Highly Efficient Phosphoproteome Capture and Analysis from Urinary Extracellular Vesicles

Analysis of protein phosphorylation in extracellular vesicles (EVs) offers an unprecedented potential for understanding cancer signaling and early stage disease diagnosis. However, prior to the phosphoproteome analysis step, the isolation of EVs from biofluids remains a challenging issue to overcome due to the low yield and impurity from current isolation methods. Here, we carry out an extensive assessment of several EV isolation methods including a novel rapid isolation method EVTRAP for highly efficient capture of extracellular vesicles from human urine sample. We demonstrate that over 95% recovery yield can be consistently achieved by EVTRAP, a significant improvement over current standard techniques. We then applied EVTRAP to identify over 16 000 unique peptides representing 2000 unique EV proteins from 200 μL urine sample, including all known EV markers with substantially increased recovery levels over ultracentrifugation. Most importantly, close to 2000 unique phosphopeptides were identified from more than 860 unique phosphoproteins using 10 mL of urine. The data demonstrated that EVTRAP is a highly effective and potentially widely implementable clinical isolation method for analysis of EV protein phosphorylation.

A Liquid Chromatography with Tandem Mass Spectrometry-Based Proteomic Analysis of Cells Cultured in DMEM 10% FBS and Chemically Defined Medium Using Human Adipose-Derived Mesenchymal Stem Cells

Human adipose-derived mesenchymal stem cells (hADSCs) are representative cell sources for cell therapy. Classically, Dulbecco's Modified Eagle's medium (DMEM) containing 10% fetal bovine serum (FBS) has been used as culture medium for hADSCs. A chemically defined medium (CDM) containing no heterologous animal components has recently been used to produce therapeutic hADSCs. However, how the culture environment using a medium without FBS affects the protein expression of hADSC is unclear. We subjected hADSCs cultured in CDM and DMEM (10% FBS) to a protein expression analysis by tandem mass spectrometry liquid chromatography and noted 98.2% agreement in the proteins expressed by the CDM and DMEM groups. We classified 761 proteins expressed in both groups by their function in a gene ontology analysis. Thirty-one groups of proteins were classified as growth-related proteins in the CDM and DMEM groups, 16 were classified as antioxidant activity-related, 147 were classified as immune system process-related, 557 were involved in biological regulation, 493 were classified as metabolic process-related, and 407 were classified as related to stimulus responses. These results show that the trend in the expression of major proteins related to the therapeutic effect of hADSCs correlated strongly in both groups.

Family-specific scaling laws in bacterial genomes

Among several quantitative invariants found in evolutionary genomics, one of the most striking is the scaling of the overall abundance of proteins, or protein domains, sharing a specific functional annotation across genomes of given size. The size of these functional categories change, on average, as power-laws in the total number of protein-coding genes. Here, we show that such regularities are not restricted to the overall behavior of high-level functional categories, but also exist systematically at the level of single evolutionary families of protein domains. Specifically, the number of proteins within each family follows family-specific scaling laws with genome size. Functionally similar sets of families tend to follow similar scaling laws, but this is not always the case. To understand this systematically, we provide a comprehensive classification of families based on their scaling properties. Additionally, we develop a quantitative score for the heterogeneity of the scaling of families belonging to a given category or predefined group. Under the common reasonable assumption that selection is driven solely or mainly by biological function, these findings point to fine-tuned and interdependent functional roles of specific protein domains, beyond our current functional annotations. This analysis provides a deeper view on the links between evolutionary expansion of protein families and the functional constraints shaping the gene repertoire of bacterial genomes.

Protein lipograms

Linguistic analysis of protein sequences is an underexploited technique. Here, we capitalize on the concept of the lipogram to characterize sequences at the proteome levels. A lipogram is a literary composition which omits one or more letters. A protein lipogram likewise omits one or more types of amino acid. In this article, we establish a usable terminology for the decomposition of a sequence collection in terms of the lipogram. Next, we characterize Uniref50 using a lipogram decomposition. At the global level, protein lipograms exhibit power-law properties. A clear correlation with metabolic cost is seen. Finally, we use the lipogram construction to assign proteomes to the four branches of the tree-of-life: archaea, bacteria, eukaryotes and viruses. We conclude from this pilot study that the lipogram demonstrates considerable potential as an additional tool for sequence analysis and proteome classification.

High-throughput metaproteomics data analysis with Unipept: A tutorial

In recent years, shotgun metaproteomics has established itself as an important tool to study the composition of complex ecosystems and microbial communities. Two key steps in metaproteomics data analysis are the inference of proteins from the identified peptides, and the determination of the taxonomic origin and function of these proteins. This tutorial therefore introduces the Unipept command line interface (http://unipept.ugent.be/clidocs) as a platform-independent tool for such metaproteomics data analyses. First, a detailed overview is given of the available Unipept commands and their functions. Next, the power of the Unipept command line interface is illustrated using two case studies that analyze a single tryptic peptide, and a set of peptides retrieved from a shotgun metaproteomics experiment, respectively. Finally, the analysis results obtained using these command line tools are compared with the interactive taxonomic analysis that is available on the Unipept website.

Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning

High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held-out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high-throughput microscopy.

Time- and radiation-dose dependent changes in the plasma proteome after total body irradiation of non-human primates: Implications for biomarker selection

Acute radiation syndrome (ARS) is a complex multi-organ disease resulting from total body exposure to high doses of radiation. Individuals can be exposed to total body irradiation (TBI) in a number of ways, including terrorist radiological weapons or nuclear accidents. In order to determine whether an individual has been exposed to high doses of radiation and needs countermeasure treatment, robust biomarkers are needed to estimate radiation exposure from biospecimens such as blood or urine. In order to identity such candidate biomarkers of radiation exposure, high-resolution proteomics was used to analyze plasma from non-human primates following whole body irradiation (Co-60 at 6.7 Gy and 7.4 Gy) with a twelve day observation period. A total of 663 proteins were evaluated from the plasma proteome analysis. A panel of plasma proteins with characteristic time- and dose-dependent changes was identified. In addition to the plasma proteomics study reported here, we recently identified candidate biomarkers using urine from these same non-human primates. From the proteomic analysis of both plasma and urine, we identified ten overlapping proteins that significantly differentiate both time and dose variables. These shared plasma and urine proteins represent optimal candidate biomarkers of radiation exposure.

Understanding the cochlear implant environment by mapping perilymph proteomes from different species

Cochlear implants operate within a bony channel of the cochlea, bathed in a fluid known as the perilymph. The perilymph is a complex fluid containing ions and proteins, which are known to actively interact with metallic electrodes. To improve our understanding of how cochlear implant performance varies in preclinical in vivo studies in comparison to human trials and patient outcomes, the protein composition (or perilymph proteome) is needed. Samples of perilymph were gathered from feline and guinea pig subjects and analyzed using liquid chromatography with tandem mass spectrometry (LC-MS/MS) to produce proteomes and compare against the recently published human proteome. Over 64% of the proteins in the guinea pig proteome were found to be common to the human proteome. The proportions of apolipoproteins, enzymes and immunoglobulins showed little variation between the two proteomes, with other classes showing similarity. This establishes a good basis for comparison of results. The results for the feline profile showed less similarity with the human proteome and would not provide a quality comparison. This work highlights the suitability of the guinea pig to model the biological environment of the human cochlear and the need to carefully select models of the biological environment of a cochlear implant to more adequately translate in vitro and in vivo studies to the clinic.

Resource: Mapping the Triticum aestivum proteome

Yield and quality improvement of bread wheat (Triticum aestivum) is a focus in efforts to meet new demands from population growth and changing human diets. As the complexity of the wheat genome is unravelled, determining how it is used to build the protein machinery of wheat plants is a key next step in explaining detailed aspects of wheat growth and development. The specific functions of wheat organs during vegetative development and the role of metabolism, protein degradation and remobilisation in driving grain production are the foundations of crop performance and have recently become accessible through studies of the wheat proteome. We present a large scale, publicly accessible proteome mapping of wheat consisting of 24 organ and developmental samples. Tissue specific sub-proteomes and ubiquitously expressed markers of the wheat proteome are identified, alongside hierarchical assessment of protein functional classes, their presence in different tissues and correlations between the abundance of functional classes of proteins. Gene-specific identifications and protein family relationships are accounted for in the organisation of the data and 202 new protein-coding transcripts identified by proteogenomic mapping. The interactive database will serve as a vehicle to build, refine and deposit confirmed targeted proteomic assays for wheat proteins and protein families to assess function (www.wheatproteome.org).

Improving Phylogeny Reconstruction at the Strain Level Using Peptidome Datasets

Typical bacterial strain differentiation methods are often challenged by high genetic similarity between strains. To address this problem, we introduce a novel in silico peptide fingerprinting method based on conventional wet-lab protocols that enables the identification of potential strain-specific peptides. These can be further investigated using in vitro approaches, laying a foundation for the development of biomarker detection and application-specific methods. This novel method aims at reducing large amounts of comparative peptide data to binary matrices while maintaining a high phylogenetic resolution. The underlying case study concerns the Bacillus cereus group, namely the differentiation of Bacillus thuringiensis, Bacillus anthracis and Bacillus cereus strains. Results show that trees based on cytoplasmic and extracellular peptidomes are only marginally in conflict with those based on whole proteomes, as inferred by the established Genome-BLAST Distance Phylogeny (GBDP) method. Hence, these results indicate that the two approaches can most likely be used complementarily even in other organismal groups. The obtained results confirm previous reports about the misclassification of many strains within the B. cereus group. Moreover, our method was able to separate the B. anthracis strains with high resolution, similarly to the GBDP results as benchmarked via Bayesian inference and both Maximum Likelihood and Maximum Parsimony. In addition to the presented phylogenomic applications, whole-peptide fingerprinting might also become a valuable complementary technique to digital DNA-DNA hybridization, notably for bacterial classification at the species and subspecies level in the future.

Towards a Proteomic Catalogue and Differential Annotation of Salivary Gland Proteins in Blood Fed Malaria Vector Anopheles culicifacies by Mass Spectrometry

In order to understand the importance of functional proteins in mosquito behavior, following blood meal, a baseline proteomic dataset is essential for providing insights into the physiology of blood feeding. Therefore, in this study as first step, in solution and 1-D electrophoresis digestion approach combined with tandem mass spectrometry (nano LC-MS/MS) and computational bioinformatics for data mining was used to prepare a baseline proteomic catalogue of salivary gland proteins of sugar fed An. culicifacies mosquitoes. A total of 106 proteins were identified and analyzed by SEQUEST algorithm against mosquito protein database from Uniprot/NCBI. Importantly, D7r1, D7r2, D7r4, salivary apyrase, anti-platelet protein, calreticulin, antigen 5 family proteins were identified and grouped on the basis of biological and functional roles. Secondly, differential protein expression and annotations between salivary glands of sugar fed vs blood fed mosquitoes was analyzed using 2-Delectrophoresis combined with MALDI-TOF mass spectrometry. The alterations in the differential expression of total 38 proteins was observed out of which 29 proteins like beclin-1, phosphorylating proteins, heme oxygenase 1, ferritin, apoptotic proteins, coagulation and immunity like, serine proteases, serpins, c-type lectin and protein in regulation of blood feeding behavior were found to be up regulated while 9 proteins related to blood feeding, juvenile hormone epoxide hydrolase ii, odorant binding proteins and energy metabolic enzymes were found to be down regulated. To our knowledge, this study provides a first time baseline proteomic dataset and functional annotations of An. culicifacies salivary gland proteins that may be involved during the blood feeding. Identification of differential salivary proteins between sugar fed and blood fed mosquitoes and their plausible role may provide insights into the physiological processes associated with feeding behavior and sporozoite transmission during the process of blood feeding.

Quantification and Classification of E. coli Proteome Utilization and Unused Protein Costs across Environments

The costs and benefits of protein expression are balanced through evolution. Expression of un-utilized protein (that have no benefits in the current environment) incurs a quantifiable fitness costs on cellular growth rates; however, the magnitude and variability of un-utilized protein expression in natural settings is unknown, largely due to the challenge in determining environment-specific proteome utilization. We address this challenge using absolute and global proteomics data combined with a recently developed genome-scale model of Escherichia coli that computes the environment-specific cost and utility of the proteome on a per gene basis. We show that nearly half of the proteome mass is unused in certain environments and accounting for the cost of this unused protein expression explains >95% of the variance in growth rates of Escherichia coli across 16 distinct environments. Furthermore, reduction in unused protein expression is shown to be a common mechanism to increase cellular growth rates in adaptive evolution experiments. Classification of the unused protein reveals that the unused protein encodes several nutrient- and stress- preparedness functions, which may convey fitness benefits in varying environments. Thus, unused protein expression is the source of large and pervasive fitness costs that may provide the benefit of hedging against environmental change.

An overarching endeavor in systems biology is to characterize and understand the allocation of an organism’s proteome. Common approaches to characterize proteome allocation are based on annotations of protein functions or transcriptional regulatory targets. Here, we develop a novel approach based on model-predicted proteome utilization. This approach reveals that in many environments, a large fraction of the proteome is unused. Unused protein expression is known to incur costs on organismal fitness. We show that changes in the allocation of the proteome to used versus unused fractions can account for the variability in growth rates observed across environments and is a common mechanism to increase growth rates in laboratory evolution experiments. We compare our approach to classify the proteome based on model-predicted utilization to more traditional approaches to reveal biological functions and transcriptional regulators underlying the expression of unused protein. Expression of these functions may reflect ecological trade-offs between growth, nutrient-readiness, and stress resistance.

Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics

Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.

Sub-cellular localisation of proteins is critical to their function in all cellular processes; proteins localising to their intended micro-environment, e.g organelles, vesicles or macro-molecular complexes, will meet the interaction partners and biochemical conditions suitable to pursue their molecular function. Therefore, sound data and methods to reliably and systematically study protein localisation, and hence their mis-localisation and the disruption of protein trafficking, that are relied upon by the cell biology community, are essential. Here we present a method to infer protein localisation relying on the optimal integration of experimental mass spectrometry-based data and auxiliary sources, such as GO annotation, outputs from third-party software, protein-protein interactions or immunocytochemistry data. We found that the application of transfer learning algorithms across these diverse data sources considerably improves on the quantity and reliability of sub-cellular protein assignment, compared to single data classifiers previously applied to infer sub-cellular localisation using experimental data only. We show how our method does not compromise biologically relevant experimental-specific signal after integration with heterogeneous freely available third-party resources. The integration of different data sources is an important challenge in the data intensive world of biology and we anticipate the transfer learning methods presented here will prove useful to many areas of biology, to unify data obtained from different but complimentary sources.

Lupus anti-ribosomal P autoantibody proteomes express convergent biclonal signatures

Lupus-specific anti-ribosomal P (anti-Rib-P) autoantibodies have been implicated in the pathogenesis of neurological complications in systemic lupus erythematosus (SLE). The aim of the present study was to determine variable (V)-region signatures of secreted autoantibody proteomes specific for the Rib-P heterocomplex and investigate the molecular basis of the reported cross-reactivity with Sm autoantigen. Anti-Rib-P immunoglobulins (IgGs) were purified from six anti-Rib-P-positive sera by elution from enzyme-linked immunosorbent assay (ELISA) plates coated with either native Rib-P proteins or an 11-amino acid peptide (11-C peptide) representing the conserved COOH-terminal P epitope. Rib-P- and 11-C peptide-specific IgGs were analysed for heavy (H) and light (L) chain clonality and V-region expression using an electrophoretic and de-novo and database-driven mass spectrometric sequencing workflow. Purified anti-Rib-P and anti-SmD IgGs were tested for cross-reactivity on ELISA and their proteome data sets analysed for shared clonotypes. Anti-Rib-P autoantibody proteomes were IgG1 kappa-restricted and comprised two public clonotypes defined by unique H/L chain pairings. The major clonotypic population was specific for the common COOH-terminal epitope, while the second shared the same pairing signature as a recently reported anti-SmD clonotype, accounting for two-way immunoassay cross-reactivity between these lupus autoantibodies. Sequence convergence of anti-Rib-P proteomes suggests common molecular pathways of autoantibody production and identifies stereotyped clonal populations that are thought to play a pathogenic role in neuropsychiatric lupus. Shared clonotypic structures for anti-Rib-P and anti-Sm responses suggest a common B cell clonal origin for subsets of these lupus-specific autoantibodies.

Label-Free Quantitative Proteomic Analysis of Puccinia psidii Uredospores Reveals Differences of Fungal Populations Infecting Eucalyptus and Guava

Puccinia psidii sensu lato (s.l.) is the causal agent of eucalyptus and guava rust, but it also attacks a wide range of plant species from the myrtle family, resulting in a significant genetic and physiological variability among populations accessed from different hosts. The uredospores are crucial to P. psidii dissemination in the field. Although they are important for the fungal pathogenesis, their molecular characterization has been poorly studied. In this work, we report the first in-depth proteomic analysis of P. psidii s.l. uredospores from two contrasting populations: guava fruits (PpGuava) and eucalyptus leaves (PpEucalyptus). NanoUPLC-MSE was used to generate peptide spectra that were matched to the UniProt Puccinia genera sequences (UniProt database) resulting in the first proteomic analysis of the phytopathogenic fungus P. psidii. Three hundred and fourty proteins were detected and quantified using Label free proteomics. A significant number of unique proteins were found for each sample, others were significantly more or less abundant, according to the fungal populations. In PpGuava population, many proteins correlated with fungal virulence, such as malate dehydrogenase, proteossomes subunits, enolases and others were increased. On the other hand, PpEucalyptus proteins involved in biogenesis, protein folding and translocation were increased, supporting the physiological variability of the fungal populations according to their protein reservoirs and specific host interaction strategies.

Isobaric Tag for Relative and Absolute Quantitation (iTRAQ)-Based Protein Profiling in Plants

Isobaric tags for relative and absolute quantitation (iTRAQ) is a technology that utilizes isobaric reagents to label the primary amines of peptides and proteins and is used in proteomics to study quantitative changes in the proteome by tandem mass spectrometry . Here, we present an adaptation of the iTRAQ experimental protocol for plants that allows the identification and quantitation of more than 12,000 plant proteins in Arabidopsis with a false discovery rate of less than 5 %.

Protein-Based Classifier to Predict Conversion from Clinically Isolated Syndrome to Multiple Sclerosis

Multiple sclerosis is an inflammatory, demyelinating, and neurodegenerative disease of the central nervous system. In most patients, the disease initiates with an episode of neurological disturbance referred to as clinically isolated syndrome, but not all patients with this syndrome develop multiple sclerosis over time, and currently, there is no clinical test that can conclusively establish whether a patient with a clinically isolated syndrome will eventually develop clinically defined multiple sclerosis. Here, we took advantage of the capabilities of targeted mass spectrometry to establish a diagnostic molecular classifier with high sensitivity and specificity able to differentiate between clinically isolated syndrome patients with a high and a low risk of developing multiple sclerosis. Based on the combination of abundances of proteins chitinase 3-like 1 and ala-β-his-dipeptidase in cerebrospinal fluid, we built a statistical model able to assign to each patient a precise probability of conversion to clinically defined multiple sclerosis. Our results are of special relevance for patients affected by multiple sclerosis as early treatment can prevent brain damage and slow down the disease progression.

Definition of a consensus integrin adhesome and its dynamics during adhesion complex assembly and disassembly

Integrin receptor activation initiates the formation of integrin adhesion complexes (IACs) at the cell membrane that transduce adhesion-dependent signals to control a multitude of cellular functions. Proteomic analyses of isolated IACs have revealed an unanticipated molecular complexity; however, a global view of the consensus composition and dynamics of IACs is lacking. Here, we have integrated several IAC proteomes and generated a 2,412-protein integrin adhesome. Analysis of this data set reveals the functional diversity of proteins in IACs and establishes a consensus adhesome of 60 proteins. The consensus adhesome is likely to represent a core cell adhesion machinery, centred around four axes comprising ILK-PINCH-kindlin, FAK-paxillin, talin-vinculin and α-actinin-zyxin-VASP, and includes underappreciated IAC components such as Rsu-1 and caldesmon. Proteomic quantification of IAC assembly and disassembly detailed the compositional dynamics of the core cell adhesion machinery. The definition of this consensus view of integrin adhesome components provides a resource for the research community.

High-resolution quantitative proteome analysis reveals substantial differences between phagosomes of RAW 264.7 and bone marrow derived macrophages

Macrophages are important immune cells operating at the forefront of innate immunity by taking up foreign particles and microbes through phagocytosis. The RAW 264.7 cell line is commonly used for experiments in the macrophage and phagocytosis field. However, little is known how its functions compare to primary macrophages. Here, we have performed an in-depth proteomics characterization of phagosomes from RAW 264.7 and bone marrow derived macrophages by quantifying more than 2500 phagosomal proteins. Our data indicate that there are significant differences for a large number of proteins including important receptors such as mannose receptor 1 and Siglec-1. Moreover, bone marrow derived macrophages phagosomes mature considerably faster by fusion with endosomes and the lysosome which we validated using fluorogenic phagocytic assays. We provide a valuable resource for researcher in the field and recommend careful use of the RAW 264.7 cell line when studying phagosome functions. All MS data have been deposited in the ProteomeXchange with identifier PXD001293 (http://proteomecentral.proteomexchange.org/dataset/PXD001293).

A Comprehensive Analysis of Chromoplast Differentiation Reveals Complex Protein Changes Associated with Plastoglobule Biogenesis and Remodeling of Protein Systems in Sweet Orange Flesh

Globular and crystalloid chromoplasts were observed to be region specifically formed in sweet orange (Citrus sinensis) flesh and converted from amyloplasts during fruit maturation, which was associated with the composition of specific carotenoids and the expression of carotenogenic genes. Subsequent isobaric tag for relative and absolute quantitation (iTRAQ)-based quantitative proteomic analyses of purified plastids from the flesh during chromoplast differentiation and senescence identified 1,386 putative plastid-localized proteins, 1,016 of which were quantified by spectral counting. The iTRAQ values reflecting the expression abundance of three identified proteins were validated by immunoblotting. Based on iTRAQ data, chromoplastogenesis appeared to be associated with three major protein expression patterns: (1) marked decrease in abundance of the proteins participating in the translation machinery through ribosome assembly; (2) increase in abundance of the proteins involved in terpenoid biosynthesis (including carotenoids), stress responses (redox, ascorbate, and glutathione), and development; and (3) maintenance of the proteins for signaling and DNA and RNA. Interestingly, a strong increase in abundance of several plastoglobule-localized proteins coincided with the formation of plastoglobules in the chromoplast. The proteomic data also showed that stable functioning of protein import, suppression of ribosome assembly, and accumulation of chromoplast proteases are correlated with the amyloplast-to-chromoplast transition; thus, these processes may play a collective role in chromoplast biogenesis and differentiation. By contrast, the chromoplast senescence process was inferred to be associated with significant increases in stress response and energy supply. In conclusion, this comprehensive proteomic study identified many potentially new plastid-localized proteins and provides insights into the potential developmental and molecular mechanisms underlying chromoplast biogenesis, differentiation, and senescence in sweet orange flesh.

The Kinetic Response of the Proteome in A549 Cells Exposed to ZnSO4 Stress

Zinc, an essential trace element, is involved in many important physiological processes. Cell responses to zinc stress show time-dependent effects besides concentration-dependence and tissue-specificity. Herein, we investigated the time-dependent differential expression of the proteome in A549 cells after administered with ZnSO4 for both 9 and 24 h using 2DE. 123 differentially expressed protein spots were detected, most of which were up-regulated by Zn2+ treatment. Interestingly, 49 proteins exhibited significant differential expression repeatedly during these two treatment periods, and moreover showed a conserved change with different ratios and four time-dependent expression patterns. Pattern 1 (up-regulated with rapid initial induction and subsequent repression) and pattern 4 (down-regulated with steady repression) were the predominant expression patterns. The abundances of the proteins in patterns 1 and 4 after 24 h of zinc treatment are always lower than that after 9 h, indicating that exogenous zinc reduced the expression of proteins in cells after 24 h or longer. Importantly, these findings could also reflect the central challenge in detecting zinc homeostasis proteins by 2DE or other high throughput analytical methods resulting from slight variation in protein expression after certain durations of exogenous zinc treatment and/or low inherent protein content in cells. These time-dependent proteome expression patterns were further validated by measuring dynamic changes in protein content in cells and in expression of two proteins using the Bradford method and western blotting, respectively. The time-dependent changes in total zinc and free Zn2+ ion contents in cells were measured using ICP-MS and confocal microscopy, respectively. The kinetic process of zinc homeostasis regulated by muffling was further revealed. In addition, we identified 50 differentially expressed proteins which are predominantly involved in metabolic process, cellular process or developmental process, and function as binding, catalytic activity or structural molecule activity. This study further elucidates our understanding of dynamic nature of the cellular response to zinc stress and the mechanism of zinc homeostasis.

The membrane proteome of stroma thylakoids from Arabidopsis thaliana studied by successive in-solution and in-gel digestion

From individual localization and large-scale proteomic studies, we know that stroma-exposed thylakoid membranes harbor part of the machinery performing the light-dependent photosynthetic reactions. The minor components of the stroma thylakoid proteome, regulating and maintaining the photosynthetic machinery, are in the process of being unraveled. In this study, we developed in-solution and in-gel proteolytic digestion methods, and used them to identify minor membrane proteins, e.g. transporters, in stroma thylakoids prepared from Arabidopsis thaliana (L.) Heynh Columbia-0 leaves. In-solution digestion with chymotrypsin yielded the largest number of peptides, but in combination with methanol extraction resulted in identification of the largest number of membrane proteins. Although less efficient in extracting peptides, in-gel digestion with trypsin and chymotrypsin led to identification of additional proteins. We identified a total of 58 proteins including 44 membrane proteins. Almost half are known thylakoid proteins with roles in photosynthetic light reactions, proteolysis and import. The other half, including many transporters, are not known as chloroplast proteins, because they have been either curated (manually assigned) to other cellular compartments or not curated at all at the plastid protein databases. Transporters include ATP-binding cassette (ABC) proteins, transporters for K(+) and other cations. Other proteins either have a role in processes probably linked to photosynthesis, namely translation, metabolism, stress and signaling or are contaminants. Our results indicate that all these proteins are present in stroma thylakoids; however, individual studies are required to validate their location and putative roles. This study also provides strategies complementary to traditional methods for identification of membrane proteins from other cellular compartments.

Vitamin D Binding Protein Isoforms and Apolipoprotein E in Cerebrospinal Fluid as Prognostic Biomarkers of Multiple Sclerosis

BACKGROUND

Multiple sclerosis (MS) is a multifactorial autoimmune disease of the central nervous system with a heterogeneous and unpredictable course. To date there are no prognostic biomarkers even if they would be extremely useful for early patient intervention with personalized therapies. In this context, the analysis of inter-individual differences in cerebrospinal fluid (CSF) proteome may lead to the discovery of biological markers that are able to distinguish the various clinical forms at diagnosis.

METHODS

To this aim, a two dimensional electrophoresis (2-DE) study was carried out on individual CSF samples from 24 untreated women who underwent lumbar puncture (LP) for suspected MS. The patients were clinically monitored for 5 years and then classified according to the degree of disease aggressiveness and the disease-modifying therapies prescribed during follow up.

RESULTS

The hierarchical cluster analysis of 2-DE dataset revealed three protein spots which were identified by means of mass spectrometry as Apolipoprotein E (ApoE) and two isoforms of vitamin D binding protein (DBP). These three protein spots enabled us to subdivide the patients into subgroups correlated with clinical classification (MS aggressive forms identification: 80%). In particular, we observed an opposite trend of values for the two protein spots corresponding to different DBP isoforms suggesting a role of a post-translational modification rather than the total protein content in patient categorization.

CONCLUSIONS

These findings proved to be very interesting and innovative and may be developed as new candidate prognostic biomarkers of MS aggressiveness, if confirmed.

Differential regulation of proteins in rice (Oryza sativa L.) under iron deficiency

Sixty-three proteins were identified to be differentially accumulated due to iron deficiency in shoot and root. The importance of these proteins alterations on shoot physiology is discussed. Iron (Fe) is an essential micronutrient for plant growth and its accumulation affects the quality of edible plant organs. To investigate the adaptive mechanism of a Chinese rice variety grown under iron deficiency, proteins differentially accumulated in leaves and roots of Yangdao 6, an indica cultivar, under Fe deficiency growth condition, were profiled using a two-dimensional electrophoresis (2-DE) and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF/MS). The accumulations of seventy-three proteins were detected to be increased or decreased upon iron deficiency, and sixty-three of them were successfully identified. Among the sixty-three proteins, a total of forty proteins were identified in rice leaves, and twenty-three proteins were in roots. Most of these proteins are involved in photosynthesis, C metabolism, oxidative stress, Adenosine triphosphate synthesis, cell growth or signal transduction. The results provide a comprehensive way to understand, at the level of proteins, the adaptive mechanism used by rice shoots and roots under iron deficiency.

Proteomic changes during tuber dormancy release process revealed by iTRAQ quantitative proteomics in potato

Given that limited information is available with regard to tuber dormancy release related proteome, we conducted proteome analysis of tuber dormancy release process at dormant tuber (DT), dormancy release tuber (DRT) and sprouting tuber (ST) using the iTRAQ technology. A total of 1,752 proteins were identified. Among them, a subset of 316 proteins was screened as significant up- (137) and down regulated (179) between DT vs DRT. A subset of 120 proteins experienced significant up- (40) or down-regulation (80) between DRT vs ST. The differentially expressed proteins were grouped into 11 functional categories. Proteins enriched in functional categories of major carbohydrate (CHO) metabolism, glycolysis, fermentation, amino acid metabolism, protein and transport were highly up-regulated, while functional categories of photosynthesis and RNA were down-regulated between DT vs DRT. Proteins enriched in functional groups of protein, cell wall, lipid metabolism, miscellaneous, and signaling were strongly up-regulated, while functional categories of photosynthesis, hormone metabolism and protein were down-regulated between DRT vs ST. Consistent with previous documented differentially expressed genes, most of differentially expressed proteins were also identified between DT and DRT, indicating the metabolism shift from growth suspension to growth activation as tubers dormancy breaking. The changes in protein profiles showed lower concordance with corresponding alterations in transcript levels, indicating possible transcriptional and posttranscriptional regulation. Furthermore, the possible mechanism of tuber dormancy release was discussed in relation to what was known in transcripts change and other plant models from carbohydrate metabolism, protein metabolism, stress response, redox regulation, transcription regulation, DNA metabolism, amino acid metabolism, development, signaling as well as hormone metabolism.