Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

A comparative Proteomics Analysis Identified Differentially Expressed Proteins in Pancreatic Cancer–Associated Stellate Cell Small Extracellular Vesicles

Human pancreatic stellate cells (HPSCs) are an essential stromal component and mediators of pancreatic ductal adenocarcinoma (PDAC) progression. Small extracellular vesicles (sEVs) are membrane-enclosed nanoparticles involved in cell-to-cell communications and are released from stromal cells within PDAC. A detailed comparison of sEVs from normal pancreatic stellate cells (HPaStec) and from PDAC-associated stellate cells (HPSCs) remains a gap in our current knowledge regarding stellate cells and PDAC. We hypothesized there would be differences in sEVs secretion and protein expression that might contribute to PDAC biology. To test this hypothesis, we isolated sEVs using ultracentrifugation followed by characterization by electron microscopy and Nanoparticle Tracking Analysis. We report here our initial observations. First, HPSC cells derived from PDAC tumors secrete a higher volume of sEVs when compared to normal pancreatic stellate cells (HPaStec). Although our data revealed that both normal and tumor-derived sEVs demonstrated no significant biological effect on cancer cells, we observed efficient uptake of sEVs by both normal and cancer epithelial cells. Additionally, intact membrane-associated proteins on sEVs were essential for efficient uptake. We then compared sEV proteins isolated from HPSCs and HPaStecs cells using liquid chromatography–tandem mass spectrometry. Most of the 1481 protein groups identified were shared with the exosome database, ExoCarta. Eighty-seven protein groups were differentially expressed (selected by 2-fold difference and adjusted p value ≤0.05) between HPSC and HPaStec sEVs. Of note, HPSC sEVs contained dramatically more CSE1L (chromosome segregation 1–like protein), a described marker of poor prognosis in patients with pancreatic cancer. Based on our results, we have demonstrated unique populations of sEVs originating from stromal cells with PDAC and suggest that these are significant to cancer biology. Further studies should be undertaken to gain a deeper understanding that could drive novel therapy.     

Identification of Tumor Antigens in the HLA Peptidome of Patient-derived Xenograft Tumors in Mouse

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Highlights

• •Sufficient tumor tissues are often unavailable large HLA peptidome discovery.
• •Using patient derived xenograft (PDX) tumors can overcome this limitation.
• •The large PDX HLA peptidomes expand significantly those of the original biopsies.
• •The HLA peptidomes of the PDX tumors included many tumor antigens.

     

Accurate MS-based Rab10 Phosphorylation Stoichiometry Determination as Readout for LRRK2 Activity in Parkinson's Disease

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Highlights

• •MS-based clinical assay that accurately determines phospho Rab10 occupancy.
• •Stable isotope labeled phosphopeptide injected as a standard with endogenous tryptic phospho Rab peptide for accurate ratio determination.
• •Determination of pRab levels in neutrophils of Parkinson disease patients.
• •Relevance of pRab levels as marker of PD.

     

Proteomic Analysis of Specific Proteins in the Root of Salt-Tolerant Barley

Comparative two-dimensional electrophoresis showed six proteins, which were significantly produced in the root of salt-tolerant barley. These proteins were identified as stress/defense-related proteins that do not scavenge reactive oxygen species directly, suggesting that salt-tolerant barley develops not only an antioxidative system, but also physical and biochemical changes to cope with salt stress.     

Proteomics analysis of meclofenamic acid-treated small cell lung carcinoma cells revealed changes in cellular energy metabolism for cancer cell survival

Small cell lung carcinoma (SCLC) is a highly aggressive cancer with low survival rate. Although initial response to chemotherapy in SCLC patients is well-rated, the treatments applied after the disease relapses are not successful. Drug resistance is accepted to be one of the main reasons for this failure. Therefore, there is an urgent need for new treatment strategies for SCLC. Meclofenamic acid, a nonsteroidal anti-inflammatory drug, has been shown to have anticancer effects on various types of cancers via different mechanisms. The aim of this study was to investigate the alterations that meclofenamic acid caused on a SCLC cell line, DMS114 using the tools of proteomics namely two-dimensional gel electrophoresis coupled to MALDI-TOF/TOF and nHPLC coupled to LC-MS/MS. Among the proteins identified by both methods, those showing significantly altered expression levels were evaluated using bioinformatics databases, PANTHER and STRING. The key altered metabolism upon meclofenamic acid treatment appeared to the cellular energy metabolism. Glycolysis was suppressed, whereas mitochondrial activity and oxidative phosphorylation were boosted. The cells underwent metabolic reprogramming to adapt into their new environment for survival. Metabolic reprogramming is known to cause drug resistance in several cancer types including SCLC. The identified differentially regulated proteins in here associated with energy metabolism hold value as the potential targets to overcome drug resistance in SCLC treatment.     

Proteomics evaluation of five economical commercial abundant protein depletion kits for enrichment of diseases-specific biomarkers from blood serum

Blood serum is arguably the most analyzed biofluid for disease prediction and diagnosis. Herein, we benchmarked five different serum abundant protein depletion (SAPD) kits with regard to the identification of disease-specific biomarkers in human serum using bottom-up proteomics. As expected, the IgG removal efficiency among the SAPD kits is highly variable, ranging from 70% to 93%. A pairwise comparison of database search results showed a 10%–19% variation in protein identification among the kits. Immunocapturing-based SAPD kits against IgG and albumin outperformed the others in the removal of these two abundant proteins. Conversely, non-antibody-based methods (i.e., kits using ion exchange resins) and kits leveraging a multi-antibody approach were proven to be less efficient in depleting IgG/albumin from samples but led to the highest number of identified peptides. Notably, our results indicate that different cancer biomarkers could be enriched up to 10% depending on the utilized SAPD kit compared with the undepleted sample. Additionally, functional analysis of the bottom-up proteomic results revealed that different SAPD kits enrich distinct disease- and pathway-specific protein sets. Overall, our study emphasizes that a careful selection of the appropriate commercial SAPD kit is crucial for the analysis of disease biomarkers in serum by shotgun proteomics.