Plastid located WHIRLY1 enhances the responsiveness of Arabidopsis seedlings toward abscisic acid

WHIRLY1 is a protein that can be translocated from the plastids to the nucleus, making it an ideal candidate for communicating information between these two compartments. Mutants of Arabidopsis thaliana lacking WHIRLY1 (why1) were shown to have a reduced sensitivity toward salicylic acid (SA) and abscisic acid (ABA) during germination. Germination assays in the presence of abamine, an inhibitor of ABA biosynthesis, revealed that the effect of SA on germination was in fact caused by a concomitant stimulation of ABA biosynthesis. In order to distinguish whether the plastid or the nuclear isoform of WHIRLY1 is adjusting the responsiveness toward ABA, sequences encoding either the complete WHIRLY1 protein or a truncated form lacking the plastid transit peptide were overexpressed in the why1 mutant background. In plants overexpressing the full-length sequence, WHIRLY1 accumulated in both plastids and the nucleus, whereas in plants overexpressing the truncated sequence, WHIRLY1 accumulated exclusively in the nucleus. Seedlings containing recombinant WHIRLY1 in both compartments were hypersensitive toward ABA. In contrast, seedlings possessing only the nuclear form of WHIRLY1 were as insensitive toward ABA as the why1 mutants. ABA was furthermore shown to lower the rate of germination of wildtype seeds even in the presence of abamine which is known to inhibit the formation of xanthoxin, the plastid located precursor of ABA. From this we conclude that plastid located WHIRLY1 enhances the responsiveness of seeds toward ABA even when ABA is supplied exogenously

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible

Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex

The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Microbial pattern recognition causes distinct functional micro-RNA signatures in primary human monocytes.

Micro-RNAs (miRNAs) are short, non-coding RNAs that regulate gene expression post transcriptionally. Several studies have demonstrated the relevance of miRNAs for a wide range of cellular mechanisms, however, the current knowledge on how miRNAs respond to relevant external stimuli, e.g. in disease scenarios is very limited. To generate a descriptive picture of the miRNA network associated to inflammatory responses, we quantified the levels of 330 miRNAs upon stimulation with a panel of pro-inflammatory components such as microbial pattern molecules (flagellin, diacylated lipopeptide lipopolysaccharide, muramyl dipeptide), infection with Listeria monocytogenes and TNF-α as pro-inflammatory control in primary human monocytes using real time PCR. As a result, we found distinct miRNA response clusters for each stimulus used. Additionally, we identified potential target genes of three selected miRNAs miR-129-5p, miR-146a and miR-378 which were part of PAMP-specific response clusters by transfecting THP1 monocytes with the corresponding pre- or anti-miRNAs and microfluidic PCR arrays. The miRNAs induced distinct transcriptomal signatures, e.g. overexpression of miRNA129-5p, which was selectively upregulated by the NOD2-elicitor MDP, led to an upregulation of DEFB1, IRAK1, FBXW7 and IKK γ (Nemo). Our findings on highly co-regulated clusters of miRNAs support the hypothesis that miRNAs act in functional groups. This study indicates that miRNAs play an important role in fine-tuning inflammatory mechanisms. Further investigation in the field of miRNA responses will help to understand their effects on gene expression and may close the regulatory gap between mRNA and protein expression in inflammatory diseases

Induction map of potential target genes.

<p>Fold-changes of target gene transcripts, as quantified via TaqMan® real time PCR in THP-1 cells, transfected with pre-miR-129-5p and stimulated with MDP, transfected with anti-miR-146a and stimulated with TNF-a and transfected with anti-miR-378 and stimulated with TNF-α are displayed (green = downregulation, yellow = no regulation, red = upregulation, grey = not detected). Target genes are arranged in rows, columns represent the different transfections. The row dendrogram (left) shows the similarities in the expression profiles of the genes, while the column dendrogram (top) shows the similarity between the different stimulations. Gene symbols are displayed right to the heatmap, a star (*) indicates that the observed induction was predicted by at least one algorithm.</p