Biomedical subjects

Yi Zhao

Publications and source records attributed to Yi Zhao.

8 recordsLinked to original sources

PubMed2026

Loss of Ku70 promotes mononucleate conidiation and homologous recombination in Phanerochaete chrysosporium.

Lignin is a major constituent of lignocellulose and the most abundant aromatic biopolymer on earth. It provides plants with rigidity and protection, but its recalcitrant nature also presents a significant barrier to lignocellulose valorization. The white-rot fungus Phanerochaete chrysosporium is among nature's most efficient lignin degraders, and its ligninolytic capabilities have been subjected to intensive investigations. Genome editing with precision is crucial for elucidating the in vivo mechanisms of its ligninolytic actions, but genetic manipulations of P. chrysosporium are often plagued by imprecision. This technical nuisance is driven primarily by canonical non-homologous end joining (c-NHEJ), a DNA repair system that requires little homology and depends on the binding of the Ku70/Ku80 heterodimer to double-strand break (DSB) ends. Loss of Ku70 or Ku80 abolishes c-NHEJ and significantly improves genome editing precision in many filamentous fungi, but it has yet to be examined and exploited in P. chrysosporium. Here, we constructed a homozygous ku70Δ mutant in a meiotic homokaryon of clear genetic background. Loss of Ku70 minimally impacts growth but significantly increases homologous recombination frequency from ~2% to ~66%, with ~32% of the latter being homozygous. Unexpectedly, loss of Ku70 also promotes mononucleate conidiation, which may facilitate isolation of homozygous mutants. Taken together, our work provides a valuable genetic tool to understand and exploit P. chrysosporium's remarkable ligninolytic capabilities.IMPORTANCEGenome editing with precision is essential to unraveling the intricacies of P. chrysosporium's exceptional ligninolytic capabilities, but the available tools are generally imprecise due to the dominance of non-homologous recombination, a problem that is further exacerbated by the discontinuation of Novozyme 234. We tackle these challenges by reestablishing protoplast-based transformation with Lywallzyme as an alternative. Importantly, we demonstrate that inactivation of c-NHEJ by deleting ku70 significantly increases gene knockout efficiency and report the unexpected involvement of c-NHEJ in regulating the number of nuclei during conidiation. Our work paves the way for future ventures into understanding ligninolysis in P. chrysosporium and building superior chassis for industrial applications.

Ku70↗

PubMed2026

Machine learning identifies ac4C-related prognostic signature and TUBA1C as therapeutic target in COAD.

To explore the role of N4-acetylcytidine (ac4C)-related genes (acRGs) in colon adenocarcinoma (COAD) and identify reliable prognostic biomarkers and potential therapeutic targets. Multi-source transcriptomic datasets (TCGA-COAD, GSE39582, GSE17536) and single-cell RNA-seq data were analyzed. Ten machine learning algorithms were integrated to construct an acRG-based prognostic signature (acRGBS). Immune microenvironment (TME) and genomic profiling were performed, with in vitro functional experiments validating TUBA1C's role. acRGBS, comprising four hub genes (SARAF, CDC42SE2, TSPYL2, TUBA1C), effectively stratified COAD patients into high- and low-risk groups with distinct survival outcomes and was an independent prognostic factor. High-risk patients exhibited increased genomic instability and immunosuppressive TME, while low-risk patients had favorable immunotherapy response. TUBA1C was overexpressed in COAD cells, and its knockdown inhibited proliferation/migration and induced apoptosis. The acRGBS is a robust prognostic tool for COAD, and TUBA1C serves as a candidate therapeutic target, providing new insights for personalized COAD management.

Humans↗

PubMed2026

Efferocytosis regulatory factors in atherosclerosis: A preclinical systematic review.

BACKGROUND: Impaired efferocytosis is a key driver of plaque instability during atherosclerosis progression. Efficient clearance of apoptotic cells through efferocytosis relies on the coordinated action of multiple regulatory factors. METHODS: PubMed, Web of Science, ScienceDirect, OVID MEDLINE, and Scopus were searched for studies published up to February 7, 2026. Eligible preclinical studies were systematically reviewed to identify endogenous factors that regulate efferocytosis in atherosclerosis. Clinical evidence was also incorporated to enable a preliminary translational assessment of these regulatory factors. RESULTS: Thirty-five endogenous regulatory factors were identified from 36 included studies, and their functional roles across distinct stages of efferocytosis were characterized. Notably, metabolic regulators such as PKM2, PFKFB3, GLS1, and Drp1 were involved in distinct efferocytosis stages. This suggests that metabolic reprogramming may provide the metabolic support require for efficient efferocytosis and inflammation resolution. Ten factors were supported by preliminary clinical evidence consistent with preclinical data. PKM2 was the only candidate biomarker with prospective observational data. However, its independent predictive value still requires validation in multicenter prospective studies. CONCLUSIONS: This review provides a systematic synthesis of 35 endogenous efferocytosis regulators and elucidates their regulatory network in atherosclerosis based on a functional stage framework. Metabolic reprogramming is identified as a central hub linking efferocytosis efficiency to inflammation resolution. This review offers a new theoretical basis for efferocytosis-targeted intervention strategies.

Animals↗

PubMed2026

LLPS-based classification and a novel prognostic signature reveal NRF1 as a therapeutic target in pancreatic cancer.

BACKGROUND: Aberrant liquid-liquid phase separation (LLPS) can alter biomolecular condensate functions and may influence pancreatic tumorigenesis and progression, but the specific role of LLPS regulators in prognosis and the tumor immune microenvironment (TIME) in pancreatic ductal adenocarcinoma (PDAC) remains unclear. METHODS: We integrated transcriptome data of LLPS regulator-related differentially expressed genes (DEGs; n = 298) in a cohort of 176 PDAC patients from TCGA. Three LLPS regulator subtypes (LS1-LS3) were identified through multi-omics analyses, and a prognostic LLPS subtype-related risk model (LRRPC) was developed and validated. Chromatin immunoprecipitation confirmed NRF1 binding to promoters of key risk genes, and in vitro and in vivo experiments assessed the effects of NRF1 targeting on tumor growth. RESULTS: The three LLPS regulator subtypes exhibited significant differences in prognosis, clinical features, genomic alterations, TIME patterns and predicted immunotherapy response. The LRRPC signature predicted prognosis and immunotherapy efficacy across cohorts and was associated with tumor biomarkers and immune infiltration. Nuclear Respiratory Factor 1 (NRF1) directly regulated hub genes such as FAM83A, RHOV and ITGB6, promoting PDAC cell proliferation, while its inhibition induced apoptosis and reduced tumor growth. CONCLUSIONS: This study proposes an LLPS-based stratification framework for PDAC, and the LRRPC model provides an LLPS subtype-related risk score that may assist personalized prognostic assessment and immunotherapy stratification. NRF1 emerges as a promising therapeutic candidate whose targeting can inhibit tumor progression in PDAC experimental models and warrants further evaluation.

Immunotherapy↗

PubMed2026

SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide clinical decisions. Although tissue-level multi-omics is widely utilized for survival prediction, its limited resolution cannot capture tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) enables dissection of the tumor microenvironment (TME) at cellular resolution, supporting personalized prognostic assessment. METHODS: We collected 213 CRC scRNA-seq samples and established a CRC-specific TME atlas comprising 339,060 cells. Using this atlas as a reference, we deconvolved bulk RNA-seq data from TCGA-CRC cohort with the EcoTyper algorithm to reconstruct TME features. Clinical, genomic, and transcriptomic data were obtained from the Xena platform; microbial data were sourced from the BIC database. We integrated TME and multi-omics features through a self-normalizing neural network to construct a deep learning model (single-cell resolution TME ecosystem with multi-omics data [SCMO]) for survival prediction. To enhance interpretability, we utilized the Integrated Gradients algorithm and spatial transcriptomic data to analyze multi-omics and TME features. We performed anticancer drug screening with tumor necrosis factor receptor-associated protein 1 (TRAP1), a critical feature according to the Integrated Gradients algorithm, as a potential target. RESULTS: We identified 13 survival-related TME features from the CRC-specific atlas: 12 cell states and one multi-cellular ecosystem. SCMO, which combined TME and multi-omics features, improved survival prediction and outperformed existing methods, achieving a concordance index of 0.762. The SCMO demonstrated robust performance for long-term predictions, achieving areas under the curve (AUCs) of 0.752, 0.772, and 0.869 for 1-, 3-, and 5-year predictions in the training set, with corresponding test set AUCs of 0.639, 0.756, and 0.772. TME features from the SCMO model revealed that ecosystem density increased with CRC malignancy. Multi-omics features included TRAP1 as a potential drug target. Drug screening identified saikosaponin A as a novel TRAP1 inhibitor, and its anticancer activity was validated in vitro. We developed SCMO-Lite, a simplified model incorporating 12 high-attribution-weight multi-omics features, which demonstrated robust risk stratification. CONCLUSIONS: SCMO combines analytical precision with biological interpretability, offering novel insights for oncology survival prediction.

Humans↗

PubMed2025

Elevated triglyceride-glucose index and risk of thymoma-associated myasthenia gravis: a prospective analysis from the UK Biobank.

BACKGROUND: Thymoma-associated myasthenia gravis (MG) is a clinically significant but uncommon condition, affecting up to half of thymoma patients and associated with worse outcomes than either disease alone. Reliable biomarkers for early risk stratification remain scarce. The triglyceride-glucose (TyG) index and triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio are established biomarkers reflecting insulin resistance and dyslipidemia. However, their clinical associations with thymoma-associated MG remain incompletely characterized. METHODS: A total of 501,954 UK Biobank participants were included. After exclusions, 422,397 (313 cases) were analyzed for TyG index and 422,691 (314 cases) for TG/HDL-C ratio. Exposures were stratified into quartiles, and assessed continuously per unit increase. Cox proportional hazards models estimated hazard ratios (HRs), restricted cubic splines (RCS) assessed non-linear associations, and subgroup analyses were stratified by body mass index (BMI). Sensitivity analyses examined TyG index and MG alone. RESULTS: An elevated TyG index was associated with increased risk of thymoma-associated MG. Compared to Q1, Q4 had higher risk (HR = 1.66, 95% CI: 1.20-2.31, P = 0.003); the overall HR per unit increase was 1.42 (95% CI: 1.17-1.73, P = 0.0005). TG/HDL-C ratio showed similar patterns: Q4 vs Q1 HR = 1.54 (95% CI: 1.11-2.15, P = 0.01); overall HR per unit increase was 1.06 (95% CI: 1.02-1.10, P = 0.002). Non-linear relationships were observed, with suggested inflection points at TyG index 8.7 and TG/HDL-C ratio 2.8, rather than strict thresholds. Subgroup analyses revealed stronger associations in normal-weight and obese participants, although tests for interaction were not statistically significant. Sensitivity analyses confirmed consistent associations between TyG index and MG risk, including for isolated MG. CONCLUSION: Elevated TyG index and TG/HDL-C ratio were independently associated with higher thymoma-associated MG risk in this large prospective cohort, with evidence of non-linear relationships and BMI-related heterogeneity. These findings provide novel epidemiologic evidence linking metabolic markers of insulin resistance with thymoma-associated MG, but clinical translation requires further validation.

Humans↗

PubMed2025

Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifies known and novel cross-population and ancestry-specific associations as novel risk loci for Alzheimer's disease.

BACKGROUND: Limited ancestral diversity has impaired our ability to detect risk variants more prevalent in ancestry groups of predominantly non-European ancestral background in genome-wide association studies (GWAS). We construct and analyze a multi-ancestry GWAS dataset in the Alzheimer's Disease Genetics Consortium (ADGC) to test for novel shared and population-specific late-onset Alzheimer's disease (LOAD) susceptibility loci and evaluate underlying genetic architecture in 37,382 non-Hispanic White (NHW), 6728 African American, 8899 Hispanic (HIS), and 3232 East Asian individuals, performing within ancestry fixed-effects meta-analysis followed by a cross-ancestry random-effects meta-analysis. RESULTS: We identify 13 loci with cross-population associations including known loci at/near CR1, BIN1, TREM2, CD2AP, PTK2B, CLU, SHARPIN, MS4A6A, PICALM, ABCA7, APOE, and two novel loci not previously reported at 11p12 (LRRC4C) and 12q24.13 (LHX5-AS1). We additionally identify three population-specific loci with genome-wide significance at/near PTPRK and GRB14 in HIS and KIAA0825 in NHW. Pathway analysis implicates multiple amyloid regulation pathways and the classical complement pathway. Genes at/near our novel loci have known roles in neuronal development (LRRC4C, LHX5-AS1, and PTPRK) and insulin receptor activity regulation (GRB14). CONCLUSIONS: Using cross-population GWAS meta-analyses, we identify novel LOAD susceptibility loci in/near LRRC4C and LHX5-AS1, both with known roles in neuronal development, as well as several novel population-unique loci. Reflecting the power of diverse ancestry in GWAS, we detect the SHARPIN locus with only 13.7% of the sample size of the NHW GWAS study (n = 409,589) in which this locus was first observed. Continued expansion into larger multi-ancestry studies will provide even more power for further elucidating the genomics of late-onset Alzheimer's disease.

Humans↗

PubMed2024

Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes.

Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer's disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.

Alzheimer Disease↗