Jonathan W. Arthur

Computational methods for protein identification from mass spectrometry data.

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology.

Gene expression of Pseudomonas aeruginosa in a mucin-containing synthetic growth medium mimicking cystic fibrosis lung sputum.

Pseudomonas aeruginosa airway infection is the leading cause of morbidity and mortality in cystic fibrosis (CF) patients. Various in vitro models have been developed to study P. aeruginosa pathobiology in the CF lung. In this study we produced a modified artificial-sputum medium (ASMDM) more closely resembling CF sputum than previous models, and extended previous work by using strain PAO1 arrays to examine the global transcription profiles of P. aeruginosa strain UCBPP-PA14 under early exponential-phase and stationary-phase growth. In early exponential phase, 38/39 nutrition-related genes were upregulated in line with data from previous in vitro models using UCBPP-PA14. Additionally, 23 type III secretion system (T3SS) genes, several anaerobic respiration genes and 24 quorum-sensing (QS)-related genes were upregulated in ASMDM, suggesting enhanced virulence factor expression and priming for anaerobic growth and biofilm formation. Under stationary phase growth in ASMDM, macroscopic clumps resembling microcolonies were evident in UCBPP-PA14 and CF strains, and over 40 potentially important genes were differentially expressed relative to stationary-phase growth in Luria broth. Most notably, QS-related and T3SS genes were downregulated in ASMDM, and iron-acquisition and assimilatory nitrate reductase genes were upregulated, simulating the iron-depleted, microaerophilic/anaerobic environment of CF sputum. ASMDM thus appears to be highly suitable for gene expression studies of P. aeruginosa in CF.

Biochemical characteristics and inhibitor selectivity of mouse indoleamine 2,3-dioxygenase-2

The first step in the kynurenine pathway of tryptophan catabolism is the cleavage of the 2,3-double bond of the indole ring of tryptophan. In mammals, this reaction is performed independently by indoleamine 2,3-dioxygenase-1 (IDO1), tryptophan 2,3-dioxygenase (TDO) and the recently discovered indoleamine 2,3-dioxygenase-2 (IDO2). Here we describe characteristics of a purified recombinant mouse IDO2 enzyme, including its pH stability, thermal stability and structural features. An improved assay system for future studies of recombinant/isolated IDO2 has been developed using cytochrome b5 as an electron donor. This, the first description of the interaction between IDO2 and cytochrome b5, provides further evidence of the presence of a physiological electron carrier necessary for activity of enzymes in the “IDO family”. Using this assay, the kinetic activity and substrate range of IDO2 were shown to be different to those of IDO1. 1-Methyl-d-tryptophan, a current lead IDO inhibitor used in clinical trials, was a poor inhibitor of both IDO1 and IDO2 activity. This suggests that its immunosuppressive effect may be independent of pharmacological inhibition of IDO enzymes, in the mouse at least. The different biochemical characteristics of the mouse IDO proteins suggest that they have evolved to have distinct biological roles.

Single nucleotide differences (SNDs) in the dbSNP database may lead to errors in genotyping and haplotyping studies.

The creation of single nucleotide polymorphism (SNP) databases (such as NCBI dbSNP) has facilitated scientific research in many fields. SNP discovery and detection has improved to the extent that there are over 17 million human reference (rs) SNPs reported to date (Build 129 of dbSNP). SNP databases are unfortunately not always complete and/or accurate. In fact, half of the reported SNPs are still only candidate SNPs and are not validated in a population. We describe the identification of SNDs (single nucleotide differences) in humans, that may contaminate the dbSNP database. These SNDs, reported as real SNPs in the database, do not exist as such, but are merely artifacts due to the presence of a paralogue (highly similar duplicated) sequence in the genome. Using sequencing we showed how SNDs could originate in two paralogous genes and evaluated samples from a population of 100 individuals for the presence/absence of SNPs. Moreover, using bioinformatics, we predicted as many as 8.32% of the biallelic, coding SNPs in the dbSNP database to be SNDs. Our identification of SNDs in the database will allow researchers to not only select truly informative SNPs for association studies, but also aid in determining accurate SNP genotypes and haplotypes. Hum Mutat 31:67–73, 2010.

Transcriptome analyses and biofilm-forming characteristics of a clonal Pseudomonas aeruginosa from the cystic fibrosis lung.

Transmissible Pseudomonas aeruginosa clones potentially pose a serious threat to cystic fibrosis (CF) patients. The AES-1 clone has been found to infect up to 40 % of patients in five CF centres in eastern Australia. Studies were carried out on clonal and non-clonal (NC) isolates from chronically infected CF patients, and the reference strain PAO1, to gain insight into the properties of AES-1. The transcriptomes of AES-1 and NC isolates, and of PAO1, grown planktonically and as a 72 h biofilm were compared using PAO1 microarrays. Microarray data were validated using real-time PCR. Overall, most differentially expressed genes were downregulated. AES-1 differentially expressed bacteriophage genes, novel motility genes, and virulence and quorum-sensing-related genes, compared with both PAO1 and NC. AES-1 but not NC biofilms significantly downregulated aerobic respiration genes compared with planktonic growth, suggesting enhanced anaerobic/microaerophilic growth by AES-1. Biofilm measurement showed that AES-1 formed significantly larger and thicker biofilms than NC or PAO1 isolates. This may be related to expression of the gene PA0729, encoding a biofilm-enhancing bacteriophage, identified by PCR in all AES-1 but few NC isolates (n=42). Links with the Liverpool epidemic strain included the presence of PA0729 and the absence of the bacteriophage gene cluster PA0632-PA0639. No common markers were found with the Manchester strain. No particular differentially expressed gene in AES-1 could definitively be ascribed a role in its infectivity, thus increasing the likelihood that AES-1 infectivity is multi-factorial and possibly involves novel genes. This study extends our understanding of the transcriptomic and genetic differences between clonal and NC strains of P. aeruginosa from CF lung.

Gene expression characteristics of a cystic fibrosis epidemic strain of Pseudomonas aeruginosa during biofilm and planktonic growth

Epidemic Pseudomonas aeruginosa have been identified in cystic fibrosis (CF) patients worldwide. The Australian Epidemic Strain-2 (AES-2) infects up to 40% of patients in three eastern Australian CF clinics. To investigate whether AES-2 isolates from chronically infected CF adults differentially express well-conserved genes potentially associated with transmissibility, we compared the transcriptomes of planktonic and biofilm-grown AES-2, infrequent P. aeruginosa clones and the reference P. aeruginosa PAO1 using the Affymetrix PAO1 array. The most interesting findings emerged from comparisons of planktonic and biofilm AES-2. AES-2 biofilms upregulated Type III secretion system genes, but downregulated quorum-sensing (QS)-regulatory genes, except lasR, QS-regulated, oxidative-stress and iron-storage genes. QS-regulated and iron-storage genes were downregulated to a greater extent in AES-2 biofilms compared with infrequent clone and PAO1 biofilms, suggesting enhanced anaerobic respiration in AES-2. Chitinase and chitin-binding protein maintained high expression in AES-2 biofilms compared with infrequent clone and PAO1 biofilms. Planktonic AES-2 upregulated QS regulators and QS-regulated genes, iron acquisition and aerobic respiration genes, and had high expression of Group III Type IV pilA compared with low expression of Group I Type IV pilA in infrequent clones. Together, these properties may enhance long-term survival of AES-2 in CF lung and contribute to its transmissibility.

BAFF is a Biological Response Marker to IFN-β Treatment in Multiple Sclerosis

Multiple sclerosis (MS) is a complex autoimmune disease characterized by the destruction of the myelin sheath of neurons. Interferon beta (IFN-beta) is currently the major drug used to treat MS. Some patients fail to respond to this treatment, in some cases due to the development of neutralizing antibodies (NAb) to IFN-beta. We used microarray analysis and RT-PCR to measure gene expression in whole blood, 9-15 h postinjection, in patients with and without NAbs to IFN-beta. The canonical marker of biological response to IFN-beta, myxovirus resistance protein A, was upregulated in all NAb- patients while remaining unchanged in NAb+ patients. Genes functioning in immune response pathways were dominant in the set of differentially expressed genes: 73 immune response genes were identified as upregulated and 29 genes were identified as downregulated. B-cell activating factor (BAFF) is a strong candidate marker for biological and clinical response as well as for predisposition to NAb development. We demonstrate that it is responsive to IFN-beta in vitro and in vivo, and that its soluble form is elevated in serum from NAb- but not NAb+ patients. We conclude BAFF is a good biomarker for IFN-beta response, and requires further studies to determine its value as a marker for clinical response and NAb predisposition.

Predicting peptide binding to Major Histocompatibility Complex molecules.

The Major Histocompatibility Complex (MHC) constitutes an important part of the human immune system. During infection, pathogenic proteins are processed into peptide fragments by the antigen processing machinery. These peptides bind to MHC molecules and the MHC-peptide complex is then transported to the cell membrane where it elicits an immune response via T-cell binding. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. One of the most challenging aspects of this area of research is understanding the specificity and sensitivity of the binding process. An empirical approach to the problem is unfeasible as there are over 512 billion potential binding peptides for each MHC molecule. Computational approaches offer the promise of predicting peptide binding, thus dramatically reducing the number of peptides proceeding to experimental verification. Various bioinformatic approaches have been developed to predict whether or not a particular peptide will bind to a particular MHC allele. Currently, peptide binding prediction methods can be categorised into three major groups: motif- and scoring matrix-based methods, artificial intelligence- (AI-) based methods, and structure-based methods. The first two are sequence-based approaches and are generally based on common sequence motifs in peptides known to bind to MHC molecules. The structure-based approach concerns the structural features and the distribution of energy between the binding peptide and the MHC molecule. Although knowledge of the molecular structure of the MHC molecules is expected to lead to better predictions of peptide binding, the development of structure-based methods has been relatively slow compared to sequence-based methods. Comparisons of various methods showed that the best sequence-based methods significantly outperform structure-based methods. This may be improved by producing more structures and binding data desperately needed by many alleles, especially class II molecules. On the other hand, the large number of verification methods and indicators used by structure-based studies hinders critical evaluation of the methods. Adopting commonly used assessment procedures can demonstrate the relative performance of structure-based methods in a straightforward comparison with other methods. This review provides an overview of current methods for predicting peptide binding to the MHC, with a focus on structure-based methods, and explores the potential for future development in this area.

ROS-activated ATM-dependent phosphorylation of cytoplasmic substrates identified by large scale phosphoproteomics screen

Ataxia-telangiectasia, mutated (ATM) protein plays a central role in phosphorylating a network of proteins in response to DNA damage. These proteins function in signaling pathways designed to maintain the stability of the genome and minimize the risk of disease by controlling cell cycle checkpoints, initiating DNA repair, and regulating gene expression. ATM kinase can be activated by a variety of stimuli, including oxidative stress. Here, we confirmed activation of cytoplasmic ATM by autophosphorylation at multiple sites. Then we employed a global quantitative phosphoproteomics approach to identify cytoplasmic proteins altered in their phosphorylation state in control and ataxia-telangiectasia (A-T) cells in response to oxidative damage. We demonstrated that ATM was activated by oxidative damage in the cytoplasm as well as in the nucleus and identified a total of 9,833 phosphorylation sites, including 6,686 high-confidence sites mapping to 2,536 unique proteins. A total of 62 differentially phosphorylated peptides were identified; of these, 43 were phosphorylated in control but not in A-T cells, and 19 varied in their level of phosphorylation. Motif enrichment analysis of phosphopeptides revealed that consensus ATM serine glutamine sites were overrepresented. When considering phosphorylation events, only observed in control cells (not observed in A-T cells), with predicted ATM sites phosphoSerine/phosphoThreonine glutamine, we narrowed this list to 11 candidate ATM-dependent cytoplasmic proteins. Two of these 11 were previously described as ATM substrates (HMGA1 and UIMCI/RAP80), another five were identified in a whole cell extract phosphoproteomic screens, and the remaining four proteins had not been identified previously in DNA damage response screens. We validated the phosphorylation of three of these proteins (oxidative stress responsive 1 (OSR1), HDGF, and ccdc82) as ATM dependent after H2O2 exposure, and another protein (S100A11) demonstrated ATM-dependence for translocation from the cytoplasm to the nucleus. These data provide new insights into the activation of ATM by oxidative stress through identification of novel substrates for ATM in the cytoplasm.

Solubility of nonpolar solutes in water: Computer simulations using the CF1 central force model

Nonpolar solutes in water are modeled by the CF1 central force model for the water-water interactions and a shifted-force Lennard-Jones potential function for the solute-water interactions. Thermodynamic integration is used to calculate the solvation free energy of solutes of various sizes. A systematic exploration of the effect of the size of the solute and the depth of the potential well is carried out. This allows an examination of the change in the solvation free energy as the solute size and potential well depth are independently varied. The solvation free energies of the noble gas series are also studied. The results compare favorably with experiment and previous studies, indicating the suitability of the CF1 model of water for use in free energy calculations. An information theory approximation is used to calculate the solvation free energy for hard spheres of various sizes from a simulation of pure CF1 water. The results of this approximation show trends similar to the results of our simulations u...