The pitfalls of proteomics experiments without the correct use of bioinformatics tools

Reporting and reproducibility: Proteomics of fish models in environmental toxicology and ecotoxicology

Environmental toxicology and ecotoxicology research efforts are employing proteomics with fish models as New Approach Methodologies, along with in silico, in vitro and other omics techniques to elucidate hazards of toxicants and toxins. We performed a critical review of toxicology studies with fish models using proteomics and reported fundamental parameters across experimental design, sample preparation, mass spectrometry, and bioinformatics of fish, which represent alternative vertebrate models in environmental toxicology, and routinely studied animals in ecotoxicology. We observed inconsistencies in reporting and methodologies among experimental designs, sample preparations, data acquisitions and bioinformatics, which can affect reproducibility of experimental results. We identified a distinct need to develop reporting guidelines for proteomics use in environmental toxicology and ecotoxicology, increased QA/QC throughout studies, and method optimization with an emphasis on reducing inconsistencies among studies. Several recommendations are offered as logical steps to advance development and application of this emerging research area to understand chemical hazards to public health and the environment.

Purification and proteomic analysis of potent fibrinolytic enzymes extracted from Lumbricus rubellus

BACKGROUND

Lumbrokinase derived from earthworms, Lumbricus rubellus is known to have fibrinolytic enzymes that have potential as therapeutic drugs due to its ability to dissolve fibrin. The current study is aimed to purify the Lumbrokinase from L. rubellus and identify its protein component.

METHODS

Water extract of local earthworm Lumbricus rubellus revealed several proteins. Therefore, to identify its protein component, purification through HiPrep DEAE fast flow and proteomic analysis were conducted prior to identifications. A combination of two-dimension gel electrophoresis (2DE) and electrospray ionization mass spectrometry analysis was used to identify the purified fractions.

RESULTS

The purified fractions contain five protein bands, namely F25-1, F25-2, F85-1, F85-2, and F85-3, which displayed strong fibrinogenolytic activity. F25 fractions showed fibrinogenolytic activity of 974.85 U/mg, while F85 fractions showed higher activity of 1,484.11 U/mg. Fractions F85-1, F85-2, and F85-3 showed molecular weights of 42.6 kDa, 27.03 kDa, and 14 kDa, respectively and were identified as Lumbrokinase iso-enzymes.

CONCLUSION

This preliminary study indicates that the F25 and F85 fractions are similar to published fibrinolytic protease-1 and lumbrokinase, respectively, in terms of their amino acid sequence.

Subproteomic profiling from renal cortices in OLETF rats reveals mutations of multiple novel genes in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN) is a serious threat to human health, but its pathogenesis is not fully understood. Otsuka Long-Evans Tokushima Fatty (OLETF) rats are very similar to human DN in many aspects such as pathological changes and processes, and are deemed to be an ideal rodent model.

OBJECTIVE

This study was aimed to explore the pathogenesis of DN by analyzing the protein expression profile from renal cortices in OLETF rats.

METHODS

Thirty-six-week-old diabetic OLETF rats and normal control Long-Evans Tokushima Otsuka (LETO) rats were nephrectomized, and the renal cortices were isolated. The proteins were separated by soluble and insoluble high-resolution subproteomics methods for the analysis and identification of differential proteins.

RESULTS

Thirty-six differentially expressed proteins were found. Among them, 11 proteins had different isoelectric points and molecular weights between OLETF and LETO rats. Further sequencing identified point mutations in genes encoding eight of these proteins, which are involved in many biological processes closely related to DN, including oxidative stress and inflammation. Five of these eight proteins have not been reported in DN.

CONCLUSION

This study reveals mutations of multiple novel genes in diabetic OLETF rats, providing some new potential targets for the pathogenesis of DN and helping to better understand the pathogenesis of DN.

Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis

Multiple sclerosis (MS) is a neuroinflammatory disorder characterized by autoimmune-mediated inflammatory lesions in CNS leading to myelin damage and axonal loss. MS is a heterogenous disease with variable and unpredictable disease course. Due to its complex nature, MS is difficult to diagnose and responses to specific treatments may vary between individuals. Therefore, there is an indisputable need for biomarkers for early diagnosis, prediction of disease exacerbations, monitoring the progression of disease, and for measuring responses to therapy. Genomic and proteomic studies have sought to understand the molecular basis of MS and find biomarker candidates. Advances in next-generation sequencing and mass-spectrometry techniques have yielded an unprecedented amount of genomic and proteomic data; yet, translation of the results into the clinic has been underwhelming. This has prompted the development of novel data science techniques for exploring these large datasets to identify biologically relevant relationships and ultimately point towards useful biomarkers. Herein we discuss optimization of omics study designs, advances in the generation of omics data, and systems biology approaches aimed at improving biomarker discovery and translation to the clinic for MS.

Immunoreactive Proteins in the Esophageal Gland Cells of Anisakis Simplex Sensu Stricto Detected by MALDI-TOF/TOF Analysis

In plant and animal nematode parasites, proteins derived from esophageal gland cells have been shown to be important in the host-nematodes relationship but little is known about the allergenic potential of these proteins in the genus Anisakis. Taking into account the increase of anisakiasis and allergies related to these nematodes, immunoreactive properties of gland cell proteins were investigated. Two hundred ventricles were manually dissected from L3 stage larvae of Aniskakis simplex s.s. to allow direct protein analysis. Denaturing gel electrophoresis followed by monochromatic silver staining which revealed the presence of differential (enriched) proteins when compared to total nematode extracts. Such comparison was performed by means of 1D and 2D electrophoresis. Pooled antisera from Anisakis spp.-allergic patients were used in western blots revealing the presence of 13 immunoreactive bands in the ventricular extracts in 1D, with 82 spots revealed in 2D. The corresponding protein bands and spots were excised from the silver-stained gel and protein assignation was made by MALDI-TOF/TOF. A total of 13 (including proteoforms) were unambiguously identified. The majority of these proteins are known to be secreted by nematodes into the external environment, of which three are described as being major allergens in other organisms with different phylogenetic origin and one is an Anisakis simplex allergen.

Effect of fosinopril on the renal cortex protein expression profile of Otsuka Long-Evans Tokushima Fatty rats

Angiotensin-converting enzyme inhibitors (ACEIs) can reduce urinary protein excretion and postpone the deterioration of renal function. However, the mechanisms of renal protection are not yet fully understood. To investigate the mechanisms of ACEIs in the treatment of diabetic nephropathy (DN), the present study determined the effects of the ACEI fosinopril (FP) on the profiling of renal cortex protein expression in Otsuka Long-Evans Tokushima Fatty (OLETF) rats using Long-Evans Tokushima Otsuka (LETO) rats as controls. Urinary protein levels at 24 h were examined using the Broadford method. PAS staining was performed to observe renal histopathological changes. The kidney cortices of OLETF, FP-treated OLETF and LETO rats were examined using soluble and insoluble high-resolution subproteomic analysis methodology at age of 36 and 56 weeks. Differentiated proteins were further confirmed using western blotting analysis. The results demonstrated that FP significantly decreased the glomerulosclerosis index and reduced the 24 h urinary protein excretion of OLETF rats. Additionally, 17 proteins significantly changed following FP-treatment. Amongst these proteins, the abundances of the stress-response protein heat shock protein family A member 9 and the antioxidant glutathione peroxidase 3 were particularly increased. These results indicated that FP ameliorated diabetic renal injuries by inhibiting oxidative stress. In conclusion, the differentially expressed proteins may improve our understanding of the mechanism of ACEIs in the OLETF rats.

Annexin A3 may play an important role in ochratoxin-induced malignant transformation of human gastric epithelium cells

Ochratoxin A (OTA), one of the most abundant food-contaminating mycotoxins, is a possible carcinogen to humans. We previously demonstrated that long-term (40 weeks) OTA exposure induces the malignant transformation of human gastric epithelium cells (GES-1) in vitro. However, the specific mechanism underlying OTA-induced gastric carcinogenesis is complex. In the present study, we used 2-DE and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI/TOF MS) combined with bioinformatics and immunoblotting to investigate the differentially expressed proteins between GES-1 and OTA-malignant transformed GES-1 cells (OTA-GES-1_T cells) in vitro. We found that four differentially expressed proteins were identified after malignant transformation, including actin, cytoplasmic 1 (ACTB), F-actin-capping protein subunit alpha-1 (CAPZA1), Annexin A3 (ANXA3), thioredoxin peroxidase B from red blood cells (TPx-B) and Fibrinogen beta B (Fibrinogen β). Among the differentially expressed proteins, the effect of Annexin A3 was analyzed by MTT assay, western blot, cell cycle analysis, wound healing assay, Transwell assay, and colony formation assay in OTA-GES-1_T cells. The results showed that inhibition of Annexin A3 by siRNA effectively prevented the proliferation, migration, and invasion abilities of OTA-GES-1_T cells. Collectively, the results of this study will guide future research on OTA carcinogenicity.

Acetylated α-Tubulin Regulated by N-Acetyl-Seryl-Aspartyl-Lysyl-Proline(Ac-SDKP) Exerts the Anti-fibrotic Effect in Rat Lung Fibrosis Induced by Silica

Silicosis is the most serious occupational disease in China. The objective of this study was to screen various proteins related to mechanisms of the pathogenesis of silicosis underlying the anti-fibrotic effect of N-acetyl-seryl-aspartyl-lysyl-proline (Ac-SDKP) using proteomic profile analysis. We also aimed to explore a potential mechanism of acetylated α-tubulin (α-Ac-Tub) regulation by Ac-SDKP. Two-dimensional electrophoresis (2-DE) and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF/TOF MS) were used to assess the different protein expression profiles between control and silicosis rats treated with or without Ac-SDKP. Twenty-nine proteins were identified to be potentially involved in the progression of silicosis and the anti-fibrotic effect of Ac-SDKP. Our current study finds that 1) the lost expression of Ac-Tub-α may be a new mechanism in rat silicosis; 2) treatment of silicotic rats with N-acetyl-Seryl-Aspartyl-Lysyl-Proline (Ac-SDKP) inhibits myofibroblast differentiation and collagen deposition accompanied by stabilizing the expression of α-Ac-Tub in vivo and in vitro, which is related with deacetylase family member 6 (HDAC6) and α-tubulin acetyl transferase (α-TAT1). Our data suggest that α-Ac-Tub regulation by Ac-SDKP may potentially be a new anti-fibrosis mechanism.

Proteome changes in banana fruit peel tissue in response to ethylene and high-temperature treatments

Banana (Musa AAA group) is one of the most consumed fruits in the world due to its flavor and nutritional value. As a typical climacteric fruit, banana responds to ethylene treatment, which induces rapid changes of color, flavor (aroma and taste), sweetness and nutritional composition. It has also been reported that ripening bananas at temperatures above 24 °C inhibits chlorophyll breakdown and color formation but increases the rate of senescence. To gain fundamental knowledge about the effects of high temperature and ethylene on banana ripening, a quantitative proteomic study employing multiplex peptide stable isotope dimethyl labeling was conducted. In this study, green (immature) untreated banana fruit were subjected to treatment with 10 μL L(-1) of ethylene for 24 h. After ethylene treatment, treated and untreated fruit were stored at 20 or 30 °C for 24 h. Fruit peel tissues were then sampled after 0 and 1 day of storage, and peel color and chlorophyll fluorescence were evaluated. Quantitative proteomic analysis was conducted on the fruit peels after 1 day of storage. In total, 413 common proteins were identified and quantified from two biological replicates. Among these proteins, 91 changed significantly in response to ethylene and high-temperature treatments. Cluster analysis on these 91 proteins identified 7 groups of changed proteins. Ethylene treatment and storage at 20 °C induced 40 proteins that are correlated with pathogen resistance, cell wall metabolism, ethylene biosynthesis, allergens and ribosomal proteins, and it repressed 36 proteins that are associated with fatty acid and lipid metabolism, redox-oxidative responses, and protein biosynthesis and modification. Ethylene treatment and storage at 30 °C induced 32 proteins, which were mainly similar to those in group 1 but also included 8 proteins in group 3 (identified as chitinase, cinnamyl alcohol dehydrogenase 1, cysteine synthase, villin-2, leucine-transfer RNA ligase, CP47 protein and calmodulin) and repressed 43 proteins in 4 groups (groups 4-7), of which 6 were associated with photosynthesis II oxygen-evolving protein, the photosynthesis I reaction center, sugar metabolism, the redox-oxidative system and fatty acid metabolism. Differences in the response to ethylene and holding temperature at 30 °C were also revealed and have been discussed. The identities and quantities of the proteins found were linked with quality changes. This study demonstrates that ethylene and high temperature influence banana fruit ripening and senescence at the proteomic level and reveals the mechanisms by which high temperature accelerates banana fruit ripening.

Epidemiologic design and analysis for proteomic studies: a primer on -omic technologies

Proteome analysis is increasingly being used in investigations elucidating the molecular basis of disease, identifying diagnostic and prognostic markers, and ultimately improving patient care. We appraised the current status of proteomic investigations using human samples, including the state of the art in proteomic technologies, from sample preparation to data evaluation approaches, as well as key epidemiologic, statistical, and translational issues. We systematically reviewed the most highly cited clinical proteomic studies published between January 2009 and March 2014 that included a minimum of 100 samples, as well as strategies that have been successfully implemented to enhance the translational relevance of proteomic investigations. Limited comparability between studies and lack of specification of biomarker context of use are frequently observed. Nevertheless, there are initial examples of successful biomarker discovery in cross-sectional studies followed by validation in high-risk longitudinal cohorts. Translational potential is currently hindered, as limitations in proteomic investigations are not accounted for. Interdisciplinary communication between proteomics experts, basic researchers, epidemiologists, and clinicians, an orchestrated assimilation of required resources, and a more systematic translational outlook for accumulation of evidence may augment the public health impact of proteomic investigations.

Proteomic profiling and characterization of differential allergens in the nematodes Anisakis simplex sensu stricto and A. pegreffii

The parasite species complex Anisakis simplex sensu lato (Anisakis simplex sensu stricto; (A. simplex s.s.), A. pegreffii, A. simplex C) is the main cause of severe anisakiasis (allergy) worldwide and is now an important health matter. In this study, the relationship of this Anisakis species complex and their allergenic capacities is assessed by studying the differences between the two most frequent species (A. simplex s.s., A. pegreffii) and their hybrid haplotype by studying active L3 larvae parasiting Merluccius merluccius. They were compared by 2D gel electrophoresis and parallel Western blot (2DE gels were hybridized with pools of sera from Anisakis allergenic patients). Unambiguous spot differences were detected and protein assignation was made by MALDI-TOF/TOF analysis or de novo sequencing. Seventy-five gel spots were detected and the corresponding proteins were identified. Differentially expressed proteins for A. simplex s.s., A. pegreffii, and their hybrid are described and results are statistically supported. Twenty-eight different allergenic proteins are classified according to different families belonging to different biological functions. These proteins are described for the first time as antigenic and potentially new allergens in Anisakis. Comparative proteomic analyses of allergenic capacities are useful for diagnosis, epidemiological surveys, and clinical research. All MS data have been deposited in the ProteomeXchange with identifier PXD000662 (http://proteomecentral.proteomexchange.org/dataset/PXD000662).

A field guide to genomics research

Portraying high-throughput genomics research as a wild frontier, Andrea Bild and colleagues use caricatures to highlight common pitfalls in genomic research and provide recommendations for navigating this terrain.

Host–parasite molecular cross-talk during the manipulative process of a host by its parasite

Many parasite taxa are able to alter a wide range of phenotypic traits of their hosts in ways that seem to improve the parasite’s chance of completing its life cycle. Host behavioural alterations are classically seen as compelling illustrations of the ‘extended phenotype’ concept, which suggests that parasite genes have phenotype effects on the host. The molecular mechanisms and the host–parasite cross-talk involved during the manipulative process of a host by its parasite are still poorly understood. In this Review, the current knowledge on proximate mechanisms related to the ‘parasite manipulation hypothesis’ is presented. Parasite genome sequences do not themselves provide a full explanation of parasite biology nor of the molecular cross-talk involved in host–parasite associations. Recently, first-generation proteomics tools have been employed to unravel some aspects of the parasite manipulation process (i.e. proximate mechanisms and evolutionary convergence) using certain model arthropod-host–parasite associations. The pioneer proteomics results obtained on the manipulative process are here highlighted, along with the many gaps in our knowledge. Candidate genes and biochemical pathways potentially involved in the parasite manipulation are presented. Finally, taking into account the environmental factors, we suggest new avenues and approaches to further explore and understand the proximate mechanisms used by parasite species to alter phenotypic traits of their hosts.

Biological and methodical challenges of blood-based proteomics in the field of neurological research

Biomarker discovery is an application of major importance in today's proteomic research. There is an urgent need for suitable biomarkers to improve diagnostic tools and treatment in various neurological diseases, such as neurodegenerative disorders. Recent years have witnessed an enormous interest in proteomics, which is currently seen as an invaluable tool to shed more light on complex interacting signalling pathways and molecular networks involved in several neuropathological conditions. However, while first results of proteomic research studies have sparked much public attention, the momentum of further proteomic biomarker research in neurological disorders may suffer by its very complex methodology which is sensitive to various sources of artefacts. A major source of variability is proteome perturbation caused by sample handling/preservation (preanalytical phase) and processing/measurement (analytical phase). The aim of the present review is to summarize the current literature focusing on the crucial role played by preanalytical and analytical factors that affect the quality of samples and the reliability of the data produced in blood-based proteomic biomarker research in neurology, which may apply to Alzheimer's disease (AD) as well as other neurological disorders. Procedures for sample preparation and protocols for the analysis of serum and plasma samples will be delineated. Finally, the potential usefulness of bioinformatics--allowing for the assembly, store, and processing of data--as well as its contribution to the execution of proteomic studies will be critically discussed.

A Bayesian model for classifying all differentially expressed proteins simultaneously in 2D PAGE gels

Background

Two-dimensional polyacrylamide gel electrophoresis (2D PAGE) is commonly used to identify differentially expressed proteins under two or more experimental or observational conditions. Wu et al (2009) developed a univariate probabilistic model which was used to identify differential expression between Case and Control groups, by applying a Likelihood Ratio Test (LRT) to each protein on a 2D PAGE. In contrast to commonly used statistical approaches, this model takes into account the two possible causes of missing values in 2D PAGE: either (1) the non-expression of a protein; or (2) a level of expression that falls below the limit of detection.

Results

We develop a global Bayesian model which extends the previously described model. Unlike the univariate approach, the model reported here is able treat all differentially expressed proteins simultaneously. Whereas each protein is modelled by the univariate likelihood function previously described, several global distributions are used to model the underlying relationship between the parameters associated with individual proteins. These global distributions are able to combine information from each protein to give more accurate estimates of the true parameters. In our implementation of the procedure, all parameters are recovered by Markov chain Monte Carlo (MCMC) integration. The 95% highest posterior density (HPD) intervals for the marginal posterior distributions are used to determine whether differences in protein expression are due to differences in mean expression intensities, and/or differences in the probabilities of expression.

Conclusions

Simulation analyses showed that the global model is able to accurately recover the underlying global distributions, and identify more differentially expressed proteins than the simple application of a LRT. Additionally, simulations also indicate that the probability of incorrectly identifying a protein as differentially expressed (i.e., the False Discovery Rate) is very low. The source code is available at https://github.com/stevenhwu/BIDE-2D.

Finding biomarkers is getting easier

Single biomarkers are rarely accurate. Even suites of biomarkers can give conflicting results. Ideally potent combinations of variables are isolated which accurately identify specific analytes and their level of toxicity. The search for such combinations can be done by reducing the thousands of candidate variables to the small number necessary for treatment classification. When the key variables are recognized by machine learning (ML) the results are quite surprising, given the apparent failure of other searching methods to produce good diagnostics. Proteins seem especially useful for portable field tests of a variety of adverse conditions. This review shows how ML, in particular artificial neural networks, can find potent biomarkers embedded in any type of expression data, mainly proteins in this article. A computer does multiple iterations to produce sets of proteins which systematically identify (to near 100% accuracy) the treatment classes of interest. Whether these proteins are useful in actual diagnoses is tested by presenting the computer model with unknown classes. Finding the biomarkers is getting easier but there still must be confirmation, by multivariable statistics and with field studies.

A systems biology approach to nutritional immunology - focus on innate immunity

Innate immunity and nutrient metabolism are complex biological systems that must work in concert to sustain and preserve life. The effector cells of the innate immune system rely on essential nutrients to generate energy, produce metabolic precursors for macromolecule biosynthesis and tune their responses to infectious agents. Thus disruptions to nutritional status have a substantial impact on immune competence and can result in increased susceptibility to infection in the case of nutrient deficiency, or chronic inflammation in the case of over-nutrition. The traditional, reductionist methods used in the study of nutritional immunology are incapable of exploring the extremely complex interactions between nutrient metabolism and innate immunity. Here, we review a relatively new analytical approach, systems biology, and highlight how it can be applied to nutritional immunology to provide a comprehensive view of the mechanisms behind nutritional regulation of the innate immune system.

Protein differences between human trapezius and vastus lateralis muscles determined with a proteomic approach

Background

The trapezius muscle is a neck muscle that is susceptible to chronic pain conditions associated with repetitive tasks, commonly referred to as chronic work-related myalgia, hence making the trapezius a muscle of clinical interest. To provide a basis for further investigations of the proteomic traits of the trapezius muscle in disease, two-dimensional difference gel electrophoresis (2D-DIGE) was performed on the healthy trapezius using vastus lateralis as a reference. To obtain as much information as possible from the vast proteomic data set, both one-way ANOVA, with and without false discovery rate (FDR) correlation, and partial least square projection to latent structures with discriminant analysis (PLS-DA) were combined to compare the outcome of the analysis.

Results

The trapezius and vastus lateralis showed significant differences in metabolic, contractile and regulatory proteins, with different results depending on choice of statistical approach and pre-processing technique. Using the standard method, FDR correlated one-way ANOVA, 42 protein spots differed significantly in abundance between the two muscles. Complementary analysis using immunohistochemistry and western blot confirmed the results from the 2D-DIGE analysis.

Conclusions

The proteomic approach used in the present study combining 2D-DIGE and multivariate modelling provided a more comprehensive comparison of the protein profiles of the human trapezius and vastus lateralis muscle, than previously possible to obtain with immunohistochemistry or SDS-PAGE alone. Although 2D-DIGE has inherent limitations it is particularly useful to comprehensively screen for important structural and metabolic proteins, and appears to be a promising tool for future studies of patients suffering from chronic work related myalgia or other muscle diseases.

Mass spectrometry-coupled techniques for viral-related disease biomarker identification

The advent of high-resolution mass spectrometers coupled with proteomic techniques has facilitated the discovery and characterization of novel viral proteins and the detection of virus-induced changes in the cellular proteome. These advances have enabled a more comprehensive characterization of viral interactions involved in infection and pathogenesis, and allowed the discovery of viral biomarkers. This article focuses on the role of mass spectrometry proteomic techniques to identify and characterize both prospective and verified viral biomarkers, and their implications on the diagnosis of disease.

Operator- and software-related post-experimental variability and source of error in 2-DE analysis

In the field of proteomics, several approaches have been developed for separating proteins and analyzing their differential relative abundance. One of the oldest, yet still widely used, is 2-DE. Despite the continuous advance of new methods, which are less demanding from a technical standpoint, 2-DE is still compelling and has a lot of potential for improvement. The overall variability which affects 2-DE includes biological, experimental, and post-experimental (software-related) variance. It is important to highlight how much of the total variability of this technique is due to post-experimental variability, which, so far, has been largely neglected. In this short review, we have focused on this topic and explained that post-experimental variability and source of error can be further divided into those which are software-dependent and those which are operator-dependent. We discuss these issues in detail, offering suggestions for reducing errors that may affect the quality of results, summarizing the advantages and drawbacks of each approach.

Plasma proteomic profiles of bovine growth hormone transgenic mice as they age

Attenuation of the growth hormone (GH)/insulin-like growth factor-1 (IGF-1) axis results in extended lifespan in many organisms including mice. Conversely, GH transgenic mice have excess GH action and die prematurely. We have studied bovine (b) GH transgenic mice (n = 9) and their wild type (WT) littermates (n = 8) longitudinally and have determined several age-related changes. Compared to WT mice, bGH mice lost fat mass, became hypoglycemic and had lower insulin levels at older ages despite being hyperinsulinemic when young. To examine plasma protein differences in bGH mice relative to controls, samples at 2, 4, 8, 12 and 16 months of age were analyzed by two-dimensional gel electrophoresis followed by identification using mass spectrometry. We found several differences in plasma proteins of bGH mice compared to controls, including increased apolipoprotein E (five isoforms), haptoglobin (four isoforms) and mannose-binding protein-C (one out of three isoforms), and decreased transthyretin (six isoforms). In addition, clusterin (two out of six isoforms) and haptoglobin (four isoforms) were up-regulated in bGH mice as a function of age. Finally, alpha-2 macroglobulin (seven isoforms) was altered in an isoform-specific manner with two isoforms increased and two decreased in bGH mouse plasma compared to controls. In conclusion, identification of these proteins suggests that bGH mice exhibit an increased inflammatory state with an adverse lipid profile, possibly contributing to their diminished life expectancy. Also, these newly discovered plasma proteins may be indicative or 'biomarkers' of a shortened lifespan.

Proteomic analysis in the identification of allergenic molecules

Conventional and innovative strategies can be exploited to identify and characterize new allergenic proteins. With the aim of obtaining suggestions for future improvements, this article describes our attempt to understand and describe some of the advantages and pitfalls of the methodologies and procedures often used in this field. The analysis includes the protein extract preparation, starting from the allergenic source, the separation of the proteins contained in a mixture and the detection, identification and characterization of IgE-binding molecules. Classic and emerging proteomic technologies, including mass spectrometry-based methodologies, Edman degradation procedure, microarray-based techniques and bioinformatics search strategies, have been explored. A comparative analysis of biochemistry-based proteomics and molecular biology strategies has also been given.

Proteomic profile of primary isolated rat mesangial cells in high-glucose culture condition and decreased expression of PSMA6 in renal cortex of diabetic rats

Diabetic nephropathy (DN) is one of the most important complications of diabetic patients and is characterized histologically by an accumulation of extracellular matrix (ECM) protein in the glomerular mesangium. Therefore, mesangial cells likely play an important role in the development of diabetic nephropathy. Here, we employed proteomic techniques to investigate the protein profile of rat mesangial cells under high-glucose culture conditions. Primary isolated rat glomerular mesangial cells were cultured under different concentrations of glucose (5.4 mmol.L-1 for normal control and 30 mmol.L-1 for high glucose) for 0, 8, 16, and 72 h, as well as for 25 days. Cellular total proteins were isolated from these cells and employed for two-dimensional gel electrophoresis (2-DE). Differentially expressed proteins were identified by matrix-assisted laser desorption - ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and some of these proteins were documented in rat models of diabetes by Western blot. Rat mesangial cells were successfully isolated in the laboratory and their proliferation rates were significantly inhibited by high glucose. Two-dimensional gel electrophoresis analyses revealed 28 differentially expressed protein spots between the normal and high-glucose groups. After MALDI-TOF-MS analysis, all 28 protein spots were successfully identified with the peptide mass fingerprint (PMF) method. Representatively, SOD1, PCBP1 and PSMA6 were validated by Western blot analysis following protein extractions from the normal and high-glucose groups. Abundance of these proteins was consistent with that found in 2-DE. Moreover, expression of SOD1, PCBP1, and PSMA6 in renal cortex was further examined in two rat models of diabetes (streptozotocin-induced and spontaneous OLETF diabetic models). Abundance of SOD1 and PCBP1 proteins did not show any significant difference between normal control and diabetic rats. However, abundance of the PSMA6 protein was significantly reduced in the renal cortex of both STZ-induced and spontaneous OLETF diabetic rats. Proteomic analysis identified 28 differentially expressed proteins in primary isolated rat mesangial cells between normal and high glucose treatments. Expression of one identified protein was found to be consistent with expression in the renal cortex of two rat diabetic models. Therefore, identification of protein expression patterns in mesangial cells can be employed to develop a therapeutic target for treatment of diabetic nephropathy.

Image analysis tools and emerging algorithms for expression proteomics

Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-DE technique of protein separation, and by first covering signal analysis for MS, we also explain the current image analysis workflow for the emerging high-throughput 'shotgun' proteomics platform of LC coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whereas existing commercial and academic packages and their workflows are described from both a user's and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models, and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.

Phylogenetic character mapping of proteomic diversity shows high correlation with subspecific phylogenetic diversity in Trypanosoma cruzi

We performed a phylogenetic character mapping on 26 stocks of Trypanosoma cruzi, the parasite responsible for Chagas disease, and 2 stocks of the sister taxon T. cruzi marinkellei to test for possible associations between T. cruzi-subspecific phylogenetic diversity and levels of protein expression, as examined by proteomic analysis and mass spectrometry. We observed a high level of correlation (P < 10(-4)) between genetic distance, as established by multilocus enzyme electrophoresis, and proteomic dissimilarities estimated by proteomic Euclidian distances. Several proteins were found to be specifically associated to T. cruzi phylogenetic subdivisions (discrete typing units). This study explores the previously uncharacterized links between infraspecific phylogenetic diversity and gene expression in a human pathogen. It opens the way to searching for new vaccine and drug targets and for identification of specific biomarkers at the subspecific level of pathogens.

OVNIp: an open source application facilitating the interpretation, the validation and the edition of proteomics data generated by MS analyses and de novo sequencing

Several academic software are available to help the validation and reporting of proteomics data generated by MS analyses. However, to our knowledge, none of them have been conceived to meet the particular needs generated by the study of organisms whose genomes are not sequenced. In that context, we have developed OVNIp, an open-source application which facilitates the whole process of proteomics results interpretation. One of its unique attributes is its capacity to compile multiple results (from several search engines and/or several databank searches) with a resolution of conflicting interpretations. Moreover, OVNIp enables automated exploitation of de novo sequences generated from unassigned MS/MS spectra leading to higher sequence coverage and enhancing confidence in the identified proteins. The exploitation of these additional spectra might also identify novel proteins through a MS-BLAST search, which can be easily ran from the OVNIp interface. Beyond this primary scope, OVNIp can also benefit to users who look for a simple standalone application to both visualize and confirm MS/MS result interpretations through a simple graphical interface and generate reports according to user-defined forms which may integrate the prerequisites for publication. Sources, documentation and a stable release for Windows are available at http://wwwappli.nantes.inra.fr:8180/OVNIp.

Proteomics of plant pathogenic fungi

Plant pathogenic fungi cause important yield losses in crops. In order to develop efficient and environmental friendly crop protection strategies, molecular studies of the fungal biological cycle, virulence factors, and interaction with its host are necessary. For that reason, several approaches have been performed using both classical genetic, cell biology, and biochemistry and the modern, holistic, and high-throughput, omic techniques. This work briefly overviews the tools available for studying Plant Pathogenic Fungi and is amply focused on MS-based Proteomics analysis, based on original papers published up to December 2009. At a methodological level, different steps in a proteomic workflow experiment are discussed. Separate sections are devoted to fungal descriptive (intracellular, subcellular, extracellular) and differential expression proteomics and interactomics. From the work published we can conclude that Proteomics, in combination with other techniques, constitutes a powerful tool for providing important information about pathogenicity and virulence factors, thus opening up new possibilities for crop disease diagnosis and crop protection.

Biomarker discovery and clinical proteomics

New biomarkers are urgently needed to accelerate efforts in developing new drugs and treatments of known diseases. New clinical and translational proteomics studies emerge almost every day. However, discovery of new diagnostic biomarkers lags behind because of variability at every step in proteomics studies (e.g., assembly of a cohort of patients, sample preparation and the nature of body fluids, selection of a profiling method and uniform protocols for data analysis).Quite often, the validation step that follows the discovery phase does not reach desired levels of sensitivity and specificity or reproducibility between laboratories. Mass spectrometry and gel-based methods do not provide enough throughput for screening thousands of clinical samples. Further development of protein arrays may address this issue.Despite many obstacles, proteomics delivers vast amounts of information useful for understanding the molecular mechanisms underlying diseases.

Changes in the proteomes of the hemocytes and fat bodies of the flesh fly Sarcophaga bullata larvae after infection by Escherichia coli

Background

Insects have an efficient self-defense system that is based on innate immunity. Recent findings have disclosed many parallels between human and insect innate immunity, and simultaneously fine differences in the processes between various species have been revealed. Studies on the immune systems of various insect species may uncover the differences in their host defense strategies.

Results

We analyzed the proteomes of the hemocytes and fat bodies of Sarcophaga bullata larvae after infection by Escherichia coli. The 2-DE gels of the hemocytes and fat bodies of infected larvae were compared with those of aseptically injured larvae. Our analysis included the construction of protein maps of the hemocyte cells and cells from fat bodies, the identification of the changed proteins, in response to infection, using LC-MS/MS, and the estimation of the trends in expression of these proteins at three time points (30 min, 6 hours and 22 hours) after infection. In total, seven changed spots were found in the hemocytes, and four changed spots were found in the fat bodies. Three types of trends in protein expression were observed. Cofilin and transgelin were undetectable at 30 min after infection but were continuously up-regulated in the induced larvae after 22 hours. A prophenoloxidase isoform and lectin subunit α were slightly up-regulated at 30 min after infection, and their protein levels reached the highest points after 6 hours but decreased after 22 hours. T-Complex subunit α, GST, ferritin-like protein and an anterior fat body protein (regucalcin homologue) were down-regulated at 22 hours after infection.

Conclusions

Many proteins identified in our study corresponded to the proteins identified in other insects. Compared to the former studies performed in insects, we presented 2-D protein maps of the hemocytes and fat bodies and showed the trends in expression of the immune-elicited proteins.

Proteogenomic studies in epithelial ovarian cancer: established knowledge and future needs

There has been a concerted effort over the last decade to improve our understanding of the complex biology of ovarian cancer. A linear growth in published proteogenomic studies has addressed a variety of questions regarding its molecular pathogenesis. A number of genes have been identified by transcriptomic approaches, some of which are being investigated as putative tumor markers (HE4, OPN, Ep-CAM and Mesothelin), whilst others are potential targets for molecular therapeutic approaches (VEGF, IO4, EGFR, MUC1, CLDN4 and SLPI). Proteogenomics has the potential to further change our current characterization and treatment of ovarian cancer. Additional advances will depend on integrated study designs, interdisciplinary collaborations, use of robust high-throughput platforms, as well as uniform guidelines for bioinformatic analyses.

A statistical model to identify differentially expressed proteins in 2D PAGE gels

Two dimensional polyacrylamide gel electrophoresis (2D PAGE) is used to identify differentially expressed proteins and may be applied to biomarker discovery. A limitation of this approach is the inability to detect a protein when its concentration falls below the limit of detection. Consequently, differential expression of proteins may be missed when the level of a protein in the cases or controls is below the limit of detection for 2D PAGE. Standard statistical techniques have difficulty dealing with undetected proteins. To address this issue, we propose a mixture model that takes into account both detected and non-detected proteins. Non-detected proteins are classified either as (a) proteins that are not expressed in at least one replicate, or (b) proteins that are expressed but are below the limit of detection. We obtain maximum likelihood estimates of the parameters of the mixture model, including the group-specific probability of expression and mean expression intensities. Differentially expressed proteins can be detected by using a Likelihood Ratio Test (LRT). Our simulation results, using data generated from biological experiments, show that the likelihood model has higher statistical power than standard statistical approaches to detect differentially expressed proteins. An R package, Slider (Statistical Likelihood model for Identifying Differential Expression in R), is freely available at http://www.cebl.auckland.ac.nz/slider.php.

Challenges, current status and future perspectives of proteomics in improving understanding, diagnosis and treatment of vascular disease

Technical advances have seen the rapid adoption of genomics and multiplex genetic polymorphism identification to research on vascular diseases. The utilization of proteomics for the study of vascular diseases has been limited by comparison. In this review we outline currently available proteomics techniques, the challenges to using these approaches and modifications which may improve the utilization of proteomics in the study of vascular diseases.

Protein expression profiling

Protein expression profiling is defined in general as identifying the proteins expressed in a particular tissue, under a specified set of conditions and at a particular time, usually compared to expression in reference samples. This information is useful in drug discovery and diagnosis as well as in understanding response mechanisms at the protein level. We may identify all the proteins responding to a particular stimulus and select those whose expression changes most. Or we may isolate significant protein variables and then identify them. These definitive sets of proteins (protein expression signatures; PES) are specific to diseases, toxicants, physical stresses, and to degrees of stress severity. Here we describe a method, based on machine learning, for isolating the sets of proteins, before identifying them by name, which classify accurately the treatment classes in a study. The principle in this chapter is that if proteins associated with known classes of interest can be used to identify unknown classes then the proteins are definitive for diagnosis.The proteins in each class, including controls, are converted to digital data and serve as input to artificial neural network (ANN) models. Multiple two-dimensional electrophoresis (2DE) gel patterns are included in each treatment class. A training subset of digitized individual, not composite, gel images is used to construct an ANN model which is then applied to a test set of images. Successful classification of the unknown (test) data confirms that the variables included in the model are indeed significant in discrimination among the classes. In the study described here the misclassifications were 5% or less using the ANN models. The ANN method seems to be a useful complement to image analysis, described in Chapter "Troubleshooting Image Analysis in 2DE". The reduction in protein variables permits multivariable statistics such as cluster and discriminant analyses.

LC-MS/MS-based proteome profiling in Daphnia pulex and Daphnia longicephala: the Daphnia pulex genome database as a key for high throughput proteomics in Daphnia

BACKGROUND

Daphniids, commonly known as waterfleas, serve as important model systems for ecology, evolution and the environmental sciences. The sequencing and annotation of the Daphnia pulex genome both open future avenues of research on this model organism. As proteomics is not only essential to our understanding of cell function, and is also a powerful validation tool for predicted genes in genome annotation projects, a first proteomic dataset is presented in this article.

RESULTS

A comprehensive set of 701,274 peptide tandem-mass-spectra, derived from Daphnia pulex, was generated, which lead to the identification of 531 proteins. To measure the impact of the Daphnia pulex filtered models database for mass spectrometry based Daphnia protein identification, this result was compared with results obtained with the Swiss-Prot and the Drosophila melanogaster database. To further validate the utility of the Daphnia pulex database for research on other Daphnia species, additional 407,778 peptide tandem-mass-spectra, obtained from Daphnia longicephala, were generated and evaluated, leading to the identification of 317 proteins.

CONCLUSION

Peptides identified in our approach provide the first experimental evidence for the translation of a broad variety of predicted coding regions within the Daphnia genome. Furthermore it could be demonstrated that identification of Daphnia longicephala proteins using the Daphnia pulex protein database is feasible but shows a slightly reduced identification rate. Data provided in this article clearly demonstrates that the Daphnia genome database is the key for mass spectrometry based high throughput proteomics in Daphnia.

"Pathogeno-proteomics"

Many scientists working on pathogens (viruses, bacteria, fungi, parasites) are betting heavily on data generated by longitudinal genomic-transcriptomic-proteomic studies to explain biochemical host-vector-pathogen interactions and thus to contribute to disease control. Availability of genome sequences of various organisms, from viruses to complex metazoans, led to the discovery of the functions of the genes themselves. The postgenomic era stimulated the development of proteomic and bioinformatics tools to identify the locations, functions, and interactions of the gene products in tissues and/or cells of living organisms. Because of the diversity of available methods and the level of integration they promote, proteomics tools are potentially able to resolve interesting issues specific not only to host-vector-pathogen interactions in cell immunobiology, but also to ecology and evolution, population biology, and adaptive processes. These new analytical tools, as all new tools, contain pitfalls directly related to experimental design, statistical treatment, and protein identification. Nevertheless, they offer the potency of building large protein-protein interaction networks for in silico analysis of novel biological entities named "interactomes," a way of modeling host-vector-pathogen interactions to define new interference strategies.

A new method for 2D gel spot alignment: application to the analysis of large sample sets in clinical proteomics

BACKGROUND

In current comparative proteomics studies, the large number of images generated by 2D gels is currently compared using spot matching algorithms. Unfortunately, differences in gel migration and sample variability make efficient spot alignment very difficult to obtain, and, as consequence most of the software alignments return noisy gel matching which needs to be manually adjusted by the user.

RESULTS

We present Sili2DGel an algorithm for automatic spot alignment that uses data from recursive gel matching and returns meaningful Spot Alignment Positions (SAP) for a given set of gels. In the algorithm, the data are represented by a graph and SAP by specific subgraphs. The results are returned under various forms (clickable synthetic gel, text file, etc.). We have applied Sili2DGel to study the variability of the urinary proteome from 20 healthy subjects.

CONCLUSION

Sili2DGel performs noiseless automatic spot alignment for variability studies (as well as classical differential expression studies) of biological samples. It is very useful for typical clinical proteomic studies with large number of experiments.

Evaluating peptide mass fingerprinting-based protein identification

Identification of proteins by mass spectrometry (MS) is an essential step in proteomic studies and is typically accomplished by either peptide mass fingerprinting (PMF) or amino acid sequencing of the peptide. Although sequence information from MS/MS analysis can be used to validate PMF-based protein identification, it may not be practical when analyzing a large number of proteins and when high- throughput MS/MS instrumentation is not readily available. At present, a vast majority of proteomic studies employ PMF. However, there are huge disparities in criteria used to identify proteins using PMF. Therefore, to reduce incorrect protein identification using PMF, and also to increase confidence in PMF-based protein identification without accompanying MS/MS analysis, definitive guiding principles are essential. To this end, we propose a value-based scoring system that provides guidance on evaluating when PMF-based protein identification can be deemed sufficient without accompanying amino acid sequence data from MS/MS analysis.

Two-dimensional electrophoretic comparison of metastatic and non-metastatic human breast tumors using in vitro cultured epithelial cells derived from the cancer tissues

BACKGROUND

Breast carcinomas represent a heterogeneous group of tumors diverse in behavior, outcome, and response to therapy. Identification of proteins resembling the tumor biology can improve the diagnosis, prediction, treatment selection, and targeting of therapy. Since the beginning of the post-genomic era, the focus of molecular biology gradually moved from genomes to proteins and proteomes and to their functionality. Proteomics can potentially capture dynamic changes in protein expression integrating both genetic and epigenetic influences.

METHODS

We prepared primary cultures of epithelial cells from 23 breast cancer tissue samples and performed comparative proteomic analysis. Seven patients developed distant metastases within three-year follow-up. These samples were included into a metastase-positive group, the others formed a metastase-negative group. Two-dimensional electrophoretical (2-DE) gels in pH range 4-7 were prepared. Spot densities in 2-DE protein maps were subjected to statistical analyses (R/maanova package) and data-mining analysis (GUHA). For identification of proteins in selected spots, liquid chromatography-tandem mass spectrometry (LC-MS/MS) was employed.

RESULTS

Three protein spots were significantly altered between the metastatic and non-metastatic groups. The correlations were proven at the 0.05 significance level. Nucleophosmin was increased in the group with metastases. The levels of 2,3-trans-enoyl-CoA isomerase and glutathione peroxidase 1 were decreased.

CONCLUSION

We have performed an extensive proteomic study of mammary epithelial cells from breast cancer patients. We have found differentially expressed proteins between the samples from metastase-positive and metastase-negative patient groups.