Biomedical subjects

Alistair M Chalk

Publications and source records attributed to Alistair M Chalk.

8 recordsLinked to original sources

PubMed2006

Differential regulation of osteoadherin (OSAD) by TGF-beta1 and BMP-2.

Osteoadherin (OSAD) is a member of the small leucine rich-repeat proteoglycan (SLRP) family. SLRPs are normally found in extracellular matrices, but OSAD is the only member restricted to mineralized tissues. We investigated the promoter region of OSAD by in silico analysis and found that the proximal promoter region contains sites for Smad-3, Smad-4, and AP-1. All are effectors of TGF-beta family signalling. We tested sensitivity of the promoter to the two TGF-beta family members TGF-beta1 and BMP-2. We found TGF-beta1 to down regulate OSAD, while BMP-2 up regulates OSAD. As a consequence of how OSAD is regulated by TGF-beta1 and BMP-2 and its temporal expression pattern in osteoblasts and bone development, we can conclude OSAD as an early marker for terminally differentiated matrix producing osteoblasts.

Animals↗

PubMed2006

Alternate transcription of the Toll-like receptor signaling cascade.

BACKGROUND: Alternate splicing of key signaling molecules in the Toll-like receptor (Tlr) cascade has been shown to dramatically alter the signaling capacity of inflammatory cells, but it is not known how common this mechanism is. We provide transcriptional evidence of widespread alternate splicing in the Toll-like receptor signaling pathway, derived from a systematic analysis of the FANTOM3 mouse data set. Functional annotation of variant proteins was assessed in light of inflammatory signaling in mouse primary macrophages, and the expression of each variant transcript was assessed by splicing arrays. RESULTS: A total of 256 variant transcripts were identified, including novel variants of Tlr4, Ticam1, Tollip, Rac1, Irak1, 2 and 4, Mapk14/p38, Atf2 and Stat1. The expression of variant transcripts was assessed using custom-designed splicing arrays. We functionally tested the expression of Tlr4 transcripts under a range of cytokine conditions via northern and quantitative real-time polymerase chain reaction. The effects of variant Mapk14/p38 protein expression on macrophage survival were demonstrated. CONCLUSION: Members of the Toll-like receptor signaling pathway are highly alternatively spliced, producing a large number of novel proteins with the potential to functionally alter inflammatory outcomes. These variants are expressed in primary mouse macrophages in response to inflammatory mediators such as interferon-gamma and lipopolysaccharide. Our data suggest a surprisingly common role for variant proteins in diversification/repression of inflammatory signaling.

Alternative Splicing↗

PubMed2006

Genome-wide review of transcriptional complexity in mouse protein kinases and phosphatases.

BACKGROUND: Alternative transcripts of protein kinases and protein phosphatases are known to encode peptides with altered substrate affinities, subcellular localizations, and activities. We undertook a systematic study to catalog the variant transcripts of every protein kinase-like and phosphatase-like locus of mouse http://variant.imb.uq.edu.au. RESULTS: By reviewing all available transcript evidence, we found that at least 75% of kinase and phosphatase loci in mouse generate alternative splice forms, and that 44% of these loci have well supported alternative 5' exons. In a further analysis of full-length cDNAs, we identified 69% of loci as generating more than one peptide isoform. The 1,469 peptide isoforms generated from these loci correspond to 1,080 unique Interpro domain combinations, many of which lack catalytic or interaction domains. We also report on the existence of likely dominant negative forms for many of the receptor kinases and phosphatases, including some 26 secreted decoys (seven known and 19 novel: Alk, Csf1r, Egfr, Epha1, 3, 5,7 and 10, Ephb1, Flt1, Flt3, Insr, Insrr, Kdr, Met, Ptk7, Ptprc, Ptprd, Ptprg, Ptprl, Ptprn, Ptprn2, Ptpro, Ptprr, Ptprs, and Ptprz1) and 13 transmembrane forms (four known and nine novel: Axl, Bmpr1a, Csf1r, Epha4, 5, 6 and 7, Ntrk2, Ntrk3, Pdgfra, Ptprk, Ptprm, Ptpru). Finally, by mining public gene expression data (MPSS and microarrays), we confirmed tissue-specific expression of ten of the novel isoforms. CONCLUSION: These findings suggest that alternative transcripts of protein kinases and phosphatases are produced that encode different domain structures, and that these variants are likely to play important roles in phosphorylation-dependent signaling pathways.

Alternative Splicing↗

PubMed2005

siRNAdb: a database of siRNA sequences.

Short interfering RNAs (siRNAs) are a popular method for gene-knockdown, acting by degrading the target mRNA. Before performing experiments it is invaluable to locate and evaluate previous knockdown experiments for the gene of interest. The siRNA database provides a gene-centric view of siRNA experimental data, including siRNAs of known efficacy and siRNAs predicted to be of high efficacy by a combination of methods. Linked to these sequences is information such as siRNA thermodynamic properties and the potential for sequence-specific off-target effects. The database enables the user to evaluate an siRNA's potential for inhibition and non-specific effects. The database is available at http://siRNA.cgb.ki.se.

Animals↗

PubMed2004

Profiled support vector machines for antisense oligonucleotide efficacy prediction.

BACKGROUND: This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1) feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE), and (2) AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions. RESULTS: In the first stage, the SVM-RFE technique was most efficient and robust in the presence of low number of samples and high input space dimension. This method yielded an optimal subset of 14 representative features, which were all related to energy and sequence motifs. The second stage evaluated the performance of the predictors (overall correlation coefficient between observed and predicted efficacy, r; mean error, ME; and root-mean-square-error, RMSE) using 8-fold and minus-one-RNA cross-validation methods. The profiled SVM produced the best results (r = 0.44, ME = 0.022, and RMSE= 0.278) and predicted high (>75% inhibition of gene expression) and low efficacy (<25%) AOs with a success rate of 83.3% and 82.9%, respectively, which is better than by previous approaches. A web server for AO prediction is available online at http://aosvm.cgb.ki.se/. CONCLUSIONS: The SVM approach is well suited to the AO prediction problem, and yields a prediction accuracy superior to previous methods. The profiled SVM was found to perform better than the standard SVM, suggesting that it could lead to improvements in other prediction problems as well.

Databases, Genetic↗

PubMed2004

Improved and automated prediction of effective siRNA.

Short interfering RNAs are used in functional genomics studies to knockdown a single gene in a reversible manner. The results of siRNA experiments are highly dependent on the choice of siRNA sequence. In order to evaluate siRNA design rules, we collected a database of 398 siRNAs of known efficacy from 92 genes. We used this database to evaluate previously proposed rules from smaller datasets, and to find a new set of rules that are optimal for the entire database. We also trained a regression tree with full cross-validation. It was however difficult to obtain the same precision as methods previously tested on small datasets from one or two genes. We show that those methods are overfitting as they work poorly on independent validation datasets from multiple genes. Our new design rules can predict siRNAs with efficacy >/= 50% in 91% of cases, and with efficacy >/=90% in 52% of cases, which is more than a twofold improvement over random selection. Software for designing siRNAs is available online via a web server at or as a standalone version for high-throughput applications.

Algorithms↗

PubMed2004

Sfixem--graphical sequence feature display in Java.

UNLABELLED: Sfixem is an sequence feature series (SFS) visualization tool implemented in Java. It is designed to visualize data from sequence analysis programs, allowing the user to view multiple sets of computationally generated analysis to assist the analysis process. SFS is used as the data exchange format. AVAILABILITY: Sfixem is available for direct usage or download for local usage at http://sfixem.cgb.ki.se. A protein sequence analysis workbench using Sfixem is available at http://sfinx.cgb.ki.se.

Algorithms↗

PubMed2002

Computational antisense oligo prediction with a neural network model.

MOTIVATION: The expression of a gene can be selectively inhibited by antisense oligonucleotides (AOs) targeting the mRNA. However, if the target site in the mRNA is picked randomly, typically 20% or less of the AOs are effective inhibitors in vivo. The sequence properties that make an AO effective are not well understood, thus many AOs need to be tested to find good inhibitors, which is time consuming and costly. So far computational models have been based exclusively on RNA structure prediction or motif searches while ignoring information from other aspects of AO design into the model. RESULTS: We present a computational model for AO prediction based on a neural network approach using a broad range of input parameters. Collecting sequence and efficacy data from AO scanning experiments in the literature generated a database of 490 AO molecules. Using a set of derived parameters based on AO sequence properties we trained a neural network model. The best model, an ensemble of 10 networks, gave an overall correlation coefficient of 0.30 (p=10(-8)). This model can predict effective AOs (>50% inhibition of gene expression) with a success rate of 92%. Using these thresholds the model predicts on average 12 effective AOs per 1000 base pairs, making it a stringent yet practical method for AO prediction.

Algorithms↗