Proteomic Analysis of Chloroplast-to-Chromoplast Transition in Tomato Reveals Metabolic Shifts Coupled with Disrupted Thylakoid Biogenesis Machinery and Elevated Energy-Production Components

A comparative proteomic approach was performed to identify differentially expressed proteins in plastids at three stages of tomato(Solanum lycopersicum) fruit ripening (mature-green, breaker, red). Stringent curation and processing of the data from three independent replicates identified 1,932 proteins among which 1,529 were quantified by spectral counting. The quantification procedures have been subsequently validated by immunoblot analysis of six proteins representative of distinct metabolic or regulatory pathways. Among the main features of the chloroplast-to-chromoplast transition revealed by the study, chromoplastogenesis appears to be associated with major metabolic shifts: (1) strong decrease in abundance of proteins of light reactions (photosynthesis, Calvin cycle, photorespiration)and carbohydrate metabolism (starch synthesis/degradation), mostly between breaker and red stages and (2) increase in terpenoid biosynthesis (including carotenoids) and stress-response proteins (ascorbate-glutathione cycle, abiotic stress, redox, heat shock). These metabolic shifts are preceded by the accumulation of plastid-encoded acetyl Coenzyme A carboxylase D proteins accounting for the generation of a storage matrix that will accumulate carotenoids. Of particular note is the high abundance of proteins involved in providing energy and in metabolites import. Structural differentiation of the chromoplast is characterized by a sharp and continuous decrease of thylakoid proteins whereas envelope and stroma proteins remain remarkably stable. This is coincident with the disruption of the machinery for thylakoids and photosystem biogenesis (vesicular trafficking, provision of material for thylakoid biosynthesis, photosystems assembly) and the loss of the plastid division machinery. Altogether, the data provide new insights on the chromoplast differentiation process while enriching our knowledge of the plant plastid proteome

A review of novel analytical diagnostics for liquid biopsies : spectroscopic and spectrometric serum profiling of primary and secondary brain tumours

With nearly 9,400 new cases of brain tumours diagnosed each year in the UK and over half of them resulting in death, the demand for a rapid, non-invasive diagnostic test permitting early detection is as vital and evident as ever. Brain tumours form when normal cells within the brain mutate, grow uncontrollably and form a mass. Brain tumours are stratified into increasing grades of malignancy determined by how they are likely to grow, the likelihood of reoccurrence and the likely best treatment, highlighted in Figure 1. Gliomas occur from the glial cells within the brain and central nervous system (CNS) are the most common primary brain tumour classified by World Health Organisation (WHO). Gliomas are often diagnosed late, are hard to treat and the prognosis is poor. Generally only 40% of patients with brain tumours are alive more than one year after diagnosis. Traditional treatment involves surgery, radiation therapy and chemotherapy but even with an extensive treatment plan survival is poor as symptoms are often diagnosed too late. The most common tumour types; astrocytomas, ependymomas and oligodendrogliomas have very different outcomes

Two-Dimensional Polyacrylamide Gel Electrophoresis (2D-PAGE): Advances and Perspectives

The recent trend in science is to assay as many biological molecules as possible within a single experiment. This trend is evident in proteomics where the aim is to characterize thousands of proteins within cells, tissues, and organisms. While advances in mass spectrometry have been critical, developments made in two-dimensional PAGE (2D-PAGE) have also played a major role in enabling proteomics. In this review, we discuss and highlight the advances made in 2D-PAGE over the past 25 years that have made it a foundational tool in proteomic research

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitud

The Use of Urine Proteomic and Metabonomic Patterns for the Diagnosis of Interstitial Cystitis and Bacterial Cystitis

The advent of systems biology approaches that have stemmed from the sequencing of the human genome has led to the search for new methods to diagnose diseases. While much effort has been focused on the identification of disease-specific biomarkers, recent efforts are underway toward the use of proteomic and metabonomic patterns to indicate disease. We have developed and contrasted the use of both proteomic and metabonomic patterns in urine for the detection of interstitial cystitis (IC). The methodology relies on advanced bioinformatics to scrutinize information contained within mass spectrometry (MS) and high-resolution proton nuclear magnetic resonance (1H-NMR) spectral patterns to distinguish IC-affected from non-affected individuals as well as those suffering from bacterial cystitis (BC). We have applied a novel pattern recognition tool that employs an unsupervised system (self-organizing-type cluster mapping) as a fitness test for a supervised system (a genetic algorithm). With this approach, a training set comprised of mass spectra and 1H-NMR spectra from urine derived from either unaffected individuals or patients with IC is employed so that the most fit combination of relative, normalized intensity features defined at precise m/z or chemical shift values plotted in n-space can reliably distinguish the cohorts used in training. Using this bioinformatic approach, we were able to discriminate spectral patterns associated with IC-affected, BC-affected, and unaffected patients with a success rate of approximately 84%

The Use of Urine Proteomic and Metabonomic Patterns for the Diagnosis of Interstitial Cystitis and Bacterial Cystitis

The advent of systems biology approaches that have stemmed from the sequencing of the human genome has led to the search for new methods to diagnose diseases. While much effort has been focused on the identification of disease-specific biomarkers, recent efforts are underway toward the use of proteomic and metabonomic patterns to indicate disease. We have developed and contrasted the use of both proteomic and metabonomic patterns in urine for the detection of interstitial cystitis (IC). The methodology relies on advanced bioinformatics to scrutinize information contained within mass spectrometry (MS) and high-resolution proton nuclear magnetic resonance ((1)H-NMR) spectral patterns to distinguish IC-affected from non-affected individuals as well as those suffering from bacterial cystitis (BC). We have applied a novel pattern recognition tool that employs an unsupervised system (self-organizing-type cluster mapping) as a fitness test for a supervised system (a genetic algorithm). With this approach, a training set comprised of mass spectra and (1)H-NMR spectra from urine derived from either unaffected individuals or patients with IC is employed so that the most fit combination of relative, normalized intensity features defined at precise m/z or chemical shift values plotted in n-space can reliably distinguish the cohorts used in training. Using this bioinformatic approach, we were able to discriminate spectral patterns associated with IC-affected, BC-affected, and unaffected patients with a success rate of approximately 84%

Widespread presence of bovine proteins in human cell lines

HPLC-MS/MS analysis of various human cell lines show the presence of a major amount of bovine protein contaminants. These likely originate from fetal bovine serum (FBS), typically used in cell cultures. If evaluated against a human protein database, on average 10% of the identified human proteins will be misleading (bovine proteins, but indicated as if they were human). Bovine contaminants therefore may cause major bias in proteomic studies of cell cultures, if not considered explicitly

Stripping voltammetric detection of insulin at liquid–liquid microinterfaces in the presence of bovine albumin

Electrochemistry at the interface between two immiscible electrolyte solutions (ITIES) provides a platform for label-free detection of biomolecules. In this study, adsorptive stripping voltammetry (AdSV) was implemented at an array of microscale ITIES for the detection of the antidiabetic hormone insulin. By exploiting the potential-controlled adsorption of insulin at the ITIES, insulin was detected at 10 nM via subsequent voltammetric desorption. This is the lowest detected concentration reported to-date for a protein by electrochemistry at the ITIES. Surface coverage calculations indicate that between 0.1 and 1 monolayer of insulin forms at the interface over the 10 – 1000 nM concentration range of the hormone. In a step toward assessment of selectivity, the optimum adsorption potentials for insulin and albumin were determined to be 0.900 V and 0.975 V, respectively. When present in an aqueous mixture with albumin, insulin was detected by tuning the adsorption potential to 0.9 V, albeit with reduced sensitivity. This provides the first example of selective detection of one protein in the presence of another by exploiting optimal adsorption potentials. The results presented here provide a route to the improvement of detection limits and achievement of selectivity for protein detection by electrochemistry at the ITIES