Correlation between mRNA and protein abundance in Desulfovibrio vulgaris: a multiple regression to identify sources of variations

Parallel profiling of mRNA and protein on a global scale and integrative analysis of these two data types could provide additional insights into the metabolic mechanisms underlying complex biological systems. However, because mRNA and protein abundance are affected by many cellular and physical processes, there have been conflicting results on their correlation. Using whole-genome microarray and LC-MS/MS proteomic data collected from Desulfovibrio vulgaris grown under three different conditions, we systematically investigate the relationship between mRNA and protein abundance by a multiple regression approach, in which some of the key covariates that may affect mRNA-protein relationship were included. The results showed that mRNA abundance alone can explain only 20-28% of the total variation of protein abundance, suggesting mRNA-protein correlation can not be determined by mRNA abundance alone. Among various covariates, analytic variation of protein abundance is the major source for the variation of mRNA-protein correlation, which contributes to 34-44% of the total variation of mRNA-protein correlation. The cellular functional category of genes/proteins contributes 10-15% of the total variation of mRNA-protein correlation, with a more pronounced correlation of the two properties was observed for "central intermediary metabolism" and "energy metabolism" categories. In addition, protein stability also contributes 5% of the total variation of mRNA-protein correlation. The study presents the first quantitative analysis of the contributions of various biochemical and physical sources to the correlation of mRNA and protein abundance in D. vulgaris.     

Proteomic analysis of altered proteins in lymphoid organ of yellow head virus infected Penaeus monodon

A comparative proteomic analysis was employed to identify altered proteins in the yellow head virus (YHV) infected lymphoid organ (LO) of Penaeus monodon. At 24 h post-infection, the infected shrimps showed obvious signs of infection, while the control shrimps remained healthy. Two-dimensional electrophoresis of proteins extracted from the LO revealed significant alterations in abundance of several proteins in the infected group. Protein identification by MALDI-TOF MS and nanoLC-ESI-MS/MS revealed significant increase of transglutaminase, protein disulfide isomerase, ATP synthase beta subunit, V-ATPase subunit A, and hemocyanin fragments. A significant decrease was also identified for Rab GDP-dissociation inhibitor, 6-phosphogluconate dehydrogenase, actin, fast tropomyosin isoform, and hemolymph clottable protein. Some of these altered proteins were further investigated at the mRNA level using real-time RT-PCR, which confirmed the proteomic data. Identification of these altered proteins in the YHV-infected shrimps may provide novel insights into the molecular responses of P. monodon to YHV infection.     

Pilot study identifying myosin heavy chain 7, desmin,insulin‐like growth factor 7, and annexin A2 as circulating biomarkers of human heart failure

In‐depth proteomic analyses offer a systematic way to investigate protein alterations in disease and, as such, can be a powerful tool for the identification of novel biomarkers. Here, we analyzed proteomic data from a transgenic mouse model with cardiac‐specific overexpression of activated calcineurin (CnA), which results in severe cardiac hypertrophy. We applied statistically filtering and false discovery rate correction methods to identify 52 proteins that were significantly different in the CnA hearts compared to controls. Subsequent informatic analysis consisted of comparison of these 52 CnA proteins to another proteomic dataset of heart failure, three available independent microarray datasets, and correlation of their expression with the human plasma and urine proteome. Following this filtering strategy, four proteins passed these selection criteria, including myosin heavy chain 7, insulin‐like growth factor‐binding protein 7, annexin A2, and desmin. We assessed expression levels of these proteins in mouse plasma by immunoblotting, and observed significantly different levels of expression between healthy and failing mice for all four proteins. We verified antibody cross‐reactivity by examining human cardiac explant tissue by immunoblotting. Finally, we assessed protein levels in plasma samples obtained from four unaffected and four heart failure patients and demonstrated that all four proteins increased between twofold and 150‐fold in heart failure. We conclude that MYH7, IGFBP7, ANXA2, and DESM are all excellent candidate plasma biomarkers of heart failure in mouse and human.     

A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers

Epithelial ovarian cancer is the most lethal gynecological malignancy, and disease-specific biomarkers are urgently needed to improve diagnosis, prognosis, and to predict and monitor treatment efficiency. We present an in-depth proteomic analysis of selected biochemical fractions of human ovarian cancer ascites, resulting in the stringent and confident identification of over 2500 proteins. Rigorous filter schemes were applied to objectively minimize the number of false-positive identifications, and we only report proteins with substantial peptide evidence. Integrated computational analysis of the ascites proteome combined with several recently published proteomic data sets of human plasma, urine, 59 ovarian cancer related microarray data sets, and protein-protein interactions from the Interologous Interaction Database I (2)D ( http://ophid.utoronto.ca/i2d) resulted in a short-list of 80 putative biomarkers. The presented proteomics analysis provides a significant resource for ovarian cancer research, and a framework for biomarker discovery.     

Tissue-based absolute quantification using large-scale TMT and LFQ experiments

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.     

A catalogue of human saliva proteins identified by free flow electrophoresis-based peptide separation and tandem mass spectrometry

Human saliva has great potential for clinical disease diagnostics. Constructing a comprehensive catalogue of saliva proteins using proteomic approaches is a necessary first step to identifying potential protein biomarkers of disease. However, because of the challenge presented in cataloguing saliva proteins with widely varying abundance, new proteomic approaches are needed. To this end, we used a newly developed approach coupling peptide separation using free flow electrophoresis with linear ion trap tandem mass spectrometry to identify proteins in whole human saliva. We identified 437 proteins with high confidence (false positive rate below 1%), producing the largest catalogue of proteins from a single saliva sample to date and providing new information on the composition and potential diagnostic utility of this fluid. The statistically validated, transparently presented, and annotated dataset provides a model for presenting large scale proteomic data of this type, which should facilitate better dissemination and easier comparisons of proteomic datasets from future studies in saliva.     

Quantitative proteomics of fractionated membrane and lumen exosome proteins from isogenic metastatic and nonmetastatic bladder cancer cells reveal differential expression of EMT factors

Cancer cells secrete soluble factors and various extracellular vesicles, including exosomes, into their tissue microenvironment. The secretion of exosomes is speculated to facilitate local invasion and metastatic spread. Here, we used an in vivo metastasis model of human bladder carcinoma cell line T24 without metastatic capacity and its two isogenic derivate cell lines SLT4 and FL3, which form metastases in the lungs and liver of mice, respectively. Cultivation in CLAD1000 bioreactors rather than conventional culture flasks resulted in a 13‐ to 16‐fold increased exosome yield and facilitated quantitative proteomics of fractionated exosomes. Exosomes from T24, SLT4, and FL3 cells were partitioned into membrane and luminal fractions and changes in protein abundance related to the gain of metastatic capacity were identified by quantitative iTRAQ proteomics. We identified several proteins linked to epithelial–mesenchymal transition, including increased abundance of vimentin and hepatoma‐derived growth factor in the membrane, and casein kinase II α and annexin A2 in the lumen of exosomes, respectively, from metastatic cells. The change in exosome protein abundance correlated little, although significant for FL3 versus T24, with changes in cellular mRNA expression. Our proteomic approach may help identification of proteins in the membrane and lumen of exosomes potentially involved in the metastatic process.     

Analyzing chromatin remodeling complexes using shotgun proteomics and normalized spectral abundance factors

Mass spectrometry-based approaches are commonly used to identify proteins from multiprotein complexes, typically with the goal of identifying new complex members or identifying post-translational modifications. However, with the recent demonstration that spectral counting is a powerful quantitative proteomic approach, the analysis of multiprotein complexes by mass spectrometry can be reconsidered in certain cases. Using the chromatography-based approach named multidimensional protein identification technology, multiprotein complexes may be analyzed quantitatively using the normalized spectral abundance factor that allows comparison of multiple independent analyses of samples. This study describes an approach to visualize multiprotein complex datasets that provides structure function information that is superior to tabular lists of data. In this method review, we describe a reanalysis of the Rpd3/Sin3 small and large histone deacetylase complexes previously described in a tabular form to demonstrate the normalized spectral abundance factor approach.