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Gang Liu

Publications and source records attributed to Gang Liu.

6 recordsLinked to original sources
PubMed2026

Protein Profiling Identifies Biomarkers for Predicting Disease Severity in Anti-NMDAR Encephalitis.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a severe autoimmune neurological disorder characterized by pathogenic antibodies against the NMDAR. A systematic protein profiling approach is warranted to identify biomarkers capable of predicting disease status. An Olink proximity extension assay (PEA) profiled 91 inflammation-related proteins from anti-NMDAR encephalitis patients. Disease severity or prognosis were assessed by CASE score or mRS score at 6-month follow-up. Patients were stratified into distinct molecular clusters using unsupervised clustering. Logistic regression models incorporating selected biomarkers were developed to predict disease severity and prognosis, followed by absolute quantification using ELISA. Patients were classified into four consensus clusters. Clusters 1 and 2 corresponded to the mild group, while Cluster 3 represented the severe group, consistent with CASE score above 6. Cluster 4 showed heterogeneous clinical features. Elevated serum levels of IL-10, IL-6, and SIRT2, as well as increased CSF levels of CXCL10, CXCL11, and MMP10, were positively associated with severe disease. Conversely, several proteins including LTA and CCL11, CCL8, TGFB1, CXCL6 were associated with severe disease or unfavorable 6-month outcomes. A logistic regression model combining serum CXCL6 and CCL11 with CSF MMP10 achieved an area under the curve (AUC) of 0.95 for predicting disease severity. Serum CCL11 alone showed predictive value for 6-month prognosis, with an AUC of 0.79. These findings delineate distinct protein signatures associated with clinical heterogeneity of anti-NMDAR encephalitis. Prediction models incorporating multiple biomarkers may provide an approach for disease severity stratification and prognosis forecast.

Humans↗
PubMed2026

Metagenomic insights into microbial drivers of organic micropollutant removal in wastewater-impacted riverbank filtration.

Organic micropollutants (OMPs) in wastewater treatment plant (WWTP) effluent pose persistent risks to aquatic ecosystems and drinking water sources. Riverbank filtration (RBF) is a nature-based treatment process, yet the compartment-specific roles of riverbed sediment and downstream soil in OMP attenuation remain poorly resolved under wastewater-impacted conditions. Here, we combined targeted chemical analysis, OMP property compilation, shotgun metagenomics, EnviPath-based biotransformation annotation, and exploratory network analysis to investigate OMP attenuation in a laboratory-scale RBF system treating real WWTP effluent for 10 months. Nineteen OMPs were monitored along a sequential sediment-soil filtration pathway. Sediment preferentially attenuated hydrophilic or charged compounds, including lidocaine, amantadine, and sotalol, whereas soil contributed more strongly to the attenuation of naproxen, atenolol, and losartan. Metagenomic profiling revealed distinct microbial communities and functional gene repertoires between sediment and soil after long-term operation. Sediment harbored higher relative abundances of genes associated with oxidative xenobiotic transformation, including cytochrome P450-related enzymes, demethylases, dehydrogenases, oxidases, and aromatic compound degradation pathways. An exploratory Spearman network further identified associations among microbial genera, EnviPath-annotated candidate biotransformation genes, and OMP removal rates, including 17 KO-OMP links supported by both correlation and pathway annotation. These findings indicate that sediment and soil develop complementary microbial functional potentials that may support compound-specific OMP attenuation. This study provides a mechanistic basis for optimizing sediment-soil configurations in wastewater-impacted RBF systems and for improving nature-based barriers against diverse OMP mixtures.

Wastewater↗
PubMed2025

MED12-STAT1-TAP2 axis regulates CD8 + T cell cytotoxicity and mediates immunotherapy outcome in non-small cell lung cancer.

Although immunotherapy for late-stage non-small cell lung carcinoma (NSCLC) has been clinically utilized, its prognosis remains highly heterogeneous, prompting us to investigate novel predictive immunotherapy biomarkers for NSCLC. We analyzed the correlations between MED12 nonsynonymous mutations and survival, clinical, genomic, transcriptomic information, and immune infiltration information through data mining across multiple datasets. We also investigated the mechanism of MED12 using luciferase assay, Western blot, ChIP-PCR, and siRNA. MED12 is significantly associated with survival in completely independent immunotherapy datasets, including MSKCC (N = 350), Naiyer2015 (N = 34), our own (N = 295) and the pan-cancer dataset, but not in the TCGA dataset, where patients received non-immunotherapy regimens. Mutations in MED12 showed no significant correlation with known metrics (TMB, IPS/CTLA4/PD1 status, PD-1/PD-L1 expression, and TCR/BCR status) or DNA Damage Repair (DDR) pathway mutations, yet they carried independent prognostic information according to the Cox multivariate regression. On the other hand, MED12 mutation is significantly associated with multiple immune-related pathways and immune infiltration of CD8 + T cells and activated NK cells. Lactate dehydrogenase assay revealed that knockdown of TAP2 restored the upregulation of CD8 + T cell cytotoxicity triggered by MED12 knockdown. ChIP-PCR, luciferase assay and siRNA knock down assay indicate that MED12 binds to the promoter region of STAT1 to suppress its transcription, while the transcription factor STAT1 promotes the transcription of TAP2, thus inhibiting the antigen processing and presentation. Collectively, MED12 mutation is an independent and valuable biomarker for predicting the response to immune checkpoint inhibitor (ICI)therapy in NSCLC by modulating CD8 + T cell cytotoxicity via the STAT1/TAP2 axis.

Humans↗
PubMed2025

Validation of breast cancer as a risk factor for anxiety and depression: Insights from Mendelian randomization analysis.

This study employed Mendelian randomization (MR) analysis to confirm the association between breast cancer and the risk of anxiety and depression, and to explore the molecular mechanisms by which lipid nanoparticles of ketamine (LNP@Ket) modulate these behaviors in a mouse model of breast cancer. Through single-cell transcriptomic analysis, the study aimed to clarify nuclear factor erythroid 2-related factor 2 (Nrf2)'s role in the development of anxiety and depression in these mice. Analysis of patient data from genome-wide association study (GWAS) databases supported the link between breast cancer, anxiety, and depression. In vivo experiments demonstrated that treating breast cancer mice with LNP@Ket significantly reduced anxiety and depression behaviors. The synthesis of LNP@Ket and its subsequent analysis highlighted its inhibitory effects on these behaviors. Single-cell transcriptomic sequencing identified key cells and genes affected by LNP@Ket treatment, particularly emphasizing Nrf2. Upregulation of Nrf2 in astrocytes increased the expression of antioxidant enzymes and reduced pro-inflammatory cytokines, alleviating anxiety and depression symptoms by inhibiting neuroinflammation and neurodegeneration. This comprehensive study highlights the pivotal role of Nrf2 in the therapeutic efficacy of LNP@Ket for treating anxiety and depression in breast cancer mice.

Anxiety and depression behaviors↗
PubMed2025

Genomic insights into the population history of fat-tailed sheep and identification of two mutations that contribute to fat tail adipogenesis.

INTRODUCTION: Since their domestication, domestic sheep (Ovis aries) have been culturally and economically significant farming animals worldwide. Fat-tailed sheep serve as a unique genetic resource for understanding adipogenesis and adaptive evolution in livestock. OBJECTIVES: Several genomic analyses have been conducted on various sheep breeds to elucidate the genome and regulation mechanism of the fat tail trait, prior genomic studies have failed to reconcile conflicting evidence about the genetic basis of tail morphology, particularly regarding the roles of PDGFD and BMP2. METHODS: Here, we conducted whole-genome resequencing of 283 sheep, encompassing 66 domestic breeds and 5 wild ovine species, to investigate the domestication history and selection signatures of fat-tailed sheep. Additionally, we performed transcriptome sequencing on adipose tissue to identify differentially expressed genes and cellular assays to validate these results. RESULTS: Demographic analysis revealed that domestic sheep descended from Asiatic mouflon and fat-tailed sheep began to diverge from thin-tailed sheep approximately 4.4-7.5 thousand years ago in East Asia. Chinese indigenous sheep were classified into Mongolian, Kazakh, Tibetan, and Yunnan populations. The Yunnan population may have experienced more recent genetic introgression from wild species, rather than an independent domestication event. Moreover, many potential regions associated with the fat-tailed phenotype (DDI1, PDGFD, and BMP2) were identified by selective sweep and genome-wide association analyses. Additionally, a fine-scale analysis of fat-tailed and thin-tailed sheep revealed two novel mutations: a G/A missense variant of PDGFD (Chr15: 3900312) and a C/T missense variant of BMP2 (Chr13: 48462350), both of which were significantly associated with tail adiposity. Functional validation demonstrated that mutant A-PDGFD significantly activated PFGFD expression and reduced fat deposition compared to wildtype. The C-BMP2 mutant activated BMP2 expression and promoted preadipocyte fat deposition. CONCLUSION: Our study provides the first evidence that these genes jointly regulate fat tail development through complementary mechanisms: PDGFD promotes adipose expansion, whereas BMP2 modulates energy partitioning. These findings offer new insights into the evolutionary history of fat-tailed sheep and identify potential targets for precision breeding in small ruminants.

Animals↗
PubMed2009

Similarities and differences between smoking-related gene expression in nasal and bronchial epithelium.

Previous studies have shown that physiological responses to cigarette smoke can be detected via bronchial airway epithelium gene expression profiling and that heterogeneity in this gene expression response to smoking is associated with lung cancer. In this study, we sought to determine the similarity of the effects of tobacco smoke throughout the respiratory tract by determining patterns of smoking-related gene expression in paired nasal and bronchial epithelial brushings collected from 14 healthy nonsmokers and 13 healthy current smokers. Using whole genome expression arrays, we identified 119 genes whose expression was affected by smoking similarly in both bronchial and nasal epithelium, including genes related to detoxification, oxidative stress, and wound healing. While the vast majority of smoking-related gene expression changes occur in both bronchial and nasal epithelium, we also identified 27 genes whose expression was affected by smoking more dramatically in bronchial epithelium than nasal epithelium. Both common and site-specific smoking-related gene expression profiles were validated using independent microarray datasets. Differential expression of select genes was also confirmed by RT-PCR. That smoking induces largely similar gene expression changes in both nasal and bronchial epithelium suggests that the consequences of cigarette smoke exposure can be measured in tissues throughout the respiratory tract. Our findings suggest that nasal epithelial gene expression may serve as a relatively noninvasive surrogate to measure physiological responses to cigarette smoke and/or other inhaled exposures in large-scale epidemiological studies.

Adult↗