Marine bacterial communities are resistant to elevated carbon dioxide levels

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd. Summary: It is well established that the release of anthropogenic-derived CO2 into the atmosphere will be mainly absorbed by the oceans, with a concomitant drop in pH, a process termed ocean acidification. As such, there is considerable interest in how changes in increased CO2 and lower pH will affect marine biota, such as bacteria, which play central roles in oceanic biogeochemical processes. Set within an ecological framework, we investigated the direct effects of elevated CO2, contrasted with ambient conditions on the resistance and resilience of marine bacterial communities in a replicated temporal seawater mesocosm experiment. The results of the study strongly indicate that marine bacterial communities are highly resistant to the elevated CO2 and lower pH conditions imposed, as demonstrated from measures of turnover using taxa-time relationships and distance-decay relationships. In addition, no significant differences in community abundance, structure or composition were observed. Our results suggest that there are no direct effects on marine bacterial communities and that the bacterial fraction of microbial plankton holds enough flexibility and evolutionary capacity to withstand predicted future changes from elevated CO2 and subsequent ocean acidification

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Riluzole: a potential therapeutic intervention in human brain tumor stem-like cells

A small subpopulation of tumor stem-like cells has the capacity to initiate tumors and mediate radio- and chemoresistance in diverse cancers hence also in glioblastoma (GBM). It has been reported that this capacity of tumor initiation in the brain is mainly dependent on the body's nutrient supply. This population of so-called brain tumor initiating or brain tumor stem-like cells (BTSCs) is able to extract nutrients like glucose with a higher affinity. Riluzole, a drug approved for treating amyotrophic lateral sclerosis (ALS), was reported to possess anticancer properties, affecting the glutamate metabolism. We report that riluzole treatment inhibits the growth of brain tumor stem-like cells enriched cultures isolated from two human glioblastomas. The effects of riluzole on these cells were associated with an inhibition of a poor prognostic indicator: glucose transporter 3 (GLUT3). A decrease in GLUT3 is associated with a decrease in the p-Akt/HIF1 alpha pathway. Further, downregulation of the DNA (Cytosine-5-)-methyltransferase 1 (DNMT1) gene that causes hypermethylation of various tumor-suppressor genes and leads to a poor prognosis in GBM, was detected. Two hallmarks of cancer cells-proliferation and cell death-were positively influenced by riluzole treatment. Finally, we observed that riluzole reduced the tumor growth in in vivo CAM assay, suggesting it could be a possible synergistic drug for the treatment of glioblastoma

Oocytes from pachytene to dictyotene can easily be analysed in neonatal rodents

We have investigated the dynamics of meiotic prophase I in neonatal ovaries from different wild rodent species, from a laboratory strain of Mus musculus and from Mus musculus × Mus spretus F1 hybrids. We found that almost all stages of prophase I were regularly present in neonatal ovaries from these species and that their transcriptional activity can be assessed by [3H]-uridine incorporation, indicating that postnatal analysis of meiotic chromosomes and synapsis may be conducted as an alternative to the investigation of foetal ovaries

Outlier-based identification of copy number variations using targeted resequencing in a small cohort of patients with Tetralogy of Fallot.

Copy number variations (CNVs) are one of the main sources of variability in the human genome. Many CNVs are associated with various diseases including cardiovascular disease. In addition to hybridization-based methods, next-generation sequencing (NGS) technologies are increasingly used for CNV discovery. However, respective computational methods applicable to NGS data are still limited. We developed a novel CNV calling method based on outlier detection applicable to small cohorts, which is of particular interest for the discovery of individual CNVs within families, de novo CNVs in trios and/or small cohorts of specific phenotypes like rare diseases. Approximately 7,000 rare diseases are currently known, which collectively affect ∼6% of the population. For our method, we applied the Dixon's Q test to detect outliers and used a Hidden Markov Model for their assessment. The method can be used for data obtained by exome and targeted resequencing. We evaluated our outlier-based method in comparison to the CNV calling tool CoNIFER using eight HapMap exome samples and subsequently applied both methods to targeted resequencing data of patients with Tetralogy of Fallot (TOF), the most common cyanotic congenital heart disease. In both the HapMap samples and the TOF cases, our method is superior to CoNIFER, such that it identifies more true positive CNVs. Called CNVs in TOF cases were validated by qPCR and HapMap CNVs were confirmed with available array-CGH data. In the TOF patients, we found four copy number gains affecting three genes, of which two are important regulators of heart development (NOTCH1, ISL1) and one is located in a region associated with cardiac malformations (PRODH at 22q11). In summary, we present a novel CNV calling method based on outlier detection, which will be of particular interest for the analysis of de novo or individual CNVs in trios or cohorts up to 30 individuals, respectively

Base qualities versus coverage values.

<p>Scatterplot indicates the average base qualities (Phred scores) and depths of coverage for samples targeted resequenced by Illumina’s Genome Analyzer IIx platform (36 bp paired-end reads).</p

Outlier-based CNV calling method.

<p>(A) Read mapping and calculation of copy number value per window. Reads are mapped to extended targeted regions, which are then joined chromosome-wise. mrCaNaVaR is used to split the joined regions into windows. For each window, its copy number value is calculated by mrCaNaVaR, where represents the value for window W in sample S. (B) Dixon’s Q test is applied for each window over all samples to identify outliers. Here, sample 1 represents an outlier (loss, L) for the first, second, third and fifth window, while sample 2 represents an outlier (gain, G) for the fourth window. (C) Assessment of outliers using a Hidden Markov Model (HMM). In the given example, the fourth window of sample 1 is considered as normal (N). After applying the HMM, it will also be considered as a loss. Similarly, the fourth window of sample 2 is considered as normal after applying the HMM. A region is called as a copy number alteration, if at least five continuous windows show the same kind of change, i.e. either gain or loss.</p