ABSTRACT
Many studies have utilized the Inbred Long Sleep and Inbred Short Sleep mouse strains to model the genetic influence on initial sensitivity to ethanol. The mechanisms underlying this divergent phenotype are still not completely understood. In this study, we attempt to identify genes that are differentially expressed between these two strains and to identify baseline networks of co-expressed genes, which may provide insight regarding their phenotypic differences. We examined the whole brain and striatal transcriptomes of both strains, using next generation RNA sequencing techniques. Many genes were differentially expressed between strains, including several in chromosomal regions previously shown to influence initial sensitivity to ethanol. These results are in concordance with a similar sample of striatal transcriptomes measured using microarrays. In addition to the higher dynamic range, RNA-Seq is not hindered by high background noise or polymorphisms in probesets as with microarray technology, and we are able to analyze exome sequence of abundant genes. Furthermore, utilizing Weighted Gene Co-expression Network Analysis, we identified several modules of co-expressed genes corresponding to strain differences. Several candidate genes were identified, including protein phosphatase 1 regulatory unit 1b (Ppp1r1b), prodynorphin (Pdyn), proenkephalin (Penk), ras association (RalGDS/AF-6) domain family member 2 (Rassf2), myosin 1d (Myo1d) and transthyretin (Ttr). In addition, we propose a role for potassium channel activity as well as map kinase signaling in the observed phenotypic differences between the two strains.
Subject(s)
Sleep/genetics , Transcriptome , Animals , Brain/metabolism , Dopamine and cAMP-Regulated Phosphoprotein 32/genetics , Enkephalins/genetics , Enkephalins/metabolism , Ethanol/pharmacology , Exome , Gene Expression Profiling , Gene Regulatory Networks , High-Throughput Nucleotide Sequencing , Male , Mice , Mice, Inbred Strains , Myosins/genetics , Myosins/metabolism , Polymorphism, Genetic , Prealbumin/genetics , Prealbumin/metabolism , Protein Precursors/genetics , Protein Precursors/metabolism , Sequence Analysis, RNA , Sleep/drug effects , Transcription, Genetic/drug effects , Transcription, Genetic/genetics , Tumor Suppressor Proteins/genetics , Tumor Suppressor Proteins/metabolismABSTRACT
We performed genomewide gene expression analysis of 35 samples representing 6 common histologic subtypes of canine lymphoma and bioinformatics analyses to define their molecular characteristics. Three major groups were defined on the basis of gene expression profiles: (1) low-grade T-cell lymphoma, composed entirely by T-zone lymphoma; (2) high-grade T-cell lymphoma, consisting of lymphoblastic T-cell lymphoma and peripheral T-cell lymphoma not otherwise specified; and (3) B-cell lymphoma, consisting of marginal B-cell lymphoma, diffuse large B-cell lymphoma, and Burkitt lymphoma. Interspecies comparative analyses of gene expression profiles also showed that marginal B-cell lymphoma and diffuse large B-cell lymphoma in dogs and humans might represent a continuum of disease with similar drivers. The classification of these diverse tumors into 3 subgroups was prognostically significant, as the groups were directly correlated with event-free survival. Finally, we developed a benchtop diagnostic test based on expression of 4 genes that can robustly classify canine lymphomas into one of these 3 subgroups, enabling a direct clinical application for our results.
Subject(s)
Biomarkers, Tumor/metabolism , Dog Diseases/classification , Lymphoma, B-Cell/veterinary , Lymphoma, T-Cell/veterinary , Animals , Cohort Studies , Computational Biology , Disease-Free Survival , Dog Diseases/mortality , Dog Diseases/pathology , Dogs , Female , Gene Expression Profiling , Gene Expression Regulation, Neoplastic , Genome-Wide Association Study/veterinary , Immunophenotyping , Lymphoma, B-Cell/classification , Lymphoma, B-Cell/metabolism , Lymphoma, B-Cell/pathology , Lymphoma, T-Cell/classification , Lymphoma, T-Cell/metabolism , Lymphoma, T-Cell/pathology , Male , Oligonucleotide Array Sequence Analysis , Prognosis , RNA, Neoplasm/geneticsABSTRACT
Trajectory clustering is a novel and statistically well-founded method for clustering time series data from gene expression arrays. Trajectory clustering uses non-parametric statistics and is hence not sensitive to the particular distributions underlying gene expression data. Each cluster is clearly defined in terms of direction of change of expression for successive time points (its 'trajectory'), and therefore has easily appreciated biological meaning. Applying the method to a dataset from mouse mammary gland development, we demonstrate that it produces different clusters than Hierarchical, K-means, and Jackknife clustering methods, even when those methods are applied to differences between successive time points. Compared to all of the other methods, trajectory clustering was better able to match a manual clustering by a domain expert, and was better able to cluster groups of genes with known related functions.
