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Key messages: Narrowing down to a single putative target gene behind a leaf senescence mutant and constructing the regulation network by proteomic method. Leaf senescence mutant is an important resource for exploring molecular mechanism of aging. To dig for potential modulation networks during maize leaf aging process, we delimited the gene responsible for a premature leaf senescence mutant els5 to a 1.1 Mb interval in the B73 reference genome using a BC1F1 population with 40,000 plants, and analyzed the leaf proteomics of the mutant and its near-isogenic wild type line. A total of 1355 differentially accumulated proteins (DAP) were mainly enriched in regulation pathways such as "photosynthesis", "ribosome", and "porphyrin and chlorophyll metabolism" by the KEGG pathway analysis. The interaction networks constructed by incorporation of transcriptome data showed that ZmELS5 likely repaired several key factors in the photosynthesis system. The putative candidate proteins for els5 were proposed based on DAPs in the fined QTL mapping interval. These results provide fundamental basis for cloning and functional research of the els5 gene, and new insights into the molecular mechanism of leaf senescence in maize. [ABSTRACT FROM AUTHOR]

Intratumor heterogeneity (ITH) is a biomarker of tumor progression, metastasis, and immune evasion. Previous studies evaluated ITH mostly based on DNA alterations. Here, we developed a new algorithm (DEPTH) for quantifying ITH based on mRNA alterations in the tumor. DEPTH scores displayed significant correlations with ITH-associated features (genomic instability, tumor advancement, unfavorable prognosis, immunosuppression, and drug response). Compared to DNA-based ITH scores (EXPANDS, PhyloWGS, MATH, and ABSOLUTE), DEPTH scores had stronger correlations with antitumor immune signatures, cell proliferation, stemness, tumor advancement, survival prognosis, and drug response. Compared to two other mRNA-based ITH scores (tITH and sITH), DEPTH scores showed stronger and more consistent associations with genomic instability, unfavorable tumor phenotypes and clinical features, and drug response. We further validated the reliability and robustness of DEPTH in 50 other datasets. In conclusion, DEPTH may provide new insights into tumor biology and potential clinical implications for cancer prognosis and treatment. Li et al develop an algorithm they call Deviating gene Expression Profiling Tumor Heterogeneity (DEPTH) to evaluate levels of intratumour heterogeneity (ITH) at the mRNA level based on the asynchrony of transcriptome alterations in tumors. Analysing TCGA and GEO data-sets, they show that this method is simpler, more effective, and more robust than other ITH scoring algorithms. [ABSTRACT FROM AUTHOR]