Quantitative proteomics of heavy metal stress responses in Sydney rock oysters.

Currently, there are few predictive biomarkers in key biomonitoring species, such as oysters, that can detect heavy metal pollution in coastal waterways. Several attributes make oysters superior to other organisms for positive biomonitoring of heavy metal pollution. In particular, they are filter feeders with a high capacity for bioaccumulation. In this study, we used two proteomics approaches, namely label-free shotgun proteomics based on SDS-PAGE gel separation and gas phase fractionation, to investigate the heavy metal stress responses of Sydney rock oysters. Protein samples were prepared from haemolymph of oysters exposed to 100 µg/L of PbCl(2), CuCl(2), or ZnCl(2) for 4 days in closed aquaria. Peptides were identified using a Bivalvia protein sequence database, due to the unavailability of a complete oyster genome sequence. Statistical analysis revealed 56 potential biomarker proteins, as well as several protein biosynthetic pathways to be greatly impacted by metal stress. These have the potential to be incorporated into bioassays for prevention and monitoring of heavy metal pollution in Australian oyster beds. The study confirms that proteomic analysis of biomonitoring species is a promising approach for assessing the effects of environmental pollution, and our experiments have provided insights into the molecular mechanisms underlying oyster stress responses.

Less label, more free: approaches in label-free quantitative mass spectrometry.

In this review we examine techniques, software, and statistical analyses used in label-free quantitative proteomics studies for area under the curve and spectral counting approaches. Recent advances in the field are discussed in an order that reflects a logical workflow design. Examples of studies that follow this design are presented to highlight the requirement for statistical assessment and further experiments to validate results from label-free quantitation. Limitations of label-free approaches are considered, label-free approaches are compared with labelling techniques, and forward-looking applications for label-free quantitative data are presented. We conclude that label-free quantitative proteomics is a reliable, versatile, and cost-effective alternative to labelled quantitation.

Quantitative proteomics analysis of high and low polyphenol expressing recombinant inbred lines (RILs) of peanut (Arachis hypogaea L.).

To facilitate selective breeding of polyphenol-rich peanuts, we looked for mass spectrometry-based proteomic evidence, investigating a subset of recombinant inbred lines (RILs) developed by the Australian peanut breeding program. To do this, we used label-free shotgun proteomics for protein and peptide quantitation, statistically analyzed normalized spectral abundance factors using R-package, as well as assayed important antioxidants. Results revealed statistically significant protein expression changes in 82 proteins classified between high or low polyphenols expressing RILs. Metabolic changes in polyphenol-rich RIL p27-362 point towards increased enzymatic breakdown of sugars and phenylalanine biosynthesis. The study revealed phenylpropanoid pathway overexpression resulting in increased polyphenols biosynthesis. Overexpression of antioxidant enzymes such as catalase, by 73.4 fold was also observed. A strong metabolic correlation exists with the observed phenotypic traits. Peanut RIL p27-362 presents a superior nutritional composition with antioxidant-rich peanut phenotype and could yield commercial profits. Data are available via ProteomeXchange with identifierPXD015493.

A label-free shotgun proteomics analysis of macadamia nut.

In this study, we present a systematic proteomic overview of macadamia nut using a label-free shotgun proteomic approach. We identified 947 proteins in 723 clusters and gene ontology analysis revealed proteins across 46 functional categories including carbohydrate metabolism (10%), protein metabolic processes (5%), amino acid metabolism (4%), transport (4%), stress response (3%), lipid metabolism (3%), protein folding (3%) and defense response (1.4%). The defense response proteins accounted for 24% of the total peptide abundance. The vicilin-like macadamia antimicrobial peptides 2-3 (MiAMP2) was the most abundant protein, followed by glyceraldehyde-3-phosphate dehydrogenase 3, 11S legumin-like protein, 2-phospho-D-glycerate hydrolase and heat shock 70 kDa protein among others. The cascading of amino acid and carbohydrate metabolic pathways in macadamia nut were constructed against reference maps from KEGG and proposed for the first time. Results were also indicative of useful protein candidates with possible allergenic potential and cross-reactivity in macadamia nut. The in-silico analysis revealed homology and linear epitope similarities to known allergens such as conglutin ß allergen from lupin, Jug r2 vicilin allergens from walnut, Ara h3 11S globulin from peanut, small rubber particle protein Hev b3, hevein, enolase 2, HSP 70kDa Cla h4, Der f28 allergen, and methylglyoxalases. Label-free shotgun proteomics reveal valuable insights into the genetic and biological makeup of macadamia nut proteome and provide guidance on protein candidates with allergenic potential for further immunological investigation. Data are available via ProteomeXchange with identifier PXD015364.

Analysis of rice proteins using SDS-PAGE shotgun proteomics.

In this chapter we describe the workflow used in our laboratory to analyze rice leaf samples using label-free shotgun proteomics based on SDS-PAGE fractionation of proteins. Rice proteomics has benefitted substantially from successful execution of shotgun proteomics techniques. We describe steps on how to proceed starting from rice protein extraction, SDS-PAGE, in-gel protein digestion with trypsin, nanoLC-MS/MS, and database searching using the GPM. Data from these experiments can be used for spectral counting, where simultaneous quantitation of several thousand proteins can be obtained.