Gene selection by cooperative competition clustering

Clustering analysis of data from DNA microarray hybridization studies is an essential task for identifying biologically relevant groups of genes. Attribute cluster algorithm (ACA) has provided an attractive way to group and select meaningful genes. However, ACA needs much prior knowledge about the genes to set the number of clusters. In practical applications, if the number of clusters is misspecified, the performance of the ACA will deteriorate rapidly. In fact, it is a very demanding to do that because of our little knowledge. We propose the Cooperative Competition Cluster Algorithm (CCCA) in this paper. In the algorithm, we assume that both cooperation and competition exist simultaneously between clusters in the process of clustering. By using this principle of Cooperative Competition, the number of clusters can be found in the process of clustering. Experimental results on a synthetic and gene expression data are demonstrated. The results show that CCCA can choose the number of clusters automatically and get excellent performance with respect to other competing methods.

A novel forward gene selection algorithm for microarray data

This paper investigates the gene selection problem for microarray data with small samples and variant correlation. Most existing algorithms usually require expensive computational effort, especially under thousands of gene conditions. The main objective of this paper is to effectively select the most informative genes from microarray data, while making the computational expenses affordable. This is achieved by proposing a novel forward gene selection algorithm (FGSA). To overcome the small samples' problem, the augmented data technique is firstly employed to produce an augmented data set. Taking inspiration from other gene selection methods, the L2-norm penalty is then introduced into the recently proposed fast regression algorithm to achieve the group selection ability. Finally, by defining a proper regression context, the proposed method can be fast implemented in the software, which significantly reduces computational burden. Both computational complexity analysis and simulation results confirm the effectiveness of the proposed algorithm in comparison with other approaches

Reference gene selection in human periodontal ligament cells

Background: Real-time quantitative PCR (qPCR) is a highly sensitive and specific method which is used extensively for determining gene expression profiles in a variety of cell and tissue types. In order to obtain accurate and reliable gene expression quantification, qPCR data are generally normalised against so-called reference or housekeeping genes. Ideally, reference genes should have abundant and stable RNA transcriptomes under the experimental conditions employed. However, reference genes are often selected rather arbitrarily and indeed some have been shown to have variable expression in a variety of in vitro experimental conditions.
Objective: The objective of the current study was to investigate reference gene expression in human periodontal ligament (PDL) cells in response to treatment with lipopolysaccharide (LPS).
Method: Primary human PDL cells were grown in Dulbecco’s Modified Eagle Medium with L-glutamine supplemented with 10% fetal bovine serum, 100UI/ml penicillin and 100µg/ml streptomycin. RNA was isolated using the RNeasy Mini Kit (Qiagen) and reverse transcribed using the QuantiTect Reverse Transcription Kit (Qiagen). The expression of a total of 19 reference genes was studied in the presence and absence of LPS treatment using the Roche Reference Gene Panel. Data were analysed using NormFinder and Bestkeeper validation programs.
Results: Treatment of human PDL cells with LPS resulted in changes in expression of several commonly used reference genes, including GAPDH. On the other hand the reference genes β-actin, G6PDH and 18S were identified as stable genes following LPS treatment.
Conclusion: Many of the reference genes studied were robust to LPS treatment (up to 100 ng/ml). However several commonly employed reference genes, including GAPDH varied with LPS treatment, suggesting they would not be ideal candidates for normalisation in qPCR gene expression studies.

Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis

Integrating evidence from multiple domains is useful in prioritizing disease candidate genes for subsequent testing. We ranked all known human genes (n = 3819) under linkage peaks in the Irish Study of High-Density Schizophrenia Families using three different evidence domains: 1) a meta-analysis of microarray gene expression results using the Stanley Brain collection, 2) a schizophrenia protein-protein interaction network, and 3) a systematic literature search. Each gene was assigned a domain-specific p-value and ranked after evaluating the evidence within each domain. For comparison to this
ranking process, a large-scale candidate gene hypothesis was also tested by including genes with Gene Ontology terms related to neurodevelopment. Subsequently, genotypes of 3725 SNPs in 167 genes from a custom Illumina iSelect array were used to evaluate the top ranked vs. hypothesis selected genes. Seventy-three genes were both highly ranked and involved in neurodevelopment (category 1) while 42 and 52 genes were exclusive to neurodevelopment (category 2) or highly ranked (category 3), respectively. The most significant associations were observed in genes PRKG1, PRKCE, and CNTN4 but no individual SNPs were significant after correction for multiple testing. Comparison of the approaches showed an excess of significant tests using the hypothesis-driven neurodevelopment category. Random selection of similar sized genes from two independent genome-wide association studies (GWAS) of schizophrenia showed the excess was unlikely by chance. In a further meta-analysis of three GWAS datasets, four candidate SNPs reached nominal significance. Although gene ranking using integrated sources of prior information did not enrich for significant results in the current experiment, gene selection using an a priori hypothesis (neurodevelopment) was superior to random selection. As such, further development of gene ranking strategies using more carefully selected sources of information is warranted.

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.

Environmental selection on transcriptome-derived SNPs in a high gene flow marine fish, the Atlantic herring (Clupea harengus)

High gene flow is considered the norm for most marine organisms and is expected to limit their ability to adapt to local environments. Few studies have directly compared the patterns of differentiation at neutral and selected gene loci in marine organisms. We analysed a transcriptome-derived panel of 281 SNPs in Atlantic herring (Clupea harengus), a highly migratory small pelagic fish, for elucidating neutral and selected genetic variation among populations and to identify candidate genes for environmental adaptation. We analysed 607 individuals from 18 spawning locations in the northeast Atlantic, including two temperature clines (5-12 °C) and two salinity clines (5-35‰). By combining genome scan and landscape genetic analyses, four genetically distinct groups of herring were identified: Baltic Sea, Baltic-North Sea transition area, North Sea/British Isles and North Atlantic; notably, samples exhibited divergent clustering patterns for neutral and selected loci. We found statistically strong evidence for divergent selection at 16 outlier loci on a global scale, and significant correlations with temperature and salinity at nine loci. On regional scales, we identified two outlier loci with parallel patterns across temperature clines and five loci associated with temperature in the North Sea/North Atlantic. Likewise, we found seven replicated outliers, of which five were significantly associated with low salinity across both salinity clines. Our results reveal a complex pattern of varying spatial genetic variation among outlier loci, likely reflecting adaptations to local environments. In addition to disclosing the fine scale of local adaptation in a highly vagile species, our data emphasize the need to preserve functionally important biodiversity.

Short interfering RNA-mediated gene silencing in Globodera pallida and Meloidogyne incognita infective stage juveniles

The analysis of gene function through RNA interference (RNAi)-based reverse genetics in plant parasitic nematodes (PPNs) remains inexplicably reliant on the use of long double-stranded RNA (dsRNA) silencing triggers; a practice inherently disadvantageous due to the introduction of superfluous dsRNA sequence. increasing chances of aberrant or off-target gene silencing through interactions between nascent short interfering RNAs (siRNAs) and non-cognate mRNA targets. Recently, we have shown that non-nematode, long dsRNAs have a propensity to elicit profound impacts on the phenotype and migrational abilities of both root knot and cyst nematodes. This study presents, to our knowledge for the first time, gene-specific knockdown of FMRFamide-like peptide (flp) transcripts, using discrete 21 bp siRNAs in potato cyst nematode Globodera pallida, and root knot nematode Meloidogyne incognita infective (J2) stage juveniles. Both knockdown at the transcript level through quantitative (q)PCR analysis and functional data derived from migration assay, indicate that siRNAs targeting certain areas of the FMRFamide-like peptide (FLP) transcripts are potent and specific in the silencing of gene function. In addition, we present a method of manipulating siRNA activity through the management of strand thermodynamics. Initial evaluation of strand thermodynamics as a determinant of RNA-induced Silencing Complex (RISC) strand selection (inferred from knockdown efficacy) in the siRNAs presented here suggested that the purported influence of 5' stand stability on guide incorporation may be somewhat promiscuous. However, we have found that on strategically incorporating base mismatches in the sense strand of a G. pallida-specific siRNA we could specifically increase or decrease the knockdown of its target (specific to the antisense strand), presumably through creating more favourable thermodynamic profiles for incorporation of either the sense (non-target-specific) or antisense (target-specific) strand into a cleavage-competent RISC. Whilst the efficacy of similar approaches to siRNA modification has been demonstrated in the context of Drosophila whole-cell lysate preparations and in mammalian cell cultures, it remained to be seen how these sense strand mismatches may impact on gene silencing in vivo, in relation to different targets and in different sequence contexts. This work presents the first application of such an approach in a whole organism; initial results show promise. (C) 2009 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

An expression vector containing a rhamnose-inducible promoter provides tightly regulated gene expression in Burkholderia cenocepacia

Infection of the respiratory tract caused by Burkholderia cepacia complex poses a serious risk for cystic fibrosis (CF) patients due to the high morbidity and mortality associated with the chronic infection and the lack of efficacious antimicrobial treatments. A detailed understanding of the pathogenicity of B. cepacia complex infections is hampered in part by the limited availability of genetic tools and the inherent resistance of these isolates to the most common antibiotics used for genetic selection. In this study, we report the construction of an expression vector which uses the rhamnose-regulated P(rhaB) promoter of Escherichia coli. The functionality of the vector was assessed by expressing the enhanced green fluorescent protein (eGFP) gene (e-gfp) and determining the levels of fluorescence emission. These experiments demonstrated that P(rhaB) is responsive to low concentrations of rhamnose and it can be effectively repressed with 0.2% glucose. We also demonstrate that the tight regulation of gene expression by P(rhaB) promoter allows us to extend the capabilities of this vector to the identification of essential genes.

Construction and evaluation of plasmid vectors optimized for constitutive and regulated gene expression in Burkholderia cepacia complex isolates

Genetic studies with Burkholderia cepacia complex isolates are hampered by the limited availability of cloning vectors and by the inherent resistance of these isolates to the most common antibiotics used for genetic selection. Also, some of the promoters widely employed for gene expression in Escherichia coli are inefficient in B. cepacia. In this study, we have utilized the backbone of the vector pME6000, a derivative of the pBBR1 plasmid that was originally isolated from Bordetella bronchiseptica, to construct a set of vectors useful for gene expression in B. cepacia. These vectors contain either the constitutive promoter of the S7 ribosomal protein gene from Burkholderia sp. strain LB400 or the arabinose-inducible P(BAD) promoter from E. coli. Promoter sequences were placed immediately upstream of multiple cloning sites in combination with the minimal sequence of pME6000 required for plasmid maintenance and mobilization. The functionality of both vectors was assessed by cloning the enhanced green fluorescent protein gene (e-gfp) and determining the levels of enhanced green fluorescent protein expression and fluorescence emission for a variety of clinical and environmental isolates of the B. cepacia complex. We also demonstrate that B. cepacia carrying these constructs can readily be detected intracellularly by fluorescence microscopy following the infection of Acanthamoeba polyphaga.

Species introduction promotes hybridization and introgression in Coregonus: is there sign of selection against hybrids?

Species introductions are considered one of the major drivers of biodiversity loss via ecological interactions and genetic admixture with local fauna. We examined two well-recognized fish species, native whitefish (Coregonus lavaretus) and introduced vendace (Coregonus albula), as well as their morphological hybrids in a single lake to test for selection against hybrids and backcrosses in the wild. A representative random subsample of 693 individuals (27.8%) was taken from the total catch of coregonids. This subsample was examined with the aim to select c. 50 individuals of pure whitefish (n = 52), pure vendace (n = 55) and putative hybrid (n = 19) for genetic analyses. The subsequent microsatellites and mitochondrial (mt) DNA analyses provided compelling evidence of hybridization and introgression. Of the 126 fish examined, four were found to be F-1, 14 backcrosses to whitefish and seven backcrosses to vendace. The estimates of historical gene flow suggested higher rates from introduced vendace into native whitefish than vice versa, whereas estimates of contemporary gene flow were equal. Mitochondrial introgression was skewed, with 18 backcrosses having vendace mtDNA and only three with whitefish mtDNA. Hybrids and backcrosses had intermediate morphology and niche utilization compared with parental species. No evidence of selection against hybrids or backcrosses was apparent, as both hybrid and backcross growth rates and fecundities were high. Hybrids (F-1) were only detected in 2 year-classes, suggesting temporal variability in mating between vendace and whitefish. However, our data show that hybrids reached sexual maturity and reproduced actively, with backcrosses recorded from six consecutive year-classes, whereas no F-2 individuals were found. The results indicate widespread introgression, as 10.8% of coregonids were estimated to be backcrosses.

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

The Fasciola hepatica genome: gene duplication and polymorphism reveals adaptation to the host environment and the capacity for rapid evolution

BACKGROUND: The liver fluke Fasciola hepatica is a major pathogen of livestock worldwide, causing huge economic losses to agriculture, as well as 2.4 million human infections annually.

RESULTS: Here we provide a draft genome for F. hepatica, which we find to be among the largest known pathogen genomes at 1.3 Gb. This size cannot be explained by genome duplication or expansion of a single repeat element, and remains a paradox given the burden it may impose on egg production necessary to transmit infection. Despite the potential for inbreeding by facultative self-fertilisation, substantial levels of polymorphism were found, which highlights the evolutionary potential for rapid adaptation to changes in host availability, climate change or to drug or vaccine interventions. Non-synonymous polymorphisms were elevated in genes shared with parasitic taxa, which may be particularly relevant for the ability of the parasite to adapt to a broad range of definitive mammalian and intermediate molluscan hosts. Large-scale transcriptional changes, particularly within expanded protease and tubulin families, were found as the parasite migrated from the gut, across the peritoneum and through the liver to mature in the bile ducts. We identify novel members of anti-oxidant and detoxification pathways and defined their differential expression through infection, which may explain the stage-specific efficacy of different anthelmintic drugs.

CONCLUSIONS: The genome analysis described here provides new insights into the evolution of this important pathogen, its adaptation to the host environment and external selection pressures. This analysis also provides a platform for research into novel drugs and vaccines.