Functional classes of SNPs related to psychiatric disorders and behavioral traits contrast with those related to neurological disorders.

We investigated the functional classes of genomic regions containing SNPS contributing most to the SNP-heritability of important psychiatric and neurological disorders and behavioral traits, as determined from recent genome-wide association studies. We employed linkage-disequilibrium score regression with several brain-specific genomic annotations not previously utilized. The classes of genomic annotations conferring substantial SNP-heritability for the psychiatric disorders and behavioral traits differed systematically from the classes associated with neurological disorders, and both differed from the classes enriched for height, a biometric trait used here as a control outgroup. The SNPs implicated in these psychiatric disorders and behavioral traits were highly enriched in CTCF binding sites, in conserved regions likely to be enhancers, and in brain-specific promoters, regulatory sites likely to affect responses to experience. The SNPs relevant for neurological disorders were highly enriched in constitutive coding regions and splice regulatory sites.

Factor Structure of the Postpartum Bonding Questionnaire in a US-Based Cohort of Mothers.

As research efforts in the field of pediatrics are oriented toward a better understanding of the synergistic relationship between different facets of early relational health (ERH) and child development and wellbeing, it is essential to focus on the quality of research instruments available for measuring different components of ERH. This study investigates the measurement characteristics of a widely used parent/caregiver-reported measure of bonding, the Postpartum Bonding Questionnaire (PBQ), in a US-based sample (n=610) of English-speaking biological mothers who completed the PBQ at 4 months postpartum. To evaluate the factor structure of the PBQ, confirmatory and exploratory statistical techniques were employed. The current study failed to replicate the PBQ's original 4-factor structure. Exploratory factor analysis results supported the creation of a 14-item abbreviated measure, the PBQ-14. The PBQ-14 showed evidence of good psychometric properties, including high internal consistency (ω=.87) and correlation with depression (r=.44, p<.001) assessed using the Patient Health Questionnaire (PHQ-9), as would be expected. The new unidimensional PBQ-14 is suitable for use in the US as a measure of general postnatal parent/caregiver-to-infant bonding.

Differential Co-Expression Analysis of RNA-Seq Data Reveals Novel Potential Biomarkers of Device-Tissue Interaction.

The biological response to electrodes implanted in the brain has been a long-standing barrier to achieving a stable tissue device-interface. Understanding the mechanisms underlying this response could explain phenomena including recording instability and loss, shifting stimulation thresholds, off-target effects of neuromodulation, and stimulation-induced depression of neural excitability. Our prior work detected differential expression in hundreds of genes following device implantation. Here, we extend upon that work by providing new analyses using differential co-expression analysis, which identifies changes in the correlation structure between groups of genes detected at the interface in comparison to control tissues. We used an "eigengene" approach to identify hub genes associated with each module. Our work adds to a growing body of literature which applies new techniques in molecular biology and computational analysis to long-standing issues surrounding electrode integration with the brain.

The Coherence Problem: Finding Meaning in GWAS Complexity.

Genome wide association studies (GWAS) for behavioral traits and psychiatric disorders have inspired both confident optimism and withering criticism. Although many recent findings from well powered GWAS have been replicated in independent data sets, the genes identified have pinned down few if any underlying causal mechanisms. Therefore, a key issue is whether or not the genes implicated by GWAS form a coherent story on their own and thus could in principle lead to insight into the biological mechanisms underlying the trait or disorder. We sketch here four scenarios for how genes may contribute to traits and disorders; genetic studies may help elucidate mechanisms under only two of our scenarios. We also describe here an approach to characterize, in an unbiased fashion, the molecular coherence of the gene sets implicated by GWAS of various behavioral and psychiatric phenotypes and we sketch how the four scenarios may be reflected in our molecular coherence measure.

Integrative functional genomic analysis of human brain development and neuropsychiatric risks.

To broaden our understanding of human neurodevelopment, we profiled transcriptomic and epigenomic landscapes across brain regions and/or cell types for the entire span of prenatal and postnatal development. Integrative analysis revealed temporal, regional, sex, and cell type-specific dynamics. We observed a global transcriptomic cup-shaped pattern, characterized by a late fetal transition associated with sharply decreased regional differences and changes in cellular composition and maturation, followed by a reversal in childhood-adolescence, and accompanied by epigenomic reorganizations. Analysis of gene coexpression modules revealed relationships with epigenomic regulation and neurodevelopmental processes. Genes with genetic associations to brain-based traits and neuropsychiatric disorders (including MEF2C, SATB2, SOX5, TCF4, and TSHZ3) converged in a small number of modules and distinct cell types, revealing insights into neurodevelopment and the genomic basis of neuropsychiatric risks.

BOTUX: bayesian-like operational taxonomic unit examiner.

Bayesian-like operational taxonomic unit examiner (BOTUX) is a new tool for the classification of 16S rRNA gene sequences into operational taxonomic units (OTUs) that addresses the problem of overestimation caused by errors introduced during PCR amplification and DNA sequencing steps. BOTUX utilises a grammar-based assignment strategy, where Bayesian models are built from each word of a given length (e.g., 8-mers). de novo analysis is possible with BOTUX as it does not require a training set, and updates probabilistic models as new sequences are recruited to an OTU. In benchmarking tests performed with real and simulated datasets of 16S rDNA sequences, BOTUX accurately identifies OTUs with comparable or better clustering efficiency and lower execution times than other OTU algorithms tested. BOTUX is the only OTU classifier, which allows incremental analysis of large datasets, and is also adept in clustering both 454 and Illumina datasets in a reasonable timeframe.

Genomic sequence analysis and characterization of Sneathia amnii sp. nov.

BACKGROUND: Bacteria of the genus Sneathia are emerging as potential pathogens of the female reproductive tract. Species of Sneathia, which were formerly grouped with Leptotrichia, can be part of the normal microbiota of the genitourinary tracts of men and women, but they are also associated with a variety of clinical conditions including bacterial vaginosis, preeclampsia, preterm labor, spontaneous abortion, post-partum bacteremia and other invasive infections. Sneathia species also exhibit a significant correlation with sexually transmitted diseases and cervical cancer. Because Sneathia species are fastidious and rarely cultured successfully in vitro; and the genomes of members of the genus had until now not been characterized, very little is known about the physiology or the virulence of these organisms. RESULTS: Here, we describe a novel species, Sneathia amnii sp. nov, which closely resembles bacteria previously designated "Leptotrichia amnionii". As part of the Vaginal Human Microbiome Project at VCU, a vaginal isolate of S. amnii sp. nov. was identified, successfully cultured and bacteriologically cloned. The biochemical characteristics and virulence properties of the organism were examined in vitro, and the genome of the organism was sequenced, annotated and analyzed. The analysis revealed a reduced circular genome of ~1.34 Mbp, containing ~1,282 protein-coding genes. Metabolic reconstruction of the bacterium reflected its biochemical phenotype, and several genes potentially associated with pathogenicity were identified. CONCLUSIONS: Bacteria with complex growth requirements frequently remain poorly characterized and, as a consequence, their roles in health and disease are unclear. Elucidation of the physiology and identification of genes putatively involved in the metabolism and virulence of S. amnii may lead to a better understanding of the role of this potential pathogen in bacterial vaginosis, preterm birth, and other issues associated with vaginal and reproductive health.

Genome-wide depletion of replication initiation events in highly transcribed regions.

This report investigates the mechanisms by which mammalian cells coordinate DNA replication with transcription and chromatin assembly. In yeast, DNA replication initiates within nucleosome-free regions, but studies in mammalian cells have not revealed a similar relationship. Here, we have used genome-wide massively parallel sequencing to map replication initiation events, thereby creating a database of all replication initiation sites within nonrepetitive DNA in two human cell lines. Mining this database revealed that genomic regions transcribed at moderate levels were generally associated with high replication initiation frequency. In genomic regions with high rates of transcription, very few replication initiation events were detected. High-resolution mapping of replication initiation sites showed that replication initiation events were absent from transcription start sites but were highly enriched in adjacent, downstream sequences. Methylation of CpG sequences strongly affected the location of replication initiation events, whereas histone modifications had minimal effects. These observations suggest that high levels of transcription interfere with formation of pre-replication protein complexes. Data presented here identify replication initiation sites throughout the genome, providing a foundation for further analyses of DNA-replication dynamics and cell-cycle progression.

Multifactorial regulation of E-cadherin expression: an integrative study.

E-cadherin (E-cad) is an adhesion molecule associated with tumor invasion and metastasis. Its down-regulation is associated with poor prognosis for many epithelial tumor types. We have profiled E-cad in the NCI-60 cancer cell lines at the DNA, RNA, and protein levels using six different microarray platforms plus bisulfite sequencing. Here we consider the effects on E-cad expression of eight potential regulatory factors: E-cad promoter DNA methylation, the transcript levels of six transcriptional repressors (SNAI1, SNAI2, TCF3, TCF8, TWIST1, and ZFHX1B), and E-cad DNA copy number. Combined bioinformatic and pharmacological analyses indicate the following ranking of influence on E-cad expression: (1) E-cad promoter methylation appears predominant, is strongly correlated with E-cad expression, and shows a 20% to 30% threshold above which E-cad expression is silenced; (2) TCF8 expression levels correlate with (-0.62) and predict (P < 0.00001) E-cad expression; (3) SNAI2 and ZFHX1B expression levels correlate positively with each other (+0.83) and also correlate with (-0.32 and -0.30, respectively) and predict (P = 0.03 and 0.01, respectively) E-cad expression; (4) TWIST1 correlates with (-0.34) but does not predict E-cad expression; and (5) SNAI1 expression, TCF3 expression, and E-cad DNA copy number do not correlate with or predict E-cad expression. Predictions of E-cad regulation based on the above factors were tested and verified by demethylation studies using 5-aza-2'-deoxycytidine treatment; siRNA knock-down of TCF8, SNAI2, or ZFHX1B expression; and combined treatment with 5-aza-2'-deoxycytidine and TCF8 siRNA. Finally, levels of cellular E-cad expression are associated with levels of cell-cell adhesion and response to drug treatment.

Allelic heterogeneity in genetic association meta-analysis: an application to DTNBP1 and schizophrenia.

BACKGROUND/AIMS: Meta-analysis of genetic association studies is a useful approach when individual investigations do not yield studywise significant results but the evidence across studies is modest and homogeneous. Current meta-analysis methods account for heterogeneity by down-weighting studies as a function of between-study variance. We contend that current approaches may obscure interesting phenomena in genetic association data. However, an appropriate approach to examining heterogeneity across studies is lacking. METHODS: We develop a novel approach, based on the EM algorithm, to detect allelic heterogeneity, identify subpopulations and assign studies to those subpopulations. We then apply these methods to the association between DTNBP1 and schizophrenia (Scz), one of the most studied relationships in complex disease genetics. We examined 32 published and unpublished population and family-based association studies containing up to 14 SNPs spanning the DTNBP1 locus. RESULTS: We explored heterogeneity in several ways including meta-regression and approaches aimed at exploring the mixture of heterogeneous studies at a particular SNP. We found significant evidence for a mixture of association distributions at multiple loci. CONCLUSION: We propose a novel approach that is broadly applicable and may be useful in large scale genetic association meta-analyses to detect significant allelic heterogeneity.

RCMAT: a regularized covariance matrix approach to testing gene sets.

BACKGROUND: Gene sets are widely used to interpret genome-scale data. Analysis techniques that make better use of the correlation structure of microarray data while addressing practical "n

Profiling SLCO and SLC22 genes in the NCI-60 cancer cell lines to identify drug uptake transporters.

Molecular and pharmacologic profiling of the NCI-60 cell panel offers the possibility of identifying pathways involved in drug resistance or sensitivity. Of these, decreased uptake of anticancer drugs mediated by efflux transporters represents one of the best studied mechanisms. Previous studies have also shown that uptake transporters can influence cytotoxicity by altering the cellular uptake of anticancer drugs. Using quantitative real-time PCR, we measured the mRNA expression of two solute carrier (SLC) families, the organic cation/zwitterion transporters (SLC22 family) and the organic anion transporters (SLCO family), totaling 23 genes in normal tissues and the NCI-60 cell panel. By correlating the mRNA expression pattern of the SLCO and SLC22 family member gene products with the growth-inhibitory profiles of 1,429 anticancer drugs and drug candidate compounds tested on the NCI-60 cell lines, we identified SLC proteins that are likely to play a dominant role in drug sensitivity. To substantiate some of the SLC-drug pairs for which the SLC member was predicted to be sensitizing, follow-up experiments were performed using engineered and characterized cell lines overexpressing SLC22A4 (OCTN1). As predicted by the statistical correlations, expression of SLC22A4 resulted in increased cellular uptake and heightened sensitivity to mitoxantrone and doxorubicin. Our results indicate that the gene expression database can be used to identify SLCO and SLC22 family members that confer sensitivity to cancer cells.

Transcript and protein expression profiles of the NCI-60 cancer cell panel: an integromic microarray study.

To evaluate the utility of transcript profiling for prediction of protein expression levels, we compared profiles across the NCI-60 cancer cell panel, which represents nine tissues of origin. For that analysis, we present here two new NCI-60 transcript profile data sets (A based on Affymetrix HG-U95 and HG-U133A chips; Affymetrix, Santa Clara, CA) and one new protein profile data set (based on reverse-phase protein lysate arrays). The data sets are available online at http://discover.nci.nih.gov in the CellMiner program package. Using the new transcript data in combination with our previously published cDNA array and Affymetrix HU6800 data sets, we first developed a "consensus set" of transcript profiles based on the four different microarray platforms. Using that set, we found that 65% of the genes showed statistically significant transcript-protein correlation, and the correlations were generally higher than those reported previously for panels of mammalian cells. Using the predictive analysis of microarray nearest shrunken centroid algorithm for functional prediction of tissue of origin, we then found that (a) the consensus mRNA set did better than did data from any of the individual mRNA platforms and (b) the protein data seemed to do somewhat better (P = 0.027) on a gene-for-gene basis in this particular study than did the consensus mRNA data, but both did well. Analysis based on the Gene Ontology showed protein levels of structure-related genes to be well predicted by mRNA levels (mean r = 0.71). Because the transcript-based technologies are more mature and are currently able to assess larger numbers of genes at one time, they continue to be useful, even when the ultimate aim is information about proteins.

Detailed DNA methylation profiles of the E-cadherin promoter in the NCI-60 cancer cells.

E-cadherin (E-cad) is a transmembrane adhesion glycoprotein, the expression of which is often reduced in invasive or metastatic tumors. To assess E-cad's distribution among different types of cancer cells, we used bisulfite-sequencing for detailed, base-by-base measurement of CpG methylation in E-cad's promoter region in the NCI-60 cell lines. The mean methylation levels of the cell lines were distributed bimodally, with values pushed toward either the high or low end of the methylation scale. The 38 epithelial cell lines showed substantially lower (28%) mean methylation levels compared with the nonepithelial cell lines (58%). The CpG site at -143 with respect to the transcriptional start was commonly methylated at intermediate levels, even in cell lines with low overall DNA methylation. We also profiled the NCI-60 cell lines using Affymetrix U133 microarrays and found E-cad expression to be correlated with E-cad methylation at highly statistically significant levels. Above a threshold of approximately 20% to 30% mean methylation, the expression of E-cad was effectively silenced. Overall, this study provides a type of detailed analysis of methylation that can also be applied to other cancer-related genes. As has been shown in recent years, DNA methylation status can serve as a biomarker for use in choosing therapy.