Исследование влияния солей лития на жизнеспособность бактерий E.coli

В работе исследовано влияние органических солей лития на жизнеспособность и метаболизм культуры E. coli. Установлено, что соли пирувата и сукцината лития не обладают токсичностью в концентрациях от 1,28 до 21,28 ммоль/л. Выявлено, что с увеличением концентрации сукцината и пирувата лития возрастает жизнеспособность культуры E. coli при культивировании на благоприятной и обеднённой питательных средах. Обнаружено, что добавление пирувата и сукцината лития влияет на биохимические процессы бактериальной клетки.The effect of organic lithium salts on the viability and metabolism of E. coli bacteria is investigated. It was found that the salts of lithium pyruvate and succinate do not have toxicity in concentrations from 1.28 to 21.28 mmol/l. It was found that lithium salts at the concentration of 12.77 and 21.28 mmol/l lead to growth increasing of E. coli bacteria in beef-extract broth and a physiological salt solution. It was found that the addition of lithium pyruvate and lithium succinate affects the biochemical processes of the bacterial cell

On the variation of the fine-structure constant: Very high resolution spectrum of QSO HE 0515-4414

We present a detailed analysis of a very high resolution (R\approx 112,000) spectrum of the quasar HE 0515-4414 obtained using the High Accuracy Radial velocity Planet Searcher (HARPS) mounted on the ESO 3.6 m telescope at the La Silla observatory. The HARPS spectrum, of very high wavelength calibration accuracy (better than 1 m\AA), is used to search for possible systematic inaccuracies in the wavelength calibration of the UV Echelle Spectrograph (UVES) mounted on the ESO Very Large Telescope (VLT). We have carried out cross-correlation analysis between the Th-Ar lamp spectra obtained with HARPS and UVES. The shift between the two spectra has a dispersion around zero of \sigma\simeq 1 m\AA. This is well within the wavelength calibration accuracy of UVES (i.e \sigma\simeq 4 m\AA). We show that the uncertainties in the wavelength calibration induce an error of about, \Delta\alpha/\alpha\le 10^{-6}, in the determination of the variation of the fine-structure constant. Thus, the results of non-evolving \Delta\alpha/\alpha reported in the literature based on UVES/VLT data should not be heavily influenced by problems related to wavelength calibration uncertainties. Our higher resolution spectrum of the z_{abs}=1.1508 damped Lyman-\alpha system toward HE 0515-4414 reveals more components compared to the UVES spectrum. Using the Voigt profile decomposition that simultaneously fits the high resolution HARPS data and the higher signal-to-noise ratio UVES data, we obtain, \Delta\alpha/\alpha=(0.05\pm0.24)x10^{-5} at z_{abs}=1.1508. This result is consistent with the earlier measurement for this system using the UVES spectrum alone.Comment: 14 pages, 13 figures, Accepted in A&

No Reliable Association between Runs of Homozygosity and Schizophrenia in a Well-Powered Replication Study

It is well known that inbreeding increases the risk of recessive monogenic diseases, but it is less certain whether it contributes to the etiology of complex diseases such as schizophrenia. One way to estimate the effects of inbreeding is to examine the association between disease diagnosis and genome-wide autozygosity estimated using runs of homozygosity (ROH) in genome-wide single nucleotide polymorphism arrays. Using data for schizophrenia from the Psychiatric Genomics Consortium (n = 21,868), Keller et al. (2012) estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is autozygous (β = 16.1, CI(β) = [6.93, 25.7], Z = 3.44, p = 0.0006). Here we describe replication results from 22 independent schizophrenia case-control datasets from the Psychiatric Genomics Consortium (n = 39,830). Using the same ROH calling thresholds and procedures as Keller et al. (2012), we were unable to replicate the significant association between ROH burden and schizophrenia in the independent PGC phase II data, although the effect was in the predicted direction, and the combined (original + replication) dataset yielded an attenuated but significant relationship between Froh and schizophrenia (β = 4.86,CI(β) = [0.90,8.83],Z = 2.40,p = 0.02). Since Keller et al. (2012), several studies reported inconsistent association of ROH burden with complex traits, particularly in case-control data. These conflicting results might suggest that the effects of autozygosity are confounded by various factors, such as socioeconomic status, education, urbanicity, and religiosity, which may be associated with both real inbreeding and the outcome measures of interest

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression

Genetic correlation between amyotrophic lateral sclerosis and schizophrenia

A. Palotie on työryhmän Schizophrenia Working Grp Psychiat jäsen.We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P = 1 x 10(-4)) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P = 8.4 x 10(-7)). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies.Peer reviewe

Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood

J. Lönnqvist on työryhmän Psychiat Genomics Consortium jäsen.Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on similar to 150,000 individuals give a higher accuracy than LDSC estimates based on similar to 400,000 individuals (from combinedmeta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.Peer reviewe

Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants with Treatment Resistance in Schizophrenia

Importance: About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts. Objective: To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples. Design, Setting, and Participants: Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10501) and individuals with non-TRS (n = 20325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]). Main Outcomes and Measures: GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition. Results: The study included a total of 85490 participants (48635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P =.001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P =.04). Conclusions and Relevance: In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance

Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

publisher: Elsevier articletitle: Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes journaltitle: Cell articlelink: https://doi.org/10.1016/j.cell.2018.05.046 content_type: article copyright: © 2018 Elsevier Inc

Subtypen-spezifisches Training bei Dyslexie

Kognitive Subtypen von Entwicklungsdyslexie können Defizite vor allem in der Phonologie oder der visuell-räumlichen Aufmerksamkeit haben, die mit hirnfunktionellen Unterschieden zu Normallesern einhergehen. Die subtypenspezifische Aktivierungsänderung durch ein visuelles Aufmerksamkeitstraining wurde bisher noch nicht untersucht.Die vorliegende fMRT-Studie untersucht den Einfluss von defizitspezifischen Trainings vs. Lesetrainings auf die neuronalen Korrelate dyslektischer Subtypen verglichen wiederum mit Normallesern. Dritt- und Viertklässler wurden psychometrischen Tests unterzogen, u. a. zu phonologischen und Aufmerksamkeitsfähigkeiten. Alle Dyslektiker erhielten ein vierwöchiges Training: Dyslektiker mit primär phonologischem Defizit ein rein phonologisches Training, Kinder mit primärem Aufmerksamkeitsdefizit ein Aufmerksamkeitstraining. Ein reines Lesetraining erhielt eine dritte Dyslexiegruppe: Dyslektiker unabhängig von ihrem kognitiven Defizit. Mittels fMRT wurde vor und nach dem Training das Posner Paradigma zur visuell-räumlichen Aufmerksamkeitsausrichtung mit den drei Trainingsgruppen und einer vierten Gruppe, den Normallesern, durchgeführt, um zu untersuchen, wie sich das Training auf die neurofunktionelle Verarbeitung der drei Dyslexiegruppen auswirkt. Die fMRT-Daten ergaben für alle Trainingsgruppen eine signifikante Veränderung im linken inferioren Frontalcortex. Das Maximum dieser Aktivierungsveränderung lag jedoch jeweils an verschiedenen Stellen innerhalb dieser Region: für die Trainingsgruppe Phonologie auf dem GFI in der Broca-Region, für die Trainingsgruppen Aufmerksamkeit und Lesen jeweils im Sulcus frontalis inferior. Die drei Gruppen verarbeiten möglicherweise verschiedene Funktionen in dieser sehr heterogenen Region. Das linkshemisphärische Aktivierungsmuster könnte somit auf unterschiedliche Kompensation nach den verschiedenen Dyslexie-Trainings hinweisen, die nicht in klassischen rechts-hemisphärischen Aufmerksamkeitsarealen, sondern im linkshemispärischen Sprachnetzwerk lokalisiert sind