A proteomic strategy for global analysis of plant protein complexes

Global analyses of protein complex assembly, composition, and location are needed to fully understand how cells coordinate diverse metabolic, mechanical, and developmental activities. The most common methods for proteome-wide analysis of protein complexes rely on affinity purification-mass spectrometry or yeast two-hybrid approaches. These methods are time consuming and are not suitable for many plant species that are refractory to transformation or genome-wide cloning of open reading frames. Here, we describe the proof of concept for a method allowing simultaneous global analysis of endogenous protein complexes that begins with intact leaves and combines chromatographic separation of extracts from subcellular fractions with quantitative label-free protein abundance profiling by liquid chromatography-coupled mass spectrometry. Applying this approach to the crude cytosolic fraction of Arabidopsis thaliana leaves using size exclusion chromatography, we identified hundreds of cytosolic proteins that appeared to exist as components of stable protein complexes. The reliability of the method was validated by protein immunoblot analysis and comparisons with published size exclusion chromatography data and the masses of known complexes. The method can be implemented with appropriate instrumentation, is applicable to any biological system, and has the potential to be further developed to characterize the composition of protein complexes and measure the dynamics of protein complex localization and assembly under different conditions.

Extracellular matrix-associated proteome changes during non-host resistance in citrus-Xanthomonas interactions

Non-host resistance (NHR) is a most durable broad-spectrum resistance employed by the plants to restrict majority of pathogens. Plant extracellular matrix (ECM) is a critical defense barrier. Understanding ECM responses during interaction with non-host pathogen will provide insights into molecular events of NHR. In this study, the ECM-associated proteome was compared during interaction of citrus with pathogen Xanthomonas axonopodis pv. citri (Xac) and non-host pathogen Xanthomonas oryzae pv. oryzae (Xoo) at 8, 16, 24 and 48 h post inoculation. Comprehensive analysis of ECM-associated proteins was performed by extracting wall-bound and soluble ECM components using both destructive and non-destructive procedures. A total of 53 proteins was differentially expressed in citrus-Xanthomonas host and non-host interaction, out of which 44 were identified by mass spectrometry. The differentially expressed proteins were related to (1) defense-response (5 pathogenesis-related proteins, 3 miraculin-like proteins (MIR, MIR1 and MIR2) and 2 proteases); (2) enzymes of reactive oxygen species (ROS) metabolism [Cu/Zn superoxide dismutase (SOD), Fe-SOD, ascorbate peroxidase and 2-cysteine-peroxiredoxin]; (3) signaling (lectin, curculin-like lectin and concanavalin A-like lectin kinase); and (4) cell-wall modification (α-xylosidase, glucan 1, 3 β-glucosidase, xyloglucan endotransglucosylase/hydrolase). The decrease in ascorbate peroxidase and cysteine-peroxiredoxin could be involved in maintenance of ROS levels. Increase in defense, cell-wall remodeling and signaling proteins in citrus-Xoo interaction suggests an active involvement of ECM in execution of NHR. Partially compromised NHR in citrus against Xoo, upon Brefeldin A pre-treatment supported the role of non-classical secretory proteins in this phenomenon.

Experimental and computational analysis of the secretome of the hyperthermophilic archaeon Pyrococcus furiosus

Although Pyrococcus furiosus is one of the best studied hyperthermophilic archaea, to date no experimental investigation of the extent of protein secretion has been performed. We describe experimental verification of the extracellular proteome of P. furiosus grown on starch. LC-MS/MS-based analysis of culture supernatants led to the identification of 58 proteins. Fifteen of these proteins had a putative N-terminal signal peptide (SP), tagging the proteins for translocation across the membrane. The detected proteins with predicted SPs and known function were almost exclusively involved in important extracellular functions, like substrate degradation or transport. Most of the 43 proteins without predicted N-terminal signal sequences are known to have intracellular functions, mainly (70 %) related to intracellular metabolism. In silico analyses indicated that the genome of P. furiosus encodes 145 proteins with N-terminal SPs, including 21 putative lipoproteins and 17 with a class III peptide. From these we identified 15 (10 %; 7 SPI, 3 SPIII and 5 lipoproteins) under the specific growth conditions of this study. The putative lipoprotein signal peptides have a unique sequence motif, distinct from the motifs in bacteria and other archaeal orders.

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

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

A proteomic comparison of formalin-fixed paraffin-embedded pancreatic tissue from autoimmune pancreatitis, chronic pancreatitis, and pancreatic cancer

CONTEXT

Formalin-fixed paraffin-embedded (FFPE) tissue is a standard for specimen preservation, and as such FFPE tissue banks are an untapped resource of histologically-characterized specimens for retrospective biomarker investigation for pancreatic disease.

OBJECTIVES

We use liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) to compare FFPE specimens from three different diseases of the exocrine pancreas.

DESIGN

We investigated the proteomic profile of FFPE pancreatic tissue from 9 archived specimens that were histologically classified as: autoimmune pancreatitis (n=3), chronic pancreatitis (n=3), and pancreatic cancer (n=3), using LC-MS/MS.

SETTING

This is a proteomic analysis experiment of FFPE pancreatic tissue in an academic center.

PATIENTS

FFPE tissue specimens were provided by Dana-Farber/Harvard Cancer Center (Boston, MA, USA).

INTERVENTIONS

FFPE tissue specimens were collected via routine surgical resection procedures.

MAIN OUTCOME MEASURES

We compared proteins identified from chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer FFPE pancreatic tissue.

RESULTS

We identified 386 non-redundant proteins from 9 specimens. Following our filtering criteria, 73, 29, and 53 proteins were identified exclusively in autoimmune pancreatitis, chronic pancreatitis, and pancreatic cancer specimens, respectively.

CONCLUSIONS

We report that differentially-expressed proteins can be identified among FFPE tissues specimens originating from individuals with different histological diagnoses. These proteins merit further confirmation with a greater number of specimens and orthogonal validation, such as immunohistochemistry. The mass spectrometry-based methodology used herein has the potential to enhance diagnostic biomarker and therapeutic target discovery, further advancing pancreatic research.

Molecular ecological responses of dinoflagellate, Karenia mikimotoi to environmental nitrate stress

Karenia mikimotoi is one of the most important harmful algal species in the Chinese coastal waters, and which produce hemolytic toxins and ichthyotoxins, resulting in devastating economic losses. Previous studies demonstrated that the increase of nitrate concentration could promote the growth and reproduction of K. mikimotoi. However, the intrinsic mechanisms regarding the effects of nitrate on the K. mikimotoi photosynthesis, nucleic acid replication and differential protein expression remain to be elucidated. Our study demonstrated that nitrate stress inhibited growth of K. mikimotoi (p<0.01). Algal chlorophyll fluorescence intensity varied slightly while algal cell cycle succession was significantly retarded by nitrate stress (p<0.05). Sixteen proteins were detected only in nitrate-limited cultures which related to nitrate transport, signal transduction, amino acid metabolism, DNA repair and hemolysin manufacture. Eleven proteins were detected only in nitrate-replete sample and were related to photorespiration, reproduction and growth, assistance of protein modification, cytoskeleton stability and signal transduction. Based on analysis of differential proteomic functional annotations, we hypothesized a proteomic response mechanism of K. mikimotoi to environmental nitrate stress.

The proteomics of colorectal cancer: identification of a protein signature associated with prognosis

Colorectal cancer is one of the commonest types of cancer and there is requirement for the identification of prognostic biomarkers. In this study protein expression profiles have been established for colorectal cancer and normal colonic mucosa by proteomics using a combination of two dimensional gel electrophoresis with fresh frozen sections of paired Dukes B colorectal cancer and normal colorectal mucosa (n = 28), gel image analysis and high performance liquid chromatography–tandem mass spectrometry. Hierarchical cluster analysis and principal components analysis showed that the protein expression profiles of colorectal cancer and normal colonic mucosa clustered into distinct patterns of protein expression. Forty-five proteins were identified as showing at least 1.5 times increased expression in colorectal cancer and the identity of these proteins was confirmed by liquid chromatography–tandem mass spectrometry. Fifteen proteins that showed increased expression were validated by immunohistochemistry using a well characterised colorectal cancer tissue microarray containing 515 primary colorectal cancer, 224 lymph node metastasis and 50 normal colonic mucosal samples. The proteins that showed the greatest degree of overexpression in primary colorectal cancer compared with normal colonic mucosa were heat shock protein 60 (p<0.001), S100A9 (p<0.001) and translationally controlled tumour protein (p<0.001). Analysis of proteins individually identified 14-3-3β as a prognostic biomarker (χ² = 6.218, p = 0.013, HR = 0.639, 95%CI 0.448–0.913). Hierarchical cluster analysis identified distinct phenotypes associated with survival and a two-protein signature consisting of 14-3-3β and aldehyde dehydrogenase 1 was identified as showing prognostic significance (χ² = 7.306, p = 0.007, HR = 0.504, 95%CI 0.303–0.838) and that remained independently prognostic (p = 0.01, HR = 0.416, 95%CI 0.208–0.829) in a multivariate model.

Quantitative phospho-proteomics to investigate the polo-like kinase 1-dependent phospho-proteome

Polo-like kinase 1 (PLK1) is a key regulator of mitotic progression and cell division, and small molecule inhibitors of PLK1 are undergoing clinical trials to evaluate their utility in cancer therapy. Despite this importance, current knowledge about the identity of PLK1 substrates is limited. Here we present the results of a proteome-wide analysis of PLK1-regulated phosphorylation sites in mitotic human cells. We compared phosphorylation sites in HeLa cells that were or were not treated with the PLK1-inhibitor BI 4834, by labeling peptides via methyl esterification, fractionation of peptides by strong cation exchange chromatography, and phosphopeptide enrichment via immobilized metal affinity chromatography. Analysis by quantitative mass spectrometry identified 4070 unique mitotic phosphorylation sites on 2069 proteins. Of these, 401 proteins contained one or multiple phosphorylation sites whose abundance was decreased by PLK1 inhibition. These include proteins implicated in PLK1-regulated processes such as DNA damage, mitotic spindle formation, spindle assembly checkpoint signaling, and chromosome segregation, but also numerous proteins that were not suspected to be regulated by PLK1. Analysis of amino acid sequence motifs among phosphorylation sites down-regulated under PLK1 inhibition in this data set identified two potential novel variants of the PLK1 consensus motif.

Classification of protein profiles from antibody microarrays using heat and detergent treatment

Antibody microarrays offer new opportunities for exploring the proteome and to identify biomarker candidates in human serum and plasma. Here, we have investigated the effect of heat and detergents on an antibody-based suspension bead array (SBA) assay using polyclonal antibodies and biotinylated plasma samples. With protein profiles from more than 2300 antibodies generated in 384-plex antibody SBAs, three major classes of heat and detergent susceptibility could be described. The results show that washing of the beads with SDS (rather than Tween) after target binding lowered intensity levels of basically all profiles and that about 50% of the profiles appeared to be lowered to a similar extent by heating of the sample. About 33% of the profiles appeared to be insensitive to heat treatment while another 17% showed a positive influence of heat to yield elevated profiles. The results suggest that the classification of antibodies is driven by the molecular properties of the antibody-antigen interaction and can generally not be predicted based on protein class or Western blot data. The experimental scheme presented here can be used to systematically categorize antibodies and thereby combine antibodies with similar properties into targeted arrays for analysis of plasma and serum.

Discovery of new molecular subtypes in oesophageal adenocarcinoma

A large number of patients suffering from oesophageal adenocarcinomas do not respond to conventional chemotherapy; therefore, it is necessary to identify new predictive biomarkers and patient signatures to improve patient outcomes and therapy selections. We analysed 87 formalin-fixed and paraffin-embedded (FFPE) oesophageal adenocarcinoma tissue samples with a reverse phase protein array (RPPA) to examine the expression of 17 cancer-related signalling molecules. Protein expression levels were analysed by unsupervised hierarchical clustering and correlated with clinicopathological parameters and overall patient survival. Proteomic analyses revealed a new, very promising molecular subtype of oesophageal adenocarcinoma patients characterised by low levels of the HSP27 family proteins and high expression of those of the HER family with positive lymph nodes, distant metastases and short overall survival. After confirmation in other independent studies, our results could be the foundation for the development of a Her2-targeted treatment option for this new patient subgroup of oesophageal adenocarcinoma.

Stage-specific proteomic expression patterns of the human filarial parasite Brugia malayi and its endosymbiont Wolbachia

Global proteomic analyses of pathogens have thus far been limited to unicellular organisms (e.g., protozoa and bacteria). Proteomic analyses of most eukaryotic pathogens (e.g., helminths) have been restricted to specific organs, specific stages, or secretomes. We report here a large-scale proteomic characterization of almost all the major mammalian stages of Brugia malayi, a causative agent of lymphatic filariasis, resulting in the identification of more than 62% of the products predicted from the Bm draft genome. The analysis also yielded much of the proteome of Wolbachia, the obligate endosymbiont of Bm that also expressed proteins in a stage-specific manner. Of the 11,610 predicted Bm gene products, 7,103 were definitively identified from adult male, adult female, blood-borne and uterine microfilariae, and infective L3 larvae. Among the 4,956 gene products (42.5%) inferred from the genome as "hypothetical," the present study was able to confirm 2,336 (47.1%) as bona fide proteins. Analysis of protein families and domains coupled with stage-specific expression highlight the important pathways that benefit the parasite during its development in the host. Gene set enrichment analysis identified extracellular matrix proteins and those with immunologic effects as enriched in the microfilarial and L3 stages. Parasite sex- and stage-specific protein expression identified those pathways related to parasite differentiation and demonstrates stage-specific expression by the Bm endosymbiont Wolbachia as well.

Eukaryotic protein kinases (ePKs) of the helminth parasite Schistosoma mansoni

BACKGROUND

Schistosomiasis remains an important parasitic disease and a major economic problem in many countries. The Schistosoma mansoni genome and predicted proteome sequences were recently published providing the opportunity to identify new drug candidates. Eukaryotic protein kinases (ePKs) play a central role in mediating signal transduction through complex networks and are considered druggable targets from the medical and chemical viewpoints. Our work aimed at analyzing the S. mansoni predicted proteome in order to identify and classify all ePKs of this parasite through combined computational approaches. Functional annotation was performed mainly to yield insights into the parasite signaling processes relevant to its complex lifestyle and to select some ePKs as potential drug targets.

RESULTS

We have identified 252 ePKs, which corresponds to 1.9% of the S. mansoni predicted proteome, through sequence similarity searches using HMMs (Hidden Markov Models). Amino acid sequences corresponding to the conserved catalytic domain of ePKs were aligned by MAFFT and further used in distance-based phylogenetic analysis as implemented in PHYLIP. Our analysis also included the ePK homologs from six other eukaryotes. The results show that S. mansoni has proteins in all ePK groups. Most of them are clearly clustered with known ePKs in other eukaryotes according to the phylogenetic analysis. None of the ePKs are exclusively found in S. mansoni or belong to an expanded family in this parasite. Only 16 S. mansoni ePKs were experimentally studied, 12 proteins are predicted to be catalytically inactive and approximately 2% of the parasite ePKs remain unclassified. Some proteins were mentioned as good target for drug development since they have a predicted essential function for the parasite.

CONCLUSIONS

Our approach has improved the functional annotation of 40% of S. mansoni ePKs through combined similarity and phylogenetic-based approaches. As we continue this work, we will highlight the biochemical and physiological adaptations of S. mansoni in response to diverse environments during the parasite development, vector interaction, and host infection.

Comparative analysis of proteome changes induced by the two spotted spider mite Tetranychus urticae and methyl jasmonate in citrus leaves

Citrus plants are currently facing biotic and abiotic stresses. Therefore, the characterization of molecular traits involved in the response mechanisms to stress could facilitate selection of resistant varieties. Although large cDNA microarray profiling has been generated in citrus tissues, the available protein expression data are scarce. In this study, to identify differentially expressed proteins in Citrus clementina leaves after infestation by the two-spotted spider mite Tetranychus urticae, a proteome comparison was undertaken using two-dimensional gel electrophoresis. The citrus leaf proteome profile was also compared with that of leaves treated over 0-72h with methyl jasmonate, a compound playing a key role in the defense mechanisms of plants to insect/arthropod attack. Significant variations were observed for 110 protein spots after spider mite infestation and 67 protein spots after MeJA treatments. Of these, 50 proteins were successfully identified by liquid chromatography-mass spectrometry-tandem mass spectrometry. The majority constituted photosynthesis- and metabolism-related proteins. Five were oxidative stress associated enzymes, including phospholipid glutathione peroxidase, a salt stressed associated protein, ascorbate peroxidase and Mn-superoxide dismutase. Seven were defense-related proteins, such as the pathogenesis-related acidic chitinase, the protease inhibitor miraculin-like protein, and a lectin-like protein. This is the first report of differentially regulated proteins after T. urticae attack and exogenous MeJA application in citrus leaves.

Proteome analysis of Borrelia burgdorferi response to environmental change

We examined global changes in protein expression in the B31 strain of Borrelia burgdorferi, in response to two environmental cues (pH and temperature) chosen for their reported similarity to those encountered at different stages of the organism's life cycle. Multidimensional nano-liquid chromatographic separations coupled with tandem mass spectrometry were used to examine the array of proteins (i.e., the proteome) of B. burgdorferi for different pH and temperature culture conditions. Changes in pH and temperature elicited in vitro adaptations of this spirochete known to cause Lyme disease and led to alterations in protein expression that are associated with increased microbial pathogenesis. We identified 1,031 proteins that represent 59% of the annotated genome of B. burgdorferi and elucidated a core proteome of 414 proteins that were present in all environmental conditions investigated. Observed changes in protein abundances indicated varied replicon usage, as well as proteome functional distributions between the in vitro cell culture conditions. Surprisingly, the pH and temperature conditions that mimicked B. burgdorferi residing in the gut of a fed tick showed a marked reduction in protein diversity. Additionally, the results provide us with leading candidates for exploring how B. burgdorferi adapts to and is able to survive in a wide variety of environmental conditions and lay a foundation for planned in situ studies of B. burgdorferi isolated from the tick midgut and infected animals.

Proteomic characterization of copper stress response in Elsholtzia splendens roots and leaves

Elsholtzia splendens is generally considered as a Cu-tolerant and -accumulating plant species, and a candidate for phytoremediation of Cu-contaminated soils. To better understand the Cu tolerance/accumulation mechanisms in E. splendens, proteomic analysis was performed on E. splendens roots and leaves exposed to 100 muM CuSO(4) for 3 and 6 days. After 6 days of treatment, Cu accumulation in roots increased much more than that in leaves. SDS-PAGE analysis showed that the proteins changed more intensively in roots than did in leaves upon Cu stress. Two-dimensional gel electrophoresis (2-DE) and image analyses found that 45 protein spots were significantly changed in roots, but only six protein spots in leaves. The abundance of protein spots mostly showed temporal changes. MALDI-TOF MS and LTQ-ESI-MS/MS were used to identify the differently expressed protein spots. The identified root proteins were involved in various cellular processes such as signal transduction, regulation of transcription and translation, energy metabolism, regulation of redox homeostasis and cell defense. The leaf proteins were mainly degraded fragments of RuBisCo and antioxidative protein. The roles of these proteins in Cu tolerance/accumulation were discussed. The resulting differences in protein expression pattern suggested that redirection of root cellular metabolism and redox homeostasis might be important survival mechanisms of E. splendens upon Cu stress.

Using a novel resource to decrease proteomic biomarker identification time

The Empirical Proteomic Ontology Knowledge Base (EPO-KB) is an online database that represents current knowledge of biomarkers and contains associations between mass-to-charge (m/z) ratios of mass-spectrometry peaks to proteins. Such a database is a useful tool for identifying putative proteins associated with a m/z ratio. At present, EPO-KB contains data that have been extracted from 120 published research papers. It has been used in successful identification of a protein associated with a biomarker.

Allergens are distributed into few protein families and possess a restricted number of biochemical functions

BACKGROUND

Existing allergen databases classify their entries by source and route of exposure, thus lacking an evolutionary, structural, and functional classification of allergens.

OBJECTIVE

We sought to build AllFam, a database of allergen families, and use it to extract common structural and functional properties of allergens.

METHODS

Allergen data from the Allergome database and protein family definitions from the Pfam database were merged into AllFam, a database that is freely accessible on the Internet at http://www.meduniwien.ac.at/allergens/allfam/. A structural classification of allergens was established by matching Pfam families with families from the Structural Classification of Proteins database. Biochemical functions of allergens were extracted from the Gene Ontology Annotation database.

RESULTS

Seven hundred seven allergens were classified by sequence into 134 AllFam families containing 184 Pfam domains (2% of 9318 Pfam families). A random set of 707 sequences with the same taxonomic distribution contained a significantly higher number of different Pfam domains (479 +/- 17). Classifying allergens by structure revealed that 5% of 3012 Structural Classification of Proteins families contained allergens. The biochemical functions of allergens most frequently found were limited to hydrolysis of proteins, polysaccharides, and lipids; binding of metal ions and lipids; storage; and cytoskeleton association.

CONCLUSION

The small number of protein families that contain allergens and the narrow functional distribution of most allergens confirm the existence of yet unknown factors that render proteins allergenic.

Functional protein divergence in the evolution of Homo sapiens

BACKGROUND

Protein-coding regions in a genome evolve by sequence divergence and gene gain and loss, altering the gene content of the organism. However, it is not well understood how this has given rise to the enormous diversity of metazoa present today.

RESULTS

To obtain a global view of human genomic evolution, we quantify the divergence of proteins by functional category at different evolutionary distances from human.

CONCLUSION

This analysis highlights some general systems-level characteristics of human evolution: regulatory processes, such as signal transducers, transcription factors and receptors, have a high degree of plasticity, while core processes, such as metabolism, transport and protein synthesis, are largely conserved. Additionally, this study reveals a dynamic picture of selective forces at short, medium and long evolutionary timescales. Certain functional categories, such as 'development' and 'organogenesis', exhibit temporal patterns of sequence divergence in eukaryotes relative to human. This framework for a grammar of human evolution supports previously postulated theories of robustness and evolvability.

Web-based tools for protein classification

Current proteomics technologies generate large number of data among which the investigator has to identify the promising diagnostic/prognostic biomarkers as well as potential therapeutic targets. For the latter, classification of proteins into meaningful families is needed. Current databases, featuring a high level of interconnectivity (cross referencing), provide the tools necessary to bring various data together, facilitating protein classification and elucidation of protein function and interoperativity. This chapter provides guidelines to explore the informationally rich peptide sequences generated by the application of the proteomics methodologies by the use of web-based tools, with the objective to predict potential protein function. After proper preprocessing (e.g., for internal repeats) of a query protein sequence, known domains can be identified, which aid in dividing the query into smaller meaningful parts. Any unclassified remainder of the protein provides the material for low-level comparative analysis for the discovery of distant homologues or candidate novel domain types to be verified experimentally.

2D-PAGE maps analysis

Due to the low reproducibility affecting 2D gel-electrophoresis and the complex maps provided by this technique, the use of effective and robust methods for the comparison and classification of 2D maps is a fundamental tool for the development of automated diagnostic methods. A review of classical and recently developed methods for the comparison of 2D maps is presented here. The methods proposed regard both the analysis of spot volume datasets through multivariate statistical tools (pattern recognition methods, cluster analysis, and classification methods) and the analysis of 2D map images through fuzzy logic, three-way PCA, and the use of moment functions. The theoretical basis of each procedure is briefly introduced, together with a review of the most interesting applications present in recent literature.

User-centered evaluation of Arizona BioPathway: an information extraction, integration, and visualization system

Explosive growth in biomedical research has made automated information extraction, knowledge integration, and visualization increasingly important and critically needed. The Arizona BioPathway (ABP) system extracts and displays biological regulatory pathway information from the abstracts of journal articles. This study uses relations extracted from more than 200 PubMed abstracts presented in a tabular and graphical user interface with built-in search and aggregation functionality. This paper presents a task-centered assessment of the usefulness and usability of the ABP system focusing on its relation aggregation and visualization functionalities. Results suggest that our graph-based visualization is more efficient in supporting pathway analysis tasks and is perceived as more useful and easier to use as compared to a text-based literature-viewing method. Relation aggregation significantly contributes to knowledge-acquisition efficiency. Together, the graphic and tabular views in the ABP Visualizer provide a flexible and effective interface for pathway relation browsing and analysis. Our study contributes to pathway-related research and biological information extraction by assessing the value of a multiview, relation-based interface that supports user-controlled exploration of pathway information across multiple granularities.

A parsimonious threshold-independent protein feature selection method through the area under receiver operating characteristic curve

MOTIVATION

Protein expression profiling for differences indicative of early cancer holds promise for improving diagnostics. Due to their high dimensionality, statistical analysis of proteomic data from mass spectrometers is challenging in many aspects such as dimension reduction, feature subset selection as well as construction of classification rules. Search of an optimal feature subset, commonly known as the feature subset selection (FSS) problem, is an important step towards disease classification/diagnostics with biomarkers.

METHODS

We develop a parsimonious threshold-independent feature selection (PTIFS) method based on the concept of area under the curve (AUC) of the receiver operating characteristic (ROC). To reduce computational complexity to a manageable level, we use a sigmoid approximation to the empirical AUC as the criterion function. Starting from an anchor feature, the PTIFS method selects a feature subset through an iterative updating algorithm. Highly correlated features that have similar discriminating power are precluded from being selected simultaneously. The classification rule is then determined from the resulting feature subset.

RESULTS

The performance of the proposed approach is investigated by extensive simulation studies, and by applying the method to two mass spectrometry data sets of prostate cancer and of liver cancer. We compare the new approach with the threshold gradient descent regularization (TGDR) method. The results show that our method can achieve comparable performance to that of the TGDR method in terms of disease classification, but with fewer features selected.

AVAILABILITY

Supplementary Material and the PTIFS implementations are available at http://staff.ustc.edu.cn/~ynyang/PTIFS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Proteome-wide identification of family member-specific natural substrate repertoire of caspases

Caspases are proteolytic enzymes that are essential for apoptosis. Understanding the many discrete and interacting signaling pathways mediated by caspases requires the identification of the natural substrate repertoire for each caspase of interest. Using an amplification-based protein selection technique called mRNA display, we developed a high-throughput screen platform for caspase family member specific substrates on a proteome-wide scale. A large number of both known and previously uncharacterized caspase-3 substrates were identified from the human proteome. The proteolytic features of these selected substrates, including their cleavage sites and specificities, were characterized. Substrates that were cleaved only by caspase-8 or granzyme B but not by caspase-3, were readily selected. The method can be widely applied for efficient and systematic identification of the family member specific natural substrate repertoire of any caspase in an organism of interest, in addition to that of numerous other proteases with high specificity.

Annotating gene function by combining expression data with a modular gene network

MOTIVATION

A promising and reliable approach to annotate gene function is clustering genes not only by using gene expression data but also literature information, especially gene networks.

RESULTS

We present a systematic method for gene clustering by combining these totally different two types of data, particularly focusing on network modularity, a global feature of gene networks. Our method is based on learning a probabilistic model, which we call a hidden modular random field in which the relation between hidden variables directly represents a given gene network. Our learning algorithm which minimizes an energy function considering the network modularity is practically time-efficient, regardless of using the global network property. We evaluated our method by using a metabolic network and microarray expression data, changing with microarray datasets, parameters of our model and gold standard clusters. Experimental results showed that our method outperformed other four competing methods, including k-means and existing graph partitioning methods, being statistically significant in all cases. Further detailed analysis showed that our method could group a set of genes into a cluster which corresponds to the folate metabolic pathway while other methods could not. From these results, we can say that our method is highly effective for gene clustering and annotating gene function.

Reconstructing the evolution of the mitochondrial ribosomal proteome

For production of proteins that are encoded by the mitochondrial genome, mitochondria rely on their own mitochondrial translation system, with the mitoribosome as its central component. Using extensive homology searches, we have reconstructed the evolutionary history of the mitoribosomal proteome that is encoded by a diverse subset of eukaryotic genomes, revealing an ancestral ribosome of alpha-proteobacterial descent that more than doubled its protein content in most eukaryotic lineages. We observe large variations in the protein content of mitoribosomes between different eukaryotes, with mammalian mitoribosomes sharing only 74 and 43% of its proteins with yeast and Leishmania mitoribosomes, respectively. We detected many previously unidentified mitochondrial ribosomal proteins (MRPs) and found that several have increased in size compared to their bacterial ancestral counterparts by addition of functional domains. Several new MRPs have originated via duplication of existing MRPs as well as by recruitment from outside of the mitoribosomal proteome. Using sensitive profile-profile homology searches, we found hitherto undetected homology between bacterial and eukaryotic ribosomal proteins, as well as between fungal and mammalian ribosomal proteins, detecting two novel human MRPs. These newly detected MRPs constitute, along with evolutionary conserved MRPs, excellent new screening targets for human patients with unresolved mitochondrial oxidative phosphorylation disorders.