Helen Hulme

A systematic strategy for large-scale analysis of genotype phenotype correlations: identification of candidate genes involved in African trypanosomiasis.

It is increasingly common to combine Microarray and Quantitative Trait Loci data to aid the search for candidate genes responsible for phenotypic variation. Workflows provide a means of systematically processing these large datasets and also represent a framework for the re-use and the explicit declaration of experimental methods. In this article, we highlight the issues facing the manual analysis of microarray and QTL data for the discovery of candidate genes underlying complex phenotypes. We show how automated approaches provide a systematic means to investigate genotype–phenotype correlations. This methodology was applied to a use case of resistance to African trypanosomiasis in the mouse. Pathways represented in the results identified Daxx as one of the candidate genes within the Tir1 QTL region. Subsequent re-sequencing in Daxx identified a deletion of an amino acid, identified in susceptible mouse strains, in the Daxx–p53 protein-binding region. This supports recent experimental evidence that apoptosis could be playing a role in the trypanosomiasis resistance phenotype. Workflows developed in this investigation, including a guide to loading and executing them with example data, are available at http://workflows.mygrid.org.uk/repository/myGrid/PaulFisher/.

Analysis of the transcriptome of the protozoan Theileria parva using MPSS reveals that the majority of genes are transcriptionally active in the schizont stage

Massively parallel signature sequencing (MPSS) was used to analyze the transcriptome of the intracellular protozoan Theileria parva. In total 1 095 000, 20 bp sequences representing 4371 different signatures were generated from T.parva schizonts. Reproducible signatures were identified within 73% of potentially detectable predicted genes and 83% had signatures in at least one MPSS cycle. A predicted leader peptide was detected on 405 expressed genes. The quantitative range of signatures was 4–52 256 transcripts per million (t.p.m.). Rare transcripts (<50 t.p.m.) were detected from 36% of genes. Sequence signatures approximated a lognormal distribution, as in microarray. Transcripts were widely distributed throughout the genome, although only 47% of 138 telomere-associated open reading frames exhibited signatures. Antisense signatures comprised 13.8% of the total, comparable with Plasmodium. Eighty five predicted genes with antisense signatures lacked a sense signature. Antisense transcripts were independently amplified from schizont cDNA and verified by sequencing. The MPSS transcripts per million for seven genes encoding schizont antigens recognized by bovine CD8 T cells varied 1000-fold. There was concordance between transcription and protein expression for heat shock proteins that were very highly expressed according to MPSS and proteomics. The data suggests a low level of baseline transcription from the majority of protein-coding genes.

maxdLoad2 and maxdBrowse: Standards-compliant tools for microarray experimental annotation, data management and dissemination

BackgroundmaxdLoad2 is a relational database schema and Java® application for microarray experimental annotation and storage. It is compliant with all standards for microarray meta-data capture; including the specification of what data should be recorded, extensive use of standard ontologies and support for data exchange formats. The output from maxdLoad2 is of a form acceptable for submission to the ArrayExpress microarray repository at the European Bioinformatics Institute. maxdBrowse is a PHP web-application that makes contents of maxdLoad2 databases accessible via web-browser, the command-line and web-service environments. It thus acts as both a dissemination and data-mining tool.ResultsmaxdLoad2 presents an easy-to-use interface to an underlying relational database and provides a full complement of facilities for browsing, searching and editing. There is a tree-based visualization of data connectivity and the ability to explore the links between any pair of data elements, irrespective of how many intermediate links lie between them. Its principle novel features are:• the flexibility of the meta-data that can be captured,• the tools provided for importing data from spreadsheets and other tabular representations,• the tools provided for the automatic creation of structured documents,• the ability to browse and access the data via web and web-services interfaces.Within maxdLoad2 it is very straightforward to customise the meta-data that is being captured or change the definitions of the meta-data. These meta-data definitions are stored within the database itself allowing client software to connect properly to a modified database without having to be specially configured. The meta-data definitions (configuration file) can also be centralized allowing changes made in response to revisions of standards or terminologies to be propagated to clients without user intervention.maxdBrowse is hosted on a web-server and presents multiple interfaces to the contents of maxd databases. maxdBrowse emulates many of the browse and search features available in the maxdLoad2 application via a web-browser. This allows users who are not familiar with maxdLoad2 to browse and export microarray data from the database for their own analysis. The same browse and search features are also available via command-line and SOAP server interfaces. This both enables scripting of data export for use embedded in data repositories and analysis environments, and allows access to the maxd databases via web-service architectures.ConclusionmaxdLoad2 http://www.bioinf.man.ac.uk/microarray/maxd/ and maxdBrowse http://dbk.ch.umist.ac.uk/maxdBrowse are portable and compatible with all common operating systems and major database servers. They provide a powerful, flexible package for annotation of microarray experiments and a convenient dissemination environment. They are available for download and open sourced under the Artistic License.

Mechanisms Controlling Anaemia in Trypanosoma congolense Infected Mice

Background Trypanosoma congolense are extracellular protozoan parasites of the blood stream of artiodactyls and are one of the main constraints on cattle production in Africa. In cattle, anaemia is the key feature of disease and persists after parasitaemia has declined to low or undetectable levels, but treatment to clear the parasites usually resolves the anaemia. Methodology/Principal Findings The progress of anaemia after Trypanosoma congolense infection was followed in three mouse strains. Anaemia developed rapidly in all three strains until the peak of the first wave of parasitaemia. This was followed by a second phase, characterized by slower progress to severe anaemia in C57BL/6, by slow recovery in surviving A/J and a rapid recovery in BALB/c. There was no association between parasitaemia and severity of anaemia. Furthermore, functional T lymphocytes are not required for the induction of anaemia, since suppression of T cell activity with Cyclosporin A had neither an effect on the course of infection nor on anaemia. Expression of genes involved in erythropoiesis and iron metabolism was followed in spleen, liver and kidney tissues in the three strains of mice using microarrays. There was no evidence for a response to erythropoietin, consistent with anaemia of chronic disease, which is erythropoietin insensitive. However, the expression of transcription factors and genes involved in erythropoiesis and haemolysis did correlate with the expression of the inflammatory cytokines Il6 and Ifng. Conclusions/Significance The innate immune response appears to be the major contributor to the inflammation associated with anaemia since suppression of T cells with CsA had no observable effect. Several transcription factors regulating haematopoiesis, Tal1, Gata1, Zfpm1 and Klf1 were expressed at consistently lower levels in C57BL/6 mice suggesting that these mice have a lower haematopoietic capacity and therefore less ability to recover from haemolysis induced anaemia after infection.

Strong position-dependent effects of sequence mismatches on signal ratios measured using long oligonucleotide microarrays

BackgroundMicroarrays are an important and widely used tool. Applications include capturing genomic DNA for high-throughput sequencing in addition to the traditional monitoring of gene expression and identifying DNA copy number variations. Sequence mismatches between probe and target strands are known to affect the stability of the probe-target duplex, and hence the strength of the observed signals from microarrays.ResultsWe describe a large-scale investigation of microarray hybridisations to murine probes with known sequence mismatches, demonstrating that the effect of mismatches is strongly position-dependent and for small numbers of sequence mismatches is correlated with the maximum length of perfectly matched probe-target duplex. Length of perfect match explained 43% of the variance in log2 signal ratios between probes with one and two mismatches. The correlation with maximum length of perfect match does not conform to expectations based on considering the effect of mismatches purely in terms of reducing the binding energy. However, it can be explained qualitatively by considering the entropic contribution to duplex stability from configurations of differing perfect match length.ConclusionThe results of this study have implications in terms of array design and analysis. They highlight the significant effect that short sequence mismatches can have upon microarray hybridisation intensities even for long oligonucleotide probes.All microarray data presented in this study are available from the GEO database [1], under accession number [GEO: GSE9669]

A systematic strategy for the discovery of candidate genes responsible for phenotypic variation.

IntroductionThe use of Quantitative Trait Loci (QTL) data is increasingly used to aid in the discovery of candidate genes involved in phenotypic variation. Tens to hundreds of genes, however, may lie within even well defined QTL. It is therefore vital that the identification, selection and functional testing of candidate Quantitative Trait genes (QTg) are carried out systematically, and without bias [1]. With the advent of microarrays, researchers are able to directly examine the expression of all genes on a genome wide scale, including those underlying QTL regions.The scale of data being generated by such high-throughput experiments has led some investigators to follow a hypothesis-driven approach [2]. Although these techniques for candidate gene identification are valid, they run the risk of overlooking genes that have less obvious associations with the phenotype. By making selections based on prior assumptions of what processes may be involved, the genes that may actually be involved in the phenotype can be overlooked. A further complication is that the use of ad hoc methods for candidate gene identification are inherently difficult to replicate and are compounded by poor documentation of the methods used to generate and capture the data from such investigations in published literature.With an ever increasing number of institutes offering programmatic access to their resources in the form of web services, however, experiments previously conducted manually can now be replaced by automated experiments, capable of processing a far greater volume of data. By reconstructing the original investigation methods in the form of workflows, we are now able to pass data directly from one service to the next. This enables us to process the data in a much more systematic, un-biased, and explicit manner.MethodsWe propose a data-driven methodology that identifies the known pathways that intersect a QTL and those derived from a set of differentially expressed genes from a microarray study. This methodology is implemented systematically through the use of web services and workflows. For the purpose of implementing this systematic pathway-driven approach, we have chosen to use the Taverna workbench [3].Results and DiscussionPreliminary studies into the modes of resistance to African Trypanosomiasis were carried out for the mouse model organism. These studies illustrated how the large-scale analysis of microarray gene expression and QTL data, investigated at the level of biological pathways, enables links between genotype and phenotype to be successfully established [4]. This approach was implemented systematically through the use of explicitly defined workflows.

Genotype and expression analysis of two inbred mouse strains and two derived congenic strains suggest that most gene expression is trans regulated and sensitive to genetic background

BackgroundDifferences in gene expression may be caused by nearby DNA polymorphisms (cis regulation) or by interactions of gene control regions with polymorphic transcription factors (trans regulation). Trans acting loci are much harder to detect than cis acting loci and their effects are much more sensitive to genetic background.ResultsTo quantify cis and trans regulation we correlated haplotype data with gene expression in two inbred mouse strains and two derived congenic lines. Upstream haplotype differences between the parental strains suggested that 30-43% of differentially expressed genes were differentially expressed because of cis haplotype differences. These cis regulated genes displayed consistent and relatively tissue-independent differential expression. We independently estimated from the congenic mice that 71-85% of genes were trans regulated. Cis regulated genes were associated with low p values (p < 0.005) for differential expression, whereas trans regulated genes were associated with values 0.005 < p < 0.05. The genes differentially expressed between congenics and controls were not a subset of those that were differentially expressed between the founder lines, showing that these were dependent on genetic background. For example, the cholesterol synthesis pathway was strongly differentially expressed in the congenic mice by indirect trans regulation but this was not observable in the parental mice.ConclusionsThe evidence that most gene regulation is trans and strongly influenced by genetic background, suggests that pathways that are modified by an allelic variant, may only exhibit differential expression in the specific genetic backgrounds in which they were identified. This has significant implications for the interpretation of any QTL mapping study.

Genomics approaches to study the biology underlying resistance to trypanosomiasis--some unexpected lessons.

An international multidisciplinary consortium is conducting a programme of research on the host response to trypanosome infection. This builds upon quantitative trait loci (QTL) mapping which identified genome regions influencing susceptibility to pathology following T. congolense infection in both cattle and mice. The approach uses expression analysis to examine the response of both susceptible and resistant strains and a series of novel informatics tools to identify pathways which are activated as a result of challenge, and those which are differentially used by resistant and susceptible strains. Of particular interest are those pathways which simultaneously satisfy both criteria, i.e. are significantly differentially activated and contain genes within QTL regions. However, it is important to stress that it is not required that the genes within the QTL region are differentially expressed themselves.