Microarray analysis of autoimmune diseases by machine learning procedures

—Microarray-based global gene expression profiling, with the use of sophisticated statistical algorithms is providing new insights into the pathogenesis of autoimmune diseases. We have applied a novel statistical technique for gene selection based on machine learning approaches to analyze microarray expression data gathered from patients with systemic lupus erythematosus (SLE) and primary antiphospholipid syndrome (PAPS), two autoimmune diseases of unknown genetic origin that share many common features. The methodology included a combination of three data discretization policies, a consensus gene selection method, and a multivariate correlation measurement. A set of 150 genes was found to discriminate SLE and PAPS patients from healthy individuals. Statistical validations demonstrate the relevance of this gene set from an univariate and multivariate perspective. Moreover, functional characterization of these genes identified an interferon-regulated gene signature, consistent with previous reports. It also revealed the existence of other regulatory pathways, including those regulated by PTEN, TNF, and BCL-2, which are altered in SLE and PAPS. Remarkably, a significant number of these genes carry E2F binding motifs in their promoters, projecting a role for E2F in the regulation of autoimmunity.

Genetic and economic analysis of a targeted marker-assisted wheat breeding strategy

The advent of molecular markers as a tool to aid selection has provided plant breeders with the opportunity to rapidly deliver superior genetic solutions to problems in agricultural production systems. However, a major constraint to the implementation of marker-assisted selection (MAS) in pragmatic breeding programs in the past has been the perceived high relative cost of MAS compared to conventional phenotypic selection. In this paper, computer simulation was used to design a genetically effective and economically efficient marker-assisted breeding strategy aimed at a specific outcome. Under investigation was a strategy involving the integration of both restricted backcrossing and doubled haploid (DH) technology. The point at which molecular markers are applied in a selection strategy can be critical to the effectiveness and cost efficiency of that strategy. The application of molecular markers was considered at three phases in the strategy: allele enrichment in the BC1F1 population, gene selection at the haploid stage and the selection for recurrent parent background of DHs prior to field testing. Overall, incorporating MAS at all three stages was the most effective, in terms of delivering a high frequency of desired outcomes and at combining the selected favourable rust resistance, end use quality and grain yield alleles. However, when costs were included in the model the combination of MAS at the BC1F1 and haploid stage was identified as the optimal strategy. A detailed economic analysis showed that incorporation of marker selection at these two stages not only increased genetic gain over the phenotypic alternative but actually reduced the over all cost by 40%.

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

The Interplay between Natural Selection and Susceptibility to Melanoma on Allele 374F of SLC45A2 Gene in a South European Population

We aimed to study the selective pressures interacting on SLC45A2 to investigate the interplay between selection and susceptibility to disease. Thus, we enrolled 500 volunteers from a geographically limited population (Basques from the North of Spain) and by resequencing the whole coding region and intron 5 of the 34 most and the 34 least pigmented individuals according to the reflectance distribution, we observed that the polymorphism Leu374Phe (L374F, rs16891982) was statistically associated with skin color variability within this sample. In particular, allele 374F was significantly more frequent among the individuals with lighter skin. Further genotyping an independent set of 558 individuals of a geographically wider population with known ancestry in the Spanish population also revealed that the frequency of L374F was significantly correlated with the incident UV radiation intensity. Selection tests suggest that allele 374F is being positively selected in South Europeans, thus indicating that depigmentation is an adaptive process. Interestingly, by genotyping 119 melanoma samples, we show that this variant is also associated with an increased susceptibility to melanoma in our populations. The ultimate driving force for this adaptation is unknown, but it is compatible with the vitamin D hypothesis. This shows that molecular evolution analysis can be used as a useful technology to predict phenotypic and biomedical consequences in humans.

Evidence of positive selection acting at the human dopamine receptor D4 gene locus

Associations have been reported of the seven-repeat (7R) allele of the human dopamine receptor D4 (DRD4) gene with both attention-deficit/hyperactivity disorder and the personality trait of novelty seeking. This polymorphism occurs in a 48-bp tandem repea

Evidence for Positive Selection on the Osteogenin (BMP3) Gene in Human Populations

Background: Human skeletal system has evolved rapidly since the dispersal of modern humans from Africa, potentially driven by selection and adaptation. Osteogenin (BMP3) plays an important role in skeletal development and bone osteogenesis as an antagonist of the osteogenic bone morphogenetic proteins, and negatively regulates bone mineral density. Methodology/Principal Findings: Here, we resequenced the BMP3 gene from individuals in four geographically separated modern human populations. Features supportive of positive selection in the BMP3 gene were found including the presence of an excess of nonsynonymous mutations in modern humans, and a significantly lower genetic diversity that deviates from neutrality. The prevalent haplotypes of the first exon region in Europeans demonstrated features of long-range haplotype homogeneity. In contrast with findings in European, the derived allele SNP Arg192Gln shows higher extended haplotype homozygosity in East Asian. The worldwide allele frequency distribution of SNP shows not only a high-derived allele frequency in Asians, but also in Americans, which is suggestive of functional adaptation. Conclusions/Significance: In conclusion, we provide evidence for recent positive selection operating upon a crucial gene in skeletal development, which may provide new insight into the evolution of the skeletal system and bone development.

Differential selection on gene translation efficiency between the filamentous fungus Ashbya gossypii and yeasts

Background: The filamentous fungus Ashbya gossypii grows into a multicellular mycelium that is distinct from the unicellular morphology of its closely related yeast species. It has been proposed that genes important for cell cycle regulation play central

Relaxed purifying selection of rhodopsin gene within a Chinese endemic cavefish genus Sinocyclocheilus (Pisces: Cypriniformes)

The cyprinid fish genus Sinocyclocheilus, as the most cavefish rich genus, includes many species showing striking adaptation to caves and convergent reduction or even loss of eyes and pigmentation. RH1 is responsible for dim vision. In order to explore the evolution of RH1 gene in this genus, we sequenced the complete gene from 28 individuals of 16 representative species of Sinocyclocheilus, with cave and surface species included. Phylogenetic analyses supported the monophyly of Sinocyclocheilus and polyphyly of the cave species. Codon models implemented in PAML were used to infer the evolution of RH1. We found that Sinocyclocheilus had a significantly higher evolutionary rate for amino acids than other cyprinid fishes compared, which might be the result of relaxation of purifying selection and could be ascribed to cave habit of this genus. In contrast to previous hypotheses, both cave and surface lineages exhibited a similar rate of molecular evolution, so the RH1 of cave species may still be functional, although these species were highly adapted to cave environment. Two amino acid substitutions (D83G and E122V) that were not reported before were found, which may be useful for site-directed mutagenesis in the future.

Retention of the developmental pluripotency in medaka embryonic stem cells after gene transfer and long-term drug selection for gene targeting in fish

Embryonic stem (ES) cells provide a unique tool for introducing random or targeted genetic alterations, because it is possible that the desired, but extremely rare recombinant genotypes can be screened by drug selection. ES cell-mediated transgenesis has so far been limited to the mouse. In the fish medaka (Oryzias latipes) several ES cell lines have been made available. Here we report the optimized conditions for gene transfer and drug selection in the medaka ES cell line MES1 as a prelude for gene targeting in fish. MES1 cells gave rise to a moderate to high transfection efficiency by the calcium phosphate co-precipitation (5%), commercial reagents Fugene (11%), GeneJuice (21%) and electroporation (>30%). Transient gene transfer and CAT reporter assay revealed that several enhancers/promoters and their combinations including CMV, RSV and ST (the SV40 virus early gene enhancer linked to the thymidine kinase promoter) were suitable regulatory sequences to drive transgene expression in the MES1 cells. We show that neo, hyg or pac conferred resistance to G418, hygromycin or puromycin for positive selection, while the HSV-tk generated sensitivity to ganciclovir for negative selection. The positive-negative selection procedure that is widely used for gene targeting in mouse ES cells was found to be effective also in MES1 cells. Importantly, we demonstrate that MES1 cells after gene transfer and long-term drug selection retained the developmental pluripotency, as they were able to undergo induced differentiation in vitro and to contribute to various tissues and organs during chimeric embryogenesis.

Development of a positive-negative selection procedure for gene targeting in fish cells

The gene targeting technique is a powerful tool for analyzing functions of cloned genes and for generating transgenic animals with site-directed integration of foreign genes. In order to develop this technique in fish, positive-negative selection (PNS) and homologous recombination vectors were constructed, and their expression was examined in fish cells. A vector (pNK) for PNS consists of the neomycin resistance gene (neo) as a positive selectable marker gene and the herpes simplex virus (HSV) thymidine kinase (tk) gene as a negative selectable marker gene. Positive selection with geneticin (G418) of epithelioma papulosum of carp (EPC) cells transfected with linearized pNK vector yielded 350 colonies, while double selection of transfected EPC cells with G418 and gancyclovir (Gc) resulted in nearly complete cell death, demonstrating that the PNS procedure is effective in fish cells. Homologous recombination vectors consist of the Xiphophorus melanoma receptor kinase (X mrk(Y)) gene as homologous sequence in addition to the neo and tk genes. Conditions for homologous recombination vector transfection and drug selection were established. After verification of the feasibility of expression of homologous recombination vectors in EPC cells, the first gene targeting experiments were attempted in the Xiphophorus melanoma cell line, PSM. Positive-negative selection of the targeting vector-transfectants led to a low enrichment in this particular cell line. The reasons for the low enrichment in PSM cells were discussed. (C) 2002 Elsevier Science B.V. All rights reserved.

Heat-shock protein70 gene expression in four hatchery Pacific Abalone Haliotis discus hannai Ino populations using for marker-assisted selection

Heat shock proteins (Hsps) are molecular chaperones that help organisms cope with stressful conditions. Here, we report on the growth rates and Hsp70 expressions in inbred and hybrid populations of abalone Haliotis discus hannai Ino. In abalone, inbred populations expressed more Hsp70 than hybrid populations at all temperatures, except at very high temperatures close to the physiological limit. At benign temperatures, there was a clear trend towards higher Hsp70 expression in inbred than hybrid populations, whereas at higher temperatures, a trend in the opposite direction was observed. The temperature of maximal Hsp70 expression (T-peak) varied with the population type. The T-peak of inbred populations (26 degrees C) was lower than that of the hybrid populations (28 degrees C). The maximal inducible Hsp70 of inbred populations was higher than that of hybrid populations. The results showed a trend towards higher expression in inbred population at a lower temperature. These results provide direct experimental evidence that hybrids can cope with the intrinsic stress even at non-stressful temperatures. The constitutive Hsp70 may therefore be used for marker-assisted selection in a breeding programme.

Aiding classification of gene expression data with feature selection: a comparative study

C. Shang and Q. Shen. Aiding classification of gene expression data with feature selection: a comparative study. Computational Intelligence Research, 1(1):68-76.

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.