Project description:A Comparative Study of Human Testes and Epididymis through the Proteomics and RNA-seq Methods
<ul><li>Dataset imported into MassIVE from <a href="https://www.iprox.cn/page/project.html?id=IPX0003098000">https://www.iprox.cn/page/project.html?id=IPX0003098000</a> on 12/10/21</li></ul>
Project description:Discovering noncanonical peptides has been a common application of proteogenomics. Recent studies suggest that certain noncanonical peptides, known as ncMAPs (noncanonical MHC-I-associated peptides), that bind to major histocompatibility complex I may make good immunotherapeutic targets. De novo peptide sequencing is a great way to find ncMAPs since it can detect peptide sequences from their tandem mass spectra without using any sequence databases. However, this strategy hasn’t been widely applied for ncMAP identification because there is not a good way to estimate its false-positive rates. In order to completely and accurately identify immunopeptides using de novo peptide sequencing, we describe a unique pipeline called pXg. In contrast to current pipelines, it makes use of genomic data, RNA-Seq abundance and sequencing quality, in addition to proteomic features to increase the sensitivity and specificity of peptide identification. We show that the peptide-spectrum match quality and genetic traits have a clear relationship, showing that they can be utilized to evaluate peptide-spectrum matches. From ten samples, we found 24,449 cMAPs (canonical MHC-I-associated peptides) and 956 ncMAPs by using a target-decoy competition. 387 ncMAPs and 1,611 cMAPs were novel identifications that had not yet been published. We discovered 11 ncMAPs produced from a squirrel monkey retrovirus in human cell lines in addition to the 2 ncMAPs originating from a complementarity determining region 3 in an antibody thanks to the unrestricted search space assumed by de novo sequencing. These entirely new identifications show that pXg can make the most of de novo peptide sequencing's advantages and its potential use in the search for new immunotherapeutic targets.
Project description:Lipids play an important role in energy storage, membrane structure stabilization and signaling. Parasitoids are excellent models to study lipidomics because a majority of them do not accumulate during their free-living life-stage. Studies on parasitoids have mostly focused on the changes in the lipids and gene transcripts in hosts and little attention has been devoted to lipidomics and transcriptomics changes in parasitoids. In this study, a relative quantitative analysis of lipids and their gene transcripts in 3-days-old Lysiphlebia japonica larva (3 days after spawning) and pupae were performed using liquid chromatography, mass spectrometry and RNA-seq. Thirty-three glycerolipids and 250 glycerophospholipids were identified in this study; all triglycerides and the vast majority of phospholipids accumulated in the pupal stage. This was accompanied by differentially regulated lipid uptake and remolding. Furthermore, our data showed that gene transcription was up-regulated in key nutrient metabolic pathways involved in lipid synthesis in 3-days-old larvae. Finally, our data suggests that larva and pupa of L. japonica may lack the ability for fatty acids synthesis. A comprehensive, quantitative, and expandable resource was provided for further studies of metabolic regulation and molecular mechanisms underlying parasitic response to hosts defense.
Project description:Grad-seq in Clostridium difficile 630. Cell lysate is analyzed in a gradient and fractionated into 21 fractions which are analysed for proteins by MS and for transcripts by RNA-sequencing.
Project description:Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools including protein quantification, imputation, and differential expression (DE) analysis were generated in the past decade. However, searching optimized tools is still an unsolved issue. Moreover, due to the rapid development of RNA-Seq technology, a vast number of DE analysis methods are created. Applying these newly developed RNA-Seq-oriented tools to proteomics data is still a question that needs to be addressed. In order to benchmark these analysis methods, a proteomics dataset constituted the proteins derived from human, yeast, and drosophila with different ratios were generated. Based on this dataset, DE analysis tools (including array-based and RNA-Seq based), imputation algorithms, and protein quantification methods were compared and benchmarked. This study provided useful information on analyzing quantitative proteomics datasets. All the methods used in this study were integrated into Perseus which are available at https://www.maxquant.org/perseus.