Briefings in Functional Genomics and Proteomics - recent issues

Editorial

Wright, P. C. — 2008-05-12

The state of proteome profiling in the fungal genus Aspergillus

Kim, Y., Nandakumar, M. P., Marten, M. R. — 2008-05-12

Aspergilli are an important genus of filamentous fungi that contribute to a multibillion dollar industry. Since many fungal genome sequencing were recently completed, it would be advantageous to profile their proteome to better understand the fungal cell factory. Here, we review proteomic data generated for the Aspergilli in recent years. Thus far, a combined total of 28 cell surface, 102 secreted and 139 intracellular proteins have been identified based on 10 different studies on Aspergillus proteomics. A summary proteome map highlighting identified proteins in major metabolic pathway is presented.

Systems biotechnology of mammalian cell factories

O'Callaghan, P. M., James, D. C. — 2008-05-12

The increasing demand for recombinant therapeutic proteins has placed significant pressure on the biopharmaceutical industry to develop high-yielding, mammalian cell-based production systems. Current efforts to increase the production of recombinant proteins by mammalian host cells largely proceed by the lengthy screening of clonal derivatives rather than by directed genetic or metabolic engineering. However, the advent of systems biology has created a new set of tools that will ensure that future engineering strategies will be informed by an understanding of the genetic/regulatory and metabolic networks that determine the functional competence of mammalian cell factories in vitro. In this review we summarize recent systems-level studies that utilize genome-scale analytical tools to analyse the functional basis for key production process characteristics such as high cell-specific productivity, correct product processing and rapid cell proliferation in the in vitro environment. We also describe the use of high-throughput -omic technologies to investigate how mammalian cell factories respond to environmental and metabolic perturbation.

Maternal communication with gametes and embryos: a complex interactome

Fazeli, A., Pewsey, E. — 2008-05-12

Maternal communication with gametes and embryos influences a broad range of events crucial to pregnancy. Events such as final maturation of gametes, gamete transport, fertilization, early embryonic development and development of foetus to term, are all dependant upon the relay of appropriate molecular signals between the mother, gametes and embryos. This signalling is temporally and spatially regulated, involving complex interactions. Disturbances in maternal communication with gametes and embryos can influence the outcome of pregnancy. Effects range from those that are immediately obvious, such as spontaneous miscarriage (due to inappropriate hormonal signalling), to more subtle consequences that may not become apparent until offspring reach adulthood (‘foetal origins’ hypothesis). Current knowledge of the factors and mechanisms involved in maternal communication with gametes and embryos is limited to only a few individual pathways. There is a need for a holistic view of all actions and interactions taking place during this crosstalk between the gametes, embryos and the female reproductive tract. Applying high-throughput genomic and proteomic analysis tools and systems biology approaches, together with mathematical modelling would allow construction of an in silico model for the temporal sequence of events involved. Ultimately this will help identify different dimensions of maternal communication with gametes and embryos in health and disease.

Analysis of iTRAQ data using Mascot and Peaks quantification algorithms

Lacerda, C. M.R., Xin, L., Rogers, I., Reardon, K. F. — 2008-05-12

The field of proteomics has been developing rapidly toward quantification of proteins. Despite the variety of experimental techniques available for peptide and protein labelling, there are few commercially available analytical tools with the ability to interpret data from any mass spectrometer. In this study, we compare two software packages, Mascot and Peaks, for the analysis of iTRAQ data from ESI-Q/TOF mass spectrometry. In the case of a six-protein mixture combined in a known proportion, the output of the Peaks algorithm deviated from the correct result by 14% on average, while the error of the Mascot quantification was nearly 200%. When the software were used to analyse iTRAQ data from a complex protein sample, the quantification results agreed within 20% for only 26% of the quantified proteins, showing significant differences in the two quantification algorithms. This comparison and analysis revealed major intricacies in peptide and protein quantification that must be taken into consideration for software development.

iTRAQPak: an R based analysis and visualization package for 8-plex isobaric protein expression data

D'Ascenzo, M., Choe, L., Lee, K. H. — 2008-05-12

The field of high throughput proteomics has spawned a number of mass spectrometry-based technologies, which enable the quantitative analysis of protein expression. One of these technologies is iTRAQ (trademarked by Applied Biosystems), which through the use of isobaric tags, enables the quantitation of up to eight complex protein samples in a single multiplexed analysis. Isobaric tagging methods are emerging as an important tool to study protein expression dynamics. In this report, we describe iTRAQPak, a free software package developed in the R statistical and visualization environment that can be applied to the analysis of 8-plex expression data. The utility of this package is demonstrated through its application to the analysis of 8-plex iTRAQ protein expression data obtained from cerebrospinal fluid samples from Alzheimer's disease subjects involved in a Phase I drug trial.

Automated extraction of meaningful pathways from quantitative proteomics data

Noirel, J., Ow, S. Y., Sanguinetti, G., Jaramillo, A., Wright, P. C. — 2008-05-12

Technological developments in the life sciences have resulted in an ever-accelerating pace of data production. Systems Biology tries to shed light upon these data by building complex models describing the interactions between biological components. However, extracting information from this morass of data requires the use of sophisticated computational techniques. Here, we propose a method suitable to integrate data drawn from quantitative proteomics into a metabolic scaffold and identify the metabolic pathways which are collectively up-regulated or down-regulated. The availability of such a tool is highly desirable as the extracted information could then be taken as a starting point for in-depth analyses, in particular in fields like Synthetic Biology, where datasets need be characterized routinely.

A review on models and algorithms for motif discovery in protein-protein interaction networks

Ciriello, G., Guerra, C. — 2008-05-12

Several algorithms have been recently designed to identify motifs in biological networks, particularly in protein–protein interaction networks. Motifs correspond to repeated modules in the network that may be of biological interest. The approaches proposed in the literature often differ in the definition of a motif, the way the occurrences of a motif are counted and the way their statistical significance is assessed. This has strong implications on the computational complexity of the discovery process and on the type of results that can be expected. This review presents in a systematic way the different computational settings outlining their main features and limitations.

A review of algorithmic techniques for disulfide-bond determination

Singh, R. — 2008-05-12

Disulfide bonds play an important role in understanding protein folding, evolution, and in studies related to determining structural and functional properties of specific proteins. At the state-of-the-art, a large number of computational techniques have been proposed for determining disulfide bonds. Operating across the gamut of input data, from pure sequence-based information to spectra from mass spectrometry, these techniques provide researchers with a variety of methodological choices and trade-offs. Techniques for disulfide-bond determination are also underpinned by a rich variety of algorithmic formulations. Analysis of these algorithms can provide valuable cues towards choosing a particular technique and understanding its results. Further, their study is critical in developing the next generation of techniques. This paper discusses the importance and applicability of disulfide-bond determination in understanding protein structure and function and provides a detailed review of computational approaches to this problem.

Functional genomics in translational cancer research: focus on breast cancer

Yulug, I. G., Gur-Dedeoglu, B. — 2008-03-27

Conventional molecular and genetic methods for studying cancer are limited to the analysis of one locus at a time. A cluster of genes that are regulated together can be identified by DNA microarray, and the functional relationships can uncover new aspects of cancer biology. Breast cancer can be used to provide a model to demonstrate the current approaches to the molecular analysis of cancer. Meta-analysis is an important tool for the identification and validation of differentially expressed genes to increase power in clinical and biological studies across different sets of data. Recently, meta-analysis approaches have been applied to large collections of microarray datasets to investigate molecular commonalities of multiple cancer types not only to find the common molecular pathways in tumour development but also to compare the individual datasets to other cancer datasets to identify new sets of genes. Several investigators agree that microarray results should be validated. One commonly used method is quantitative reverse transcription PCR (qRT-PCR) to validate the expression profiles of the target genes obtained through microarray experiments. qRT-PCR is attractive for clinical use, since it can be automated and performed on fresh or archived formalin-fixed, paraffin-embedded tissue samples. The outcome of these analyses might accelerate the application of basic research findings into daily clinical practice through translational research and may have an impact on foreseeing the clinical outcome, predicting tumour response to specific therapy, identification of new prognostic biomarkers, discovering targets for the development of novel therapies and providing further insights into tumour biology.

Profiling killers; unravelling the pathways of human natural killer cell function

Scott, G. B., Meade, J. L., Cook, G. P. — 2008-03-27

Natural killer (NK) cells are lymphocytes with an innate ability to recognize and kill infected cells and tumour cells. Unlike B and T cells, NK cells do not express an antigen receptor. Instead, NK cells detect changes in the phenotype of the target cell surface; malignant transformation or infection resulting in the loss or gain of particular molecules that are detected by inhibitory or activating receptors on the NK cell surface. The identification and characterization of NK cells and their receptors was made possible by monoclonal antibody technology. The ease with which genes and gene products can now be identified and manipulated has accelerated our understanding of NK cell function. Furthermore, gene and protein profiling studies are beginning to refine our understanding of NK cells, their interactions with other cells and their effector mechanisms. This review illustrates some of the basic features of NK cell biology and highlights the contribution made by post-genomic technology in defining the molecular mechanisms by which NK cells identify and kill susceptible targets.

Directional and quantitative phosphorylation networks

Jorgensen, C., Linding, R. — 2008-03-27

Directionality in protein signalling networks is due to modulated protein–protein interactions and is fundamental for proper signal progression and response to external and internal cues. This property is in part enabled by linear motifs embedding post-translational modification sites. These serve as recognition sites, guiding phosphorylation by kinases and subsequent binding of modular domains (e.g. SH2 and BRCT). Characterization of such modification-modulated interactions on a proteome-wide scale requires extensive computational and experimental analysis. Here, we review the latest advances in methods for unravelling phosphorylation-mediated cellular interaction networks. In particular, we will discuss how the combination of new quantitative mass-spectrometric technologies and computational algorithms together are enhancing mapping of these largely uncharted dynamic networks. By combining quantitative measurements of phosphorylation events with computational approaches, we argue that systems level models will help to decipher complex diseases through the ability to predict cellular systems trajectories.

Apoptotic blocks and chemotherapy resistance: strategies to identify Bcl-2 protein signatures

Gul, O., Basaga, H., Kutuk, O. — 2008-03-27

Acquired or innate resistance to chemotherapy is a major drawback of cancer therapeutics, which is frequently seen in epithelial cancers. However, the molecular mechanisms underlying chemotherapy resistance remain poorly understood. The mitochondrial pathway is a critical death pathway common to many different types of chemotherapy. Aberrations in this pathway can result in resistance to chemotherapy. The Bcl-2 family of proteins control commitment to programmed cell death by mitochondrial apoptosis. In this review, we will summarize the strategies in determining the components of apoptotic defects responsible for chemotherapy resistance, mainly focused on Bcl-2 protein network.

The plasma proteome, adductome and idiosyncratic toxicity in toxicoproteomics research

Merrick, B. A. — 2008-03-27

Toxicoproteomics uses the discovery potential of proteomics in toxicology research by applying global protein measurement technologies to biofluids and tissues after host exposure to injurious agents. Toxicoproteomic studies thus far have focused on protein profiling of major organs and biofluids such as liver and blood in preclinical species exposed to model toxicants. The slow pace of discovery for new biomarkers, toxicity signatures and mechanistic insights is partially due to the limited proteome coverage derived from analysis of native organs, tissues and body fluids by traditional proteomic platforms. Improved toxicoproteomic analysis would result by combining higher data density LC-MS/MS platforms with stable isotope labelled peptides and parallel use of complementary platforms. Study designs that remove abundant proteins from biofluids, enrich subcellular structures and include cell specific isolation from heterogeneous tissues would greatly increase differential expression capabilities. By leveraging resources from immunology, cell biology and nutrition research communities, toxicoproteomics could make particular contributions in three inter-related areas to advance mechanistic insights and biomarker development: the plasma proteome and circulating microparticles, the adductome and idiosyncratic toxicity.

Proteogenomics: needs and roles to be filled by proteomics in genome annotation

Ansong, C., Purvine, S. O., Adkins, J. N., Lipton, M. S., Smith, R. D. — 2008-03-27

While genome sequencing efforts reveal the basic building blocks of life, a genome sequence alone is insufficient for elucidating biological function. Genome annotation—the process of identifying genes and assigning function to each gene in a genome sequence—provides the means to elucidate biological function from sequence. Current state-of-the-art high-throughput genome annotation uses a combination of comparative (sequence similarity data) and non-comparative (ab initio gene prediction algorithms) methods to identify protein-coding genes in genome sequences. Because approaches used to validate the presence of predicted protein-coding genes are typically based on expressed RNA sequences, they cannot independently and unequivocally determine whether a predicted protein-coding gene is translated into a protein. With the ability to directly measure peptides arising from expressed proteins, high-throughput liquid chromatography-tandem mass spectrometry-based proteomics approaches can be used to verify coding regions of a genomic sequence. Here, we highlight several ways in which high-throughput tandem mass spectrometry-based proteomics can improve the quality of genome annotations and suggest that it could be efficiently applied during the gene calling process so that the improvements are propagated through the subsequent functional annotation process.

The prediction of protein subcellular localization from sequence: a shortcut to functional genome annotation

Casadio, R., Martelli, P. L., Pierleoni, A. — 2008-03-27

Automated sequence annotation is a major goal of post-genomic era with hundreds of genomes in the databases, from both prokaryotes and eukaryotes. While the number of fully sequenced chromosomes from microbial organisms exponentially increased in the last decade above 600, presently we know the whole DNA content of only 25 eukaryotic organisms, including Homo sapiens. However, the process of genome annotation is far from being completed. This is particularly relevant in eukaryotes, whose cells contain several subcellular compartments, or organelles, enclosed by membranes, where different relevant functions are performed. Translocation across the membrane into the organelles is a highly regulated and complex cellular process. Indeed different proteins and/or protein isoforms, originated from genes by alternative splicing, may be conveyed to different cell compartments, depending on their specific role in the cell. During recent years the prediction of subcellular localization (SL) by computational means has been an active research area. Several methods are presently available based on different notions and addressing different aspects of SL. This review provides a short overview of the most well performing methods described in the literature, highlighting their predictive capabilities and different applications.

Mass spectrometry is only one piece of the puzzle in clinical proteomics

McGuire, J. N., Overgaard, J., Pociot, F. — 2008-03-27

Biomarker discovery in clinical proteomics is being performed on relatively large patient cohorts by utilizing the high throughput of laser desorption/ionization mass spectrometry (MALDI- and SELDI-TOF-MS). Dealing directly with patient samples as opposed to working in cell or animal systems requires a host of considerations both before and after mass spectrometric analysis to obtain robust biomarker candidates. The challenges associated with the heterogeneity of typical samples are amplified by the ability to detect hundreds to thousands of proteins simultaneously. Adherence to protocols and consistency, however, can ensure optimal results. A study starts necessarily with a relevant clinical question and proceeds to a planning phase where sample availability, statistical test selection, logistics and bias reduction are key points. The physical analysis requires consistency and standardized protocols that are helped significantly through automation. Data analysis is broken into two stages, screening and final testing, which can detect either single candidates or a pattern of proteins. Biomarker identification can be performed at this point and will help significantly in the last stage, interpretation. Replication should be performed in an independent sample set in a separate study. The candidate biomarkers from an initial study give a wealth of information that can help to pinpoint patient subpopulations for a more exhaustive proteomic study using complementary platforms with limited capacity but extremely high information content. A clinical proteomics pilot project can also lead to better selection of model systems by providing a direct link with patient samples.

IG, TR and IgSF, MHC and MhcSF: what do we learn from the IMGT Colliers de Perles?

Kaas, Q., Ehrenmann, F., Lefranc, M.-P. — 2008-01-27

The immunoglobulin superfamily (IgSF) comprises the immunoglobulins (IG), T cell receptors (TR) and proteins that have the common feature of having at least one Ig-like domain. The major histocompatibility complex (MHC) superfamily (MhcSF) comprises, in addition to the MHC, proteins which share the common feature of having Mhc-like domains. IMGT®, the international ImMunoGeneTics information system® (http://imgt.cines.fr) has set up a unique numbering system and standardized 2D graphical representations, or IMGT Colliers de Perles, which take into account the structural features of the Ig-like and Mhc-like domains. In this article, we review the IMGT Scientific chart rules for the description of the IgSF (V and C types) and of the MhcSF (G type) domains. These rules are based on the IMGT-ONTOLOGY axioms and concepts and are applicable for the sequence and structure analysis, whatever the species, the IgSF or MhcSF protein, or the chain type. These IMGT Colliers de Perles are particularly useful for antibody engineering, sequence–structure analysis, visualization and comparison of positions for mutations, polymorphisms and contact analysis.

Microarray data analysis and mining approaches

Cordero, F., Botta, M., Calogero, R. A. — 2008-01-27

Microarray based transcription profiling is now a consolidated methodology and has widespread use in areas such as pharmacogenomics, diagnostics and drug target identification. Large-scale microarray studies are also becoming crucial to a new way of conceiving experimental biology. A main issue in microarray transcription profiling is data analysis and mining. When microarrays became a methodology of general use, considerable effort was made to produce algorithms and methods for the identification of differentially expressed genes. More recently, the focus has switched to algorithms and database development for microarray data mining. Furthermore, the evolution of microarray technology is allowing researchers to grasp the regulative nature of transcription, integrating basic expression analysis with mRNA characteristics, i.e. exon-based arrays, and with DNA characteristics, i.e. comparative genomic hybridization, single nucleotide polymorphism, tiling and promoter structure. In this article, we will review approaches used to detect differentially expressed genes and to link differential expression to specific biological functions.

Genomics of mRNA turnover

Perez-Ortin, J. E. — 2008-01-27

Most studies on eukaryotic gene regulation have focused on mature mRNA levels. Nevertheless, the steady-state mRNA level is the result of two opposing biological processes: transcription and degradation, both of which can be important points to regulate gene expression. It is now possible to determine the transcription and degradation rates (TR and DR), as well as the mRNA amount, for each gene using DNA chip technologies. In this way, each individual contribution to gene expression can be analysed. This review will deal with the techniques used for the genomic evaluation of TR and DR developed for the yeast Saccharomyces cerevisiae. They will be described in detail and their potential drawbacks discussed. I will also discuss the integration of the data obtained to fully analyse the expression strategies used by yeast and other eukaryotic cells.

Phenobabelomics--mouse phenotype data resources

Hancock, J. M., Mallon, A.-M. — 2008-01-27

An essential aspect to understanding the functional significance of individual genes in the mouse genome is an understanding of the phenotypic consequences of gene mutations. A wide variety of online sites exist that provide different types of phenotypic information on the laboratory mouse. In this review, we describe the major resources that are currently available and discuss some of the bioinformatics requirements that will be necessary to make more seamless searching, comparison and analysis of these various data types possible.

Improving yeast two-hybrid screening systems

Koegl, M., Uetz, P. — 2008-01-27

Yeast two-hybrid (Y2H) screening methods are an effective means for the detection of protein–protein interactions. Optimisation and automation has increased the throughput of the method to an extent that allows the systematic mapping of protein–protein interactions on a proteome-wide scale. Since two-hybrid screens fail to detect a great number of interactions, parallel high-throughput approaches are needed for proteome-wide interaction screens. In this review, we discuss and compare different approaches for adaptation of Y2H screening to high-throughput, the limits of the method and possible alternative approaches to complement the mapping of organism-wide protein–protein interactions.

Molecular approaches in pig breeding to improve meat quality

Davoli, R., Braglia, S. — 2008-01-27

This article reviews the advances in molecular genetics that have led to the identification of genes and markers associated with meat quality in pig. The development of a considerable number of annotated livestock genome sequences represents an incredibly rich source of information that can be used to identify candidate genes responsible for complex traits and quantitative trait loci effects. In pig, the huge amount of information emerging from the study of the genome has helped in the acquisition of new knowledge concerning biological systems and it is opening new opportunities for the genetic selection of this specie. Among the new fields of genomics recently developed, functional genomics and proteomics that allow considering many genes and proteins at the same time are very useful tools for a better understanding of the function and regulation of genes, and how these participate in complex networks controlling the phenotypic characteristics of a trait. In particular, global gene expression profiling at the mRNA and protein level can provide a better understanding of gene regulation that underlies biological functions and physiology related to the delivery of a better pig meat quality. Moreover, the possibility to realize an integrated approach of genomics and proteomics with bioinformatics tools is essential to obtain a complete exploitation of the available molecular genetics information. The development of this knowledge will benefit scientists, industry and breeders considering that the efficiency and accuracy of the traditional pig selection schemes will be improved by the implementation of molecular data into breeding programs.

A proteomic approach to iron and copper homeostasis in cyanobacteria

2008-01-27

Cyanobacteria, which are considered to be the chloroplast precursors, are significant contributors to global photosynthetic productivity. The ample variety of membrane and soluble proteins containing different metals (mainly, iron and copper) has made these organisms develop a complex homeostasis with different mechanisms and tight regulation processes to fulfil their metal requirements in a changing environment. Cell metabolism is so adapted as to synthesize alternative proteins depending on the relative metal availabilities. In particular, plastocyanin, a copper protein, and cytochrome c₆, a haem protein, can replace each other to play the same physiological role as electron carriers in photosynthesis and respiration, with the synthesis of one protein or another being regulated by copper concentration in the medium. The unicellular cyanobacterium Synechocystis sp. PCC 6803 has been widely used as a model system because of completion of its genome sequence and the ease of its genetic manipulation, with a lot of proteomic work being done. In this review article, we focus on the functional characterization of knockout Synechocystis mutants for plastocyanin and cytochrome c₆, and discuss the ongoing proteomic analyses performed at varying copper concentrations to investigate the cyanobacterial metal homeostasis and cell response to changing environmental conditions.