Conifer defence against insects: microarray gene expression profiling of Sitka spruce (Picea sitchensis) induced by mechanical wounding or feeding by spruce budworms (Choristoneura occidentalis) or white pine weevils (Pissodes strobi) reveals large

Conifers are resistant to attack from a large number of potential herbivores or pathogens. Previous molecular and biochemical characterization of selected conifer defence systems support a model of multigenic, constitutive and induced defences that act on invading insects via physical, chemical, biochemical or ecological (multitrophic) mechanisms. However, the genomic foundation of the complex defence and resistance mechanisms of conifers is largely unknown. As part of a genomics strategy to characterize inducible defences and possible resistance mechanisms of conifers against insect herbivory, we developed a cDNA microarray building upon a new spruce (Picea spp.) expressed sequence tag resource. This first-generation spruce cDNA microarray contains 9720 cDNA elements representing c. 5500 unique genes. We used this array to monitor gene expression in Sitka spruce (Picea sitchensis) bark in response to herbivory by white pine weevils (Pissodes strobi, Curculionidae) or wounding, and in young shoot tips in response to western spruce budworm (Choristoneura occidentalis, Lepidopterae) feeding. Weevils are stem-boring insects that feed on phloem, while budworms are foliage feeding larvae that consume needles and young shoot tips. Both insect species and wounding treatment caused substantial changes of the host plant transcriptome detected in each case by differential gene expression of several thousand array elements at 1 or 2 d after the onset of treatment. Overall, there was considerable overlap among differentially expressed gene sets from these three stress treatments. Functional classification of the induced transcripts revealed genes with roles in general plant defence, octadecanoid and ethylene signalling, transport, secondary metabolism, and transcriptional regulation. Several genes involved in primary metabolic processes such as photosynthesis were down-regulated upon insect feeding or wounding, fitting with the concept of dynamic resource allocation in plant defence. Refined expression analysis using gene-specific primers and real-time PCR for selected transcripts was in agreement with microarray results for most genes tested. This study provides the first large-scale survey of insect-induced defence transcripts in a gymnosperm and provides a platform for functional investigation of plant-insect interactions in spruce. Induction of spruce genes of octadecanoid and ethylene signalling, terpenoid biosynthesis, and phenolic secondary metabolism are discussed in more detail.

Microarray expression profiling in melanoma reveals a BRAF mutation signature

We have used microarray gene expression profiling and machine learning to predict the presence of BRAF mutations in a panel of 61 melanoma cell lines. The BRAF gene was found to be mutated in 42 samples (69%) and intragenic mutations of the NRAS gene were detected in seven samples (11%). No cell line carried mutations of both genes. Using support vector machines, we have built a classifier that differentiates between melanoma cell lines based on BRAF mutation status. As few as 83 genes are able to discriminate between BRAF mutant and BRAF wild-type samples with clear separation observed using hierarchical clustering. Multidimensional scaling was used to visualize the relationship between a BRAF mutation signature and that of a generalized mitogen-activated protein kinase (MAPK) activation (either BRAF or NRAS mutation) in the context of the discriminating gene list. We observed that samples carrying NRAS mutations lie somewhere between those with or without BRAF mutations. These observations suggest that there are gene-specific mutation signals in addition to a common MAPK activation that result from the pleiotropic effects of either BRAF or NRAS on other signaling pathways, leading to measurably different transcriptional changes.

Saliva-derived DNA performs well in large-scale, high-density single-nucleotide polymorphism microarray studies

As of June 2009, 361 genome-wide association studies (GWAS) had been referenced by the HuGE database. GWAS require DNA from many thousands of individuals, relying on suitable DNA collections. We recently performed a multiple sclerosis (MS) GWAS where a substantial component of the cases (24%) had DNA derived from saliva. Genotyping was done on the Illumina genotyping platform using the Infinium Hap370CNV DUO microarray. Additionally, we genotyped 10 individuals in duplicate using both saliva- and blood-derived DNA. The performance of blood- versus saliva-derived DNA was compared using genotyping call rate, which reflects both the quantity and quality of genotyping per sample and the “GCScore,” an Illumina genotyping quality score, which is a measure of DNA quality. We also compared genotype calls and GCScores for the 10 sample pairs. Call rates were assessed for each sample individually. For the GWAS samples, we compared data according to source of DNA and center of origin. We observed high concordance in genotyping quality and quantity between the paired samples and minimal loss of quality and quantity of DNA in the saliva samples in the large GWAS sample, with the blood samples showing greater variation between centers of origin. This large data set highlights the usefulness of saliva DNA for genotyping, especially in high-density single-nucleotide polymorphism microarray studies such as GWAS.

Determining epithelial contribution to in vivo mesenchymal tumour expression signature using species-specific microarray profiling analysis of xenografts

Gene expression profiling using microarrays and xenograft transplants of human cancer cell lines are both popular tools to investigate human cancer. However, the undefined degree of cross hybridization between the mouse and human genomes hinders the use of microarrays to characterize gene expression of both the host and the cancer cell within the xenograft. Since an increasingly recognized aspect of cancer is the host response (or cancer-stroma interaction), we describe here a bioinformatic manipulation of the Affymetrix profiling that allows interrogation of the gene expression of both the mouse host and the human tumour. Evidence of microenvironmental regulation of epithelial mesenchymal transition of the tumour component in vivo is resolved against a background of mesenchymal gene expression. This tool could allow deeper insight to the mechanism of action of anti-cancer drugs, as typically novel drug efficacy is being tested in xenograft systems.

Meta-analysis of microarray data identifies GAS6 expression as an independent predictor of poor survival in ovarian cancer

Seeking new biomarkers for epithelial ovarian cancer, the fifth most common cause of death from all cancers in women and the leading cause of death from gynaecological malignancies, we performed a meta-analysis of three independent studies and compared the results in regard to clinicopathological parameters. This analysis revealed that GAS6 was highly expressed in ovarian cancer and therefore was selected as our candidate of choice. GAS6 encodes a secreted protein involved in physiological processes including cell proliferation, chemotaxis, and cell survival. We performed immunohistochemistry on various ovarian cancer tissues and found that GAS6 expression was elevated in tumour tissue samples compared to healthy control samples (P < 0.0001). In addition, GAS6 expression was also higher in tumours from patients with residual disease compared to those without. Our data propose GAS6 as an independent predictor of poor survival, suggesting GAS6, both on the mRNA and on the protein level, as a potential biomarker for ovarian cancer. In clinical practice, the staining of a tumour biopsy for GAS6 may be useful to assess cancer prognosis and/or to monitor disease progression.

Optimisation algorithms for microarray biclustering

In providing simultaneous information on expression profiles for thousands of genes, microarray technologies have, in recent years, been largely used to investigate mechanisms of gene expression. Clustering and classification of such data can, indeed, highlight patterns and provide insight on biological processes. A common approach is to consider genes and samples of microarray datasets as nodes in a bipartite graphs, where edges are weighted e.g. based on the expression levels. In this paper, using a previously-evaluated weighting scheme, we focus on search algorithms and evaluate, in the context of biclustering, several variations of Genetic Algorithms. We also introduce a new heuristic “Propagate”, which consists in recursively evaluating neighbour solutions with one more or one less active conditions. The results obtained on three well-known datasets show that, for a given weighting scheme,optimal or near-optimal solutions can be identified.

High-resolution SNP microarray investigation of copy number variations on chromosome 18 in a control cohort

Copy number variations (CNVs) as described in the healthy population are purported to contribute significantly to genetic heterogeneity. Recent studies have described CNVs using lymphoblastoid cell lines or by application of specifically developed algorithms to interrogate previously described data. However, the full extent of CNVs remains unclear. Using high-density SNP array, we have undertaken a comprehensive investigation of chromosome 18 for CNV discovery and characterisation of distribution and association with chromosome architecture. We identified 399 CNVs, of which loss represents 98%, 58% are less than 2.5 kb in size and 71% are intergenic. Intronic deletions account for the majority of copy number changes with gene involvement. Furthermore, one-third of CNVs do not have putative breakpoints within repetitive sequences. We conclude that replicative processes, mediated either by repetitive elements or microhomology, account for the majority of CNVs in the healthy population. Genomic instability involving the formation of a non-B structure is demonstrated in one region.

The Gerbera cDNA Microarray: A Tool for Large-Scale Identification of Genes Involved in Flower Development

During the past ten years, large-scale transcript analysis using microarrays has become a powerful tool to identify and predict functions for new genes. It allows simultaneous monitoring of the expression of thousands of genes and has become a routinely used tool in laboratories worldwide. Microarray analysis will, together with other functional genomics tools, take us closer to understanding the functions of all genes in genomes of living organisms. Flower development is a genetically regulated process which has mostly been studied in the traditional model species Arabidopsis thaliana, Antirrhinum majus and Petunia hybrida. The molecular mechanisms behind flower development in them are partly applicable in other plant systems. However, not all biological phenomena can be approached with just a few model systems. In order to understand and apply the knowledge to ecologically and economically important plants, other species also need to be studied. Sequencing of 17 000 ESTs from nine different cDNA libraries of the ornamental plant Gerbera hybrida made it possible to construct a cDNA microarray with 9000 probes. The probes of the microarray represent all different ESTs in the database. From the gerbera ESTs 20% were unique to gerbera while 373 were specific to the Asteraceae family of flowering plants. Gerbera has composite inflorescences with three different types of flowers that vary from each other morphologically. The marginal ray flowers are large, often pigmented and female, while the central disc flowers are smaller and more radially symmetrical perfect flowers. Intermediate trans flowers are similar to ray flowers but smaller in size. This feature together with the molecular tools applied to gerbera, make gerbera a unique system in comparison to the common model plants with only a single kind of flowers in their inflorescence. In the first part of this thesis, conditions for gerbera microarray analysis were optimised including experimental design, sample preparation and hybridization, as well as data analysis and verification. Moreover, in the first study, the flower and flower organ-specific genes were identified. After the reliability and reproducibility of the method were confirmed, the microarrays were utilized to investigate transcriptional differences between ray and disc flowers. This study revealed novel information about the morphological development as well as the transcriptional regulation of early stages of development in various flower types of gerbera. The most interesting finding was differential expression of MADS-box genes, suggesting the existence of flower type-specific regulatory complexes in the specification of different types of flowers. The gerbera microarray was further used to profile changes in expression during petal development. Gerbera ray flower petals are large, which makes them an ideal model to study organogenesis. Six different stages were compared and specifically analysed. Expression profiles of genes related to cell structure and growth implied that during stage two, cells divide, a process which is marked by expression of histones, cyclins and tubulins. Stage 4 was found to be a transition stage between cell division and expansion and by stage 6 cells had stopped division and instead underwent expansion. Interestingly, at the last analysed stage, stage 9, when cells did not grow any more, the highest number of upregulated genes was detected. The gerbera microarray is a fully-functioning tool for large-scale studies of flower development and correlation with real-time RT-PCR results show that it is also highly sensitive and reliable. Gene expression data presented here will be a source for gene expression mining or marker gene discovery in the future studies that will be performed in the Gerbera Laboratory. The publicly available data will also serve the plant research community world-wide.

Characterization of genomic diversity in extraintestinal pathogenic Escherichia coli (ExPEC) and development of a diagnostic DNA microarray for the differentiation of ExPEC isolates causing urinary tract infections

Extraintestinal pathogenic Escherichia coli (ExPEC) represent a diverse group of strains of E. coli, which infect extraintestinal sites, such as the urinary tract, the bloodstream, the meninges, the peritoneal cavity, and the lungs. Urinary tract infections (UTIs) caused by uropathogenic E. coli (UPEC), the major subgroup of ExPEC, are among the most prevalent microbial diseases world wide and a substantial burden for public health care systems. UTIs are responsible for serious morbidity and mortality in the elderly, in young children, and in immune-compromised and hospitalized patients. ExPEC strains are different, both from genetic and clinical perspectives, from commensal E. coli strains belonging to the normal intestinal flora and from intestinal pathogenic E. coli strains causing diarrhea. ExPEC strains are characterized by a broad range of alternate virulence factors, such as adhesins, toxins, and iron accumulation systems. Unlike diarrheagenic E. coli, whose distinctive virulence determinants evoke characteristic diarrheagenic symptoms and signs, ExPEC strains are exceedingly heterogeneous and are known to possess no specific virulence factors or a set of factors, which are obligatory for the infection of a certain extraintestinal site (e. g. the urinary tract). The ExPEC genomes are highly diverse mosaic structures in permanent flux. These strains have obtained a significant amount of DNA (predictably up to 25% of the genomes) through acquisition of foreign DNA from diverse related or non-related donor species by lateral transfer of mobile genetic elements, including pathogenicity islands (PAIs), plasmids, phages, transposons, and insertion elements. The ability of ExPEC strains to cause disease is mainly derived from this horizontally acquired gene pool; the extragenous DNA facilitates rapid adaptation of the pathogen to changing conditions and hence the extent of the spectrum of sites that can be infected. However, neither the amount of unique DNA in different ExPEC strains (or UPEC strains) nor the mechanisms lying behind the observed genomic mobility are known. Due to this extreme heterogeneity of the UPEC and ExPEC populations in general, the routine surveillance of ExPEC is exceedingly difficult. In this project, we presented a novel virulence gene algorithm (VGA) for the estimation of the extraintestinal virulence potential (VP, pathogenicity risk) of clinically relevant ExPECs and fecal E. coli isolates. The VGA was based on a DNA microarray specific for the ExPEC phenotype (ExPEC pathoarray). This array contained 77 DNA probes homologous with known (e.g. adhesion factors, iron accumulation systems, and toxins) and putative (e.g. genes predictably involved in adhesion, iron uptake, or in metabolic functions) ExPEC virulence determinants. In total, 25 of DNA probes homologous with known virulence factors and 36 of DNA probes representing putative extraintestinal virulence determinants were found at significantly higher frequency in virulent ExPEC isolates than in commensal E. coli strains. We showed that the ExPEC pathoarray and the VGA could be readily used for the differentiation of highly virulent ExPECs both from less virulent ExPEC clones and from commensal E. coli strains as well. Implementing the VGA in a group of unknown ExPECs (n=53) and fecal E. coli isolates (n=37), 83% of strains were correctly identified as extraintestinal virulent or commensal E. coli. Conversely, 15% of clinical ExPECs and 19% of fecal E. coli strains failed to raster into their respective pathogenic and non-pathogenic groups. Clinical data and virulence gene profiles of these strains warranted the estimated VPs; UPEC strains with atypically low risk-ratios were largely isolated from patients with certain medical history, including diabetes mellitus or catheterization, or from elderly patients. In addition, fecal E. coli strains with VPs characteristic for ExPEC were shown to represent the diagnostically important fraction of resident strains of the gut flora with a high potential of causing extraintestinal infections. Interestingly, a large fraction of DNA probes associated with the ExPEC phenotype corresponded to novel DNA sequences without any known function in UTIs and thus represented new genetic markers for the extraintestinal virulence. These DNA probes included unknown DNA sequences originating from the genomic subtractions of four clinical ExPEC isolates as well as from five novel cosmid sequences identified in the UPEC strains HE300 and JS299. The characterized cosmid sequences (pJS332, pJS448, pJS666, pJS700, and pJS706) revealed complex modular DNA structures with known and unknown DNA fragments arranged in a puzzle-like manner and integrated into the common E. coli genomic backbone. Furthermore, cosmid pJS332 of the UPEC strain HE300, which carried a chromosomal virulence gene cluster (iroBCDEN) encoding the salmochelin siderophore system, was shown to be part of a transmissible plasmid of Salmonella enterica. Taken together, the results of this project pointed towards the assumptions that first, (i) homologous recombination, even within coding genes, contributes to the observed mosaicism of ExPEC genomes and secondly, (ii) besides en block transfer of large DNA regions (e.g. chromosomal PAIs) also rearrangements of small DNA modules provide a means of genomic plasticity. The data presented in this project supplemented previous whole genome sequencing projects of E. coli and indicated that each E. coli genome displays a unique assemblage of individual mosaic structures, which enable these strains to successfully colonize and infect different anatomical sites.