Dana Pascovici

Proteomic analysis indicates massive changes in metabolism prior to the inhibition of growth and photosynthesis of grapevine (Vitis vinifera L.) in response to water deficit

BackgroundCabernet Sauvignon grapevines were exposed to a progressive, increasing water defict over 16 days. Shoot elongation and photosynthesis were measured for physiological responses to water deficit. The effect of water deficit over time on the abundance of individual proteins in growing shoot tips (including four immature leaves) was analyzed using nanoflow liquid chromatography - tandem mass spectrometry (nanoLC-MS/MS).ResultsWater deficit progressively decreased shoot elongation, stomatal conductance and photosynthesis after Day 4; 2277 proteins were identified by shotgun proteomics with an average CV of 9% for the protein abundance of all proteins. There were 472 out of 942 (50%) proteins found in all samples that were significantly affected by water deficit. The 472 proteins were clustered into four groups: increased and decreased abundance of early- and late-responding protein profiles. Vines sensed the water deficit early, appearing to acclimate to stress, because the abundance of many proteins changed before decreases in shoot elongation, stomatal conductance and photosynthesis. Predominant functional categories of the early-responding proteins included photosynthesis, glycolysis, translation, antioxidant defense and growth-related categories (steroid metabolism and water transport), whereas additional proteins for late-responding proteins were largely involved with transport, photorespiration, antioxidants, amino acid and carbohydrate metabolism.ConclusionsProteomic responses to water deficit were dynamic with early, significant changes in abundance of proteins involved in translation, energy, antioxidant defense and steroid metabolism. The abundance of these proteins changed prior to any detectable decreases in shoot elongation, stomatal conductance or photosynthesis. Many of these early-responding proteins are known to be regulated by post-transcriptional modifications such as phosphorylation. The proteomics analysis indicates massive and substantial changes in plant metabolism that appear to funnel carbon and energy into antioxidant defenses in the very early stages of plant response to water deficit before any significant injury.

Differential metabolic response of cultured rice (Oryza sativa) cells exposed to high- and low-temperature stress

Global mean temperatures are expected to rise by 2–4.5°C by 2100, accompanied by an increase in frequency and amplitude of extreme temperature events. Greater climatic extremes and an expanded range of cultivation will expose rice to increasing stress in the future. Understanding gene expression in disparate thermal regimes is important for the engineering of cultivars with tolerance to nonoptimal temperatures. Our study investigated the proteomic responses of rice cell suspension cultures to sudden temperature changes. Cell cultures grown at 28°C were subjected to 3‐day exposure to 12 or 20°C for low‐temperature stress, and 36 or 44°C for high‐temperature stress. Quantitative label‐free shotgun proteomic analysis was performed on biological triplicates of each treatment. Over 1900 proteins were expressed in one or more temperature treatments, and, of these, more than 850 were found to be responsive to either of the temperature extremes. These temperature‐responsive proteins included more than 300 proteins which were uniquely expressed at either 12 or 44°C. Our study also identified 40 novel stress–response proteins and observed that switching between the classical and the alternative pathways of sucrose metabolism occurs in response to extremes of temperature.

Shotgun proteomic analysis of long-distance drought signaling in rice roots.

Rice (Oryza sativa L. cv. IR64) was grown in split-root systems to analyze long-distance drought signaling within root systems. This in turn underpins how root systems in heterogeneous soils adapt to drought. The approach was to compare four root tissues: (1) fully watered; (2) fully droughted and split-root systems where (3) one-half was watered and (4) the other half was droughted. This was specifically aimed at identifying how droughted root tissues altered the proteome of adjacent wet roots by hormone signals and how wet roots reciprocally affected dry roots hydraulically. Quantitative label-free shotgun proteomic analysis of four different root tissues resulted in identification of 1487 nonredundant proteins, with nearly 900 proteins present in triplicate in each treatment. Drought caused surprising changes in expression, most notably in partially droughted roots where 38% of proteins were altered in level compared to adjacent watered roots. Specific functional groups changed consistently in drought. Pathogenesis-related proteins were generally up-regulated in response to drought and heat-shock proteins were totally absent in roots of fully watered plants. Proteins involved in transport and oxidation-reduction reactions were also highly dependent upon drought signals, with the former largely absent in roots receiving a drought signal while oxidation-reduction proteins were strongly present during drought. Finally, two functionally contrasting protein families were compared to validate our approach, showing that nine tubulins were strongly reduced in droughted roots while six chitinases were up-regulated, even when the signal arrived remotely from adjacent droughted roots.

Analysis of human leukaemias and lymphomas using extensive immunophenotypes from an antibody microarray

A novel antibody microarray has been developed that provides an extensive immunophenotype of leukaemia cells. The assay is a solid phase cell‐capture technique in which 82 antigens are studied simultaneously. This paper presents the analysis of 733 patients with a variety of leukaemias and lymphomas from peripheral blood and bone marrow. Discriminant Function Analysis of the expression profiles from these 733 patients and 63 normal subjects were clustered and showed high levels of consistency with diagnoses obtained using conventional clinical and laboratory criteria. The overall levels of consensus for classification using the microarray compared with established criteria were 93·9% (495/527 patients) for peripheral blood and 97·6% (201/206 patients) for bone marrow aspirates, showing that the extensive phenotype alone was frequently able to classify the disease when the leukaemic clone was the dominant cell population present. Immunophenotypes for neoplastic cells were distinguishable from normal cells when the leukaemic cell count was at least 5 × 109 cells/l in peripheral blood, or 20% of cells obtained from bone marrow aspirates. This technique may be a useful adjunct to flow cytometry and other methods when an extensive phenotype of the leukaemia cell is desired for clinical trials, research and prognostic factor analysis.

Differential proteomic response of rice (Oryza sativa) leaves exposed to high- and low-temperature stress

Global mean surface temperature has been predicted to increase by 1.8–4°C within this century, accompanied by an increase in the magnitude and frequency of extreme temperature events. Developing rice cultivars better adapted to non‐optimal temperatures is essential to increase rice yield in the future and, hence, understanding the molecular response of rice to temperature stress is necessary. In this study, we investigated the proteomic responses of leaves of 24‐day‐old rice seedlings to sudden temperature changes. Rice seedlings grown at 28/20°C (day/night) were subjected to 3‐day exposure to 12/5°C or 20/12°C (day/night) for low‐temperature stress, and 36/28°C or 44/36°C (day/night) for high‐temperature stress, followed by quantitative label‐free shotgun proteomic analysis on biological triplicates of each treatment. Out of over 1100 proteins identified in one or more temperature treatments, more than 400 were found to be responsive to temperature stress. Of these, 43, 126 and 47 proteins were exclusively found at 12/5, 20/12 and 44/36°C (day/night), respectively. Our results showed that a greater change occurs in the rice leaf proteome at 20/12°C (day/night) in comparison to other non‐optimal temperature regimes. In addition, our study identified more than 20 novel stress–response proteins.

Differential regulation of aquaporins, small GTPases and V‐ATPases proteins in rice leaves subjected to drought stress and recovery

Mechanisms of drought tolerance are complex, interacting, and polygenic. This paper describes patterns of gene expression at precise physiological stages of drought in 35‐day‐old seedlings of Oryza sativa cv. Nipponbare. Drought was imposed gradually for up to 15 days, causing abscisic acid levels to rise and growth to cease, and plants were then re‐watered. Proteins were identified from leaf samples after moderate drought, extreme drought, and 3 and 6 days of re‐watering. Label‐free quantitative shotgun proteomics resulted in identification of 1548 non‐redundant proteins. More proteins were down‐regulated in early stages of drought but more were up‐regulated as severe drought developed. After re‐watering, there was notable down regulation, suggesting that stress‐related proteins were being degraded. Proteins involved in signalling and transport became dominant as severe drought took hold but decreased again on re‐watering. Most of the nine aquaporins identified were responsive to drought, with six decreasing rapidly in abundance as plants were re‐watered. Nine G‐proteins appeared in large amounts during severe drought and dramatically degraded once plants were re‐watered. We speculate that water transport and drought signalling are critical elements of the overall response to drought in rice and might be the key to biotechnological approaches to drought tolerance.

SWATH mass spectrometry performance using extended peptide MS/MS assay libraries

The use of data-independent acquisition methods such as SWATH for mass spectrometry based proteomics is usually performed with peptide MS/MS assay libraries which enable identification and quantitation of peptide peak areas. Reference assay libraries can be generated locally through information dependent acquisition, or obtained from community data repositories for commonly studied organisms. However, there have been no studies performed to systematically evaluate how locally generated or repository-based assay libraries affect SWATH performance for proteomic studies. To undertake this analysis, we developed a software workflow, SwathXtend, which generates extended peptide assay libraries by integration with a local seed library and delivers statistical analysis of SWATH-quantitative comparisons. We designed test samples using peptides from a yeast extract spiked into peptides from human K562 cell lysates at three different ratios to simulate protein abundance change comparisons. SWATH-MS performance was assessed using local and external assay libraries of varying complexities and proteome compositions. These experiments demonstrated that local seed libraries integrated with external assay libraries achieve better performance than local assay libraries alone, in terms of the number of identified peptides and proteins and the specificity to detect differentially abundant proteins. Our findings show that the performance of extended assay libraries is influenced by the MS/MS feature similarity of the seed and external libraries, while statistical analysis using multiple testing corrections increases the statistical rigor needed when searching against large extended assay libraries.

Label-Free Quantitative Shotgun Proteomics Using Normalized Spectral Abundance Factors

In this chapter we describe the workflow used in our laboratory for label-free quantitative shotgun proteomics based on spectral counting. The main tools used are a series of R modules known collectively as the Scrappy program. We describe how to go from peptide to spectrum matching in a shotgun proteomics experiment using the XTandem algorithm, to simultaneous quantification of up to thousands of proteins, using normalized spectral abundance factors. The outputs of the software are described in detail, with illustrative examples provided for some of the graphical images generated. While it is not strictly within the scope of this chapter, some consideration is given to how best to extract meaningful biological information from quantitative shotgun proteomics data outputs.

PloGO: plotting gene ontology annotation and abundance in multi-condition proteomics experiments.

We describe the PloGO R package, a simple open‐source tool for plotting gene ontology (GO) annotation and abundance information, which was developed to aid with the bioinformatics analysis of multi‐condition label‐free proteomics experiments using quantitation based on spectral counting. PloGO can incorporate abundance (raw spectral counts) or normalized spectral abundance factors (NSAF) data in addition to the GO annotation, as well as handle multiple files and allow for a targeted collection of GO categories of interest. Our main aims were to help identify interesting subsets of proteins for further analysis such as those arising from a protein data set partition based on the presence and absence or multiple pair‐wise comparisons, as well as provide GO summaries that can be easily used in subsequent analyses. Though developed with label‐free proteomics experiments in mind it is not specific to that approach and can be used for any multi‐condition experiment for which GO information has been generated.

Cell surface phenotype profiles distinguish stable and progressive chronic lymphocytic leukemia.

Abstract Chronic lymphocytic leukemia (CLL) is clinically heterogeneous. While some patients have indolent disease for many years, 20–30% will progress and ultimately die of their disease. CLL may be classified by the Rai or Binet staging system, mutational status of the immunoglobulin variable heavy-chain gene (IGVH), ZAP-70 overexpression, cytogenetic abnormalities (13q−, + 12, 11q−, 17p−) and expression of several cell surface antigens (CD38, CD49d) that correlate with risk of disease progression. However, none of these markers identify all cases of CLL at risk. In a recent review, we summarized those CD antigens known to correlate with the prognosis of CLL. The present study has identified surface profiles of CD antigens that distinguish clinically progressive CLL from slow-progressive and stable CLL. Using an extended DotScan™ CLL antibody microarray (Version 3; 182 CD antibodies), and with refined analysis of purified CD19 + B-cells, the following 27 CD antigens were differentially abundant for progressive CLL: CD11a, CD11b, CD11c, CD18, CD19, CD20 (two epitopes), CD21, CD22, CD23, CD24, CD25, CD38, CD40, CD43, CD45, CD45RA, CD52, CD69, CD81, CD84, CD98, CD102, CD148, CD180, CD196 and CD270. The extensive surface profiles obtained provide disease signatures with an accuracy of 79.2%, a sensitivity of 83.9% and a specificity of 72.5% that could provide the basis for a rapid test to triage patients with CLL according to probability of clinical progression and potential earlier requirement for treatment.