Label-free shotgun proteomics and metabolite analysis reveal a significant metabolic shift during citrus fruit development

Label-free LC-MS/MS-based shot-gun proteomics was used to quantify the differential protein synthesis and metabolite profiling in order to assess metabolic changes during the development of citrus fruits. Our results suggested the occurrence of a metabolic change during citrus fruit maturation, where the organic acid and amino acid accumulation seen during the early stages of development shifted into sugar synthesis during the later stage of citrus fruit development. The expression of invertases remained unchanged, while an invertase inhibitor was up-regulated towards maturation. The increased expression of sucrose-phosphate synthase and sucrose-6-phosphate phosphatase and the rapid sugar accumulation suggest that sucrose is also being synthesized in citrus juice sac cells during the later stage of fruit development.     

A phase synchronization clustering algorithm for identifying interesting groups of genes from cell cycle expression data

Background

Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.

Results

We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling.

Conclusion

The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.     

The aconitate hydratase family from <Emphasis Type="Italic">Citrus</Emphasis>

Background  

Research on citrus fruit ripening has received considerable attention because of the importance of citrus fruits for the human diet. Organic acids are among the main determinants of taste and organoleptic quality of fruits and hence the control of fruit acidity loss has a strong economical relevance. In citrus, organic acids accumulate in the juice sac cells of developing fruits and are catabolized thereafter during ripening. Aconitase, that transforms citrate to isocitrate, is the first step of citric acid catabolism and a major component of the citrate utilization machinery. In this work, the citrus aconitase gene family was first characterized and a phylogenetic analysis was then carried out in order to understand the evolutionary history of this family in plants. Gene expression analyses of the citrus aconitase family were subsequently performed in several acidic and acidless genotypes to elucidate their involvement in acid homeostasis.     

Metaprotein expression modeling for label-free quantitative proteomics

Background

Label-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.

Results

We describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides??a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.

Conclusions

Mass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.     

System-based proteomic analysis of the interferon response in human liver cells

Background

Interferons (IFNs) play a critical role in the host antiviral defense and are an essential component of current therapies against hepatitis C virus (HCV), a major cause of liver disease worldwide. To examine liver-specific responses to IFN and begin to elucidate the mechanisms of IFN inhibition of virus replication, we performed a global quantitative proteomic analysis in a human hepatoma cell line (Huh7) in the presence and absence of IFN treatment using the isotope-coded affinity tag (ICAT) method and tandem mass spectrometry (MS/MS).

Results

In three subcellular fractions from the Huh7 cells treated with IFN (400 IU/ml, 16 h) or mock-treated, we identified more than 1,364 proteins at a threshold that corresponds to less than 5% false-positive error rate. Among these, 54 were induced by IFN and 24 were repressed by more than two-fold, respectively. These IFN-regulated proteins represented multiple cellular functions including antiviral defense, immune response, cell metabolism, signal transduction, cell growth and cellular organization. To analyze this proteomics dataset, we utilized several systems-biology data-mining tools, including Gene Ontology via the GoMiner program and the Cytoscape bioinformatics platform.

Conclusions

Integration of the quantitative proteomics with global protein interaction data using the Cytoscape platform led to the identification of several novel and liver-specific key regulatory components of the IFN response, which may be important in regulating the interplay between HCV, interferon and the host response to virus infection.     

Differential expression of proteomics models of colorectal cancer, colorectal benign disease and healthy controls

Background

Colorectal cancer (CRC) is often diagnosed at a late stage with concomitant poor prognosis. The hypersensitive analytical technique of proteomics can detect molecular changes before the tumor is palpable. The surface-enhanced laser desorption/ionization-time of flight-mass spectra (SELDI-TOF-MS) is a newly-developed technique of evaluating protein separation in recent years. The protein chips have established the expression of tumor protein in the serum specimens and become the newly discovered markers for tumor diagnosis. The objective of this study was to find new markers of the diagnosis among groups of CRC, colorectal benign diseases (CBD) and healthy controls. The assay of SELDI-TOF-MS with analytical technique of protein-chip bioinformatics was used to detect the expression of protein mass peaks in the sera of patients or controls. One hundred serum samples, including 52 cases of colorectal cancer, 27 cases of colorectal benign disease, and 21 cases of healthy controls, were examined by SELDI-TOF-MS with WCX2 protein-chips.

Results

The diagnostic models (I, II and III) were setup by analyzed the data and sieved markers using Ciphergen - Protein-Chip-Software 5.1. These models were combined with 3 protein mass peaks to discriminate CRC, CBD, and healthy controls. The accuracy, the sensitivity and the particularity of cross verification of these models are all highly over 80%.

Conclusions

The SELDI-TOF-MS is a useful tool to help diagnose colorectal cancer, especially during the early stage. However, identification of the significantly differentiated proteins needs further study.     

Aconitase activity and expression during the development of lemon fruit

Citrus fruits are characterized by the accumulation of high levels of citric acid in the juice sac cells and a decline in acid level toward maturation. It has been suggested that changes in mitochondrial aconitase (EC 4.2.1.3) activity affect fruit acidity. Recently, a cytosolic aconitase (cyt-Aco) homologous to mammalian iron-regulated proteins was identified in plants, leading us to re-evaluate the role of aconitase in acid accumulation. Aconitase activity was studied in 2 contrasting citrus varieties, sweet lime ( Citrus limettioides Tan., low acid) and sour lemon ( Citrus limon var. Eureka, high acid). Two aconitase isozymes were detected. One declined early in sour lemon fruit development, but was constant throughout sweet lime fruit development. Its reduction in sour lemon was associated with a decrease in aconitase activity in the mitochondrial fraction. Another isozyme was detected in sour lemon toward maturation, and was associated with an increase in aconitase activity in the soluble fraction, suggesting a cytosolic localization. The cyt-Aco was cloned from lemon juice sac cells, but in contrast to the changes in isozyme activity, its expression was constant during fruit development. We present a model, which suggests that reduction of the mitochondrial aconitase activity plays a role in acid accumulation, while an increase in the cyt-Aco activity reduces acid level toward fruit maturation.     

AtRBOH I confers submergence tolerance and is involved in auxin-mediated signaling pathways under hypoxic stress

Satsuma mandarin fruit (Citrus unshiu Mark.) photosynthesizes as comparable to leaf at about 100 days after full bloom (DAFB). In this study, translocation and accumulation of fruit-fixed photosynthate were investigated by using ¹⁴CO₂. When fruit at 108 DAFB was exposed to ¹⁴CO₂ for 48 h under 135 photosynthetic photon flux density (PPFD), ¹⁴C-sucrose, ¹⁴C-glucose and ¹⁴C-fructose were detected not only in flavedo but juice sac; more than 50?% of fruit assimilated ¹⁴C-sugars were present in juice sac. Thus, majority of rind-fixed photosynthate are infiltrated into juice sac and accumulated there within 48 h after assimilation. Although ¹⁴C-sucrose was predominant at flavedo where high SS (sucrose synthase) activity toward synthesis was present, the amount decreased gradually from the outside (flavedo) to the inside (juice sac) of fruit. In vascular bundle, strong SS toward cleavage and soluble acid invertase activities were involved, and ¹⁴C-fructose was predominant in juice sac. Accordingly, rind-fixed photosynthate is once converted to sucrose, the translocated sugar in Citrus, at flavedo by SS toward synthesis, and loaded on vascular bundle through symplastic and/or apoplastic movement in the albedo tissue. In the vascular bundle, sucrose may be degraded by SS toward cleavage and invertase, and resulting hexoses transported symplastically to the juice sac through juice stalk.     

Label-free protein profiling of formalin-fixed paraffin-embedded (FFPE) heart tissue reveals immediate mitochondrial impairment after ionising radiation

Qualitative proteome profiling of formalin-fixed, paraffin-embedded (FFPE) tissue is advancing the field of clinical proteomics. However, quantitative proteome analysis of FFPE tissue is hampered by the lack of an efficient labelling method. The usage of conventional protein labelling on FFPE tissue has turned out to be inefficient. Classical labelling targets lysine residues that are blocked by the formalin treatment. The aim of this study was to establish a quantitative proteomics analysis of FFPE tissue by combining the label-free approach with optimised protein extraction and separation conditions. As a model system we used FFPE heart tissue of control and exposed C57BL/6 mice after total body irradiation using a gamma ray dose of 3 gray. We identified 32 deregulated proteins (p≤0.05) in irradiated hearts 24h after the exposure. The proteomics data were further evaluated and validated by bioinformatics and immunoblotting investigation. In good agreement with our previous results using fresh-frozen tissue, the analysis indicated radiation-induced alterations in three main biological pathways: respiratory chain, lipid metabolism and pyruvate metabolism. The label-free approach enables the quantitative measurement of radiation-induced alterations in FFPE tissue and facilitates retrospective biomarker identification using clinical archives.     

Protein Analysis of Atrial Fibrosis via Label-Free Proteomics in Chronic Atrial Fibrillation Patients with Mitral Valve Disease

Background

Atrial fibrosis, as a hallmark of atrial structure remodeling, plays an important role in maintenance of chronic atrial fibrillation, but interrelationship of atrial fibrosis and atrial fibrillation is uncertain. Label-free proteomics can implement high throughput screening for finding and analyzing pivotal proteins related to the disease.. Therefore, we used label-free proteomics to explore and analyze differentially proteins in chronic atrial fibrillation patients with mitral valve disease.

Methods

Left and right atrial appendages obtained from patients with mitral valve disease were both in chronic atrial fibrillation (CAF, AF≥6 months, n = 6) and in sinus rhythm (SR, n = 6). One part of the sample was used for histological analysis and fibrosis quantification; other part were analyzed by label-free proteomic combining liquid chromatography with mass spectrometry (LC-MS), we utilized bioinformatics analysis to identify differential proteins.

Results

Degree of atrial fibrosis was higher in CAF patients than that of SR patients. 223 differential proteins were detected between two groups. These proteins mainly had vital functions such as cell proliferation, stress response, focal adhesion apoptosis. We evaluated that serine/threonine protein kinase N2 (PKN2), dermatopontin(DP), S100 calcium binding protein B(S100B), protein tyrosine kinase 2(PTK2) and discoidin domain receptor tyrosine kinase 2(DDR2) played important roles in fibrotic process related to atrial fibrillation.

Conclusion

The study presented differential proteins responsible for atrial fibrosis in chronic atrial fibrillation patients through label-free proteomic analysis. We assessed some vital proteins including their characters and roles. These findings may open up new realm for mechanism research of atrial fibrillation.     

HotSwap for bioinformatics: A STRAP tutorial

Background

Bioinformatics applications are now routinely used to analyze large amounts of data. Application development often requires many cycles of optimization, compiling, and testing. Repeatedly loading large datasets can significantly slow down the development process. We have incorporated HotSwap functionality into the protein workbench STRAP, allowing developers to create plugins using the Java HotSwap technique.

Results

Users can load multiple protein sequences or structures into the main STRAP user interface, and simultaneously develop plugins using an editor of their choice such as Emacs. Saving changes to the Java file causes STRAP to recompile the plugin and automatically update its user interface without requiring recompilation of STRAP or reloading of protein data. This article presents a tutorial on how to develop HotSwap plugins. STRAP is available at http://strapjava.de and http://www.charite.de/bioinf/strap.

Conclusion

HotSwap is a useful and time-saving technique for bioinformatics developers. HotSwap can be used to efficiently develop bioinformatics applications that require loading large amounts of data into memory.