributed to Tao Guo.

5 recordsLinked to original sources

PubMed2026

Genetic Analysis of Genomic and Methylomic Variation and Identification of Multi-Trait Mutants in Rice Carried on Chang'e-5.

Global food security is facing challenges from population growth to diminishing arable land. Space mutation breeding holds promise for overcoming the variation limitations in conventional breeding; however, the mutagenic effects of the deep-space environment on rice and the transgenerational inheritance patterns of induced variations remain unclear. In this study, rice seeds carried by the Chang'e-5 spacecraft were used as materials. Whole-genome sequencing and whole-genome bisulfite sequencing were performed on the first (SP1) and second generations (SP2) of space-mutagenized plants after their return to Earth. The results showed that the number of genomic variants in the SP2 generation increased significantly compared with SP1, and SNPs, homozygous sites, and variants in coding regions were more heritable. The genome-wide methylation level was elevated in the SP2 generation, and among differentially methylated cytosines, those in the CG context exhibited the highest heritability. Furthermore, large-scale screening for nitrogen efficiency, tolerance to PEG-induced stress, and germination-stage cold resistant mutants was conducted in the SP2 generation, and phenotypic validation was performed in the third generation (SP3). By integrating multi-omics analyses of representative mutants to mine candidate genes, a number of heritable elite mutants were obtained, and seven candidate genes for key traits were identified. This study systematically elucidates the transgenerational inheritance patterns of deep-space-induced variation in rice. The multi-trait mutants obtained provide valuable germplasm resources for gene cloning and breeding applications in rice.

DNA methylation↗

PubMed2026

A Risk Score for Polycystic Ovary Syndrome Based on Meta-Analysis and Machine Learning of Gut Microbiota Signatures.

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine and metabolic disorder among reproductive-age women, in which emerging evidence suggests a substantial role played by the gut microbiota. To comprehensively evaluate gut microbiota alterations in PCOS and identify microbial biomarkers through integrated analysis, a systematic search of PubMed, Web of Science, and Embase was conducted for studies employing 16S rRNA gene sequencing of fecal samples from PCOS cohorts. Ten eligible PCOS cohorts, comprising 858 individuals, were included in the study, from which a risk score was derived using a 20-gene gut microbial signature associated with PCOS. Meta-analysis at the genus level identified that Subdoligranulum, NK4A214_group, and Collinsella significantly decreased, and Bacteroides increased in PCOS across multiple cohorts. Machine learning analysis identified a 20-genus microbial signature using the least absolute shrinkage and selection operator (LASSO) method, which was used to construct a risk score with an AUC of 0.835 in diagnosis prediction. Network analysis further identified Negativibacillus and Lachnospiraceae_UCG_010 as potential driver microbes in PCOS. The analysis in this study highlights key alterations in the gut microbiota across PCOS cohorts. The identified gut microbial signature and derived LASSO-based risk model offer novel insights and a potential tool for PCOS diagnosis.

Polycystic Ovary Syndrome↗

PubMed2026

PPRC1 is a prognostic biomarker and key regulator of mitochondrial oxidative phosphorylation in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) remains an incurable haematological malignancy, underscoring the need for novel prognostic biomarkers and therapeutic targets. This study aimed to investigate the clinical and biological significance of peroxisome proliferator-activated receptor gamma coactivator-related protein 1 (PPRC1) in MM. METHODS: Expression and clinical data were obtained from public databases and an independent local cohort. Kaplan-Meier and Cox regression analyses were performed to evaluate prognostic value. Differential expression analysis, pathway enrichment analysis and single-cell RNA-seq data analysis were used to explore biological functions. PPRC1 was silenced in MM cell lines using siRNA to assess its effects on cell survival and oxidative phosphorylation. RESULTS: PPRC1 was significantly upregulated in MM and was associated with advanced disease stage and poor overall survival. Multivariate Cox analysis identified PPRC1 as an independent prognostic factor. A nomogram incorporating PPRC1 and revised-ISS improved survival prediction. Functional analyses revealed that PPRC1 was positively correlated with oxidative phosphorylation and oncogenic signalling pathways. A potential connection between PPRC1 expression and immune cell infiltration was observed. PPRC1 knockdown inhibited cell proliferation, induced cell cycle arrest and apoptosis and impaired oxidative phosphorylation in MM. CONCLUSIONS: PPRC1 acts as a prognostic biomarker and metabolic regulator in MM by sustaining mitochondrial oxidative phosphorylation. These findings highlight PPRC1 as a potential therapeutic target in MM.

Humans↗

PubMed2025

Association of MTHFD1 G1958A (rs2236225) gene polymorphism with the risk of congenital heart disease: a systematic review and meta-analysis.

BACKGROUND: We did this study to better clarify the correlations of methylenetetrahydrofolate dehydrogenase 1 (MTHFD1)-G1958A (rs2236225) gene polymorphism with the risk of congenital heart diseases (CHD) and its subgroups. METHODS: Relevant articles were searched in PubMed, Web of Science, Cochrane Library, Embase, CNKI, VIP database and Wanfang DATA until October 2023. We will use odds ratios (ORs) and 95% confidence intervals (CIs) to examine the potential associations of MTHFD1- G1958A gene polymorphism with CHD and its subgroups. RESULTS: We included a total of 9 eligible studies, encompassing 1917 children with CHD, 1863 healthy children, 1717 mothers of the children with CHD and 1666 mothers of healthy children. In our study, the meta-analysis of fetal group revealed no significant association between any of the five genetic models for the MTHFD1-G1958A polymorphism and the risk of CHD. Subgroup analysis showed that associations between the MTHFD1-G1958A polymorphism and Tetralogy of Fallot (TOF) risk in the homozygote model (AA vs. GG, OR = 2.82, 95%CI [1.16, 6.86], P = 0.02) and recessive model (AA vs. GG + GA, OR = 3.09, 95%CI [1.36, 7.03], P = 0.007). In addition, the MTHFD1-G1958A polymorphism was associated with the risk of CHD in racial subgroup, increasing the risk of CHD in Caucasians. In maternal analysis, 2 genetic models of MTHFD1-G1958A polymorphism increased the risk of CHD: the heterozygote model (GA vs. GG, OR = 1.22, 95%CI [1.04, 1.42], P = 0.01), and the dominance model (GA + AA vs. GG, OR = 1.17, 95%CI [1.01, 1.34], P = 0.03). CONCLUSIONS: The fetal MTHFD1-G1958A (rs2236225) gene polymorphism increase their risk of TOF. The maternal MTHFD1-G1958A polymorphism has a strong correlation with the risk of CHD, and there are racial differences in this correlation. Compared with GG genotype, the GA genotype increases the risk of CHD.

Humans↗

PubMed2024

The prognostic significance of ubiquitination-related genes in multiple myeloma by bioinformatics analysis.

BACKGROUND: Immunoregulatory drugs regulate the ubiquitin-proteasome system, which is the main treatment for multiple myeloma (MM) at present. In this study, bioinformatics analysis was used to construct the risk model and evaluate the prognostic value of ubiquitination-related genes in MM. METHODS AND RESULTS: The data on ubiquitination-related genes and MM samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The consistent cluster analysis and ESTIMATE algorithm were used to create distinct clusters. The MM prognostic risk model was constructed through single-factor and multiple-factor analysis. The ROC curve was plotted to compare the survival difference between high- and low-risk groups. The nomogram was used to validate the predictive capability of the risk model. A total of 87 ubiquitination-related genes were obtained, with 47 genes showing high expression in the MM group. According to the consistent cluster analysis, 4 clusters were determined. The immune infiltration, survival, and prognosis differed significantly among the 4 clusters. The tumor purity was higher in clusters 1 and 3 than in clusters 2 and 4, while the immune score and stromal score were lower in clusters 1 and 3. The proportion of B cells memory, plasma cells, and T cells CD4 naïve was the lowest in cluster 4. The model genes KLHL24, HERC6, USP3, TNIP1, and CISH were highly expressed in the high-risk group. AICAr and BMS.754,807 exhibited higher drug sensitivity in the low-risk group, whereas Bleomycin showed higher drug sensitivity in the high-risk group. The nomogram of the risk model demonstrated good efficacy in predicting the survival of MM patients using TCGA and GEO datasets. CONCLUSIONS: The risk model constructed by ubiquitination-related genes can be effectively used to predict the prognosis of MM patients. KLHL24, HERC6, USP3, TNIP1, and CISH genes in MM warrant further investigation as therapeutic targets and to combat drug resistance.

Humans↗