Adaptive Copy Number Evolution in Malaria Parasites

Copy number polymorphism (CNP) is ubiquitous in eukaryotic genomes, but the degree to which this reflects the action of positive selection is poorly understood. The first gene in the Plasmodium folate biosynthesis pathway, GTP-cyclohydrolase I (gch1), shows extensive CNP. We provide compelling evidence that gch1 CNP is an adaptive consequence of selection by antifolate drugs, which target enzymes downstream in this pathway. (1) We compared gch1 CNP in parasites from Thailand (strong historical antifolate selection) with those from neighboring Laos (weak antifolate selection). Two percent of chromosomes had amplified copy number in Laos, while 72% carried multiple (2–11) copies in Thailand, and differentiation exceeded that observed at 73 synonymous SNPs. (2) We found five amplicon types containing one to greater than six genes and spanning 1 to >11 kb, consistent with parallel evolution and strong selection for this gene amplification. gch1 was the only gene occurring in all amplicons suggesting that this locus is the target of selection. (3) We observed reduced microsatellite variation and increased linkage disequilibrium (LD) in a 900-kb region flanking gch1 in parasites from Thailand, consistent with rapid recent spread of chromosomes carrying multiple copies of gch1. (4) We found that parasites bearing dhfr-164L, which causes high-level resistance to antifolate drugs, carry significantly (p = 0.00003) higher copy numbers of gch1 than parasites bearing 164I, indicating functional association between genes located on different chromosomes but linked in the same biochemical pathway. These results demonstrate that CNP at gch1 is adaptive and the associations with dhfr-164L strongly suggest a compensatory function. More generally, these data demonstrate how selection affects multiple enzymes in a single biochemical pathway, and suggest that investigation of structural variation may provide a fast-track to locating genes underlying adaptation

Can Survival Prediction Be Improved By Merging Gene Expression Data Sets?

BACKGROUND:High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS:Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS:Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression

Molecular mechanism for 3:1 subunit stoichiometry of rod cyclic nucleotide-gated ion channels

Molecular determinants of ion channel tetramerization are well characterized, but those involved in heteromeric channel assembly are less clearly understood. The heteromeric composition of native channels is often precisely controlled. Cyclic nucleotide-gated (CNG) channels from rod photoreceptors exhibit a 3:1 stoichiometry of CNGA1 and CNGB1 subunits that tunes the channels for their specialized role in phototransduction. Here we show, using electrophysiology, fluorescence, biochemistry, and X-ray crystallography, that the mechanism for this controlled assembly is the formation of a parallel 3-helix coiled-coil domain of the carboxy-terminal leucine zipper region of CNGA1 subunits, constraining the channel to contain three CNGA1 subunits, followed by preferential incorporation of a single CNGB1 subunit. Deletion of the carboxy-terminal leucine zipper domain relaxed the constraint and permitted multiple CNGB1 subunits in the channel. The X-ray crystal structures of the parallel 3-helix coiled-coil domains of CNGA1 and CNGA3 subunits were similar, suggesting that a similar mechanism controls the stoichiometry of cone CNG channels

Measuring social capital: The development of the social capital and cohesion scale and the associations between social capital and mental health

Over the last two decades, social capital has received increasing attention in the international literature. Despite the popularity of the construct, problems concerning definition, theoretical conceptualisation, and measurement continue to plague research and policy in this area. This investigation aimed to address this gap by developing a new social capital instrument to test the theorised nature of the construct. Utilising a sample of 1371 young Australians living in disadvantaged communities, the newly developed Social Capital and Cohesion Scale (SCCS) combined the commonalities in the current theoretical conceptualisations of social capital defining it as a multi-level, multidimensional construct consisting of trust and reciprocity across family, peer, neighbour, and institutional networks. To test the convergent validity of the scale, relations with mental health were also examined. Confirmatory factor analysis results demonstrated that the SCCS was a valid and reliable multidimensional scale, which was invariant across both regional and gender groups. Correlational analysis demonstrated that associations with depression, anxiety, and stress were consistent with past research thereby strengthening the validity of the SCCS measure

Questioning new directions in understanding student motivation: An investigation into the domain specificity of motivational goals

Most past models of student motivation have assumed that student motivation generalises across various achievement situations and curriculum domains; however, research has not fully explored the extent to which motivation may be domain-specific (Green, Martin, & Marsh, 2007; Martin, 2008). The purpose of the present investigation was to explore this issue by comparing and contrasting generalised models of motivation with domain-specific models and how they relate to achievement outcomes in mathematics and English. Secondary students (N = 476) completed both the domain-general (ISM, McInerney, 2003) and the researcher-derived domain-specific motivational measure (DSSM) followed by a standardised achievement test (WRAT-3, Wilkinson, 1993). Overall, the study resulted in mixed findings. There was some indication that there was enough independent variance between the domain-specific goal types to suggest they were tapping distinct constructs as found in previous research (Green et al., 2007). However, the small and often inconsistent correlations with achievement outcomes brings into question the usefulness for educators and the research practicality of pursuing such a division. © 2013 Australian Psychological Society Ltd

Uncovering the structure of and gender and developmental differences in cyber bullying

Although literature on traditional bullying is abundant, a limited body of sound empirical research exists regarding its newest form: cyber bullying. The sample comprised Australian secondary students (N = 803) and aimed to identify the underlying structure of cyber bullying, and differences in traditional and cyber bullying behaviors across gender and grade. Reliability analyses, confirmatory factor analyses, and factorial invariance testing demonstrated that the newly extended measure of traditional and cyber bullying was psychometrically sound. Multiple-Indicators-Multiple-Causes models demonstrated gender, grade, and gender by grade interaction effects for traditional and cyber forms of bullying and being bullied. Findings were interpreted in the context of bullying theory. Moreover, potential limitations of the investigation and implications for theory, research, and practice were discussed. © 2012 Taylor & Francis Group, LLC

GCV for Tikhonov regularization by partial SVD

Tikhonov regularization is commonly used for the solution of linear discrete ill-posed problems with error-contaminated data. A regularization parameter that determines the quality of the computed solution has to be chosen. One of the most popular approaches to choosing this parameter is to minimize the Generalized Cross Validation (GCV) function. The minimum can be determined quite inexpensively when the matrix A that defines the linear discrete ill-posed problem is small enough to rapidly compute its singular value decomposition (SVD). We are interested in the solution of linear discrete ill-posed problems with a matrix A that is too large to make the computation of its complete SVD feasible, and show how upper and lower bounds for the numerator and denominator of the GCV function can be determined fairly inexpensively for large matrices A by computing only a few of the largest singular values and associated singular vectors of A. These bounds are used to determine a suitable value of the regularization parameter. Computed examples illustrate the performance of the proposed method