As a crucial molecular mechanism, post-translational modifications (PTMs) play critical roles in a wide range of biological processes in plants. Recent advances in mass spectrometry-based proteomic technologies have greatly accelerated the profiling and quantification of plant PTM events. Although several databases have been constructed to store plant PTM data, a resource including more plant species and more PTM types with quantitative dynamics still remains to be developed. In this paper, we present an integrative database of quantitative PTMs in plants named qPTMplants (http://qptmplants.omicsbio.info), which hosts 1 242 365 experimentally identified PTM events for 429 821 nonredundant sites on 123 551 proteins under 583 conditions for 23 PTM types in 43 plant species from 293 published studies, with 620 509 quantification events for 136 700 PTM sites on 55 361 proteins under 354 conditions. Moreover, the experimental details, such as conditions, samples, instruments and methods, were manually curated, while a variety of annotations, including the sequence and structural characteristics, were integrated into qPTMplants. Then, various search and browse functions were implemented to access the qPTMplants data in a user-friendly manner. Overall, we anticipate that the qPTMplants database will be a valuable resource for further research on PTMs in plants.

As important post-translational modifications, protein cysteine modifications (PCMs) occurring at cysteine thiol group play critical roles in the regulation of various biological processes in eukaryotes. Due to the rapid advancement of high-throughput proteomics technologies, a large number of PCM events have been identified but remain to be curated. Thus, an integrated resource of eukaryotic PCMs will be useful for the research community. In this work, we developed an integrative database for protein cysteine modifications in eukaryotes (iCysMod), which curated and hosted 108 030 PCM events for 85 747 experimentally identified sites on 31 483 proteins from 48 eukaryotes for 8 types of PCMs, including oxidation, S-nitrosylation (-SNO), S-glutathionylation (-SSG), disulfide formation (-SSR), S-sulfhydration (-SSH), S-sulfenylation (-SOH), S-sulfinylation (-SO2H) and S-palmitoylation (-S-palm). Then, browse and search options were provided for accessing the dataset, while various detailed information about the PCM events was well organized for visualization. With human dataset in iCysMod, the sequence features around the cysteine modification sites for each PCM type were analyzed, and the results indicated that various types of PCMs presented distinct sequence recognition preferences. Moreover, different PCMs can crosstalk with each other to synergistically orchestrate specific biological processes, and 37 841 PCM events involved in 119 types of PCM co-occurrences at the same cysteine residues were finally obtained. Taken together, we anticipate that the database of iCysMod would provide a useful resource for eukaryotic PCMs to facilitate related researches, while the online service is freely available at http://icysmod.omicsbio.info.

Abstract Yeast autolysis refers to the process in which cells degrade and release intracellular contents under specific conditions by endogenous enzymes such as proteases, nucleases and lipid enzymes. Protein-rich baker's yeast is widely used to produce yeast extract in food industry, however, the molecular mechanism related to baker's yeast autolysis is still unclear. In this study, RNA-seq technology and biochemical analysis were performed to analyze the autolysis processes in baker's yeast. The differentially expressed genes (DEGs), 27 autolysis-related euKaryotic Ortholog Groups (KOG) and three types of autolysis-induced Gene Ontology (GO) were identified and analyzed in detail. A total of 143 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways under autolysis were also assigned. Interestingly, the DEGs were significantly enriched in the mitogen-activated protein kinase (MAPK) signaling pathways and metabolic pathways, and key genes MID2, MTL1, SLT2, PTP2, HKR1 and GPD1 may play important roles in autolysis. Further quantitative PCR was performed to verify the expression pattern in baker's yeast autolysis. Together, all these results indicated that MAPK pathways might play an essential role during autolysis process through inhibiting the metabolism and disrupting cell wall in baker's yeast. This result may provide important clues for the in-depth interpretation of the yeast autolysis mechanism.