Clinical and proteomic profiles of chronic kidney disease in heart failure with reduced and preserved ejection fraction.

BACKGROUND: Chronic kidney disease (CKD) is prevalent and related to poor clinical outcomes in patients with heart failure (HF). The pathophysiology of CKD in HF with a reduced ejection fraction (HFrEF) and HF with a preserved ejection fraction (HFpEF) is not well defined. In this study we compared clinical and proteomic profiles of CKD between patients with HFrEF and HFpEF. METHODS: We included 478 patients of the Scottish BIOSTAT-CHF cohort, of which 246 had HFrEF and 232 had HFpEF. CKD was defined as an eGFR <60 mL/min/1.73m2. We compared HFrEF-patients with CKD to HFpEF-patients with CKD using logistic- and Cox-regression. We performed a differential expression analysis using 6376 proteins. RESULTS: The prevalence of CKD was 36 % and 32 % in patients with HFpEF and HFrEF, respectively. CKD patients were on average 7 years older. BMI, higher NT-proBNP, ACE-inhibitors, HDL-cholesterol and Stroke were associated with CKD- patients with HFrEF. In HFpEF, CKD was associated with MRA-use and higher platelet count. CKD was associated with increased risk of death or heart failure hospitalization (HR 1.82, p < 0.001), with similar effect in HFrEF and HFpEF. The pattern of differentially expressed proteins between patients with and without CKD was similar in both HF-groups. CONCLUSION: Clinical profiles related to CKD- patients with HFrEF were different from CKD-patients with HFrEF. CKD was associated with an increased risk of death or heart failure hospitalization, which was not different between HFpEF and HFrEF. Patterns of circulating proteins were similar between CKD-patients with HFpEF and HFrEF, suggesting no major differences in CKD-pathophysiology.

DeepPeptide predicts cleaved peptides in proteins using conditional random fields.

MOTIVATION: Peptides are ubiquitous throughout life and involved in a wide range of biological processes, ranging from neural signaling in higher organisms to antimicrobial peptides in bacteria. Many peptides are generated post-translationally by cleavage of precursor proteins and can thus not be detected directly from genomics data, as the specificities of the responsible proteases are often not completely understood. RESULTS: We present DeepPeptide, a deep learning model that predicts cleaved peptides directly from the amino acid sequence. DeepPeptide shows both improved precision and recall for peptide detection compared to previous methodology. We show that the model is capable of identifying peptides in underannotated proteomes. AVAILABILITY AND IMPLEMENTATION: DeepPeptide is available online at ku.biolib.com/DeepPeptide.

Deorphanizing Peptides Using Structure Prediction.

Many endogenous peptides rely on signaling pathways to exert their function, but identifying their cognate receptors remains a challenging problem. We investigate the use of AlphaFold-Multimer complex structure prediction together with transmembrane topology prediction for peptide deorphanization. We find that AlphaFold's confidence metrics have strong performance for prioritizing true peptide-receptor interactions. In a library of 1112 human receptors, the method ranks true receptors in the top percentile on average for 11 benchmark peptide-receptor pairs.

Combining mass spectrometry and machine learning to discover bioactive peptides.

Peptides play important roles in regulating biological processes and form the basis of a multiplicity of therapeutic drugs. To date, only about 300 peptides in human have confirmed bioactivity, although tens of thousands have been reported in the literature. The majority of these are inactive degradation products of endogenous proteins and peptides, presenting a needle-in-a-haystack problem of identifying the most promising candidate peptides from large-scale peptidomics experiments to test for bioactivity. To address this challenge, we conducted a comprehensive analysis of the mammalian peptidome across seven tissues in four different mouse strains and used the data to train a machine learning model that predicts hundreds of peptide candidates based on patterns in the mass spectrometry data. We provide in silico validation examples and experimental confirmation of bioactivity for two peptides, demonstrating the utility of this resource for discovering lead peptides for further characterization and therapeutic development.

The EndoC-ßH1 cell line is a valid model of human beta cells and applicable for screenings to identify novel drug target candidates.

OBJECTIVE: To characterize the EndoC-ßH1 cell line as a model for human beta cells and evaluate its beta cell functionality, focusing on insulin secretion, proliferation, apoptosis and ER stress, with the objective to assess its potential as a screening platform for identification of novel anti-diabetic drug candidates. METHODS: EndoC-ßH1 was transplanted into mice for validation of in vivo functionality. Insulin secretion was evaluated in cells cultured as monolayer and as pseudoislets, as well as in diabetic mice. Cytokine induced apoptosis, glucolipotoxicity, and ER stress responses were assessed. Beta cell relevant mRNA and protein expression were investigated by qPCR and antibody staining. Hundreds of proteins or peptides were tested for their effect on insulin secretion and proliferation. RESULTS: Transplantation of EndoC-ßH1 cells restored normoglycemia in streptozotocin induced diabetic mice. Both in vitro and in vivo, we observed a clear insulin response to glucose, and, in vitro, we found a significant increase in insulin secretion from EndoC-ßH1 pseudoislets compared to monolayer cultures for both glucose and incretins. Apoptosis and ER stress were inducible in the cells and caspase 3/7 activity was elevated in response to cytokines, but not affected by the saturated fatty acid palmitate. By screening of various proteins and peptides, we found Bombesin (BB) receptor agonists and Pituitary Adenylate Cyclase-Activating Polypeptides (PACAP) to significantly induce insulin secretion and the proteins SerpinA6, STC1, and APOH to significantly stimulate proliferation. ER stress was readily induced by Tunicamycin and resulted in a reduction of insulin mRNA. Somatostatin (SST) was found to be expressed by 1% of the cells and manipulation of the SST receptors was found to significantly affect insulin secretion. CONCLUSIONS: Overall, the EndoC-ßH1 cells strongly resemble human islet beta cells in terms of glucose and incretin stimulated insulin secretion capabilities. The cell line has an active cytokine induced caspase 3/7 apoptotic pathway and is responsive to ER stress initiation factors. The cells' ability to proliferate can be further increased by already known compounds as well as by novel peptides and proteins. Based on its robust performance during the functionality assessment assays, the EndoC-ßH1 cell line was successfully used as a screening platform for identification of novel anti-diabetic drug candidates.

Xenotropic retrovirus Bxv1 in human pancreatic ß cell lines.

It has been reported that endogenous retroviruses can contaminate human cell lines that have been passaged as xenotransplants in immunocompromised mice. We previously developed and described 2 human pancreatic ß cell lines (EndoC-ßH1 and EndoC-ßH2) that were generated in this way. Here, we have shown that B10 xenotropic virus 1 (Bxv1), a xenotropic endogenous murine leukemia virus (MuLV), is present in these 2 recently described cell lines. We determined that Bxv1 was also present in SCID mice that were used for in vivo propagation of EndoC-ßH1/2 cells, suggesting that contamination occurred during xenotransplantation. EndoC-ßH1/2 cells released Bxv1 particles that propagated to human 293T and Mus dunni cells. Mobilization assays demonstrated that Bxv1 transcomplements defective MuLV-based retrovectors. In contrast, common rodent ß cell lines, rat INS-1E and RIN-5F cells and mouse MIN6 and ßTC3 cells, displayed either no or extremely weak xenotropic helper activity toward MuLV-based retrovectors, although xenotropic retrovirus sequences and transcripts were detected in both mouse cell lines. Bxv1 propagation from EndoC-ßH1/2 to 293T cells occurred only under optimized conditions and was overall poorly efficient. Thus, although our data imply that MuLV-based retrovectors should be cautiously used in EndoC-ßH1/2 cells, our results indicate that an involuntary propagation of Bxv1 from these cells can be easily avoided with good laboratory practices.

SLP-65 phosphorylation dynamics reveals a functional basis for signal integration by receptor-proximal adaptor proteins.

Understanding intracellular signal transduction by cell surface receptors requires information about the precise order of relevant modifications on the early transducer elements. Here we introduce the B cell line DT40 and its genetically engineered variants as a model system to determine and functionally characterize post-translational protein modifications in general. This is accomplished by a customized strategy that combines mass spectrometric analyses of protein modifications with subsequent mutational studies. When applied to the B cell receptor (BCR)-proximal effector SLP-65, this approach uncovered a differential and highly dynamic engagement of numerous newly identified phospho-acceptor sites. Some of them serve as kinase substrates in resting cells and undergo rapid dephosphorylation upon BCR ligation. Stimulation-induced phosphorylation of SLP-65 can be early and transient, or early and sustained, or late. Functional elucidation of conspicuous phosphorylation at serine 170 in SLP-65 revealed a BCR-distal checkpoint for some but not all possible B cell responses. Our data show that SLP-65 phosphorylation acts upstream for signal initiation and also downstream during selective processing of the BCR signal. Such a phenomenon defines a receptor-specific signal integrator.

Differential membrane proteomics using 18O-labeling to identify biomarkers for cholangiocarcinoma.

Quantitative proteomic methodologies allow profiling of hundreds to thousands of proteins in a high-throughput fashion. This approach is increasingly applied to cancer biomarker discovery to identify proteins that are differentially regulated in cancers. Fractionation of protein samples based on enrichment of cellular subproteomes prior to mass spectrometric analysis can provide increased coverage of certain classes of molecules. We used a membrane protein enrichment strategy coupled with 18O labeling based quantitative proteomics to identify proteins that are highly expressed in cholangiocarcinomas. In addition to identifying several proteins previously known to be overexpressed in cholangiocarcinoma, we discovered a number of molecules that were previously not associated with cholangiocarcinoma. Using immunoblotting and immunohistochemical labeling of tissue microarrays, we validated Golgi membrane protein 1, Annexin IV and Epidermal growth factor receptor pathway substrate 8 (EPS8) as candidate biomarkers for cholangiocarcinomas. Golgi membrane protein 1 was observed to be overexpressed in 89% of cholangiocarcinoma cases analyzed by staining tissue microarrays. In light of recent reports showing that Golgi membrane protein 1 is detectable in serum, further investigation into validation of this protein has the potential to provide a biomarker for early detection of cholangiocarcinomas.

Identification of c-Src tyrosine kinase substrates using mass spectrometry and peptide microarrays.

c-Src tyrosine kinase plays a critical role in signal transduction downstream of growth factor receptors, integrins and G protein-coupled receptors. We used stable isotope labeling with amino acids in cell culture (SILAC) approach to identify additional substrates of c-Src tyrosine kinase in human embryonic kidney 293T cells. We have identified 10 known substrates and interactors of c-Src and Src family kinases along with 26 novel substrates. We have experimentally validated 4 of the novel proteins (NICE-4, RNA binding motif 10, FUSE-binding protein 1 and TRK-fused gene) as direct substrates of c-Src using in vitro kinase assays and cotransfection experiments. Significantly, using a c-Src specific inhibitor, we were also able to implicate 3 novel substrates (RNA binding motif 10, EWS1 and Bcl-2 associated transcription factor) in PDGF signaling. Finally, to identify the exact tyrosine residues that are phosphorylated by c-Src on the novel c-Src substrates, we designed custom peptide microarrays containing all possible tyrosine-containing peptides (312 unique peptides) and their mutant counterparts containing a Tyr --> Phe substitution from 14 of the identified substrates. Using this platform, we identified 34 peptides that are phosphorylated by c-Src. We have demonstrated that SILAC-based quantitative proteomics approach is suitable for identification of substrates of nonreceptor tyrosine kinases and can be coupled with peptide microarrays for high-throughput identification of substrate phosphopeptides.

Target discovery and validation in pancreatic cancer.

Pancreatic cancer is a lethal disease and rational strategies for early detection and targeted therapies are urgently required to alleviate the dismal prognosis of this neoplasm. The use of global RNA and protein expression-profiling technologies, such as DNA microarrays, serial analysis of gene expression, and mass spectrometric analysis of proteins, have led to identification of cellular targets with considerable potential for clinical application and patient care. These studies underscore the importance of pursuing large-scale profiling of human cancers not only for furthering our understanding of the pathogenesis of these malignancies but also for developing strategies to improve patient outcomes.

Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach.

Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can subsequently be tested using other techniques for use in early detection of cancers. Here we demonstrate the use of stable isotope labeling with amino acids in cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5-fold change), several of which were previously reported as either up-regulated (e.g. cathepsin D, macrophage colony stimulation factor, and fibronectin receptor) or down-regulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have not been correlated previously with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin beta1), and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins not described previously to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, apoE, and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly the protein expression data from SILAC were compared with mRNA expression data obtained using gene expression microarrays for the two cell lines (Panc1 and human pancreatic duct epithelial), and a correlation coefficient (r) of 0.28 was obtained, confirming previously reported poor associations between RNA and protein expression studies.

A functional annotation of subproteomes in human plasma.

The data collected by Human Proteome Organization's Plasma Proteome Pilot project phase was analyzed by members of our working group. Accordingly, a functional annotation of the human plasma proteome was carried out. Here, we report the findings of our analyses. First, bioinformatic analyses were undertaken to determine the likely sources of plasma proteins and to develop a protein interaction network of proteins identified in this project. Second, annotation of these proteins was performed in the context of functional subproteomes involved in the coagulation pathway, the mononuclear phagocytic system, the inflammation pathway, the cardiovascular system, and the liver; as well as the subset of proteins associated with DNA binding activities. Our analyses contributed to the Plasma Proteome Database (http://www.plasmaproteomedatabase.org), an annotated database of plasma proteins identified by HPPP as well as from other published studies. In addition, we address several methodological considerations including the selective enrichment of post-translationally modified proteins by the use of multi-lectin chromatography as well as the use of peptidomic techniques to characterize the low molecular weight proteins in plasma. Furthermore, we have performed additional analyses of peptide identification data to annotate cleavage of signal peptides, sites of intra-membrane proteolysis and post-translational modifications. The HPPP-organized, multi-laboratory effort, as described herein, resulted in much synergy and was essential to the success of this project.

Comprehensive proteomic analysis of human pancreatic juice.

Proteomic technologies provide an excellent means for analysis of body fluids for cataloging protein constituents and identifying biomarkers for early detection of cancers. The biomarkers currently available for pancreatic cancer, such as CA19-9, lack adequate sensitivity and specificity contributing to late diagnosis of this deadly disease. In this study, we carried out a comprehensive characterization of the "pancreatic juice proteome" in patients with pancreatic adenocarcinoma. Pancreatic juice was first fractionated by 1-dimensional gel electrophoresis and subsequently analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS). A total of 170 unique proteins were identified including known pancreatic cancer tumor markers (e.g., CEA, MUC1) and proteins overexpressed in pancreatic cancers (e.g., hepatocarcinoma-intestine-pancreas/pancreatitis-associated protein (HIP/PAP) and lipocalin 2). In addition, we identified a number of proteins that have not been previously described in pancreatic juice (e.g., tumor rejection antigen (pg96) and azurocidin). Interestingly, a novel protein that is 85% identical to HIP/PAP was identified, which we have designated as PAP-2. The proteins identified in this study could be directly assessed for their potential as biomarkers for pancreatic cancer by quantitative proteomics methods or immunoassays.

A proteomic analysis of human bile.

We have carried out a comprehensive characterization of human bile to define the bile proteome. Our approach involved fractionation of bile by one-dimensional gel electrophoresis and lectin affinity chromatography followed by liquid chromatography tandem mass spectrometry. Overall, we identified 87 unique proteins, including several novel proteins as well as known proteins whose functions are unknown. A large majority of the identified proteins have not been previously described in bile. Using lectin affinity chromatography and enzymatically labeling of asparagine residues carrying glycan moieties by (18)O, we have identified a total of 33 glycosylation sites. The strategy described in this study should be generally applicable for a detailed proteomic analysis of most body fluids. In combination with "tagging" approaches for differential proteomics, our method could be used for identification of cancer biomarkers from any body fluid.

Development of human protein reference database as an initial platform for approaching systems biology in humans.

Human Protein Reference Database (HPRD) is an object database that integrates a wealth of information relevant to the function of human proteins in health and disease. Data pertaining to thousands of protein-protein interactions, posttranslational modifications, enzyme/substrate relationships, disease associations, tissue expression, and subcellular localization were extracted from the literature for a nonredundant set of 2750 human proteins. Almost all the information was obtained manually by biologists who read and interpreted >300,000 published articles during the annotation process. This database, which has an intuitive query interface allowing easy access to all the features of proteins, was built by using open source technologies and will be freely available at http://www.hprd.org to the academic community. This unified bioinformatics platform will be useful in cataloging and mining the large number of proteomic interactions and alterations that will be discovered in the postgenomic era.