GARBAN: genomic analysis and rapid biological annotation of cDNA microarray and proteomic data

SUMMARY: Genomic Analysis and Rapid Biological ANnotation (GARBAN) is a new tool that provides an integrated framework to analyze simultaneously and compare multiple data sets derived from microarray or proteomic experiments. It carries out automated classifications of genes or proteins according to the criteria of the Gene Ontology Consortium at a level of depth defined by the user. Additionally, it performs clustering analysis of all sets based on functional categories or on differential expression levels. GARBAN also provides graphical representations of the biological pathways in which all the genes/proteins participate. AVAILABILITY: http://garban.tecnun.es.     

PeptideDepot: Flexible relational database for visual analysis of quantitative proteomic data and integration of existing protein information

Recently, dramatic progress has been achieved in expanding the sensitivity, resolution, mass accuracy, and scan rate of mass spectrometers able to fragment and identify peptides through MS/MS. Unfortunately, this enhanced ability to acquire proteomic data has not been accompanied by a concomitant increase in the availability of flexible tools allowing users to rapidly assimilate, explore, and analyze this data and adapt to various experimental workflows with minimal user intervention. Here we fill this critical gap by providing a flexible relational database called PeptideDepot for organization of expansive proteomic data sets, collation of proteomic data with available protein information resources, and visual comparison of multiple quantitative proteomic experiments. Our software design, built upon the synergistic combination of a MySQL database for safe warehousing of proteomic data with a FileMaker‐driven graphical user interface for flexible adaptation to diverse workflows, enables proteomic end‐users to directly tailor the presentation of proteomic data to the unique analysis requirements of the individual proteomics lab. PeptideDepot may be deployed as an independent software tool or integrated directly with our high throughput autonomous proteomic pipeline used in the automated acquisition and post‐acquisition analysis of proteomic data.     

Statistical Application and Challenges in Global Gel-Free Proteomic Analysis by Mass Spectrometry

Global gel-free proteomic analysis by mass spectrometry has been widely used as an important tool for exploring complex biological systems at the whole genome level. Simultaneous analysis of a large number of protein species is a complicated and challenging task. The challenges exist throughout all stages of a global gel-free proteomic analysis: experimental design, peptide/protein identification, data preprocessing and normalization, and inferential analysis. In addition to various efforts to improve the analytical technologies, statistical methodologies have been applied in all stages of proteomic analyses to help extract relevant information efficiently from large proteomic datasets. In this review, we summarize current applications of statistics in several stages of global gel-free proteomic analysis by mass spectrometry. We discuss the challenges associated with the applications of various statistical tools. Whenever possible, we also propose potential solutions on how to improve the data collection and interpretation for mass-spectrometry-based global proteomic analysis using more sophisticated and/or novel statistical approaches.     

Challenges and solutions in proteomics

The accelerated growth of proteomics data presents both opportunities and challenges. Large-scale proteomic profiling of biological samples such as cells, organelles or biological fluids has led to discovery of numerous key and novel proteins involved in many biological/disease processes including cancers, as well as to the identification of novel disease biomarkers and potential therapeutic targets. While proteomic data analysis has been greatly assisted by the many bioinformatics tools developed in recent years, a careful analysis of the major steps and flow of data in a typical highthroughput analysis reveals a few gaps that still need to be filled to fully realize the value of the data. To facilitate functional and pathway discovery for large-scale proteomic data, we have developed an integrated proteomic expression analysis system, iProXpress, which facilitates protein identification using a comprehensive sequence library and functional interpretation using integrated data. With its modular design, iProXpress complements and can be integrated with other software in a proteomic data analysis pipeline. This novel approach to complex biological questions involves the interrogation of multiple data sources, thereby facilitating hypothesis generation and knowledge discovery from the genomic-scale studies and fostering disease diagnosis and drug development.     

HTAPP: High‐throughput autonomous proteomic pipeline

Recent advances in the speed and sensitivity of mass spectrometers and in analytical methods, the exponential acceleration of computer processing speeds, and the availability of genomic databases from an array of species and protein information databases have led to a deluge of proteomic data. The development of a lab‐based automated proteomic software platform for the automated collection, processing, storage, and visualization of expansive proteomic data sets is critically important. The high‐throughput autonomous proteomic pipeline described here is designed from the ground up to provide critically important flexibility for diverse proteomic workflows and to streamline the total analysis of a complex proteomic sample. This tool is composed of a software that controls the acquisition of mass spectral data along with automation of post‐acquisition tasks such as peptide quantification, clustered MS/MS spectral database searching, statistical validation, and data exploration within a user‐configurable lab‐based relational database. The software design of high‐throughput autonomous proteomic pipeline focuses on accommodating diverse workflows and providing missing software functionality to a wide range of proteomic researchers to accelerate the extraction of biological meaning from immense proteomic data sets. Although individual software modules in our integrated technology platform may have some similarities to existing tools, the true novelty of the approach described here is in the synergistic and flexible combination of these tools to provide an integrated and efficient analysis of proteomic samples.     

Utility of mass spectrometry for proteome analysis: part II. Ion-activation methods,statistics, bioinformatics and annotation

This is the second article in a series, intended as a tutorial to provide the interested reader with an overview of the concepts not covered in part I, such as: the principles of ion-activation methods, the ability of mass-spectrometric methods to interface with various proteomic strategies, analysis techniques, bioinformatics and data interpretation and annotation. Although these are different topics, it is important that a reader has a basic and collective understanding of all of them for an overall appreciation of how to carry out and analyze a proteomic experiment. Different ion-activation methods for MS/MS, such as collision-induced dissociation (including postsource decay) and surface-induced dissociation, electron capture and electron-transfer dissociation, infrared multiphoton and blackbody infrared radiative dissociation have been discussed since they are used in proteomic research. The high dimensionality of data generated from proteomic studies requires an understanding of the underlying analytical procedures used to obtain these data, as well as the development of improved bioinformatics tools and data-mining approaches for efficient and accurate statistical analyses of biological samples from healthy and diseased individuals, in addition to determining the utility of the interpreted data. Currently available strategies for the analysis of the proteome by mass spectrometry, such as those employed for the analysis of substantially purified proteins and complex peptide mixtures, as well as hypothesis-driven strategies, have been elaborated upon. Processing steps prior to the analysis of mass spectrometry data, statistics and the several informatics steps currently used for the analysis of shotgun proteomic experiments, as well as proteomics ontology, are also discussed.     

Quantitative proteomic analyses of the response of acidophilic microbial communities to different pH conditions

Extensive genomic characterization of multi-species acid mine drainage microbial consortia combined with laboratory cultivation has enabled the application of quantitative proteomic analyses at the community level. In this study, quantitative proteomic comparisons were used to functionally characterize laboratory-cultivated acidophilic communities sustained in pH 1.45 or 0.85 conditions. The distributions of all proteins identified for individual organisms indicated biases for either high or low pH, and suggests pH-specific niche partitioning for low abundance bacteria and archaea. Although the proteome of the dominant bacterium, Leptospirillum group II, was largely unaffected by pH treatments, analysis of functional categories indicated proteins involved in amino acid and nucleotide metabolism, as well as cell membrane/envelope biogenesis were overrepresented at high pH. Comparison of specific protein abundances indicates higher pH conditions favor Leptospirillum group III, whereas low pH conditions promote the growth of certain archaea. Thus, quantitative proteomic comparisons revealed distinct differences in community composition and metabolic function of individual organisms during different pH treatments. Proteomic analysis revealed other aspects of community function. Different numbers of phage proteins were identified across biological replicates, indicating stochastic spatial heterogeneity of phage outbreaks. Additionally, proteomic data were used to identify a previously unknown genotypic variant of Leptospirillum group II, an indication of selection for a specific Leptospirillum group II population in laboratory communities. Our results confirm the importance of pH and related geochemical factors in fine-tuning acidophilic microbial community structure and function at the species and strain level, and demonstrate the broad utility of proteomics in laboratory community studies.     

拟南芥叶绿体中DEG蛋白酶功能的研究进展

DEGP家族蛋白酶广泛分布于原核生物和真核生物细胞中。在拟南芥中有16个DEGP类似的蛋白酶,根据蛋白质组学数据,其中有4个定位于叶绿体中,分别命名为DEG1、DEG2、DEG5和DEG8。结合生物化学和分子生物学等研究手段对拟南芥叶绿体中的DEGP蛋白酶进行了分析,现有的研究初步证明了这些蛋白酶参与光系统II（PSII）复合物反应中心D1蛋白的降解,从而在PSII复合物的修复循环和功能维护中起重要作用。该文概述了拟南芥叶绿体中DEG蛋白酶的结构和功能的最新研究进展。     

拟南芥叶绿体中DEG 蛋白酶功能的研究进展

DEGP家族蛋白酶广泛分布于原核生物和真核生物细胞中。在拟南芥中有16个DEGP类似的蛋白酶, 根据蛋白质组学数据, 其中有4个定位于叶绿体中, 分别命名为DEG1、DEG2、DEG5和DEG8。结合生物化学和分子生物学等研究手段对拟 南芥叶绿体中的DEGP蛋白酶进行了分析, 现有的研究初步证明了这些蛋白酶参与光系统II (PSII)复合物反应中心D1蛋白的降解, 从而在PSII复合物的修复循环和功能维护中起重要作用。该文概述了拟南芥叶绿体中DEG蛋白酶的结构和功能的最新研究进展。     

Impact of fixation time on GeLC-MS/MS proteomic profiling of formalin-fixed, paraffin-embedded tissues

Formalin-fixed, paraffin-embedded (FFPE) tissue banks represent an invaluable resource for biomarker discovery. Recently, the combination of full-length protein extraction, GeLC-MS/MS analysis, and spectral counting quantification has been successfully applied to mine proteomic information from these tissues. However, several sources of variability affect these samples; among these, the duration of the fixation process is one of the most important and most easily controllable ones. To assess its influence on quality of GeLC-MS/MS data, the impact of fixation time on efficiency of full-length protein extraction efficiency and on quality of label-free quantitative data was evaluated. As a result, although proteins were successfully extracted from FFPE liver samples fixed for up to eight days, fixation time appeared to negatively influence both protein extraction yield and GeLC-MS/MS quantitative proteomic data. Particularly, MS identification efficiency decreased with increasing fixation times. Moreover, amino acid modifications putatively induced by formaldehyde were detected and characterized. These results demonstrate that proteomic information can be achieved also from tissue samples fixed for relatively long times, but suggest that variations in fixation time need to be carefully taken into account when performing proteomic biomarker discovery studies on fixed tissue archives.     

iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates

The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline, it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organism-specific composite data sets.     

Identification of nuclear phosphatidylinositol 4,5-bisphosphate-interacting proteins by neomycin extraction

Considerable insight into phosphoinositide-regulated cytoplasmic functions has been gained by identifying phosphoinositide-effector proteins. Phosphoinositide-regulated nuclear functions however are fewer and less clear. To address this, we established a proteomic method based on neomycin extraction of intact nuclei to enrich for nuclear phosphoinositide-effector proteins. We identified 168 proteins harboring phosphoinositide-binding domains. Although the vast majority of these contained lysine/arginine-rich patches with the following motif, K/R-(X(n= 3-7)-K-X-K/R-K/R, we also identified a smaller subset of known phosphoinositide-binding proteins containing pleckstrin homology or plant homeodomain modules. Proteins with no prior history of phosphoinositide interaction were identified, some of which have functional roles in RNA splicing and processing and chromatin assembly. The remaining proteins represent potentially other novel nuclear phosphoinositide-effector proteins and as such strengthen our appreciation of phosphoinositide-regulated nuclear functions. DNA topology was exemplar among these: Biochemical assays validated our proteomic data supporting a direct interaction between phosphatidylinositol 4,5-bisphosphate and DNA Topoisomerase IIα. In addition, a subset of neomycin extracted proteins were further validated as phosphatidyl 4,5-bisphosphate-interacting proteins by quantitative lipid pull downs. In summary, data sets such as this serve as a resource for a global view of phosphoinositide-regulated nuclear functions.     

Hyperlipidemic versus healthy pancreases: a proteomic analysis using an animal model

Hyperlipidemia is associated with a variety of pancreatic diseases; however, the underlying pathophysiology and molecular mechanisms remain undefined. Here, we performed a comparative proteomic analysis of pancreatic tissue obtained from hyperlipidemic rats to identify proteins that may be involved in mediating hyperlipidemia-associated pancreatic injury. Rats were fed a high-fat diet to induce hyperlipidemia. Control rats were fed a diet with normal fat content. Pancreatic tissue samples were obtained after 6 or 12 weeks and comparative proteomic analysis, using gel electrophoresis and mass spectrometry, was conducted to identify proteins, the expression of which were altered in pancreases from hyperlipidemic compared with control rat pancreases. The expression levels of 3 of 13 proteins were significantly altered in pancreatic samples from hyperlipidemic rats. Alpha-amylase and arginase II were dysregulated by more than twofold. These modulations persisted in pancreatic tissue obtained from late-stage hyperlipidemic rats. The levels of alpha-amylase and arginase II were significantly altered in pancreases obtained from rats with hyperlipidemia. These enzymes may be putative biomarkers of hyperlipidemia-mediated pancreatic injury.     

Human Proteinpedia as a resource for clinical proteomics

Clinical proteomics is an emerging field that deals with the use of proteomic technologies for medical applications. With a major objective of identifying proteins involved in pathological processes and as potential biomarkers, this field is already gaining momentum. Consequently, clinical proteomics data are being generated at a rapid pace, although mechanisms of sharing such data with the biomedical community lag far behind. Most of these data are either provided as supplementary information through journal web sites or directly made available by the authors through their own web resources. Integration of these data within a single resource that displays information in the context of individual proteins is likely to enhance the use of proteomic data in biomedical research. Human Proteinpedia is one such portal that unifies human proteomic data under a single banner. The goal of this resource is to ultimately capture and integrate all proteomic data obtained from individual studies on normal and diseased tissues. We anticipate that harnessing of these data will help prioritize experiments related to protein targets and also permit meta-analysis to uncover molecular signatures of disease. Finally, we encourage all biomedical investigators to maximize dissemination of their valuable proteomic data to rest of the community by active participation in existing repositories such as Human Proteinpedia.     

Proteomic Profiling Reveals the Molecular Control of Oocyte Maturation

Meiotic maturation is an intricate and precisely regulated process orchestrated by various pathways and numerous proteins. However, little is known about the proteome landscape during oocytes maturation. Here, we obtained the temporal proteomic profiles of mouse oocytes during in vivo maturation. We successfully quantified 4694 proteins from 4500 oocytes in three key stages (germinal vesicle, germinal vesicle breakdown, and metaphase II). In particular, we discovered the novel proteomic features during oocyte maturation, such as the active Skp1–Cullin–Fbox pathway and an increase in mRNA decay–related proteins. Using functional approaches, we further identified the key factors controlling the histone acetylation state in oocytes and the vital proteins modulating meiotic cell cycle. Taken together, our data serve as a broad resource on the dynamics occurring in oocyte proteome and provide important knowledge to better understand the molecular mechanisms during germ cell development.     

Proteomic Studies on the Development of the Central Nervous System and Beyond

Neuroproteomics has become a ‘symbol’ or even a ‘sign’ for neuroscientists in the post-genomic era. During the last several decades, a number of proteomic approaches have been used widely to decipher the complexity of the brain, including the study of embryonic stages of human or non-human animal brain development. The use of proteomic techniques has allowed for great scientific advancements, including the quantitative analysis of proteomic data using 2D-DIGE, ICAT and iTRAQ. In addition, proteomic studies of the brain have expanded into fields such as subproteomics, synaptoproteomics, neural plasma membrane proteomics and even mitochondrial proteomics. The rapid progress that has been made in this field will not only increase the knowledge based on the neuroproteomics of the developing brain but also help to increase the understanding of human neurological diseases. This paper will focus on proteomic studies in the central nervous system and especially those conducted on the development of the brain in order to summarize the advances in this rapidly developing field.