PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-11 (11)
 

Clipboard (0)
None

Select a Filter Below

Journals
Year of Publication
Document Types
1.  Carbonic Anhydrase 9 Expression Increases with Vascular Endothelial Growth Factor–Targeted Therapy and Is Predictive of Outcome in Metastatic Clear Cell Renal Cancer 
European Urology  2014;66(5):956-963.
Background
There is a lack of biomarkers to predict outcome with targeted therapy in metastatic clear cell renal cancer (mccRCC). This may be because dynamic molecular changes occur with therapy.
Objective
To explore if dynamic, targeted-therapy-driven molecular changes correlate with mccRCC outcome.
Design, setting, and participants
Multiple frozen samples from primary tumours were taken from sunitinib-naïve (n = 22) and sunitinib-treated mccRCC patients (n = 23) for protein analysis. A cohort (n = 86) of paired, untreated and sunitinib/pazopanib-treated mccRCC samples was used for validation. Array comparative genomic hybridisation (CGH) analysis and RNA interference (RNAi) was used to support the findings.
Intervention
Three cycles of sunitinib 50 mg (4 wk on, 2 wk off).
Outcome measurements and statistical analysis
Reverse phase protein arrays (training set) and immunofluorescence automated quantitative analysis (validation set) assessed protein expression.
Results and limitations
Differential expression between sunitinib-naïve and treated samples was seen in 30 of 55 proteins (p < 0.05 for each). The proteins B-cell CLL/lymphoma 2 (BCL2), mutL homolog 1 (MLH1), carbonic anhydrase 9 (CA9), and mechanistic target of rapamycin (mTOR) (serine/threonine kinase) had both increased intratumoural variance and significant differential expression with therapy. The validation cohort confirmed increased CA9 expression with therapy. Multivariate analysis showed high CA9 expression after treatment was associated with longer survival (hazard ratio: 0.48; 95% confidence interval, 0.26–0.87; p = 0.02). Array CGH profiles revealed sunitinib was associated with significant CA9 region loss. RNAi CA9 silencing in two cell lines inhibited the antiproliferative effects of sunitinib. Shortcomings of the study include selection of a specific protein for analysis, and the specific time points at which the treated tissue was analysed.
Conclusions
CA9 levels increase with targeted therapy in mccRCC. Lower CA9 levels are associated with a poor prognosis and possible resistance, as indicated by the validation cohort.
Patient summary
Drug treatment of advanced kidney cancer alters molecular markers of treatment resistance. Measuring carbonic anhydrase 9 levels may be helpful in determining which patients benefit from therapy.
Take Home Message
By exploring dynamic changes to protein expression in sunitinib-treated and untreated metastatic renal cell carcinoma tissue, carbonic anhydrase 9 (CA9) was identified as a potential biomarker. CA9 levels were prognostic, increasing with therapy. Lower CA9 levels are associated with a poor prognosis and possible resistance.
doi:10.1016/j.eururo.2014.04.007
PMCID: PMC4410300  PMID: 24821582
Renal cancer; Biomarker; VEGF TKI; CA9
2.  The ADAR RNA editing enzyme controls neuronal excitability in Drosophila melanogaster 
Nucleic Acids Research  2013;42(2):1139-1151.
RNA editing by deamination of specific adenosine bases to inosines during pre-mRNA processing generates edited isoforms of proteins. Recoding RNA editing is more widespread in Drosophila than in vertebrates. Editing levels rise strongly at metamorphosis, and Adar5G1 null mutant flies lack editing events in hundreds of CNS transcripts; mutant flies have reduced viability, severely defective locomotion and age-dependent neurodegeneration. On the other hand, overexpressing an adult dADAR isoform with high enzymatic activity ubiquitously during larval and pupal stages is lethal. Advantage was taken of this to screen for genetic modifiers; Adar overexpression lethality is rescued by reduced dosage of the Rdl (Resistant to dieldrin), gene encoding a subunit of inhibitory GABA receptors. Reduced dosage of the Gad1 gene encoding the GABA synthetase also rescues Adar overexpression lethality. Drosophila Adar5G1 mutant phenotypes are ameliorated by feeding GABA modulators. We demonstrate that neuronal excitability is linked to dADAR expression levels in individual neurons; Adar-overexpressing larval motor neurons show reduced excitability whereas Adar5G1 null mutant or targeted Adar knockdown motor neurons exhibit increased excitability. GABA inhibitory signalling is impaired in human epileptic and autistic conditions, and vertebrate ADARs may have a relevant evolutionarily conserved control over neuronal excitability.
doi:10.1093/nar/gkt909
PMCID: PMC3902911  PMID: 24137011
3.  TMA Navigator: network inference, patient stratification and survival analysis with tissue microarray data 
Nucleic Acids Research  2013;41(Web Server issue):W562-W568.
Tissue microarrays (TMAs) allow multiplexed analysis of tissue samples and are frequently used to estimate biomarker protein expression in tumour biopsies. TMA Navigator (www.tmanavigator.org) is an open access web application for analysis of TMA data and related information, accommodating categorical, semi-continuous and continuous expression scores. Non-biological variation, or batch effects, can hinder data analysis and may be mitigated using the ComBat algorithm, which is incorporated with enhancements for automated application to TMA data. Unsupervised grouping of samples (patients) is provided according to Gaussian mixture modelling of marker scores, with cardinality selected by Bayesian information criterion regularization. Kaplan–Meier survival analysis is available, including comparison of groups identified by mixture modelling using the Mantel-Cox log-rank test. TMA Navigator also supports network inference approaches useful for TMA datasets, which often constitute comparatively few markers. Tissue and cell-type specific networks derived from TMA expression data offer insights into the molecular logic underlying pathophenotypes, towards more effective and personalized medicine. Output is interactive, and results may be exported for use with external programs. Private anonymous access is available, and user accounts may be generated for easier data management.
doi:10.1093/nar/gkt529
PMCID: PMC3692046  PMID: 23761446
4.  Computational approaches to selecting and optimising targets for structural biology 
Methods (San Diego, Calif.)  2011;55(1):3-11.
Highlights
► Identifies key considerations in target selection and optimisation. ► Approaches to assign useful protein features and structure/function relationships. ► Comparison of latest crystallisation propensity predictors on nonredundant data. ► Discusses single point of reference target selection/optimisation resources. ► Guidance on using the SSPF Target Optimisation Utility (TarO).
Selection of protein targets for study is central to structural biology and may be influenced by numerous factors. A key aim is to maximise returns for effort invested by identifying proteins with the balance of biophysical properties that are conducive to success at all stages (e.g. solubility, crystallisation) in the route towards a high resolution structural model. Selected targets can be optimised through construct design (e.g. to minimise protein disorder), switching to a homologous protein, and selection of experimental methodology (e.g. choice of expression system) to prime for efficient progress through the structural proteomics pipeline.
Here we discuss computational techniques in target selection and optimisation, with more detailed focus on tools developed within the Scottish Structural Proteomics Facility (SSPF); namely XANNpred, ParCrys, OB-Score (target selection) and TarO (target optimisation). TarO runs a large number of algorithms, searching for homologues and annotating the pool of possible alternative targets. This pool of putative homologues is presented in a ranked, tabulated format and results are also visualised as an automatically generated and annotated multiple sequence alignment. The target selection algorithms each predict the propensity of a selected protein target to progress through the experimental stages leading to diffracting crystals. This single predictor approach has advantages for target selection, when compared with an approach using two or more predictors that each predict for success at a single experimental stage. The tools described here helped SSPF achieve a high (21%) success rate in progressing cloned targets to diffraction-quality crystals.
doi:10.1016/j.ymeth.2011.08.014
PMCID: PMC3202631  PMID: 21906678
MSA, Multiple Sequence Alignment; PTM, Post Translational Modification; SSPF, Scottish Structural Proteomics Facility; MCC, Matthew’s correlation coefficient; AROC, Area Under the Receiver Operator Characteristic curve; Target selection; Crystallisation; Structural genomics; Structural biology; Bioinformatics; Construct design
5.  Global network analysis of drug tolerance, mode of action and virulence in methicillin-resistant S. aureus 
BMC Systems Biology  2011;5:68.
Background
Staphylococcus aureus is a major human pathogen and strains resistant to existing treatments continue to emerge. Development of novel treatments is therefore important. Antimicrobial peptides represent a source of potential novel antibiotics to combat resistant bacteria such as Methicillin-Resistant Staphylococcus aureus (MRSA). A promising antimicrobial peptide is ranalexin, which has potent activity against Gram-positive bacteria, and particularly S. aureus. Understanding mode of action is a key component of drug discovery and network biology approaches enable a global, integrated view of microbial physiology, including mechanisms of antibiotic killing. We developed a systems-wide functional association network approach to integrate proteome and transcriptome profiles, enabling study of drug resistance and mode of action.
Results
The functional association network was constructed by Bayesian logistic regression, providing a framework for identification of antimicrobial peptide (ranalexin) response modules from S. aureus MRSA-252 transcriptome and proteome profiling. These signatures of ranalexin treatment revealed multiple killing mechanisms, including cell wall activity. Cell wall effects were supported by gene disruption and osmotic fragility experiments. Furthermore, twenty-two novel virulence factors were inferred, while the VraRS two-component system and PhoU-mediated persister formation were implicated in MRSA tolerance to cationic antimicrobial peptides.
Conclusions
This work demonstrates a powerful integrative approach to study drug resistance and mode of action. Our findings are informative to the development of novel therapeutic strategies against Staphylococcus aureus and particularly MRSA.
doi:10.1186/1752-0509-5-68
PMCID: PMC3123200  PMID: 21569391
6.  The Scottish Structural Proteomics Facility: targets, methods and outputs 
The Scottish Structural Proteomics Facility was funded to develop a laboratory scale approach to high throughput structure determination. The effort was successful in that over 40 structures were determined. These structures and the methods harnessed to obtain them are reported here. This report reflects on the value of automation but also on the continued requirement for a high degree of scientific and technical expertise. The efficiency of the process poses challenges to the current paradigm of structural analysis and publication. In the 5 year period we published ten peer-reviewed papers reporting structural data arising from the pipeline. Nevertheless, the number of structures solved exceeded our ability to analyse and publish each new finding. By reporting the experimental details and depositing the structures we hope to maximize the impact of the project by allowing others to follow up the relevant biology.
Electronic supplementary material
The online version of this article (doi:10.1007/s10969-010-9090-y) contains supplementary material, which is available to authorized users.
doi:10.1007/s10969-010-9090-y
PMCID: PMC2883930  PMID: 20419351
High-throughput; Protein crystallography; Structural proteomics; SSPF
7.  Purification, crystallization and data collection of methicillin-resistant Staphylococcus aureus Sar2676, a pantothenate synthetase 
Sar2676, a pantothenate synthetase with a molecular weight of 31 419 Da from methicillin-resistant Staphylococcus aureus, has been expressed, purified and crystallized at 293 K.
Sar2676, a pantothenate synthetase with a molecular weight of 31 419 Da from methicillin-resistant Staphylococcus aureus, has been expressed, purified and crystallized at 293 K. The protein crystallizes in a primitive triclinic lattice, with unit-cell parameters a = 45.3, b = 60.5, c = 117.6 Å, α = 87.2, β = 81.2, γ = 68.4°. A complete data set has been collected to 2.3 Å resolution at the ESRF. Consideration of the likely solvent content suggested the asymmetric unit to contain four molecules. This has been confirmed by molecular-replacement phasing calculations, which give a solution with four monomers using a monomer of pantothenate synthetase from Escherichia coli (PDB code 1iho), which is 41% identical to Sar2676, as a search model.
doi:10.1107/S1744309107020362
PMCID: PMC2335074  PMID: 17554169
Sar2676; pantothenate synthetase; methicillin-resistant Staphylococcus aureus
8.  Expression, purification, crystallization, data collection and preliminary biochemical characterization of methicillin-resistant Staphylococcus aureus Sar2028, an aspartate/tyrosine/phenylalanine pyridoxal-5′-phosphate-dependent aminotransferase 
As part of work on S. aureus, the crystallization of Sar2028, a protein that is upregulated in MRSA, is reported.
Sar2028, an aspartate/tyrosine/phenylalanine pyridoxal-5′-phosphate-dependent aminotransferase with a molecular weight of 48 168 Da, was overexpressed in methicillin-resistant Staphylococcus aureus compared with a methicillin-sensitive strain. The protein was expressed in Escherichia coli, purified and crystallized. The protein crystallized in a primitive orthorhombic Laue group with unit-cell parameters a = 83.6, b = 91.3, c = 106.0 Å, α = β = γ = 90°. Analysis of the systematic absences along the three principal axes indicated the space group to be P212121. A complete data set was collected to 2.5 Å resolution.
doi:10.1107/S1744309107019562
PMCID: PMC2335000  PMID: 17565195
Sar2028; Staphylococcus aureus; aminotransferases
9.  TarO: a target optimisation system for structural biology 
Nucleic Acids Research  2008;36(Web Server issue):W190-W196.
TarO (http://www.compbio.dundee.ac.uk/taro) offers a single point of reference for key bioinformatics analyses relevant to selecting proteins or domains for study by structural biology techniques. The protein sequence is analysed by 17 algorithms and compared to 8 databases. TarO gathers putative homologues, including orthologues, and then obtains predictions of properties for these sequences including crystallisation propensity, protein disorder and post-translational modifications. Analyses are run on a high-performance computing cluster, the results integrated, stored in a database and accessed through a web-based user interface. Output is in tabulated format and in the form of an annotated multiple sequence alignment (MSA) that may be edited interactively in the program Jalview. TarO also simplifies the gathering of additional annotations via the Distributed Annotation System, both from the MSA in Jalview and through links to Dasty2. Routes to other information gateways are included, for example to relevant pages from UniProt, COG and the Conserved Domains Database. Open access to TarO is available from a guest account with private accounts for academic use available on request. Future development of TarO will include further analysis steps and integration with the Protein Information Management System (PIMS), a sister project in the BBSRC ‘Structural Proteomics of Rational Targets’ initiative
doi:10.1093/nar/gkn141
PMCID: PMC2447720  PMID: 18385152
10.  XANNpred: Neural nets that predict the propensity of a protein to yield diffraction-quality crystals 
Proteins  2010;79(4):1027-1033.
Production of diffracting crystals is a critical step in determining the three-dimensional structure of a protein by X-ray crystallography. Computational techniques to rank proteins by their propensity to yield diffraction-quality crystals can improve efficiency in obtaining structural data by guiding both protein selection and construct design. XANNpred comprises a pair of artificial neural networks that each predict the propensity of a selected protein sequence to produce diffraction-quality crystals by current structural biology techniques. Blind tests show XANNpred has accuracy and Matthews correlation values ranging from 75% to 81% and 0.50 to 0.63 respectively; values of area under the receiver operator characteristic (ROC) curve range from 0.81 to 0.88. On blind test data XANNpred outperforms the other available algorithms XtalPred, PXS, OB-Score, and ParCrys. XANNpred also guides construct design by presenting graphs of predicted propensity for diffraction-quality crystals against residue sequence position. The XANNpred-SG algorithm is likely to be most useful to target selection in structural genomics consortia, while the XANNpred-PDB algorithm is more suited to the general structural biology community. XANNpred predictions that include sliding window graphs are freely available from http://www.compbio.dundee.ac.uk/xannpred Proteins 2011. © 2010 Wiley-Liss, Inc.
doi:10.1002/prot.22914
PMCID: PMC3084997  PMID: 21246630
computational biology; bioinformatics; crystallization; software; artificial neural network; predictor
11.  Addressing Statistical Biases in Nucleotide-Derived Protein Databases for Proteogenomic Search Strategies 
Journal of Proteome Research  2012;11(11):5221-5234.
Proteogenomics has the potential to advance genome annotation through high quality peptide identifications derived from mass spectrometry experiments, which demonstrate a given gene or isoform is expressed and translated at the protein level. This can advance our understanding of genome function, discovering novel genes and gene structure that have not yet been identified or validated. Because of the high-throughput shotgun nature of most proteomics experiments, it is essential to carefully control for false positives and prevent any potential misannotation. A number of statistical procedures to deal with this are in wide use in proteomics, calculating false discovery rate (FDR) and posterior error probability (PEP) values for groups and individual peptide spectrum matches (PSMs). These methods control for multiple testing and exploit decoy databases to estimate statistical significance. Here, we show that database choice has a major effect on these confidence estimates leading to significant differences in the number of PSMs reported. We note that standard target:decoy approaches using six-frame translations of nucleotide sequences, such as assembled transcriptome data, apparently underestimate the confidence assigned to the PSMs. The source of this error stems from the inflated and unusual nature of the six-frame database, where for every target sequence there exists five “incorrect” targets that are unlikely to code for protein. The attendant FDR and PEP estimates lead to fewer accepted PSMs at fixed thresholds, and we show that this effect is a product of the database and statistical modeling and not the search engine. A variety of approaches to limit database size and remove noncoding target sequences are examined and discussed in terms of the altered statistical estimates generated and PSMs reported. These results are of importance to groups carrying out proteogenomics, aiming to maximize the validation and discovery of gene structure in sequenced genomes, while still controlling for false positives.
doi:10.1021/pr300411q
PMCID: PMC3703792  PMID: 23025403
proteogenomics; peptide spectrum match; false discovery rate; posterior error probability; expressed sequence tag

Results 1-11 (11)