Christina L. Zheng

Transcription restores DNA repair to heterochromatin, determining regional mutation rates in cancer genomes

Somatic mutations in cancer are more frequent in heterochromatic and late-replicating regions of the genome. We report that regional disparities in mutation density are virtually abolished within transcriptionally silent genomic regions of cutaneous squamous cell carcinomas (cSCCs) arising in an XPC(-/-) background. XPC(-/-) cells lack global genome nucleotide excision repair (GG-NER), thus establishing differential access of DNA repair machinery within chromatin-rich regions of the genome as the primary cause for the regional disparity. Strikingly, we find that increasing levels of transcription reduce mutation prevalence on both strands of gene bodies embedded within H3K9me3-dense regions, and only to those levels observed in H3K9me3-sparse regions, also in an XPC-dependent manner. Therefore, transcription appears to reduce mutation prevalence specifically by relieving the constraints imposed by chromatin structure on DNA repair. We model this relationship among transcription, chromatin state, and DNA repair, revealing a new, personalized determinant of cancer risk.

Effects of selection for ethanol preference on gene expression in the nucleus accumbens of HS-CC mice

Previous studies on changes in murine brain gene expression associated with the selection for ethanol preference have used F2 intercross or heterogeneous stock (HS) founders, derived from standard laboratory strains. However, these populations represent only a small proportion of the genetic variance available in Mus musculus. To investigate a wider range of genetic diversity, we selected mice for ethanol preference using an HS derived from the eight strains of the collaborative cross. These HS mice were selectively bred (four generations) for high and low ethanol preference. The nucleus accumbens shell of naive S4 mice was interrogated using RNA sequencing (RNA‐Seq). Gene networks were constructed using the weighted gene coexpression network analysis assessing both coexpression and cosplicing. Selection targeted one of the network coexpression modules (greenyellow) that was significantly enriched in genes associated with receptor signaling activity including Chrna7, Grin2a, Htr2a and Oprd1. Connectivity in the module as measured by changes in the hub nodes was significantly reduced in the low preference line. Of particular interest was the observation that selection had marked effects on a large number of cell adhesion molecules, including cadherins and protocadherins. In addition, the coexpression data showed that selection had marked effects on long non‐coding RNA hub nodes. Analysis of the cosplicing network data showed a significant effect of selection on a large cluster of Ras GTPase‐binding genes including Cdkl5, Cyfip1, Ndrg1, Sod1 and Stxbp5. These data in part support the earlier observation that preference is linked to Ras/Mapk pathways.

The genetics of gene expression in complex mouse crosses as a tool to study the molecular underpinnings of behavior traits

Complex Mus musculus crosses provide increased resolution to examine the relationships between gene expression and behavior. While the advantages are clear, there are numerous analytical and technological concerns that arise from the increased genetic complexity that must be considered. Each of these issues is discussed, providing an initial framework for complex cross study design and planning.

On the relationships in rhesus macaques between chronic ethanol consumption and the brain transcriptome

This is the first description of the relationship between chronic ethanol self‐administration and the brain transcriptome in a non‐human primate (rhesus macaque). Thirty‐one male animals self‐administered ethanol on a daily basis for over 12 months. Gene transcription was quantified with RNA‐Seq in the central nucleus of the amygdala (CeA) and cortical Area 32. We constructed coexpression and cosplicing networks, and we identified areas of preservation and areas of differentiation between regions and network types. Correlations between intake and transcription included largely distinct gene sets and annotation categories across brain regions and between expression and splicing; positive and negative correlations were also associated with distinct annotation groups. Membrane, synaptic and splicing annotation categories were over‐represented in the modules (gene clusters) enriched in positive correlations (CeA); our cosplicing analysis further identified the genes affected only at the exon inclusion level. In the CeA coexpression network, we identified Rab6b, Cdk18 and Igsf21 among the intake‐correlated hubs, while in the Area 32, we identified a distinct hub set that included Ppp3r1 and Myeov2. Overall, the data illustrate that excessive ethanol self‐administration is associated with broad expression and splicing mechanisms that involve membrane and synapse genes.

Use of semantic workflows to enhance transparency and reproducibility in clinical omics

BackgroundRecent highly publicized cases of premature patient assignment into clinical trials, resulting from non-reproducible omics analyses, have prompted many to call for a more thorough examination of translational omics and highlighted the critical need for transparency and reproducibility to ensure patient safety. The use of workflow platforms such as Galaxy and Taverna have greatly enhanced the use, transparency and reproducibility of omics analysis pipelines in the research domain and would be an invaluable tool in a clinical setting. However, the use of these workflow platforms requires deep domain expertise that, particularly within the multi-disciplinary fields of translational and clinical omics, may not always be present in a clinical setting. This lack of domain expertise may put patient safety at risk and make these workflow platforms difficult to operationalize in a clinical setting. In contrast, semantic workflows are a different class of workflow platform where resultant workflow runs are transparent, reproducible, and semantically validated. Through semantic enforcement of all datasets, analyses and user-defined rules/constraints, users are guided through each workflow run, enhancing analytical validity and patient safety.MethodsTo evaluate the effectiveness of semantic workflows within translational and clinical omics, we have implemented a clinical omics pipeline for annotating DNA sequence variants identified through next generation sequencing using the Workflow Instance Generation and Specialization (WINGS) semantic workflow platform.ResultsWe found that the implementation and execution of our clinical omics pipeline in a semantic workflow helped us to meet the requirements for enhanced transparency, reproducibility and analytical validity recommended for clinical omics. We further found that many features of the WINGS platform were particularly primed to help support the critical needs of clinical omics analyses.ConclusionsThis is the first implementation and execution of a clinical omics pipeline using semantic workflows. Evaluation of this implementation provides guidance for their use in both translational and clinical settings.

Splicing landscape of the eight collaborative cross founder strains

BackgroundThe Collaborative Cross (CC) is a large panel of genetically diverse recombinant inbred mouse strains specifically designed to provide a systems genetics resource for the study of complex traits. In part, the utility of the CC stems from the extensive genome-wide annotations of founder strain sequence and structural variation. Still missing, however, are transcriptome-specific annotations of the CC founder strains that could further enhance the utility of this resource.ResultsWe provide a comprehensive survey of the splicing landscape of the 8 CC founder strains by leveraging the high level of alternative splicing within the brain. Using deep transcriptome sequencing, we found that a majority of the splicing landscape is conserved among the 8 strains, with ~65% of junctions being shared by at least 2 strains. We, however, found a large number of potential strain-specific splicing events as well, with an average of ~3000 and ~500 with ≥3 and ≥10 sequence read coverage, respectively, within each strain. To better understand strain-specific splicing within the CC founder strains, we defined criteria for and identified high-confidence strain-specific splicing events. These splicing events were defined as exon-exon junctions 1) found within only one strain, 2) with a read coverage ≥10, and 3) defined by a canonical splice site. With these criteria, a total of 1509 high-confidence strain-specific splicing events were identified, with the majority found within two of the wild-derived strains, CAST and PWK. Strikingly, the overwhelming majority, 94%, of these strain-specific splicing events are not yet annotated. Strain-specific splicing was also located within genomic regions recently reported to be over- and under-represented within CC populations.ConclusionsPhenotypic characterization of CC populations is increasing; thus these results will not only aid in further elucidating the transcriptomic architecture of the individual CC founder strains, but they will also help in guiding the utilization of the CC populations in the study of complex traits. This report is also the first to establish guidelines in defining and identifying strain-specific splicing across different mouse strains.

Regional Differences and Similarities in the Brain Transcriptome for Mice Selected for Ethanol Preference From HS-CC Founders

The high genetic complexity found in heterogeneous stock (HS-CC) mice, together with selective breeding, can be used to detect new pathways and mechanisms associated with ethanol preference and excessive ethanol consumption. We predicted that these pathways would provide new targets for therapeutic manipulation. Previously (Colville et al., 2017), we observed that preference selection strongly affected the accumbens shell (SH) genes associated with synaptic function and in particular genes associated with synaptic tethering. Here we expand our analyses to include substantially larger sample sizes and samples from two additional components of the “addiction circuit,” the central nucleus of the amygdala (CeA) and the prelimbic cortex (PL). At the level of differential expression (DE), the majority of affected genes are region-specific; only in the CeA did the DE genes show a significant enrichment in GO annotation categories, e.g., neuron part. In all three brain regions the differentially variable genes were significantly enriched in a single network module characterized by genes associated with cell-to-cell signaling. The data point to glutamate plasticity as being a key feature of selection for ethanol preference. In this context the expression of Dlg2 which encodes for PSD-93 appears to have a key role. It was also observed that the expression of the clustered protocadherins was strongly associated with preference selection.

Analysis Considerations for Utilizing RNA-Seq to Characterize the Brain Transcriptome

RNA-Seq allows one to examine only gene expression as well as expression of noncoding RNAs, alternative splicing, and allele-specific expression. With this increased sensitivity and dynamic range, there are computational and statistical considerations that need to be contemplated, which are highly dependent on the biological question being asked. We highlight these to provide an overview of their importance and the impact they can have on downstream interpretation of the brain transcriptome.