SLAC1 is required for plant guard cell S-type anion channel function in stomatal signalling

Stomatal pores, formed by two surrounding guard cells in the epidermis of plant leaves, allow influx of atmospheric carbon dioxide in exchange for transpirational water loss. Stomata also restrict the entry of ozone--an important air pollutant that has an increasingly negative impact on crop yields, and thus global carbon fixation and climate change. The aperture of stomatal pores is regulated by the transport of osmotically active ions and metabolites across guard cell membranes. Despite the vital role of guard cells in controlling plant water loss, ozone sensitivity and CO2 supply, the genes encoding some of the main regulators of stomatal movements remain unknown. It has been proposed that guard cell anion channels function as important regulators of stomatal closure and are essential in mediating stomatal responses to physiological and stress stimuli. However, the genes encoding membrane proteins that mediate guard cell anion efflux have not yet been identified. Here we report the mapping and characterization of an ozone-sensitive Arabidopsis thaliana mutant, slac1. We show that SLAC1 (SLOW ANION CHANNEL-ASSOCIATED 1) is preferentially expressed in guard cells and encodes a distant homologue of fungal and bacterial dicarboxylate/malic acid transport proteins. The plasma membrane protein SLAC1 is essential for stomatal closure in response to CO2, abscisic acid, ozone, light/dark transitions, humidity change, calcium ions, hydrogen peroxide and nitric oxide. Mutations in SLAC1 impair slow (S-type) anion channel currents that are activated by cytosolic Ca2+ and abscisic acid, but do not affect rapid (R-type) anion channel currents or Ca2+ channel function. A low homology of SLAC1 to bacterial and fungal organic acid transport proteins, and the permeability of S-type anion channels to malate suggest a vital role for SLAC1 in the function of S-type anion channels.     

Genome-wide association study identifies multiple loci influencing human serum metabolite levels

Nuclear magnetic resonance assays allow for measurement of a wide range of metabolic phenotypes. We report here the results of a GWAS on 8,330 Finnish individuals genotyped and imputed at 7.7 million SNPs for a range of 216 serum metabolic phenotypes assessed by NMR of serum samples. We identified significant associations (P < 2.31 × 10(-10)) at 31 loci, including 11 for which there have not been previous reports of associations to a metabolic trait or disorder. Analyses of Finnish twin pairs suggested that the metabolic measures reported here show higher heritability than comparable conventional metabolic phenotypes. In accordance with our expectations, SNPs at the 31 loci associated with individual metabolites account for a greater proportion of the genetic component of trait variance (up to 40%) than is typically observed for conventional serum metabolic phenotypes. The identification of such associations may provide substantial insight into cardiometabolic disorders.     

Haplotype tagging for the identification of common disease genes

Genome-wide linkage disequilibrium (LD) mapping of common disease genes could be more powerful than linkage analysis if the appropriate density of polymorphic markers were known and if the genotyping effort and cost of producing such an LD map could be reduced. Although different metrics that measure the extent of LD have been evaluated, even the most recent studies have not placed significant emphasis on the most informative and cost-effective method of LD mapping-that based on haplotypes. We have scanned 135 kb of DNA from nine genes, genotyped 122 single-nucleotide polymorphisms (SNPs; approximately 184,000 genotypes) and determined the common haplotypes in a minimum of 384 European individuals for each gene. Here we show how knowledge of the common haplotypes and the SNPs that tag them can be used to (i) explain the often complex patterns of LD between adjacent markers, (ii) reduce genotyping significantly (in this case from 122 to 34 SNPs), (iii) scan the common variation of a gene sensitively and comprehensively and (iv) provide key fine-mapping data within regions of strong LD. Our results also indicate that, at least for the genes studied here, the current version of dbSNP would have been of limited utility for LD mapping because many common haplotypes could not be defined. A directed re-sequencing effort of the approximately 10% of the genome in or near genes in the major ethnic groups would aid the systematic evaluation of the common variant model of common disease.     

The Effect of Worker Learning on Scheduling Jobs in a Hybrid Flow Shop: A Bi-Objective Approach

This paper studies learning effect as a resource utilization technique that can model improvement in worker’s ability as a result of repeating similar tasks. By considering learning of workers while performing setup times, a schedule can be determined to place jobs that share similar tools and fixtures next to each other. The purpose of this paper is to schedule a set of jobs in a hybrid flow shop (HFS) environment with learning effect while minimizing two objectives that are in conflict: namely maximum completion time (makespan) and total tardiness. Minimizing makespan is desirable from an internal efficiency viewpoint, but may result in individual jobs being scheduled past their due date, causing customer dissatisfaction and penalty costs. A bi-objective mixed integer programming model is developed, and the complexity of the developed bi-objective model is compared against the bi-criteria one through numerical examples. The effect of worker learning on the structure of assigned jobs to machines and their sequences is analyzed. Two solution methods based on the hybrid water flow like algorithm and non-dominated sorting and ranking concepts are proposed to solve the problem. The quality of the approximated sets of Pareto solutions is evaluated using several performance criteria. The results show that the proposed algorithms with learning effect perform well in reducing setup times and eliminate the need for setups itself through proper scheduling.     

Unification and mechanistic detail as drivers of model construction: Models of networks in economics and sociology

We examine the diversity of strategies of modelling networks in (micro) economics and (analytical) sociology. Field-specific conceptions of what explaining (with) networks amounts to or systematic preference for certain kinds of explanatory factors are not sufficient to account for differences in modelling methodologies. We argue that network models in both sociology and economics are abstract models of network mechanisms and that differences in their modelling strategies derive to a large extent from field-specific conceptions of the way in which a good model should be a general one. Whereas the economics models aim at unification, the sociological models aim at a set of mechanism schemas that are extrapolatable to the extent that the underlying psychological mechanisms are general. These conceptions of generality induce specific biases in mechanistic explanation and are related to different views of when knowledge from different fields should be seen as relevant.     

Mutations in mRNA export mediator GLE1 result in a fetal motoneuron disease

The most severe forms of motoneuron disease manifest in utero are characterized by marked atrophy of spinal cord motoneurons and fetal immobility. Here, we report that the defective gene underlying lethal motoneuron syndrome LCCS1 is the mRNA export mediator GLE1. Our finding of mutated GLE1 exposes a common pathway connecting the genes implicated in LCCS1, LCCS2 and LCCS3 and elucidates mRNA processing as a critical molecular mechanism in motoneuron development and maturation.     

Parameters for reliable results in genetic association studies in common disease.

It is increasingly apparent that the identification of true genetic associations in common multifactorial disease will require studies comprising thousands rather than the hundreds of individuals employed to date. Using 2,873 families, we were unable to confirm a recently published association of the interleukin 12B gene in 422 type I diabetic families. These results emphasize the need for large datasets, small P values and independent replication if results are to be reliable.     

The genetically isolated populations of Finland and sardinia may not be a panacea for linkage disequilibrium mapping of common disease genes

The choice of which population to study in the mapping of common disease genes may be critical. Isolated founder populations, such as that found in Finland, have already proved extremely useful for mapping the genes for specific rare monogenic disorders and are being used in attempts to map the genes underlying common, complex diseases. But simulation results suggest that, under the common disease-common variant hypothesis, most isolated populations will prove no more useful for linkage disequilibrium (LD) mapping of common disease genes than large outbred populations. There is very little empirical data to either support or refute this conclusion at present. Therefore, we evaluated LD between 21 common microsatellite polymorphisms on chromosome 18q21 in 2 genetic isolates (Finland and Sardinia) and compared the results with those observed in two mixed populations (United Kingdom and United States of America). Mean levels of LD were similar across all four populations. Our results provide empirical support for the expectation that genetic isolates like Finland and Sardinia will not prove significantly more valuable than general populations for LD mapping of common variants underlying complex disease.