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Biomedical subjects

Long Zhang

Publications and source records attributed to Long Zhang.

2 recordsLinked to original sources

Human m6A demethylase FTO modulates the flowering time of tomato plants under low-temperature stress.

N6-methyladenosine (m6A) RNA modification plays an important role in plant development and environmental stress responses. However, whether m6A demethylation modulates flowering under low-temperature (LT) stress in tomatoes remains unclear. Here, we investigated whether ectopic expression of FTO, a well-characterized human m6A demethylase, influences flowering and post-transcriptional behaviour in tomato (Solanum lycopersicum) under LT conditions. Flowering of transgenic tomato plants expressing FTO was analyzed under LT and normal conditions (NC), and the impacts of FTO on transcripts-specific m6A level, mRNA stability and splicing efficiency of flowering-related genes were evaluated using RT-qPCR, LC-MS/MS, m6A-IP-qPCR, and RNA decay and splicing analyses. FTO-expressing plants exhibited accelerated flowering specifically under LT, whereas no significant differences were observed under normal growth conditions. This phenotype was accompanied by increased expression of positive floral regulators (SlMC, SlFCA, and SlJ2) and decreased expression of negative regulators (SlSVP, SlSP, and SlTMF) under LT conditions. Notably, these expression changes were associated with altered mRNA stability, with positive regulators showing increased stability and negative regulators showing reduced stability under LT conditions. m6A-IP-qPCR analysis indicated reduced m6A enrichment in these selected transcripts in FTO-expressing plants. In addition to effects on mRNA stability, FTO expression was associated with changes in the splicing efficiency of SlMC transcripts. Collectively, our findings indicate that human FTO functions as an mRNA m6A demethylase in tomatoes and is associated with altered RNA regulatory processes under LT conditions. These findings suggest that m6A-mediated post-transcriptional regulation contributes to stress-induced flowering plasticity under LT conditions, rather than direct activation of canonical flowering pathways.

Abiotic stress

Machine learning and multi-omics clustering to map cellular rewiring and immune evasion in ccRCC.

Immune checkpoint blockade (ICB) efficacy in clear cell renal cell carcinoma (ccRCC) is limited by tumor microenvironment (TME) heterogeneity. Because traditional bulk-derived models lack spatial resolution, we developed an integrated framework connecting macroscopic survival risks to microscopic TME structures. We applied ten algorithms to establish multi-omics subtypes and evaluated 101 machine-learning combinations across three independent cohorts to generate a Consensus Machine Learning-driven Signature (CMLS). The signature's spatial and cellular origins were decoded using spatial transcriptomics (ST) and a 140,000-cell scRNA-seq atlas. Expression of key genes was experimentally validated via RT-qPCR in 17 paired ccRCC clinical tissues. We identified two molecular subtypes with distinct clinical and epigenetic profiles. SuperPC optimization yielded a 24-gene CMLS serving as an independent prognostic factor. scRNA-seq and ST deconvolution revealed these signals predominantly originate from cancer-associated fibroblasts (CAFs) and malignant epithelial cells, which collaborate to drive spatial immune exclusion. RT-qPCR confirmed significant overexpression of five core CMLS genes in ccRCC versus adjacent normal tissues. Low CMLS scores correlated with enhanced ICB responsiveness, whereas high-CMLS tumors demonstrated specific vulnerability to dasatinib and dabrafenib. The CMLS translates spatial immune-exclusion dynamics into a quantifiable metric, outperforming tumor mutational burden in predicting ICB benefits, providing a robust tool for patient stratification in ccRCC.

Humans