Medical Biology - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 27
-
ItemLoss of PUMA (BBC3) does not prevent thrombocytopenia caused by the loss of BCL-XL (BCL2L1)(WILEY, 2016-09)Apoptosis is required to maintain tissue homeostasis in multicellular organisms. Platelets, the anucleate cells that are essential for blood clotting, are a prime example. Their brief life span in the circulation is regulated by the intrinsic apoptosis pathway. Pro-survival BCL-XL (also termed BCL2L1) is essential for platelet viability. It functions to restrain the pro-apoptotic BCL-2 family members BAK (also termed BAK1) and BAX, the essential mediators of intrinsic apoptosis. Genetic deletion or pharmacological inhibition of BCL-XL results in thrombocytopenia. Conversely, deletion of BAK in platelets doubles their circulating life span. However, what triggers platelet apoptosis in vivo remains unclear. The pro-apoptotic BH3-only proteins are essential for initiating apoptosis in nucleated cells, and there is some evidence to suggest they also play a role in platelet biology. We investigated whether PUMA (also termed BBC3), a potent BH3-only protein that can inhibit all pro-survival BCL-2 family members as well as directly activate BAX, regulates the death of platelets. Surprisingly, loss of PUMA had no impact on the loss of platelets caused by loss of BCL-XL. It therefore remains to be established whether other BH3-only proteins play a critical role in induction of apoptosis in platelets or whether their death is controlled solely by the interactions between BCL-XL with BAK and BAX.
-
ItemKRLMM: an adaptive genotype calling method for common and low frequency variants(BIOMED CENTRAL LTD, 2014-05-23)BACKGROUND: SNP genotyping microarrays have revolutionized the study of complex disease. The current range of commercially available genotyping products contain extensive catalogues of low frequency and rare variants. Existing SNP calling algorithms have difficulty dealing with these low frequency variants, as the underlying models rely on each genotype having a reasonable number of observations to ensure accurate clustering. RESULTS: Here we develop KRLMM, a new method for converting raw intensities into genotype calls that aims to overcome this issue. Our method is unique in that it applies careful between sample normalization and allows a variable number of clusters k (1, 2 or 3) for each SNP, where k is predicted using the available data. We compare our method to four genotyping algorithms (GenCall, GenoSNP, Illuminus and OptiCall) on several Illumina data sets that include samples from the HapMap project where the true genotypes are known in advance. All methods were found to have high overall accuracy (> 98%), with KRLMM consistently amongst the best. At low minor allele frequency, the KRLMM, OptiCall and GenoSNP algorithms were observed to be consistently more accurate than GenCall and Illuminus on our test data. CONCLUSIONS: Methods that tailor their approach to calling low frequency variants by either varying the number of clusters (KRLMM) or using information from other SNPs (OptiCall and GenoSNP) offer improved accuracy over methods that do not (GenCall and Illuminus). The KRLMM algorithm is implemented in the open-source crlmm package distributed via the Bioconductor project (http://www.bioconductor.org).
-
Itemlimma powers differential expression analyses for RNA-sequencing and microarray studies(OXFORD UNIV PRESS, 2015-04-20)limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
-
ItemedgeR: a versatile tool for the analysis of shRNA-seq and CRISPR-Cas9 genetic screens.(F1000Research, 2014)Pooled library sequencing screens that perturb gene function in a high-throughput manner are becoming increasingly popular in functional genomics research. Irrespective of the mechanism by which loss of function is achieved, via either RNA interference using short hairpin RNAs (shRNAs) or genetic mutation using single guide RNAs (sgRNAs) with the CRISPR-Cas9 system, there is a need to establish optimal analysis tools to handle such data. Our open-source processing pipeline in edgeR provides a complete analysis solution for screen data, that begins with the raw sequence reads and ends with a ranked list of candidate genes for downstream biological validation. We first summarize the raw data contained in a fastq file into a matrix of counts (samples in the columns, genes in the rows) with options for allowing mismatches and small shifts in sequence position. Diagnostic plots, normalization and differential representation analysis can then be performed using established methods to prioritize results in a statistically rigorous way, with the choice of either the classic exact testing methodology or generalized linear modeling that can handle complex experimental designs. A detailed users' guide that demonstrates how to analyze screen data in edgeR along with a point-and-click implementation of this workflow in Galaxy are also provided. The edgeR package is freely available from http://www.bioconductor.org.
-
ItemA pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1(BMC, 2015-04-03)BACKGROUND: The molecular regulators that orchestrate stem cell renewal, proliferation and differentiation along the mammary epithelial hierarchy remain poorly understood. Here we have performed a large-scale pooled RNAi screen in primary mouse mammary stem cell (MaSC)-enriched basal cells using 1295 shRNAs against genes principally involved in transcriptional regulation. METHODS: MaSC-enriched basal cells transduced with lentivirus pools carrying shRNAs were maintained as non-adherent mammospheres, a system known to support stem and progenitor cells. Integrated shRNAs that altered culture kinetics were identified by next generation sequencing as relative frequency changes over time. RNA-seq-based expression profiling coupled with in vitro progenitor and in vivo transplantation assays was used to confirm a role for candidate genes in mammary stem and/or progenitor cells. RESULTS: Utilizing a mammosphere-based assay, the screen identified several candidate regulators. Although some genes had been previously implicated in mammary gland development, the vast majority of genes uncovered have no known function within the mammary gland. RNA-seq analysis of freshly purified primary mammary epithelial populations and short-term cultured mammospheres was used to confirm the expression of candidate regulators. Two genes, Asap1 and Prox1, respectively implicated in breast cancer metastasis and progenitor cell function in other systems, were selected for further analysis as their roles in the normal mammary gland were unknown. Both Prox1 and Asap1 were shown to act as negative regulators of progenitor activity in vitro, and Asap1 knock-down led to a marked increase in repopulating activity in vivo, implying a role in stem cell activity. CONCLUSIONS: This study has revealed a number of novel genes that influence the activity or survival of mammary stem and/or progenitor cells. Amongst these, we demonstrate that Prox1 and Asap1 behave as negative regulators of mammary stem/progenitor function. Both of these genes have also been implicated in oncogenesis. Our findings provide proof of principle for the use of short-term cultured primary MaSC/basal cells in functional RNAi screens.
-
Itemilluminaio: An open source IDAT parsing tool for Illumina microarrays.(F1000 Research Ltd, 2013)The IDAT file format is used to store BeadArray data from the myriad of genomewide profiling platforms on offer from Illumina Inc. This proprietary format is output directly from the scanner and stores summary intensities for each probe-type on an array in a compact manner. A lack of open source tools to process IDAT files has hampered their uptake by the research community beyond the standard step of using the vendor's software to extract the data they contain in a human readable text format. To fill this void, we have developed the illuminaio package that parses IDAT files from any BeadArray platform, including the decryption of files from Illumina's gene expression arrays. illuminaio provides the first open-source package for this task, and will promote wider uptake of the IDAT format as a standard for sharing Illumina BeadArray data in public databases, in the same way that the CEL file serves as the standard for the Affymetrix platform.
-
ItemSetdb1-mediated H3K9 methylation is enriched on the inactive X and plays a role in its epigenetic silencing(BMC, 2016-05-18)BACKGROUND: The presence of histone 3 lysine 9 (H3K9) methylation on the mouse inactive X chromosome has been controversial over the last 15 years, and the functional role of H3K9 methylation in X chromosome inactivation in any species has remained largely unexplored. RESULTS: Here we report the first genomic analysis of H3K9 di- and tri-methylation on the inactive X: we find they are enriched at the intergenic, gene poor regions of the inactive X, interspersed between H3K27 tri-methylation domains found in the gene dense regions. Although H3K9 methylation is predominantly non-genic, we find that depletion of H3K9 methylation via depletion of H3K9 methyltransferase Set domain bifurcated 1 (Setdb1) during the establishment of X inactivation, results in failure of silencing for around 150 genes on the inactive X. By contrast, we find a very minor role for Setdb1-mediated H3K9 methylation once X inactivation is fully established. In addition to failed gene silencing, we observed a specific failure to silence X-linked long-terminal repeat class repetitive elements. CONCLUSIONS: Here we have shown that H3K9 methylation clearly marks the murine inactive X chromosome. The role of this mark is most apparent during the establishment phase of gene silencing, with a more muted effect on maintenance of the silent state. Based on our data, we hypothesise that Setdb1-mediated H3K9 methylation plays a role in epigenetic silencing of the inactive X via silencing of the repeats, which itself facilitates gene silencing through alterations to the conformation of the whole inactive X chromosome.
-
ItemCombining multiple tools outperforms individual methods in gene set enrichment analyses(OXFORD UNIV PRESS, 2017-02-01)MOTIVATION: Gene set enrichment (GSE) analysis allows researchers to efficiently extract biological insight from long lists of differentially expressed genes by interrogating them at a systems level. In recent years, there has been a proliferation of GSE analysis methods and hence it has become increasingly difficult for researchers to select an optimal GSE tool based on their particular dataset. Moreover, the majority of GSE analysis methods do not allow researchers to simultaneously compare gene set level results between multiple experimental conditions. RESULTS: The ensemble of genes set enrichment analyses (EGSEA) is a method developed for RNA-sequencing data that combines results from twelve algorithms and calculates collective gene set scores to improve the biological relevance of the highest ranked gene sets. EGSEA's gene set database contains around 25 000 gene sets from sixteen collections. It has multiple visualization capabilities that allow researchers to view gene sets at various levels of granularity. EGSEA has been tested on simulated data and on a number of human and mouse datasets and, based on biologists' feedback, consistently outperforms the individual tools that have been combined. Our evaluation demonstrates the superiority of the ensemble approach for GSE analysis, and its utility to effectively and efficiently extrapolate biological functions and potential involvement in disease processes from lists of differentially regulated genes. AVAILABILITY AND IMPLEMENTATION: EGSEA is available as an R package at http://www.bioconductor.org/packages/EGSEA/ . The gene sets collections are available in the R package EGSEAdata from http://www.bioconductor.org/packages/EGSEAdata/ . CONTACTS: monther.alhamdoosh@csl.com.au mritchie@wehi.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
-
ItemRNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR.(F1000 Research Ltd, 2016)The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular edgeR package to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.
-
ItemTranscriptional profiling of the epigenetic regulator Smchd1(ELSEVIER SCIENCE BV, 2016-03)Smchd1 is an epigenetic repressor with important functions in healthy cellular processes and disease. To elucidate its role in transcriptional regulation, we performed two independent genome-wide RNA-sequencing studies comparing wild-type and Smchd1 null samples in neural stem cells and lymphoma cell lines. Using an R-based analysis pipeline that accommodates observational and sample-specific weights in the linear modeling, we identify key genes dysregulated by Smchd1 deletion such as clustered protocadherins in the neural stem cells and imprinted genes in both experiments. Here we provide a detailed description of this analysis, from quality control to read mapping and differential expression analysis. These data sets are publicly available from the Gene Expression Omnibus database (accession numbers GSE64099 and GSE65747).