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.

Four generations of sequencing: Is it ready for the clinic yet?

Next-generation sequencing techniques have revolutionized over the last decade providing researchers with low cost, high-throughput alternatives compared to the traditional Sanger sequencing methods. These sequencing techniques have rapidly evolved from first-generation to fourth-generation with very broad applications such as unravelling the complexity of the genome, in terms of genetic variations, and having a high impact on the biological field. In this review, we discuss the transition of sequencing from the second-generation to the third- and fourth-generations, and describe some of their novel biological applications. With the advancement in technology, the earlier challenges of minimal size of the instrument, flexibility of throughput, ease of data analysis and short run times are being addressed. However, the need for prospective analysis and effectiveness to test whether the knowledge of any given new variants identified has an effect on clinical outcome may need improvement.

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.

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.

Heritability of working memory brain activation

Although key to understanding individual variation in task-related brain activation, the genetic contribution to these individual differences remains largely unknown. Here we report voxel-by-voxel genetic model fitting in a large sample of 319 healthy, young adult, human identical and fraternal twins (mean ± SD age, 23.6 ±1.8 years) who performed an n-back working memory task during functional magnetic resonance imaging (fMRI) at a high magnetic field (4 tesla). Patterns of task-related brain response (BOLD signal difference of 2-back minus 0-back) were significantly heritable, with the highest estimates (40 - 65%) in the inferior, middle, and superior frontal gyri, left supplementary motor area, precentral and postcentral gyri, middle cingulate cortex, superior medial gyrus, angular gyrus, superior parietal lobule, including precuneus, and superior occipital gyri. Furthermore, high test-retest reliability for a subsample of 40 twins indicates that nongenetic variance in the fMRI brain response is largely due to unique environmental influences rather than measurement error. Individual variations in activation of the working memory network are therefore significantly influenced by genetic factors. By establishing the heritability of cognitive brain function in a large sample that affords good statistical power, and using voxel-by-voxel analyses, this study provides the necessary evidence for task-related brain activation to be considered as an endophenotype for psychiatric or neurological disorders, and represents a substantial new contribution to the field of neuroimaging genetics. These genetic brain maps should facilitate discovery of gene variants influencing cognitive brain function through genome-wide association studies, potentially opening up new avenues in the treatment of brain disorders.

Common Alzheimer's disease risk variant within the CLU gene affects white matter microstructure in young adults

There is a strong genetic risk for late-onset Alzheimer's disease (AD), but so far few gene variants have been identified that reliably contribute to that risk. A newly confirmed genetic risk allele C of the clusterin (CLU) gene variant rs11136000 is carried by ~88% of Caucasians. The C allele confers a 1.16 greater odds of developing late-onset AD than the T allele. AD patients have reductions in regional white matter integrity. We evaluated whether the CLU risk variant was similarly associated with lower white matter integrity in healthy young humans. Evidence of early brain differences would offer a target for intervention decades before symptom onset. We scanned 398 healthy young adults (mean age, 23.6 ± 2.2 years) with diffusion tensor imaging, a variation of magnetic resonance imaging sensitive to white matter integrity in the living brain. We assessed genetic associations using mixed-model regression at each point in the brain to map the profile of these associations with white matter integrity. Each C allele copy of the CLUvariant was associated with lower fractional anisotropy-a widely accepted measure of white matter integrity-in multiple brain regions, including several known to degenerate in AD. These regions included the splenium of the corpus callosum, the fornix, cingulum, and superior and inferior longitudinal fasciculi in both brain hemispheres. Young healthy carriers of the CLU gene risk variant showed a distinct profile of lower white matter integrity that may increase vulnerability to developing AD later in life.

A tensor-based morphometry study of genetic influences on brain structure using a new fluid registration method

We incorporated a new Riemannian fluid registration algorithm into a general MRI analysis method called tensor-based morphometry to map the heritability of brain morphology in MR images from 23 monozygotic and 23 dizygotic twin pairs. All 92 3D scans were fluidly registered to a common template. Voxelwise Jacobian determinants were computed from the deformation fields to assess local volumetric differences across subjects. Heritability maps were computed from the intraclass correlations and their significance was assessed using voxelwise permutation tests. Lobar volume heritability was also studied using the ACE genetic model. The performance of this Riemannian algorithm was compared to a more standard fluid registration algorithm: 3D maps from both registration techniques displayed similar heritability patterns throughout the brain. Power improvements were quantified by comparing the cumulative distribution functions of the p-values generated from both competing methods. The Riemannian algorithm outperformed the standard fluid registration.

A new registration method based on Log-Euclidean tensor metrics and its application to genetic studies

In structural brain MRI, group differences or changes in brain structures can be detected using Tensor-Based Morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerous registration methods that have recently been developed differ in their detection sensitivity when used for TBM, and detection power is paramount in epidemological studies or drug trials. We therefore developed a new fluid registration method that computes the mappings and performs statistics on them in a consistent way, providing a bridge between TBM registration and statistics. We used the Log-Euclidean framework to define a new regularizer that is a fluid extension of the Riemannian elasticity, which assures diffeomorphic transformations. This regularizer constrains the symmetrized Jacobian matrix, also called the deformation tensor. We applied our method to an MRI dataset from 40 fraternal and identical twins, to revealed voxelwise measures of average volumetric differences in brain structure for subjects with different degrees of genetic resemblance.