Multi-variant Genetic Panel for Genetic Risk of Opioid Addiction.

Over 116 million people worldwide have chronic pain and prescription dependence. In the US, opioids account for the majority of overdose deaths, and in 2014, almost 2 million Americans abused or were dependent on prescription opioids. Genetic factors may play a key role in opioid prescription addiction. Herein, we describe genetic variations between opioid addicted and non-addicted populations and derive a predictive model determining risk of opioid addiction. This case cohort study compares the frequency of 16 single nucleotide polymorphisms involved in the brain reward pathways in patients with and without opioid addiction. Data from 37 patients with prescription opioid or heroin addiction and 30 age and gender matched controls were used to design the predictive score. The predictive score was then tested on an additional 138 samples to determine generalizabilty. Results for Method Derivation of Observed data: ROC statistic=0.92, sensitivity=82% (95% CI: 66-90), specificity=75% (95% CI:56-87). TreeNet "learn" data: ROC statistic=0.92, sensitivity=92%, specificity=90%, precision=92%, and overall correct=91%. Results of Generalizability data: Sensitivity=97% (95% CI: 90 to 100), specificity=87% (95% CI: 86 to 93), positive likelihood ratio=7.3 (95% CI: 4.0 to 13.5), and negative likelihood ratio=0.03 (95% CI: 0.01 to 0.13). This negative likelihood ratio can be used as an evidence based measure to exclude patients with a high risk of opioid addicition or substance use disorder. By identifying patients with a lower risk for opioid addiction, our model may inform therapeutic decisions.

Genetic and environmental factors strongly influence risk, severity and progression of age-related macular degeneration.

Age-related macular degeneration (AMD) is characterized by complex interactions between genetic and environmental factors. Here we genotyped the selected 25 single-nucleotide polymorphisms (SNPs) in 983 cases with advanced AMD and 271 cases with intermediate AMD and build an AMD life-risk score model for assessment of progression from intermediate to advanced AMD. We analyzed the performance of the prediction model for geographic atrophy progressors or choroidal neovascularization progressors versus non-progressors based on the 25 SNPs plus body mass index and smoking status. Our results suggest that a class prediction algorithm can be used for the risk assessment of progression from intermediate to late AMD stages. The algorithm could also be potentially applied for therapeutic response, and toward personalized care and precision medicine.

Erratum: Genetic and environmental factors strongly influence risk, severity and progression of age-related macular degeneration.

[This corrects the article DOI: 10.1038/sigtrans.2016.16.].

The global role of ppGpp synthesis in morphological differentiation and antibiotic production in Streptomyces coelicolor A3(2).

BACKGROUND: Regulation of production of the translational apparatus via the stringent factor ppGpp in response to amino acid starvation is conserved in many bacteria. However, in addition to this core function, it is clear that ppGpp also exhibits genus-specific regulatory effects. In this study we used Affymetrix GeneChips to more fully characterize the regulatory influence of ppGpp synthesis on the biology of Streptomyces coelicolor A3(2), with emphasis on the control of antibiotic biosynthesis and morphological differentiation. RESULTS: Induction of ppGpp synthesis repressed transcription of the major sigma factor hrdB, genes with functions associated with active growth, and six of the thirteen conservons present in the S. coelicolor genome. Genes induced following ppGpp synthesis included the alternative sigma factor SCO4005, many for production of the antibiotics CDA and actinorhodin, the regulatory genes SCO4198 and SCO4336, and two alternative ribosomal proteins. Induction of the CDA and actinorhodin clusters was accompanied by an increase in transcription of the pathway regulators cdaR and actII-ORF4, respectively. Comparison of transcriptome profiles of a relA null strain, M570, incapable of ppGpp synthesis with its parent M600 suggested the occurrence of metabolic stress in the mutant. The failure of M570 to sporulate was associated with a stalling between production of the surfactant peptide SapB, and of the hydrophobins: it overproduced SapB but failed to express the chaplin and rodlin genes. CONCLUSION: In S. coelicolor, ppGpp synthesis influences the expression of several genomic elements that are particularly characteristic of streptomycete biology, notably antibiotic gene clusters, conservons, and morphogenetic proteins.

Target preparation for DNA microarray hybridization.

DNA microarrays are widely used to analyze genome-wide gene expression patterns and to study genotypic variations. They are miniaturized collections of thousands of DNA fragments arrayed on a surface. Based on nucleic acid complementary binding, they serve as a tool to interrogate complex populations of nucleic acids for abundance or binding affinity of particular sequences. Before a nucleic acid (target) can be used for hybridization to the probes of a microarray, it needs to be extracted from the tissue and labeled. Frequently, it also needs to be amplified to increase detection sensitivity. During a hybridization process, labeled target molecules with sequences complementary to the probes are captured quantitatively. Subsequently, a reader measures the amount of label on each probe. To generate accurate and informative data, one of the most critical aspects of these experiments is the quality of both the isolated and the labeled nucleic acid samples. This chapter describes detailed procedures for the preparation of labeled RNA samples for DNA microarray analysis.

Environmental whole-genome amplification to access microbial populations in contaminated sediments.

Low-biomass samples from nitrate and heavy metal contaminated soils yield DNA amounts that have limited use for direct, native analysis and screening. Multiple displacement amplification (MDA) using phi29 DNA polymerase was used to amplify whole genomes from environmental, contaminated, subsurface sediments. By first amplifying the genomic DNA (gDNA), biodiversity analysis and gDNA library construction of microbes found in contaminated soils were made possible. The MDA method was validated by analyzing amplified genome coverage from approximately five Escherichia coli cells, resulting in 99.2% genome coverage. The method was further validated by confirming overall representative species coverage and also an amplification bias when amplifying from a mix of eight known bacterial strains. We extracted DNA from samples with extremely low cell densities from a U.S. Department of Energy contaminated site. After amplification, small-subunit rRNA analysis revealed relatively even distribution of species across several major phyla. Clone libraries were constructed from the amplified gDNA, and a small subset of clones was used for shotgun sequencing. BLAST analysis of the library clone sequences showed that 64.9% of the sequences had significant similarities to known proteins, and "clusters of orthologous groups" (COG) analysis revealed that more than half of the sequences from each library contained sequence similarity to known proteins. The libraries can be readily screened for native genes or any target of interest. Whole-genome amplification of metagenomic DNA from very minute microbial sources, while introducing an amplification bias, will allow access to genomic information that was not previously accessible. The reported SSU rRNA sequences and library clone end sequences are listed with their respective GenBank accession numbers, DQ 404590 to DQ 404652, DQ 404654 to DQ 404938, and DX 385314 to DX 389173.

Contribution of transcriptional regulation to natural variations in Arabidopsis.

BACKGROUND: Genetic control of gene transcription is a key component in genome evolution. To understand the transcriptional basis of natural variation, we have studied genome-wide variations in transcription and characterized the genetic variations in regulatory elements among Arabidopsis accessions. RESULTS: Among five accessions (Col-0, C24, Ler, WS-2, and NO-0) 7,508 probe sets with no detectable genomic sequence variations were identified on the basis of the comparative genomic hybridization to the Arabidopsis GeneChip microarray, and used for accession-specific transcriptome analysis. Two-way ANOVA analysis has identified 60 genes whose mRNA levels differed in different accession backgrounds in an organ-dependent manner. Most of these genes were involved in stress responses and late stages of plant development, such as seed development. Correlation analysis of expression patterns of these 7,508 genes between pairs of accessions identified a group of 65 highly plastic genes with distinct expression patterns in each accession. CONCLUSION: Genes that show substantial genetic variation in mRNA level are those with functions in signal transduction, transcription and stress response, suggesting the existence of variations in the regulatory mechanisms for these genes among different accessions. This is in contrast to those genes with significant polymorphisms in the coding regions identified by genomic hybridization, which include genes encoding transposon-related proteins, kinases and disease-resistance proteins. While relatively fewer sequence variations were detected on average in the coding regions of these genes, a number of differences were identified from the upstream regions, several of which alter potential cis-regulatory elements. Our results suggest that nucleotide polymorphisms in regulatory elements of genes encoding controlling factors could be primary targets of natural selection and a driving force behind the evolution of Arabidopsis accessions.

Quantitative genomics of starvation stress resistance in Drosophila.

BACKGROUND: A major challenge of modern biology is to understand the networks of interacting genes regulating complex traits, and the subset of these genes that affect naturally occurring quantitative genetic variation. Previously, we used P-element mutagenesis and quantitative trait locus (QTL) mapping in Drosophila to identify candidate genes affecting resistance to starvation stress, and variation in resistance to starvation stress between the Oregon-R (Ore) and 2b strains. Here, we tested the efficacy of whole-genome transcriptional profiling for identifying genes affecting starvation stress resistance. RESULTS: We evaluated whole-genome transcript abundance for males and females of Ore, 2b, and four recombinant inbred lines derived from them, under control and starved conditions. There were significant differences in transcript abundance between the sexes for nearly 50% of the genome, while the transcriptional response to starvation stress involved approximately 25% of the genome. Nearly 50% of P-element insertions in 160 genes with altered transcript abundance during starvation stress had mutational effects on starvation tolerance. Approximately 5% of the genome exhibited genetic variation in transcript abundance, which was largely attributable to regulation by unlinked genes. Genes exhibiting variation in transcript abundance among lines did not cluster within starvation resistance QTLs, and none of the candidate genes affecting variation in starvation resistance between Ore and 2b exhibited significant differences in transcript abundance between lines. CONCLUSIONS: Expression profiling is a powerful method for identifying networks of pleiotropic genes regulating complex traits, but the relationship between variation in transcript abundance among lines used to map QTLs and genes affecting variation in quantitative traits is complicated.

Expression profiling of phyB mutant demonstrates substantial contribution of other phytochromes to red-light-regulated gene expression during seedling de-etiolation.

Different Arabidopsis phytochrome (phy) family members (phyA through phyE) display differential photosensory and/or physiological functions in regulating growth and developmental responses to light signals. To identify the genes regulated by phyB in response to continuous monochromatic red light (Rc) during the induction of seedling de-etiolation, we have performed time-course, microarray-based expression profiling of wild type (WT) and phyB null mutants. Comparison of the observed expression patterns with those induced by continuous monochromatic far-red light (FRc; perceived exclusively by phyA) in WT and phyA null-mutant seedlings suggests early convergence of the FRc and Rc photosensory pathways to control a largely common transcriptional network. phyB mutant seedlings retain a surprisingly high level of responsiveness to Rc for the majority of Rc-regulated genes on the microarray, indicating that one or more other phys have a major role in regulating their expression. Combined with the robust visible morphogenic phenotype of the phyB mutant in Rc, these data suggest that different members of the phy family act in organ-specific fashion in regulating seedling de-etiolation. Specifically, phyB appears to be the dominant, if not exclusive, photoreceptor in regulating a minority population of genes involved in suppression of hypocotyl cell elongation in response to Rc signals. By contrast, this sensory function is apparently shared by one or more other phys in regulating the majority Rc-responsive gene set involved in other important facets of the de-etiolation process in the apical region, such as cotyledon cell expansion.

The genetic architecture of odor-guided behavior in Drosophila: epistasis and the transcriptome.

We combined transcriptional profiling and quantitative genetic analysis to elucidate the genetic architecture of olfactory behavior in Drosophila melanogaster. We applied whole-genome expression analysis to five coisogenic smell-impaired (smi) mutant lines and their control. We used analysis of variance to partition variation in transcript abundance between males and females and between smi genotypes and to determine the genotype-by-sex interaction. A total of 666 genes showed sexual dimorphism in transcript abundance, and 530 genes were coregulated in response to one or more smi mutations, showing considerable epistasis at the level of the transcriptome in response to single mutations. Quantitative complementation tests of mutations at these coregulated genes with the smi mutations showed that in most cases (67%) epistatic interactions for olfactory behavior mirrored epistasis at the level of transcription, thus identifying new candidate genes regulating olfactory behavior.