Prenatal maternal depressive symptoms are associated with smaller amygdalar volumes of four-year-old children

Prenatal maternal depressive symptoms are related to an increased offspring susceptibility to psychiatric disorders over the life course. Alterations in fetal brain development might partly mediate this association. The relation of prenatal depressive symptoms with child's amygdalar volumes is still underexplored, and this study aimed to address this gap. We explored the association of prenatal maternal depressive symptoms with amygdalar volumes in 28 4-year-old children (14 female). Amygdalar volumes were assessed using the volBrain pipeline and manual segmentation. Prenatal depressive symptoms were self-reported by mothers at gestational weeks 14, 24 and 34 (Edinburgh Postnatal Depression Scale). Sex differences were probed, and possible pre- and postnatal confounders, such as maternal general anxiety, were controlled for. We observed that elevated depressive symptoms of the early second trimester, after controlling for prenatal maternal general anxiety, were significantly related to smaller right amygdalar volumes in the whole sample. Higher depressive symptoms of the third trimester were associated with significantly smaller right amygdalar volumes in boys compared to girls. Altogether, our data suggest that offspring limbic brain development might be affected by maternal depressive symptoms in early pregnancy, and might also be more vulnerable to depressive symptoms in late pregnancy in boys compared to girls

Sex-specific association between infant caudate volumes and a polygenic risk score for major depressive disorder

Polygenic risk scores for major depressive disorder (PRS-MDD) have been identified in large genome-wide association studies, and recent findings suggest that PRS-MDD might interact with environmental risk factors to shape human limbic brain development as early as in the prenatal period. Striatal structures are crucially involved in depression; however, the association of PRS-MDD with infant striatal volumes is yet unknown. In this study, 105 Finnish mother-infant dyads (44 female, 11-54 days old) were investigated to reveal how infant PRS-MDD is associated with infant dorsal striatal volumes (caudate, putamen) and whether PRS-MDD interacts with prenatal maternal depressive symptoms (Edinburgh Postnatal Depression Scale, gestational weeks 14, 24, 34) on infant striatal volumes. A robust sex-specific main effect of PRS-MDD on bilateral infant caudate volumes was observed. PRS-MDD were more positively associated with caudate volumes in boys compared to girls. No significant interaction effects of genotype PRS-MDD with the environmental risk factor "prenatal maternal depressive symptoms" (genotype-by-environment interaction) nor significant interaction effects of genotype with prenatal maternal depressive symptoms and sex (genotype-by-environment-by-sex interaction) were found for infant dorsal striatal volumes. Our study showed that a higher PRS-MDD irrespective of prenatal exposure to maternal depressive symptoms is associated with smaller bilateral caudate volumes, an indicator of greater susceptibility to major depressive disorder, in female compared to male infants. This sex-specific polygenic effect might lay the ground for the higher prevalence of depression in women compared to men

Mre11 modulates the fidelity of fusion between short telomeres in human cells

The loss of telomere function can result in the fusion of telomeres with other telomeric loci, or non-telomeric double-stranded DNA breaks. Sequence analysis of fusion events between short dysfunctional telomeres in human cells has revealed that fusion is characterized by a distinct molecular signature consisting of extensive deletions and micro-homology at the fusion points. This signature is consistent with alternative error-prone end-joining processes. We have examined the role that Mre11 may play in the fusion of short telomeres in human cells; to do this, we have analysed telomere fusion events in cells derived from ataxia-telangiectasia-like disorder (ATLD) patients that exhibit hypomorphic mutations in MRE11. The telomere dynamics of ATLD fibroblasts were indistinguishable from wild-type fibroblasts and they were proficient in the fusion of short telomeres. However, we observed a high frequency of insertion of DNA sequences at the fusion points that created localized sequence duplications. These data indicate that Mre11 plays a role in the fusion of short dysfunctional telomeres in human cells and are consistent with the hypothesis that as part of the MRN complex it serves to stabilize the joining complex, thereby controlling the fidelity of the fusion reaction

RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib.

Pre-clinical models of tumour biology often rely on propagating human tumour cells in a mouse. In order to gain insight into the alignment of these models to human disease segments or investigate the effects of different therapeutics, approaches such as PCR or array based expression profiling are often employed despite suffering from biased transcript coverage, and a requirement for specialist experimental protocols to separate tumour and host signals. Here, we describe a computational strategy to profile transcript expression in both the tumour and host compartments of pre-clinical xenograft models from the same RNA sample using RNA-Seq. Key to this strategy is a species-specific mapping approach that removes the need for manipulation of the RNA population, customised sequencing protocols, or prior knowledge of the species component ratio. The method demonstrates comparable performance to species-specific RT-qPCR and a standard microarray platform, and allowed us to quantify gene expression changes in both the tumour and host tissue following treatment with cediranib, a potent vascular endothelial growth factor receptor tyrosine kinase inhibitor, including the reduction of multiple murine transcripts associated with endothelium or vessels, and an increase in genes associated with the inflammatory response in response to cediranib. In the human compartment, we observed a robust induction of hypoxia genes and a reduction in cell cycle associated transcripts. In conclusion, the study establishes that RNA-Seq can be applied to pre-clinical models to gain deeper understanding of model characteristics and compound mechanism of action, and to identify both tumour and host biomarkers

Catalytic Mechanism of Bacteriophage T4 Rad50 ATP Hydrolysis

Spontaneous double-strand breaks (DSBs) are one of the most deleterious forms of DNA damage, and their improper repair can lead to cellular dysfunction. The Mre11 and Rad50 proteins, a nuclease and an ATPase, respectively, form a well-conserved complex that is involved in the initial processing of DSBs. Here we examine the kinetic and catalytic mechanism of ATP hydrolysis by T4 Rad50 (gp46) in the presence and absence of Mre11 (gp47) and DNA. Single-turnover and pre-steady state kinetics on the wild-type protein indicate that the rate-limiting step for Rad50, the MR complex, and the MR-DNA complex is either chemistry or a conformational change prior to catalysis. Pre-steady state product release kinetics, coupled with viscosity steady state kinetics, also supports that the binding of DNA to the MR complex does not alter the rate-limiting step. The lack of a positive deuterium solvent isotope effect for the wild type and several active site mutants, combined with pH–rate profiles, implies that chemistry is rate-limiting and the ATPase mechanism proceeds via an asymmetric, dissociative-like transition state. Mutation of the Walker A/B and H-loop residues also affects the allosteric communication between Rad50 active sites, suggesting possible routes for cooperativity between the ATP active sites

Systematic Bias in Genomic Classification Due to Contaminating Non-neoplastic Tissue in Breast Tumor Samples

Abstract Background Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification. Methods To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors. Results Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability. Conclusions Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor

Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size

Abstract Background Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required. Thus, the performances of co-expression network analysis methods on large co-expression networks consisting of a few thousand nodes, with only a small number of profiles with a single perturbation, which more accurately reflect normal experimental conditions, are generally uncharacterized and unknown. Methods We proposed a novel network inference methods based on Relevance Low order Partial Correlation (RLowPC). RLowPC method uses a two-step approach to select on the high-confidence edges first by reducing the search space by only picking the top ranked genes from an intial partial correlation analysis and, then computes the partial correlations in the confined search space by only removing the linear dependencies from the shared neighbours, largely ignoring the genes showing lower association. Results We selected six co-expression-based methods with good performance in evaluation studies from the literature: Partial correlation, PCIT, ARACNE, MRNET, MRNETB and CLR. The evaluation of these methods was carried out on simulated time-series data with various network sizes ranging from 100 to 3000 nodes. Simulation results show low precision and recall for all of the above methods for large networks with a small number of expression profiles. We improved the inference significantly by refinement of the top weighted edges in the pre-inferred partial correlation networks using RLowPC. We found improved performance by partitioning large networks into smaller co-expressed modules when assessing the method performance within these modules. Conclusions The evaluation results show that current methods suffer from low precision and recall for large co-expression networks where only a small number of profiles are available. The proposed RLowPC method effectively reduces the indirect edges predicted as regulatory relationships and increases the precision of top ranked predictions. Partitioning large networks into smaller highly co-expressed modules also helps to improve the performance of network inference methods. The RLowPC R package for network construction, refinement and evaluation is available at GitHub: https://github.com/wyguo/RLowPC

Unique DNA Repair Gene Variations and Potential Associations with the Primary Antibody Deficiency Syndromes IgAD and CVID

BACKGROUND: Despite considerable effort, the genetic factors responsible for >90% of the antibody deficiency syndromes IgAD and CVID remain elusive. To produce a functionally diverse antibody repertoire B lymphocytes undergo class switch recombination. This process is initiated by AID-catalyzed deamination of cytidine to uridine in switch region DNA. Subsequently, these residues are recognized by the uracil excision enzyme UNG2 or the mismatch repair proteins MutSalpha (MSH2/MSH6) and MutLalpha (PMS2/MLH1). Further processing by ubiquitous DNA repair factors is thought to introduce DNA breaks, ultimately leading to class switch recombination and expression of a different antibody isotype. METHODOLOGY/PRINCIPAL FINDINGS: Defects in AID and UNG2 have been shown to result in the primary immunodeficiency hyper-IgM syndrome, leading us to hypothesize that additional, potentially more subtle, DNA repair gene variations may underlie the clinically related antibody deficiencies syndromes IgAD and CVID. In a survey of twenty-seven candidate DNA metabolism genes, markers in MSH2, RAD50, and RAD52 were associated with IgAD/CVID, prompting further investigation into these pathways. Resequencing identified four rare, non-synonymous alleles associated with IgAD/CVID, two in MLH1, one in RAD50, and one in NBS1. One IgAD patient carried heterozygous non-synonymous mutations in MLH1, MSH2, and NBS1. Functional studies revealed that one of the identified mutations, a premature RAD50 stop codon (Q372X), confers increased sensitivity to ionizing radiation. CONCLUSIONS: Our results are consistent with a class switch recombination model in which AID-catalyzed uridines are processed by multiple DNA repair pathways. Genetic defects in these DNA repair pathways may contribute to IgAD and CVID

Integrating Diverse Datasets Improves Developmental Enhancer Prediction

Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology. © 2014 Erwin et al