Temporal Proteomic Profiling During Differentiation of Normal and Dystrophin-Deficient Human Muscle Cells

Background: Myogenesis is a dynamic process involving temporal changes in the expression of many genes. Lack of dystrophin protein such as in Duchenne muscular dystrophy might alter the natural course of gene expression dynamics during myogenesis. Objective: To gain insight into the dynamic temporal changes in protein expression during differentiation of normal and dystrophin deficient myoblasts to myotubes. Method: A super SILAC spike-in strategy in combination and LC-MS/MS was used for temporal proteome profiling of normal and dystrophin deficient myoblasts during differentiation. The acquired data was analyzed using Proteome Discoverer 2.2. and data clustering using R to define significant temporal changes in protein expression. Results: sFour major temporal protein clusters that showed sequential dynamic expression profiles during myogenesis of normal myoblasts were identified. Clusters 1 and 2, consisting mainly of proteins involved mRNA splicing and processing expression, were elevated at days 0 and 0.5 of differentiation then gradually decreased by day 7 of differentiation, then remained lower thereafter. Cluster 3 consisted of proteins involved contractile muscle and actomyosin organization. They increased in their expression reaching maximum at day 7 of differentiation then stabilized thereafter. Cluster 4 consisting of proteins involved in skeletal muscle development glucogenesis and extracellular remodeling had a lower expression during myoblast stage then gradually increased in their expression to reach a maximum at days 11–15 of differentiation. Lack of dystrophin expression in DMD muscle myoblast caused major alteration in temporal expression of proteins involved in cell adhesion, cytoskeleton, and organelle organization as well as the ubiquitination machinery. Conclusion: Time series proteome profiling using super SILAC strategy is a powerful method to assess temporal changes in protein expression during myogenesis and to define the downstream consequences of lack of dystrophin on these temporal protein expressions. Key alterations were identified in dystrophin deficient myoblast differentiation compared to normal myoblasts. These alterations could be an attractive therapeutic target.

Proteomic and Metabolomic Analyses of Maggots in Porcine Corpses for Post-Mortem Interval Estimation

Post-mortem interval (PMI) estimation is a critical task in forensic science. In this study, we used maggots collected from pig carcasses and applied an integrated proteomics and metabolomics approach to determine potential candidate substances for the estimation of PMI. After methanol precipitation, the supernatant containing metabolites and the protein pellet were separated and subjected to metabolomic and proteomic analyses using liquid chromatography-tandem mass spectrometry (LC-MS/MS). MS/MS data were analyzed for identification and quantification using Proteome Discoverer and Compound Discoverer software. A total of 573 metabolites and more than 800 porcine proteins were identified in maggots. This is the first dataset of proteins and metabolites in maggots collected from porcine carcasses. In this study, guanosine monophosphate, xanthine, inosine, adenosine, and guanine were detected with a similar tendency to increase during early days of maggot development and then decreased gradually. We broadly profiled various biomolecules through analysis in the spot of incident. Especially, we confirmed that proteome and metabolome profiling could be performed directly and indirectly.

Zika virus infection of the placenta alters extracellular matrix proteome

Zika virus (ZIKV) infection has been associated with fetal abnormalities by compromising placental integrity, but the mechanisms by which this occurs are unknown. Flavivirus can deregulate the host proteome, especially extracellular matrix (ECM) proteins. We hypothesize that a deregulation of specific ECM proteins by ZIKV, affects placental integrity. Using twelve different placental samples collected during the 2016 ZIKV Puerto Rico epidemic, we compared the proteome of five ZIKV infected samples with four uninfected controls followed by validation of most significant proteins by immunohistochemistry. Quantitative proteomics was performed using tandem mass tag TMT10plex™ Isobaric Label Reagent Set followed by Q Exactive™ Hybrid Quadrupole Orbitrap Mass Spectrometry. Identification of proteins was performed using Proteome Discoverer 2.1. Proteins were compared based on the fold change and p value using Limma software. Significant proteins pathways were analyzed using Ingenuity Pathway (IPA). TMT analysis showed that ZIKV infected placentas had 94 reviewed differentially abundant proteins, 32 more abundant, and 62 less abundant. IPA analysis results indicate that 45 of the deregulated proteins are cellular components of the ECM and 16 play a role in its structure and organization. Among the most significant proteins in ZIKV positive placenta were fibronectin, bone marrow proteoglycan, and fibrinogen. Of these, fibrinogen was further validated by immunohistochemistry in 12 additional placenta samples and found significantly increased in ZIKV infected placentas. The upregulation of this protein in the placental tissue suggests that ZIKV infection is promoting the coagulation of placental tissue and restructuration of ECM potentially affecting the integrity of the tissue and facilitating dissemination of the virus from mother to the fetus.

Clinical Amyloid Typing by Proteomics: Performance Evaluation and Data Sharing Between Two Centres

Amyloidosis is a relatively rare human disease caused by the deposition of abnormal protein fibres in the extracellular space of various tissues, impairing their normal function. Proteomic analysis of patients’ biopsies, developed by Dogan and colleagues at the Mayo Clinic, has become crucial for clinical diagnosis and for identifying the amyloid type. Currently, the proteomic approach is routinely used at National Amyloidosis Centre (NAC, London, UK) and Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche (ITB-CNR, Milan, Italy). Both centres are members of the European Proteomics Amyloid Network (EPAN), which was established with the aim of sharing and discussing best practice in the application of amyloid proteomics. One of the EPAN’s activities was to evaluate the quality and the confidence of the results achieved using different software and algorithms for protein identification. In this paper, we report the comparison of proteomics results obtained by sharing NAC proteomics data with the ITB-CNR centre. Mass spectrometric raw data were analysed using different software platforms including Mascot, Scaffold, Proteome Discoverer, Sequest and bespoke algorithms developed for an accurate and immediate amyloid protein identification. Our study showed a high concordance of the obtained results, suggesting a good accuracy of the different bioinformatics tools used in the respective centres. In conclusion, inter-centre data exchange is a worthwhile approach for testing and validating the performance of software platforms and the accuracy of results, and is particularly important where the proteomics data contribute to a clinical diagnosis.

Proteome Discoverer—A Community Enhanced Data Processing Suite for Protein Informatics

Proteomics researchers today face an interesting challenge: how to choose among the dozens of data processing and analysis pipelines available for converting tandem mass spectrometry files to protein identifications. Due to the dominance of Orbitrap technology in proteomics in recent history, many researchers have defaulted to the vendor software Proteome Discoverer. Over the fourteen years since the initial release of the software, it has evolved in parallel with the increasingly complex demands faced by proteomics researchers. Today, Proteome Discoverer exists in two distinct forms with both powerful commercial versions and fully functional free versions in use in many labs today. Throughout the 11 main versions released to date, a central theme of the software has always been the ability to easily view and verify the spectra from which identifications are made. This ability is, even today, a key differentiator from other data analysis solutions. In this review I will attempt to summarize the history and evolution of Proteome Discoverer from its first launch to the versions in use today.

Label-Free Liquid Chromatography–Mass Spectrometry Proteomic Analysis of Urinary Identification in Diabetic Vascular Dementia in a Han Chinese Population

Objective: This study aimed to identify potential diagnostic biomarkers of diabetic vascular dementia (DVD) and unravel the underlying mechanisms using mass spectrometry (MS).Methods: Label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomic analysis was applied to urine samples from four groups, including 14 patients with vascular dementia (VD), 22 patients with type 2 diabetes mellitus (T2DM), 12 patients with DVD, and 21 normal controls (NCs). Searching the MS data by Proteome Discoverer software (ThermoFisher Scientific; Waltham, MA, USA), protein abundances were analyzed qualitatively and quantitatively and compared between these groups. Combining bioinformatics analysis using Gene Ontology (GO), pathway crosstalk analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG), protein–protein interaction (PPI) network analysis using STRING, and literature searching, the differentially expressed proteins (DEPs) of DVD can be comprehensively judged and were further quantified by receiver operating characteristic (ROC) curve methods.Results: The proteomic findings showed quantitative changes in patients with DVD compared to patients with NC, T2DM, and VD groups; among 4,744 identified urine proteins, 1,222, 1,152, and 1,180 proteins displayed quantitative changes unique to DVD vs. NC, T2DM, and VD, respectively, including 481 overlapped common DEPs. Then, nine unique proteins [including HP, SERPIND, ATP5PB, VNN2, ALDH3A1, U2AF2, C6, A0A5C2GRG5 (no name), and A0A5C2FZ29 (no name)] and two composite markers (CM) (A0A5C2GRG5+U2AF2 and U2AF2+C6) were confirmed by a ROC curve method.Conclusion: This study provided an insight into the potential pathogenesis of DVD and elucidated a method for early detection.

Data Employed in the Construction of a Composite Protein Database for Proteogenomic Analyses of Cephalopods Salivary Apparatus

Here we provide all datasets and details applied in the construction of a composite protein database required for the proteogenomic analyses of the article “Putative Antimicrobial Peptides of the Posterior Salivary Glands from the Cephalopod Octopus vulgaris Revealed by Exploring a Composite Protein Database”. All data, subdivided into six datasets, are deposited at the Mendeley Data repository as follows. Dataset_1 provides our composite database “All_Databases_5950827_sequences.fasta” derived from six smaller databases composed of (i) protein sequences retrieved from public databases related to cephalopods’ salivary glands, (ii) proteins identified with Proteome Discoverer software using our original data obtained by shotgun proteomic analyses of posterior salivary glands (PSGs) from three Octopus vulgaris specimens (provided as Dataset_2) and (iii) a non-redundant antimicrobial peptide (AMP) database. Dataset_3 includes the transcripts obtained by de novo assembly of 16 transcriptomes from cephalopods’ PSGs using CLC Genomics Workbench. Dataset_4 provides the proteins predicted by the TransDecoder tool from the de novo assembly of 16 transcriptomes of cephalopods’ PSGs. Further details about database construction, as well as the scripts and command lines used to construct them, are deposited within Dataset_5 and Dataset_6. The data provided in this article will assist in unravelling the role of cephalopods’ PSGs in feeding strategies, toxins and AMP production.

Mass-Spectrometry Analysis of Urinary Biomarkers of Endothelial Injury in Sickle Cell Anemia Patients

BACKGROUND : Sickle cell anemia (SCA) patients are predisposed to the development of chronic kidney disease (CKD). Vascular dysfunction plays an important role in the etiology of CKD and is a major complication in SCA. Biomarkers of endothelial dysfunction VCAM and Activin A were recently described for diagnostics of CKD in Diabetic Kidney Disease and Systemic Lupus Erythematosus. Previously, we identified several urinary biomarkers of CKD in SCA patients using mass-spectrometry analysis. These biomarkers reflect the pathophysiology of SCA, including markers of iron homeostasis (ceruloplasmin (CP), transferrin (TrF), hemoglobin (Hgb), ferritin (FrT)); inflammation (orosomucoid (ORM)); and glomerular hyperfiltration (HGFL). However, the urinary biomarkers of endothelial injury in SCA patients are unknown. HYPOTHESIS: We hypothesized that urinary biomarkers of endothelial injury can be identified by mass-spectrometry and that they correlate with hemoglobinuria and hyperfiltration. METHODS: We tested mass-spectrometry data obtained from spot urine samples of 19 SCA patients without CKD in a steady state from the University of Illinois at Chicago. Samples were run in triplicates (total 57 spectra) and mass spectrometry analysis was carried out using Proteome Discoverer 2.2 (Thermo Fisher Scientific) and Ingenuity Pathway Analysis (IPA, Qiagen) software. Protein abundances were calculated using Proteome Discoverer 2.2 and correlated with hyperfiltration and hemoglobinuria. RESULTS: We re-analyzed previously obtained mass-spectra using the recently released Proteome Discoverer 2.2 program and identified 819 proteins that were further analyzed with IPA software. Proteins were sorted into the categories associated with renal necrosis (31 proteins), glomerular injury (16 proteins), and renal hypoplasia (9 proteins) using IPA. Because of a lack of vascular injury pathways in IPA, nine proteins associated with endothelial or vascular injury were manually picked including VCAM and Activin A (Table 1). Abundances were calculated for each of the nine selected proteins related to endothelial injury in 57 samples and normalized to the urinary creatinine. Results were stratified for hemoglobinuria and hyperfiltration. Four proteins showed 2-4 fold higher abundances in the samples with hemoglobinuria (BMPER, NRG3, MKI67, and TJP2), but the increase was statistically significant for NRG3 and MKI67 only. In the samples from patients with hyperfiltration, BAMBI and NOS2 abundances were increased over 2-fold but the abundance of NRG3 was decreased. Ingenuity network analysis showed changes in immunoglobulin production for MKI67 network; ERK1/2 signaling for NRG3 pathway; cell cycle and NF-kB signaling for BAMBI and cell cycle proteins and RNA polymerase II transcription for NOS2 network. CONCLUSIONS: Markers of endothelial injury are found in the urinary proteome in correlation with hemoglobinuria and hyperfiltration. Further analysis to validate these biomarkers and correlate them with CKD progression is needed. LIMITATION: Research participants were from a small cohort of patients from one center. ACKNOWLEDGMENTS: This work was supported by NIH Research Grants (1P50HL118006, 1R01HL125005, 5G12MD007597 and 1SC1HL150685). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Disclosures Saraf: Novartis, Global Blood Therapeutics: Membership on an entity's Board of Directors or advisory committees; Global Blood Therapeutics: Membership on an entity's Board of Directors or advisory committees, Other: Advisory Boards, Speakers Bureau; Pfizer, Global Blood Therapeutics, Novartis: Research Funding. Gordeuk:Imara: Research Funding; Ironwood: Research Funding; Novartis: Consultancy; CSL Behring: Consultancy, Research Funding; Global Blood Therapeutics: Consultancy, Research Funding.