Compiling a list of optimal surrogate peptides for target proteins to be analyzed by LC/MRM-MS has been a cumbersome process, in which expert researchers retrieved information from different online repositories and used their own reasoning to find the most appropriate peptides. Our scientific workflow automates this process by integrating information from different data sources including UniProt, Global Proteome Machine, NCBI's dbSNP, and PeptideAtlas, simulating the researchers' reasoning, and incorporating their knowledge of how to select the best proteotypic peptides for an MRM analysis. The developed software can help to standardize the selection of peptides, eliminate human error, and increase productivity.

This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-HPP) consortium, which develops new technologies to identify yet-to-be annotated proteins (termed "missing proteins") in biological samples that lack sufficient experimental evidence at the protein level for confident protein identification. The C-HPP also aims to identify new protein forms that may be caused by genetic variability, post-translational modifications, and alternative splicing. Proteogenomic data integration forms the basis of the C-HPP's activities; therefore, we have summarized some of the key approaches and their roles in the project. We present new analytical technologies that improve the chemical space and lower detection limits coupled to bioinformatics tools and some publicly available resources that can be used to improve data analysis or support the development of analytical assays. Most of this paper's content has been compiled from posters, slides, and discussions presented in the series of C-HPP workshops held during 2014. All data (posters, presentations) used are available at the C-HPP Wiki (http://c-hpp.webhosting.rug.nl/) and in the Supporting Information.

Background: Laser Interstitial ThermoTherapy (LITT) is a well established surgical method. The use of LITT is so far limited to homogeneous tissues, e.g. the liver. One of the reasons is the limited capability of existing treatment planning models to calculate accurately the damage zone. The treatment planning in inhomogeneous tissues, especially of regions near main vessels, poses still a challenge. In order to extend the application of LITT to a wider range of anatomical regions new simulation methods are needed. The model described with this article enables efficient simulation for predicting damaged tissue as a basis for a future laser-surgical planning system. Previously we described the dependency of the model on geometry. With the presented paper including two video files we focus on the methodological, physical and mathematical background of the model.

Targeting metabolism through bioactive key metabolites is an upcoming future therapeutic strategy. We questioned how modifying intracellular lipid metabolism could be a possible means for alleviating inflammation. Using a recently developed chemical probe (SH42), we inhibited distal cholesterol biosynthesis through selective inhibition of Δ24-dehydrocholesterol reductase (DHCR24). Inhibition of DHCR24 led to an antiinflammatory/proresolving phenotype in a murine peritonitis model. Subsequently, we investigated several omics layers in order to link our phenotypic observations with key metabolic alterations. Lipidomic analysis revealed a significant increase in endogenous polyunsaturated fatty acid (PUFA) biosynthesis. These data integrated with gene expression analysis, revealing increased expression of the desaturase Fads6 and the key proresolving enzyme Alox-12/15. Protein array analysis, as well as immune cell phenotype and functional analysis, substantiated these results confirming the antiinflammatory/proresolving phenotype. Ultimately, lipid mediator (LM) analysis revealed the increased production of bioactive lipids, channeling the observed metabolic alterations into a key class of metabolites known for their capacity to change the inflammatory phenotype.

An experimental and computational approach for identification of protein-protein interactions by ex vivo chemical crosslinking and mass spectrometry (CLMS) has been developed that takes advantage of the specific characteristics of cyanurbiotindipropionylsuccinimide (CBDPS), an affinity-tagged isotopically coded mass spectrometry (MS)-cleavable crosslinking reagent. Utilizing this reagent in combination with a crosslinker-specific data-dependent acquisition strategy based on MS2 scans, and a software pipeline designed for integrating crosslinker-specific mass spectral information led to demonstrated improvements in the application of the CLMS technique, in terms of the detection, acquisition, and identification of crosslinker-modified peptides. This approach was evaluated on intact yeast mitochondria, and the results showed that hundreds of unique protein-protein interactions could be identified on an organelle proteome-wide scale. Both known and previously unknown protein-protein interactions were identified. These interactions were assessed based on their known sub-compartmental localizations. Additionally, the identified crosslinking distance constraints are in good agreement with existing structural models of protein complexes involved in the mitochondrial electron transport chain.

HLA-DP alleles can be classified into functional T cell epitope (TCE) groups. TCE-1 and TCE-2 are clearly defined, but TCE-3 still represents an heterogeneous group. Because polymorphisms in HLA-DP influence the presented peptidome, we investigated whether the composition of peptides binding in HLA-DP may be used to refine the HLA-DP group classification. Peptidomes of human HLA-DP-typed B cell lines were analyzed with mass spectrometry after immunoaffinity chromatography and peptide elution. Gibbs clustering was performed to identify motifs of binding peptides. HLA-DP peptide-binding motifs showed a clear association with the HLA-DP allele-specific sequences of the binding groove. Hierarchical clustering of HLA-DP immunopeptidomes was performed to investigate the similarities and differences in peptidomes of different HLA-DP molecules, and this clustering resulted in the categorization of HLA-DP alleles into 3-DP peptidome clusters (DPC). The peptidomes of HLA-DPB1*09:01, -10:01, and -17:01 (TCE-1 alleles) and HLA-DPB1*04:01, -04:02, and -02:01 (TCE-3 alleles) were separated in two maximal distinct clusters, DPC-1 and DPC-3, respectively, reflecting their previous TCE classification. HLA-DP alleles categorized in DPC-2 shared certain similar peptide-binding motifs with DPC-1 or DPC-3 alleles, but significant differences were observed for other positions. Within DPC-2, divergence between the alleles was observed based on the preference for different peptide residues at position 9. In summary, immunopeptidome analysis was used to unravel functional hierarchies among HLA-DP alleles, providing new molecular insights into HLA-DP classification.

Modern biomarker and translational research as well as personalized health care studies rely heavily on powerful omics’ technologies, including metabolomics and lipidomics. However, to translate metabolomics and lipidomics discoveries into a high-throughput clinical setting, standardization is of utmost importance. Here, we compared and benchmarked a quantitative lipidomics platform. The employed Lipidyzer platform is based on lipid class separation by means of differential mobility spectrometry with subsequent multiple reaction monitoring. Quantitation is achieved by the use of 54 deuterated internal standards and an automated informatics approach. We investigated the platform performance across nine laboratories using NIST SRM 1950–Metabolites in Frozen Human Plasma, and three NIST Candidate Reference Materials 8231–Frozen Human Plasma Suite for Metabolomics (high triglyceride, diabetic, and African-American plasma). In addition, we comparatively analyzed 59 plasma samples from individuals with familial hypercholesterolemia from a clinical cohort study. We provide evidence that the more practical methyl-tert-butyl ether extraction outperforms the classic Bligh and Dyer approach and compare our results with two previously published ring trials. In summary, we present standardized lipidomics protocols, allowing for the highly reproducible analysis of several hundred human plasma lipids, and present detailed molecular information for potentially disease relevant and ethnicity-related materials.

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