Optimising ketocarotenoid production in potato tubers : effect of genetic background, transgene combinations and environment

Astaxanthin is a high value carotenoid produced by some bacteria, a few green algae, several fungi but only a limited number of plants from the genus Adonis. Astaxanthin has been industrially exploited as a feed supplement in poultry farming and aquaculture. Consumption of ketocarotenoids, most notably astaxanthin, is also increasingly associated with a wide range of health benefits,as demonstrated in numerous clinical studies. Currently astaxanthin is produced commercially by chemical synthesis or from algal production systems. Several studies have used a metabolic engineering approach to produce astaxanthin in transgenic plants. Previous attempts to produce transgenic potato tubers biofortified with astaxanthin have met with limited success. In this study we have investigated approaches to optimising tuber astaxanthin content. It is demonstrated that the selection of appropriate parental genotype for transgenic approaches and stacking carotenoid biosynthetic pathway genes with the cauliflower Or gene result in enhanced astaxanthin content, to give six-fold higher tuber astaxanthin content than has been achieved previously. Additionally we demonstrate the effects of growth environment on tuber carotenoid content in both wild type and astaxanthin-producing transgenic lines and describe the associated transcriptome and metabolome restructuring.

Efficiency of parallelisation of genetic algorithms in the data analysis context

Most real-life data analysis problems are difficult to solve using exact methods, due to the size of the datasets and the nature of the underlying mechanisms of the system under investigation. As datasets grow even larger, finding the balance between the quality of the approximation and the computing time of the heuristic becomes non-trivial. One solution is to consider parallel methods, and to use the increased computational power to perform a deeper exploration of the solution space in a similar time. It is, however, difficult to estimate a priori whether parallelisation will provide the expected improvement. In this paper we consider a well-known method, genetic algorithms, and evaluate on two distinct problem types the behaviour of the classic and parallel implementations.

Genetic characterisation and structural analysis of the O-specific polysaccharide of Yersinia pseudotuberculosis serotype O:1c

Many, but not all, of the current 21 serotypes of Yersinia pseudotuberculosis have been investigated with regard to the chemical structures of their O-specific polysaccharide (OPS) and the genetic basis of their biosynthesis. Completion of the genetics and structures of the remaining serotypes will enhance our understanding of the emerging relationship between genetics and structures within this species. Here, we present a structural and genetic analysis of the Y. pseudotuberculosis serotype O:1c OPS. Our results showed that this OPS has the same backbone as Y. pseudotuberculosis O:2b, but with a 3,6-dideoxy-D-ribo-hexofuranose (paratofuranose, Parf) side-branch instead of a 3,6-dideoxy-D-xylo-hexopyranose (abequopyranose, Abep). The 3'-end of the gene cluster is the same as for O:2b and has the genes for synthesis of the backbone and for processing the completed repeat unit. The 5'-end of the cluster consists of the same genes as O:1b for synthesis of Parf and a related gene for its transfer to the repeating unit backbone.

Genetic risk score analysis indicates migraine with and without comorbid depression are genetically different disorders

Migraine and major depressive disorder (MDD) are comorbid, moderately heritable and to some extent influenced by the same genes. In a previous paper, we suggested the possibility of causality (one trait causing the other) underlying this comorbidity. We present a new application of polygenic (genetic risk) score analysis to investigate the mechanisms underlying the genetic overlap of migraine and MDD. Genetic risk scores were constructed based on data from two discovery samples in which genome-wide association analyses (GWA) were performed for migraine and MDD, respectively. The Australian Twin Migraine GWA study (N = 6,350) included 2,825 migraine cases and 3,525 controls, 805 of whom met the diagnostic criteria for MDD. The RADIANT GWA study (N = 3,230) included 1,636 MDD cases and 1,594 controls. Genetic risk scores for migraine and for MDD were used to predict pure and comorbid forms of migraine and MDD in an independent Dutch target sample (NTR-NESDA, N = 2,966), which included 1,476 MDD cases and 1,058 migraine cases (723 of these individuals had both disorders concurrently). The observed patterns of prediction suggest that the 'pure' forms of migraine and MDD are genetically distinct disorders. The subgroup of individuals with comorbid MDD and migraine were genetically most similar to MDD patients. These results indicate that in at least a subset of migraine patients with MDD, migraine may be a symptom or consequence of MDD. © 2013 Springer-Verlag Berlin Heidelberg.

The future for genetic studies in reproduction

Genetic factors contribute to risk of many common diseases affecting reproduction and fertility. In recent years, methods for genome-wide association studies(GWAS) have revolutionized gene discovery forcommontraits and diseases. Results of GWAS are documented in the Catalog of Published Genome-Wide Association Studies at the National Human Genome Research Institute and report over 70 publications for 32 traits and diseases associated with reproduction. These include endometriosis, uterine fibroids, age at menarche and age at menopause. Results that pass appropriate stringent levels of significance are generally well replicated in independent studies. Examples of genetic variation affecting twinning rate, infertility, endometriosis and age at menarche demonstrate that the spectrum of disease-related variants for reproductive traits is similar to most other common diseases.GWAS 'hits' provide novel insights into biological pathways and the translational value of these studies lies in discovery of novel gene targets for biomarkers, drug development and greater understanding of environmental factors contributing to disease risk. Results also show that genetic data can help define sub-types of disease and co-morbidity with other traits and diseases. To date, many studies on reproductive traits have used relatively small samples. Future genetic marker studies in large samples with detailed phenotypic and clinical information will yield new insights into disease risk, disease classification and co-morbidity for many diseases associated with reproduction and infertility.

Genetic variants underlying risk of endometriosis: Insights from meta-analysis of eight genome-wide association and replication datasets

BACKGROUND Endometriosis is a heritable common gynaecological condition influenced by multiple genetic and environmental factors. Genome-wide association studies (GWASs) have proved successful in identifying common genetic variants of moderate effects for various complex diseases. To date, eight GWAS and replication studies from multiple populations have been published on endometriosis. In this review, we investigate the consistency and heterogeneity of the results across all the studies and their implications for an improved understanding of the aetiology of the condition. METHODS Meta-analyses were conducted on four GWASs and four replication studies including a total of 11 506 cases and 32 678 controls, and on the subset of studies that investigated associations for revised American Fertility Society (rAFS) Stage III/IV including 2859 cases. The datasets included 9039 cases and 27 343 controls of European (Australia, Belgium, Italy, UK, USA) and 2467 cases and 5335 controls of Japanese ancestry. Fixed and Han and Elkin random-effects models, and heterogeneity statistics (Cochran's Q test), were used to investigate the evidence of the nine reported genome-wide significant loci across datasets and populations. RESULTS Meta-analysis showed that seven out of nine loci had consistent directions of effect across studies and populations, and six out of nine remained genome-wide significant (P < 5 × 10(-8)), including rs12700667 on 7p15.2 (P = 1.6 × 10(-9)), rs7521902 near WNT4 (P = 1.8 × 10(-15)), rs10859871 near VEZT (P = 4.7 × 10(-15)), rs1537377 near CDKN2B-AS1 (P = 1.5 × 10(-8)), rs7739264 near ID4 (P = 6.2 × 10(-10)) and rs13394619 in GREB1 (P = 4.5 × 10(-8)). In addition to the six loci, two showed borderline genome-wide significant associations with Stage III/IV endometriosis, including rs1250248 in FN1 (P = 8 × 10(-8)) and rs4141819 on 2p14 (P = 9.2 × 10(-8)). Two independent inter-genic loci, rs4141819 and rs6734792 on chromosome 2, showed significant evidence of heterogeneity across datasets (P < 0.005). Eight of the nine loci had stronger effect sizes among Stage III/IV cases, implying that they are likely to be implicated in the development of moderate to severe, or ovarian, disease. While three out of nine loci were inter-genic, the remaining were in or near genes with known functions of biological relevance to endometriosis, varying from roles in developmental pathways to cellular growth/carcinogenesis. CONCLUSIONS Our meta-analysis shows remarkable consistency in endometriosis GWAS results across studies, with little evidence of population-based heterogeneity. They also show that the phenotypic classifications used in GWAS to date have been limited. Stronger associations with Stage III/IV disease observed for most loci emphasize the importance for future studies to include detailed sub-phenotype information. Functional studies in relevant tissues are needed to understand the effect of the variants on downstream biological pathways.

Different approaches of applying single-objective binary genetic algorithm on the wind farm design

This paper discusses three different ways of applying the single-objective binary genetic algorithm into designing the wind farm. The introduction of different applications is through altering the binary encoding methods in GA codes. The first encoding method is the traditional one with fixed wind turbine positions. The second involves varying the initial positions from results of the first method, and it is achieved by using binary digits to represent the coordination of wind turbine on X or Y axis. The third is the mixing of the first encoding method with another one, which is by adding four more binary digits to represent one of the unavailable plots. The goal of this paper is to demonstrate how the single-objective binary algorithm can be applied and how the wind turbines are distributed under various conditions with best fitness. The main emphasis of discussion is focused on the scenario of wind direction varying from 0° to 45°. Results show that choosing the appropriate position of wind turbines is more significant than choosing the wind turbine numbers, considering that the former has a bigger influence on the whole farm fitness than the latter. And the farm has best performance of fitness values, farm efficiency, and total power with the direction between 20°to 30°.

Molecular genetic investigation of mitochondrial dysfunction in relation to migraine susceptibility

The aim of this research was to assess the role of genetic variation in mitochondrial function and how this relates to migraine pathophysiology. Using our unique Norfolk Island population, a custom in-house next generation sequencing methodology was developed. This data for the first time showed that there is a molecular genetic link between mitochondrial dysfunction and migraine susceptibility. This work has provided the foundation for further studies aimed at utilising the identified markers in improved migraine diagnostic and therapeutic strategies.

A practical guide to terminology for kimberlite facies: A systematic progression from descriptive to genetic, including a pocket guide

Kimberlite terminology remains problematic because both descriptive and genetic terms are mixed together in most existing terminology schemes. In addition, many terms used in existing kimberlite terminology schemes are not used in mainstream volcanology, even though kimberlite bodies are commonly the remains of kimberlite volcanic vents and edifices. We build on our own recently published approach to kimberlite facies terminology, involving a systematic progression from descriptive to genetic. The scheme can be used for both coherent kimberlite (i.e. kimberlite that was emplaced without undergoing any fragmentation processes and therefore preserving coherent igneous textures) and fragmental kimberlites. The approach involves documentation of components, textures and assessing the degree and effects of alteration on both components and original emplacement textures. This allows a purely descriptive composite component, textural and compositional petrological rock or deposit name to be constructed first, free of any biases about emplacement setting and processes. Then important facies features such as depositional structures, contact relationships and setting are assessed, leading to a composite descriptive and genetic name for the facies or rock unit that summarises key descriptive characteristics, emplacement processes and setting. Flow charts summarising the key steps in developing a progressive descriptive to genetic terminology are provided for both coherent and fragmental facies/deposits/rock units. These can be copied and used in the field, or in conjunction with field (e.g. drill core observations) and petrographic data. Because the approach depends heavily on field scale observations, characteristics and process interpretations, only the first descriptive part is appropriate where only petrographic observations are being made. Where field scale observations are available the progression from developing descriptive to interpretative terminology can be used, especially where some petrographic data also becomes available.

A new approach to kimberlite facies terminology using a revised general approach to the nomenclature of all volcanic rocks and deposits: Descriptive to genetic

Although kimberlite pipes/bodies are usually the remains of volcanic vents, in-vent deposits, and subvolcanic intrusions, the terminology used for kimberlite rocks has largely developed independently of that used in mainstream volcanology. Existing kimberlite terminology is not descriptive and includes terms that are rarely used, used differently, and even not used at all in mainstream volcanology. In addition, kimberlite bodies are altered to varying degrees, making application of genetic terminology difficult because original components and depositional textures are commonly masked by alteration. This paper recommends an approach to the terminology for kimberlite rocks that is consistent with usage for other volcanic successions. In modern terrains the eruption and emplacement origins of deposits can often be readily deduced, but this is often not the case for old, variably altered and deformed rock successions. A staged approach is required whereby descriptive terminology is developed first, followed by application of genetic terminology once all features, including the effects of alteration on original texture and depositional features, together with contact relationships and setting, have been evaluated. Because many volcanic successions consist of both primary volcanic deposits as well as volcanic sediments, terminology must account for both possibilities.

Structural identification of a laboratory-based steel through truss cantilevered bridge with suspended span using a layered genetic algorithm with patternsearch step (GAPS)

Structural identification (St-Id) can be considered as the process of updating a finite element (FE) model of a structural system to match the measured response of the structure. This paper presents the St-Id of a laboratory-based steel through-truss cantilevered bridge with suspended span. There are a total of 600 degrees of freedom (DOFs) in the superstructure plus additional DOFs in the substructure. The St-Id of the bridge model used the modal parameters from a preliminary modal test in the objective function of a global optimisation technique using a layered genetic algorithm with patternsearch step (GAPS). Each layer of the St-Id process involved grouping of the structural parameters into a number of updating parameters and running parallel optimisations. The number of updating parameters was increased at each layer of the process. In order to accelerate the optimisation and ensure improved diversity within the population, a patternsearch step was applied to the fittest individuals at the end of each generation of the GA. The GAPS process was able to replicate the mode shapes for the first two lateral sway modes and the first vertical bending mode to a high degree of accuracy and, to a lesser degree, the mode shape of the first lateral bending mode. The mode shape and frequency of the torsional mode did not match very well. The frequencies of the first lateral bending mode, the first longitudinal mode and the first vertical mode matched very well. The frequency of the first sway mode was lower and that of the second sway mode was higher than the true values, indicating a possible problem with the FE model. Improvements to the model and the St-Id process will be presented at the upcoming conference and compared to the results presented in this paper. These improvements will include the use of multiple FE models in a multi-layered, multi-solution, GAPS St-Id approach.

Genetic and environmental influences on neuroimaging phenotypes: A meta-analytical perspective on twin imaging studies

Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all metaanalyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary.

Genetic effects on the cerebellar role in working memory: Same brain, different genes?

Over the past several years, evidence has accumulated showing that the cerebellum plays a significant role in cognitive function. Here we show, in a large genetically informative twin sample (n= 430; aged 16-30. years), that the cerebellum is strongly, and reliably (n=30 rescans), activated during an n-back working memory task, particularly lobules I-IV, VIIa Crus I and II, IX and the vermis. Monozygotic twin correlations for cerebellar activation were generally much larger than dizygotic twin correlations, consistent with genetic influences. Structural equation models showed that up to 65% of the variance in cerebellar activation during working memory is genetic (averaging 34% across significant voxels), most prominently in the lobules VI, and VIIa Crus I, with the remaining variance explained by unique/unshared environmental factors. Heritability estimates for brain activation in the cerebellum agree with those found for working memory activation in the cerebral cortex, even though cerebellar cyto-architecture differs substantially. Phenotypic correlations between BOLD percent signal change in cerebrum and cerebellum were low, and bivariate modeling indicated that genetic influences on the cerebellum are at least partly specific to the cerebellum. Activation on the voxel-level correlated very weakly with cerebellar gray matter volume, suggesting specific genetic influences on the BOLD signal. Heritable signals identified here should facilitate discovery of genetic polymorphisms influencing cerebellar function through genome-wide association studies, to elucidate the genetic liability to brain disorders affecting the cerebellum.