Proteogenomic and metabolomic characterization of human glioblastoma.

Glioblastoma (GBM) is the most aggressive nervous system cancer, with median survival under 2 years. Understanding its molecular pathogenesis is crucial for improving diagnosis and treatment. We performed an integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs. We identified key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation as well as potential targets for EGFR-, TP53- and RB1-altered tumors. We detected immune subtypes, driven by the presence of distinct immune cell populations using bulk omics, validated by single nulcei RNA sequencing (snRNA-seq), and they were correlated with specific expression and histone acetylation patterns. Acetylation of histone H2B in classical-like and immune-low GBM was driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identified specific lipid distributions across subtypes and distinct global metabolic changes in IDH mutated tumors. This work highlights biological relationships which could potentially aid GBM patient stratifications for more effective treatments.

Glioblastoma (GBM) is the most aggressive nervous system cancer, with median survival under 2 years. Understanding its molecular pathogenesis is crucial for improving diagnosis and treatment. We performed an integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs. We identified key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation as well as potential targets for EGFR-, TP53- and RB1-altered tumors. We detected immune subtypes, driven by the presence of distinct immune cell populations using bulk omics, validated by snRNA-seq, and they were correlated with specific expression and histone acetylation patterns. Acetylation of histone H2B in classical-like and immune-low GBM was driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identified specific lipid distributions across subtypes and distinct global metabolic changes in IDH mutated tumors. By comparing the adult GBM proteomics to the adolescent and young adult (AYA) GBM cohort from the HOPE study, we found downregulated IDH1 expression and up-regulated expression of genes in the NADH dehydrogenase family, including NDUFB1, and NDUFB3 among others, which may be related to high IDH1 mutation frequency in AYA. This work highlights biological relationships which could potentially aid GBM patient stratifications for more effective treatments. Citation Format: Liang-Bo Wang, Alla Karpova, Marina A. Gritsenko, Jennifer E. Kyle, Song Cao, Yize Li, Dmitry Rykunov, Antonio Colaprico, Joseph Rothstein, Runyu Hong, Vasileios Stathias, MacIntosh Cornwell, Francesca Petralia, Richard D. Smith, Antonio Iavarone, Milan G. Chheda, Jill S. Barnholtz-Sloan, Karin D. Rodland, Tao Liu, Li Ding, Clinical Proteomic Tumor Analysis Consortium. Proteogenomic and metabolomic characterization of human glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2170.

Glioblastoma (GBM) is the most common type of primary malignant tumor in adults and contributes disproportionately to cancer morbidity and mortality. Understanding its molecular pathogenesis is crucial for improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomics data on 99-treatment naïve GBMs provided insights to GBM biology. Multiple key findings emerged that were not known from traditional genomic analyses: (1) Analysis of protein phosphorylation identified key signaling intermediates in the RTK/RAS path-way common to multiple RTK genomic alterations (PTPN11 and PLCG1), potentially offering common therapeutic targets for different oncogenic drivers in GBM. (2) Phosphoproteomics also identified potential druggable targets based on kinase-substrate pathway analysis, as well as novel phosphoprotein targets associated with the regulation of telomere length by ATRX in IDH mutants. (3) TP53 protein abundance in GBM is regulated by protein/phosphoprotein effectors (4) Four immune GBM subtypes exist, characterized by distinct immune cell population differences – Immune High and Immune Low phenotypes in GBM were driven by tumor-associated macrophage markers, and associated with distinct epigenetic modifications and histone acetylation patterns. (5) The mesenchymal subtype displays EMT signatures specific in tumor cells, distinct from infiltrating immune cells. (6) Histone H2B acetylation and immune-low GBM was driven largely by BRDs, CREBBP, and EP300. (7) Identification of key metabolic changes in IDH mutants facilitating the accumulation of onco-metabolite 2-HG were also associated with lipidomic changes. This work identifies additional therapeutic channels for GBM and novel information useful for more accurate stratification patients for effective treatment. Citation Format: Jill S. Barnholtz-Sloan. Gaining biologic insights into glioblastoma using proteomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr SY07-02.

Motivation: The availability of modern sequencing techniques has led to a rapid increase in the amount of reconstructed metabolic networks. Using these models as a platform for the analysis of high throughput transcriptomic, proteomic and metabolomic data can provide valuable insight into conditional changes in the metabolic activity of an organism. While transcriptomics and proteomics provide important insights into the hierarchical regulation of metabolic flux, metabolomics shed light on the actual enzyme activity through metabolic regulation and mass action effects. Here we introduce a new method, termed integrative omics-metabolic analysis (IOMA) that quantitatively integrates proteomic and metabolomic data with genome-scale metabolic models, to more accurately predict metabolic flux distributions. The method is formulated as a quadratic programming (QP) problem that seeks a steady-state flux distribution in which flux through reactions with measured proteomic and metabolomic data, is as consistent as possible with kinetically derived flux estimations.Results: IOMA is shown to successfully predict the metabolic state of human erythrocytes (compared to kinetic model simulations), showing a significant advantage over the commonly used methods flux balance analysis and minimization of metabolic adjustment. Thereafter, IOMA is shown to correctly predict metabolic fluxes in Escherichia coli under different gene knockouts for which both metabolomic and proteomic data is available, achieving higher prediction accuracy over the extant methods. Considering the lack of high-throughput flux measurements, while high-throughput metabolomic and proteomic data are becoming readily available, we expect IOMA to significantly contribute to future research of cellular metabolism.Contacts: kerenyiz@post.tau.ac.il; tomersh@cs.technion.ac.il

Design and analysis of experiments with high throughput biological assay data

The prostate-specific Pten-knockout (KO) mouse carcinogenesis model is highly desirable for prostate cancer chemoprevention studies due to its close resemblance of many histopathological features of human prostate cancer including disease progression from prostatic intraepithelial neoplasia (PIN) to invasive adenocarcinomas. Here, we profiled the prostate proteome, transcriptome and aqueous metabolome of Pten-KO mice to identify reference molecular signatures that can be used for designing chemopreventive and/or therapeutic intervention and for selection of molecular biomarkers of responses to intervention. For proteomics, 4 pairs of whole prostates from Pten-KO mice (12-15 weeks of age, corresponding to high grade PIN) and their wild type littermate housed in same cages were obtained from the NCI Mouse Model Repository and analyzed by 8-plex iTRAQTM. For transcriptomic/microarray and metabolomic analyses, 3 additional matched pairs of prostate/tumor specimens at older age (22-20 weeks) were used. Proteomic and transcriptomic analyses using manual annotation methods with references from PubMed revealed top signatures that were up- and down-regulated by Pten deletion, particularly those implicated in immune function, inflammatory response, cancer, drug metabolism, cellular functions, prostate functions, and endoplasmic stress regulation. Similar to the manual annotation approach, each network analysis of 203 genes and 22 proteins (≥ 2- fold changes) by a bioinformatics software, Ingenuity Pathway Analysis (IPA) showed that inflammatory response, cellular movement, immune cell trafficking, immunological disease, and cancer were top 5 disease and biological functions in Pten-KO mice. Using references from PubMed, we manually assigned the unmapped prostate metabolites to functional categories, which included altered methionine-cysteine cycle fluxes and purine metabolites, increased nucleotide pools, cholesterol and polyamine synthesis and suppressed pools of sugar and choline derivatives, glycolysis intermediates, and purine bases. IPA network analysis of 25 metabolites (≥ 2- fold changes) revealed the biological functions related to molecular transport, amino acid metabolism, and small molecule biochemistry. In addition, we integrated transcriptomic, proteomic, and metabolomic data sets to identify latent biological relationships and to gain a comprehensive understanding of Pten-deficient prostate carcinogenesis. The integrative analysis predicted activation of inflammatory response and several central signaling nodes, such as IRF7, NF-κB, and IL-6. Collectively, the integrative analyses identify both active and latent reference molecular signatures and provide more insight into Pten-deficient prostate cancer than single omic approaches. Citation Format: Jinhui Zhang, Li Li, Sangyub Kim, LeeAnn Higgins, Yibin Deng, Christopher J. Kemp, Cheng Jiang, Junxuan Lu. Integrative analysis of transcriptomic, proteomic, and metabolomic data of Pten-knockout carcinogenic mouse prostate [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5249. doi:10.1158/1538-7445.AM2017-5249

See the article by Baldock et al, on pages 779–786. The molecular genetics of glioblastoma (GBM) are complex, and efforts are underway to systematically investigate their association with tumor aggressiveness and patient outcome1–3. Mutation in the isocitrate dehydrogenase 1 (IDH1) gene produces an alternative metabolite, 2-hydroxyglutarate instead of alpha-ketoglutarate and is strongly associated with lower grade gliomas4,5. The majority of GBMs containing the IDH1 mutation are secondary, and diagnostic approaches to distinguish IDH1 wild-type and mutant tumors as well as primary and secondary GBM are under development. Magnetic resonance imaging (MRI) is a standard diagnostic approach used to provide glioma grading based on contrast enhancement, edema, as well as necrosis6; however its diagnostic power is limited as MRI alone cannot provide an accurate measure of tumor aggressiveness based on just these parameters. Efforts have been made to increase the accuracy of MRI using additional parameters such as blood volume and metabolite measurements that enhance diagnostic sensitivity7. Despite these advances, the ability to distinguish invasive tumor cells remains a challenge. Mathematical modeling and extrapolation of MRI data is being evaluated as an alternative approach to enhance the diagnostic power of MRI, including estimating the extent of invasion, that does not involve additional imaging or probes8–12. Integrating computational modeling into predictions of GBM classification (primary versus secondary) as well as mutation status (such as wild-type or IDH1 mutant) represents the next steps for these approaches. Using a previously described model based on serial MRI data that calculates aggressiveness based on rate constants that represent proliferation (ρ) and dispersion (D)10, Swanson and colleagues compare wild-type and IDH1 mutant gliomas. By combining these two parameters, an aggressiveness ratio can be generated (ρ/D) that predicts prognosis, extent of hypoxia, and possibly primary GBM characterized by increased proliferation and secondary GBM characterized by decreased proliferation and increased diffusion13. In a well-characterized patient cohort of 178 gliomas (158 of which were GBM) from the Cancer Genome Atlas (TCGA) and two academic medical centers, the authors found that while tumors may display identical rates of proliferation, the IDH1 mutant tumors often have increased rates of dispersion. Overall, the wild-type tumors had a significantly higher aggressiveness ratio as compared to the IDH1 mutant tumors. These differences were present both in primary and secondary GBM, with a more significant difference present in secondary GBM. Taken together, the model presented by the authors distinguished between wild-type and IDH1 mutant tumors based on differences in proliferation and dispersion (Fig. 1). The authors confirm that IDH1 mutant tumors are less aggressive and possess an elevated invasion profile based on MRI. These findings can be applied to pre-operative MRI data and allow for predictions based on IDH1 status that can be integrated into the clinical management plan. Figure 1. Computational models predict differences in invasion and aggressiveness between wild-type and IDH1 mutant tumors. Schematic depicting differences between wild-type (left) and IHD1 mutant (right) tumors based on proliferation (ρ) and dispersion ... The results of the computational modeling also support the go or grow hypothesis14 that predicts that rapidly proliferating cells have a lower invasion capacity while cells with a higher propensity to invade have lower proliferation. Invasion is a hallmark of GBM and remains a challenge to the treatment of the disease. While the signaling pathways responsible for invasion are being evaluated for the development of additional anti-GBM therapies, recent observations that anti-angiogenic therapies can increase the invasiveness of GBM15 suggest that invasion can also be a therapeutic resistance mechanism. How IDH1 mutation status relates to invasion remains largely unexplored. The authors' model predicts that IDH1 mutant tumors would be more invasive, and this has been confirmed by an independent group based on histology demonstrating elevated numbers of IDH1 mutant cells in invading areas16. However, the molecular pathways responsible for this possible increase in invasion and decrease in proliferation are yet to be determined and may yield additional insight into the consequence of IDH1 mutation. The ability to accurately predict and detect IDH1 mutation status remains a priority for the neuro-oncology community. The authors' computational modeling approach is a complementary method to antibody17 and metabolite18 based detection methods. Moving forward, accurately identifying key mutations linked to tumor grade and outcome will be essential to ensure that the most accurate diagnosis and treatment plan is provided for each patient.

BackgroundLung cancer brain metastases (LC-BrMs) are frequently associated with dismal mortality rates in patients with lung cancer; however, standard of care therapies for LC-BrMs are still limited in their efficacy. A deep understanding of molecular mechanisms and tumor microenvironment of LC-BrMs will provide us with new insights into developing novel therapeutics for treating patients with LC-BrMs.MethodsHere, we performed integrated analyses of genomic, transcriptomic, proteomic, metabolomic, and single-cell RNA sequencing data which were derived from a total number of 154 patients with paired and unpaired primary lung cancer and LC-BrM, spanning four published and two newly generated patient cohorts on both bulk and single cell levels.ResultsWe uncovered that LC-BrMs exhibited a significantly greater intra-tumor heterogeneity. We also observed that mutations in a subset of genes were almost always shared by both primary lung cancers and LC-BrM lesions, including TTN, TP53, MUC16, LRP1B, RYR2, and EGFR. In addition, the genome-wide landscape of somatic copy number alterations was similar between primary lung cancers and LC-BrM lesions. Nevertheless, several regions of focal amplification were significantly enriched in LC-BrMs, including 5p15.33 and 20q13.33. Intriguingly, integrated analyses of transcriptomic, proteomic, and metabolomic data revealed mitochondrial-specific metabolism was activated but tumor immune microenvironment was suppressed in LC-BrMs. Subsequently, we validated our results by conducting real-time quantitative reverse transcription PCR experiments, immunohistochemistry, and multiplexed immunofluorescence staining of patients’ paired tumor specimens. Therapeutically, targeting oxidative phosphorylation with gamitrinib in patient-derived organoids of LC-BrMs induced apoptosis and inhibited cell proliferation. The combination of gamitrinib plus anti-PD-1 immunotherapy significantly improved survival of mice bearing LC-BrMs. Patients with a higher expression of mitochondrial metabolism genes but a lower expression of immune genes in their LC-BrM lesions tended to have a worse survival outcome.ConclusionsIn conclusion, our findings not only provide comprehensive and integrated perspectives of molecular underpinnings of LC-BrMs but also contribute to the development of a potential, rationale-based combinatorial therapeutic strategy with the goal of translating it into clinical trials for patients with LC-BrMs.

Psoriatic arthritis (PsA) is a relatively common inflammatory arthritis, a spondyloarthritis (SpA), that occurs most often in patients with psoriasis, a common immune-mediated inflammatory skin disease. Both psoriasis and PsA are highly heritable. Genetic and recent genomic studies have identified variants associated with psoriasis and PsA, but variants differentiating psoriasis from PsA are few. In this review, we describe recent developments in understanding the genetic burden of PsA, linkage, association and epigenetic studies. Using pathway analysis, we provide further insights into the similarities and differences between PsA and psoriasis, as well as between PsA and other immune-mediated inflammatory diseases, particularly ankylosing spondylitis, another SpA. Environmental factors that may trigger PsA in patients with psoriasis are also reviewed. To further understand the pathogenetic differences between PsA and psoriasis as well as other SpA, larger cohort studies of well-phenotyped subjects with integrated analysis of genomic, epigenomic, transcriptomic, proteomic and metabolomic data using interomic system biology approaches are required.

The integration of multi-omics technologies has ushered in a transformative era for biomedical research, enabling a systems-level exploration of complex pathophysiological processes. By moving beyond the limitations of single-omics approaches, concurrent analysis of genomic, transcriptomic, proteomic, and metabolomic data provides a holistic view of disease mechanisms, revealing intricate molecular networks and dynamic interactions. This paradigm shift is critically advancing our capabilities in early disease detection, prognostic stratification, and the discovery of novel therapeutic targets, thereby paving the way for precision medicine. This Research Topic, Advances in Multi-Omics Technologies in Pathophysiological Processes and Disease Diagnostics, presents five pioneering contributions that exemplify how integrative omics, combined with advanced computational tools, can address unmet clinical needs across a spectrum of human diseases—from neonatal disorders to cardiovascular and neurological conditions. Metabolomics and neonatal sepsis Bian et al. applied metabolomics coupled with machine learning to tackle the challenge of early neonatal sepsis diagnosis (Bian et al.). Their work identified specific metabolic biomarkers with strong discriminatory power, providing a promising non-invasive strategy for timely and accurate diagnosis in this vulnerable population. Lactylation in CNS disorders Tian et al. provided a comprehensive review of lactylation, a novel post-translational modification, and its implications in central nervous system disorders (Tian et al.). By linking cellular metabolism to epigenetic regulation, this work illustrates how multi-omics frameworks are essential for unraveling mechanistic connections between metabolic rewiring and neuronal function. Cardiotoxicity in cancer therapy Ding et al. focused on doxorubicin-induced cardiotoxicity, a major hurdle in oncology, by performing metabolomic profiling (Ding et al.). Their findings identified plasma metabolite signatures that serve as sensitive early indicators of cardiac damage, offering opportunities for patient monitoring and the development of cardioprotective interventions. Mitochondrial biomarkers in ischemic stroke Zhang et al. integrated bulk and single-cell RNA sequencing with machine learning to identify biomarkers associated with the mitochondrial unfolded protein response in ischemic stroke (Zhang et al.). This multi-layered approach revealed cell-type-specific contributions to stroke pathology and validated mitochondrial stress responses as potential therapeutic targets. Lipid metabolism in myocardial infarction Chen et al. explored plasma lipidomic profiles in patients with acute myocardial infarction of different coronary occlusion types (Chen et al.). Their study uncovered distinct lipid signatures that provide insights into disease heterogeneity and could inform more precise diagnostic and risk stratification strategies. Concluding remarks Together, these contributions underscore a common message: the integration of multiple omics layers, enhanced by computational modeling and machine learning, is indispensable for deconvoluting the complexity of human diseases. From neonatal sepsis to myocardial infarction, and from epigenetic regulation in the brain to cardiotoxicity and stroke, these studies demonstrate the capacity of multi-omics to uncover novel mechanisms, identify robust biomarkers, and reveal therapeutic opportunities not visible to single-level analyses. At the same time, challenges remain, including the standardization of omics protocols, the development of robust integration pipelines, and the need for validation in large and diverse cohorts. Addressing these hurdles will be critical to translating multi-omics discoveries into clinical applications. We extend our sincere gratitude to all the authors and reviewers for their invaluable contributions. We hope this Research Topic will inspire further interdisciplinary collaboration and accelerate the translation of multi-omics science into improved diagnostics and personalized medicine.

Glioblastoma (GBM) is a fatal brain cancer known for its rapid and aggressive growth, with some studies indicating that females may have better survival outcomes compared to males. While sex differences in GBM have been observed, the underlying biological mechanisms remain poorly understood. Feature selection can lead to the identification of discriminative key biomarkers by reducing dimensionality from high-dimensional medical datasets to improve machine learning model performance, explainability, and interpretability. Feature selection can uncover unique sex-specific biomarkers, determinants, and molecular profiles in patients with GBM. We analyzed high-dimensional proteomic and metabolomic profiles from serum biospecimens obtained from 109 patients with pathology-proven glioblastoma (GBM) on NIH IRB-approved protocols with full clinical annotation (local dataset). Serum proteomic analysis was performed using Somalogic aptamer-based technology (measuring 7289 proteins) and serum metabolome analysis using the University of Florida's SECIM (Southeast Center for Integrated Metabolomics) platform (measuring 6015 metabolites). Machine learning-based feature selection was employed to identify proteins and metabolites associated with male and female labels in high-dimensional datasets. Results were compared to publicly available proteomic and metabolomic datasets (CPTAC and TCGA) using the same methodology and TCGA data previously structured for glioma grading. Employing a machine learning-based and hybrid feature selection approach, utilizing both LASSO and mRMR, in conjunction with a rank-based weighting method (i.e., GLIO-Select), we linked proteomic and metabolomic data to clinical data for the purposes of feature reduction to identify molecular biomarkers associated with biological sex in patients with GBM and used a separate TCGA set to explore possible linkages between biological sex and mutations associated with tumor grading. Serum proteomic and metabolomic data identified several hundred features that were associated with the male/female class label in the GBM datasets. Using the local serum-based dataset of 109 patients, 17 features (100% ACC) and 16 features (92% ACC) were identified for the proteomic and metabolomic datasets, respectively. Using the CPTAC tissue-based dataset (8828 proteomic and 59 metabolomic features), 5 features (99% ACC) and 13 features (80% ACC) were identified for the proteomic and metabolomic datasets, respectively. The proteomic data serum or tissue (CPTAC) achieved the highest accuracy rates (100% and 99%, respectively), followed by serum metabolome and tissue metabolome. The local serum data yielded several clinically known features (PSA, PZP, HCG, and FSH) which were distinct from CPTAC tissue data (RPS4Y1 and DDX3Y), both providing methodological validation, with PZP and defensins (DEFA3 and DEFB4A) representing shared proteomic features between serum and tissue. Metabolomic features shared between serum and tissue were homocysteine and pantothenic acid. Several signals emerged that are known to be associated with glioma or GBM but not previously known to be associated with biological sex, requiring further research, as well as several novel signals that were previously not linked to either biological sex or glioma. EGFR, FAT4, and BCOR were the three features associated with 64% ACC using the TCGA glioma grading set. GLIO-Select shows remarkable results in reducing feature dimensionality when different types of datasets (e.g., serum and tissue-based) were used for our analyses. The proposed approach successfully reduced relevant features to less than twenty biomarkers for each GBM dataset. Serum biospecimens appear to be highly effective for identifying biologically relevant sex differences in GBM. These findings suggest that serum-based noninvasive biospecimen-based analyses may provide more accurate and clinically detailed insights into sex as a biological variable (SABV) as compared to other biospecimens, with several signals linking sex differences and glioma pathology via immune response, amino acid metabolism, and cancer hallmark signals requiring further research. Our results underscore the importance of biospecimen choice and feature selection in enhancing the interpretation of omics data for understanding sex-based differences in GBM. This discovery holds significant potential for enhancing personalized treatment plans and patient outcomes.

The aim of this study was to integrate multi omics data to characterize underlying functional pathways and candidate genes for drip loss in pigs. The consideration of different omics levels allows elucidating the black box of phenotype expression. Metabolite and protein profiling was applied in Musculus longissimus dorsi samples of 97 Duroc Pietrain pigs. In total, 126 and 35 annotated metabolites and proteins were quantified, respectively. In addition, all animals were genotyped with the porcine 60 k Illumina beadchip. An enrichment analysis resulted in 10 pathways, amongst others, sphingolipid metabolism and glycolysis/gluconeogenesis, with significant influence on drip loss. Drip loss and 22 metabolic components were analyzed as intermediate phenotypes within a genome-wide association study (GWAS). We detected significantly associated genetic markers and candidate genes for drip loss and for most of the metabolic components. On chromosome 18, a region with promising candidate genes was identified based on SNPs associated with drip loss, the protein phosphoglycerate mutase 2 and the metabolite glycine. We hypothesize that association studies based on intermediate phenotypes are able to provide comprehensive insights in the genetic variation of genes directly involved in the metabolism of performance traits. In this way, the analyses contribute to identify reliable candidate genes.

The aim of this study was to integrate multi omics data to characterize underlying functional pathways and candidate genes for drip loss in pigs. The consideration of different omics levels allows elucidating the black box of phenotype expression. Metabolite and protein profiling was applied in Musculus longissimus dorsi samples of 97 Duroc × Pietrain pigs. In total, 126 and 35 annotated metabolites and proteins were quantified, respectively. In addition, all animals were genotyped with the porcine 60 k Illumina beadchip. An enrichment analysis resulted in 10 pathways, amongst others, sphingolipid metabolism and glycolysis/gluconeogenesis, with significant influence on drip loss. Drip loss and 22 metabolic components were analyzed as intermediate phenotypes within a genome-wide association study (GWAS). We detected significantly associated genetic markers and candidate genes for drip loss and for most of the metabolic components. On chromosome 18, a region with promising candidate genes was identified based on SNPs associated with drip loss, the protein “phosphoglycerate mutase 2” and the metabolite glycine. We hypothesize that association studies based on intermediate phenotypes are able to provide comprehensive insights in the genetic variation of genes directly involved in the metabolism of performance traits. In this way, the analyses contribute to identify reliable candidate genes.

The comprehensive analysis of the proteome and its modulation by post-translational modification (PTM) is increasingly used in biological and biomedical studies. As a result, proteomics data analysis is ever more carried out by scientists with limited expertise in this type of data. While excellent software solutions for comprehensive and rigorous analysis of quantitative proteomic data exist, most are complex and not well suited for non-proteomics scientists. Integrative analysis of multi-level proteomics data on protein and diverse PTMs, like phosphorylation or proteolytic processing, remains particularly challenging and inaccessible to most biologists. To fill this void, we developed SQuAPP, an R-Shiny web-based analysis pipeline for the quantitative analysis of proteomic data. SQuAPP uses a streamlined workflow model to guide expert and novice users through quality control, data pre-processing, statistical analysis and visualization steps. Processing the protein, peptide and PTM datasets in parallel and their quantitative integration enable rapid identification of protein-level-independent modulation of protein modifications and intuitive interpretation of dynamic dependencies between different protein modifications. SQuAPP is available at http://squapp.langelab.org/. The source code and local setup instructions can be accessed from https://github.com/LangeLab/SQuAPP.

Metabolomics and proteomics analyses of zebrafish exposed to mesaconitine from Aconitum plants reveal marked reductions in lipid, glycolytic pathways.