Briefings in Functional Genomics and Proteomics - current issue

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.