Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by datadependent acquisition (DDA) experiments are required prior to DIA analysis, which is timeconsuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics.
Pathogenesis of colorectal cancer (CRC) is associated with alterations in gut microbiome. Previous studies have focused on the changes of taxonomic abundances by metagenomics. Variations of the function of intestinal bacteria in CRC patients compared to healthy crowds remain largely unknown. Here we collected fecal samples from CRC patients and healthy volunteers and characterized their microbiome using quantitative metaproteomic method. We have identified and quantified 91,902 peptides, 30,062 gut microbial protein groups, and 195 genera of microbes. Among the proteins, 341 were found significantly different in abundance between the CRC patients and the healthy volunteers. Microbial proteins related to iron intake/transport; oxidative stress; and DNA replication, recombination, and repair were significantly alternated in abundance as a result of high local concentration of iron and high oxidative stress in the large intestine of CRC patients. Our study shows that metaproteomics can provide functional information on intestinal microflora that is of great value for pathogenesis research, and can help guide clinical diagnosis in the future.
M. thermoacetica-CdS biohybrid, the first artificial photosynthetic microbial system, has gained a wide variety of scientific attention. Proteomic and metabolomic results indicate that a number of electron carriers and enzymes in the Wood-Ljungdahl pathway play very important roles in electron transfer from CdS to cytoplasm and CO 2 fixation. Targeted metabolomics together with proteome data reveal that glycolysis and the TCA cycle are involved in ATP production in the biohybrid system.
Proteolysis affects every protein at some point in its life cycle. Many biomarkers of disease or cancer are stable proteolytic fragments in biological fluids. There is great interest and a challenge in proteolytically modified protein study to identify physiologic protease-substrate relationships and find potential biomarkers. In this study, two human hepatocellular carcinoma (HCC) cell lines with different metastasis potential, MHCC97L, and HCCLM6, were researched with a high-throughput and sensitive PROTOMAP platform. In total 391 proteins were found to be proteolytically processed and many of them were cleaved into persistent fragments instead of completely degraded. Fragments related to 161 proteins had different expressions in these two cell lines. Through analyzing these significantly changed fragments with bio-informatic tools, several bio-functions such as tumor cell migration and anti-apoptosis were enriched. A proteolysis network was also built up, of which the CAPN2 centered subnetwork, including SPTBN1, ATP5B, and VIM, was more active in highly metastatic HCC cell line. Interestingly, proteolytic modifications of CD44 and FN1 were found to affect their secretion. This work suggests that proteolysis plays an important role in human HCC metastasis.
Metaproteomics can provide valuable insights into the functions of human gut microbiota (GM), but is challenging due to the extreme complexity and heterogeneity of GM. Data-independent acquisition (DIA) mass spectrometry (MS) has been an emerging quantitative technique in conventional proteomics, but is still at the early stage of development in the field of metaproteomics. Herein, we applied library-free DIA (directDIA)-based metaproteomics and compared the directDIA with other MS-based quantification techniques for metaproteomics on simulated microbial communities and feces samples spiked with bacteria with known ratios, demonstrating the superior performance of directDIA by a comprehensive consideration of proteome coverage in identification as well as accuracy and precision in quantification. We characterized human GM in two cohorts of clinical fecal samples of pancreatic cancer (PC) and mild cognitive impairment (MCI). About 70,000 microbial proteins were quantified in each cohort and annotated to profile the taxonomic and functional characteristics of GM in different diseases. Our work demonstrated the utility of directDIA in quantitative metaproteomics for investigating intestinal microbiota and its related disease pathogenesis.
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