Probing the Drosophila retinal determination gene network in Tribolium (II): The Pax6 genes eyeless and twin of eyeless

AbstractThe Pax6 genes eyeless (ey) and twin of eyeless (toy) are upstream regulators in the retinal determination gene network (RDGN), which instructs the formation of the adult eye primordium in Drosophila. Most animals possess a singleton Pax6 ortholog, but the dependence of eye development on Pax6 is widely conserved. A rare exception is given by the larval eyes of Drosophila, which develop independently of ey and toy. To obtain insight into the origin of differential larval and adult eye regulation, we studied the function of toy and ey in the red flour beetle Tribolium castaneum. We find that single and combinatorial knockdown of toy and ey affect larval eye development strongly but adult eye development only mildly in this primitive hemimetabolous species. Compound eye-loss, however, was provoked when ey and toy were RNAi-silenced in combination with the early retinal gene dachshund (dac). We propose that these data reflect a role of Pax6 during regional specification in the developing head and that the subsequent maintenance and growth of the adult eye primordium is regulated partly by redundant and partly by specific functions of toy, ey and dac in Tribolium. The results from embryonic knockdown and comparative protein sequence analysis lead us further to conclude that Tribolium represents an ancestral state of redundant control by ey and toy

White Matter Changes in Patients with Friedreich Ataxia after Treatment with Erythropoietin

Background and Purpose—Erythropoietin (EPO) has received growing attention because of its neuro-regenerative properties. Preclinical and clinical evidence supports its therapeutic potential in brain conditions like stroke, multiple sclerosis and schizophrenia. Also in Friedreich ataxia, clinical improvement after EPO therapy was shown. The aim of the present study was to assess possible therapy-associated brain white-matter changes in these patients.Methods—Nine patients with Friedreich ataxia underwent Diffusion Tensor Imaging (DTI) before and after EPO treatment. Tract-based spatial statistics (TBSS) was used for longitudinal comparison. Results—We detected widespread longitudinal increase in fractional anisotropy (FA) and axial diffusivity (D||) in cerebral hemispheres bilaterally (p<0.05, corrected), while no changes were observed within the cerebellum, medulla oblongata and pons. Conclusions—To the best of our knowledge, this is the first DTI study to investigate the effects of erythropoietin in a neurodegenerative disease. Anatomically, the diffusivity changes appear disease-unspecific, and their biological underpinnings deserve further study

Polygenic risk scores across the extended psychosis spectrum

As early detection of symptoms in the subclinical to clinical psychosis spectrum may improve health outcomes, knowing the probabilistic susceptibility of developing a disorder could guide mitigation measures and clinical intervention. In this context, polygenic risk scores (PRSs) quantifying the additive effects of multiple common genetic variants hold the potential to predict complex diseases and index severity gradients. PRSs for schizophrenia (SZ) and bipolar disorder (BD) were computed using Bayesian regression and continuous shrinkage priors based on the latest SZ and BD genome-wide association studies (Psychiatric Genomics Consortium, third release). Eight well-phenotyped groups (n = 1580; 56% males) were assessed: control (n = 305), lower (n = 117) and higher (n = 113) schizotypy (both groups of healthy individuals), at-risk for psychosis (n = 120), BD type-I (n = 359), BD type-II (n = 96), schizoaffective disorder (n = 86), and SZ groups (n = 384). PRS differences were investigated for binary traits and the quantitative Positive and Negative Syndrome Scale. Both BD-PRS and SZ-PRS significantly differentiated controls from at-risk and clinical groups (Nagelkerke’s pseudo-R2: 1.3–7.7%), except for BD type-II for SZ-PRS. Out of 28 pairwise comparisons for SZ-PRS and BD-PRS, 9 and 12, respectively, reached the Bonferroni-corrected significance. BD-PRS differed between control and at-risk groups, but not between at-risk and BD type-I groups. There was no difference between controls and schizotypy. SZ-PRSs, but not BD-PRSs, were positively associated with transdiagnostic symptomology. Overall, PRSs support the continuum model across the psychosis spectrum at the genomic level with possible irregularities for schizotypy. The at-risk state demands heightened clinical attention and research addressing symptom course specifiers. Continued efforts are needed to refine the diagnostic and prognostic accuracy of PRSs in mental healthcare

An Investigation of Psychosis Subgroups With Prognostic Validation and Exploration of Genetic Underpinnings The PsyCourse Study

This cohort study aims to detect psychosis subgroups and examine their illness courses over 1.5 years and their polygenic scores for schizophrenia, bipolar disorder, major depression disorder, and educational achievement. Question Will data-driven clustering using high-dimensional clinical data reveal psychosis subgroups with relevance to prognoses and polygenic risk? Findings In this cohort study including 1223 individuals, in the discovery sample of 765 individuals with predominantly bipolar and schizophrenia diagnoses, 5 subgroups were detected with different clinical signatures, illness trajectories, and genetic scores for educational attainment. Results were validated in a sample of 458 individuals. Meaning New data-driven clustering paired with rigorous validation may offer a means to extend symptom-based psychosis taxonomies toward functional outcomes, genetic markers, and trajectory-based stratifications. Importance Identifying psychosis subgroups could improve clinical and research precision. Research has focused on symptom subgroups, but there is a need to consider a broader clinical spectrum, disentangle illness trajectories, and investigate genetic associations. Objective To detect psychosis subgroups using data-driven methods and examine their illness courses over 1.5 years and polygenic scores for schizophrenia, bipolar disorder, major depression disorder, and educational achievement. Design, Setting, and Participants This ongoing multisite, naturalistic, longitudinal (6-month intervals) cohort study began in January 2012 across 18 sites. Data from a referred sample of 1223 individuals (765 in the discovery sample and 458 in the validation sample) with DSM-IV diagnoses of schizophrenia, bipolar affective disorder (I/II), schizoaffective disorder, schizophreniform disorder, and brief psychotic disorder were collected from secondary and tertiary care sites. Discovery data were extracted in September 2016 and analyzed from November 2016 to January 2018, and prospective validation data were extracted in October 2018 and analyzed from January to May 2019. Main Outcomes and Measures A clinical battery of 188 variables measuring demographic characteristics, clinical history, symptoms, functioning, and cognition was decomposed using nonnegative matrix factorization clustering. Subtype-specific illness courses were compared with mixed models and polygenic scores with analysis of covariance. Supervised learning was used to replicate results in validation data with the most reliably discriminative 45 variables. Results Of the 765 individuals in the discovery sample, 341 (44.6%) were women, and the mean (SD) age was 42.7 (12.9) years. Five subgroups were found and labeled as affective psychosis (n = 252), suicidal psychosis (n = 44), depressive psychosis (n = 131), high-functioning psychosis (n = 252), and severe psychosis (n = 86). Illness courses with significant quadratic interaction terms were found for psychosis symptoms (R-2 = 0.41; 95% CI, 0.38-0.44), depression symptoms (R-2 = 0.28; 95% CI, 0.25-0.32), global functioning (R-2 = 0.16; 95% CI, 0.14-0.20), and quality of life (R-2 = 0.20; 95% CI, 0.17-0.23). The depressive and severe psychosis subgroups exhibited the lowest functioning and quadratic illness courses with partial recovery followed by reoccurrence of severe illness. Differences were found for educational attainment polygenic scores (mean [SD] partial eta(2) = 0.014 [0.003]) but not for diagnostic polygenic risk. Results were largely replicated in the validation cohort. Conclusions and Relevance Psychosis subgroups were detected with distinctive clinical signatures and illness courses and specificity for a nondiagnostic genetic marker. New data-driven clinical approaches are important for future psychosis taxonomies. The findings suggest a need to consider short-term to medium-term service provision to restore functioning in patients stratified into the depressive and severe psychosis subgroups