Loss of H3K27 trimethylation is frequent in IDH1-R132H but not in non-canonical IDH1/2 mutated and 1p/19q codeleted oligodendroglioma: a Japanese cohort study.

Oligodendrogliomas are defined by mutation in isocitrate dehydrogenase (NADP(+)) (IDH)1/2 genes and chromosome 1p/19q codeletion. World Health Organisation diagnosis endorses testing for 1p/19q codeletion to distinguish IDH mutant (Mut) oligodendrogliomas from astrocytomas because these gliomas require different treatments and they have different outcomes. Several methods have been used to identify 1p/19q status; however, these techniques are not routinely available and require substantial infrastructure investment. Two recent studies reported reduced immunostaining for trimethylation at lysine 27 on histone H3 (H3K27me3) in IDH Mut 1p/19q codeleted oligodendroglioma. However, the specificity of H3K27me3 immunostaining in this setting is controversial. Therefore, we developed an easy-to-implement immunohistochemical surrogate for IDH Mut glioma subclassification and evaluated a validated adult glioma cohort. We screened 145 adult glioma cases, consisting of 45 IDH Mut and 1p/19q codeleted oligodendrogliomas, 30 IDH Mut astrocytomas, 16 IDH wild-type (Wt) astrocytomas, and 54 IDH Wt glioblastomas (GBMs). We compared immunostaining with DNA sequencing and fluorescent in situ hybridization analysis and assessed differences in H3K27me3 staining between oligodendroglial and astrocytic lineages and between IDH1-R132H and non-canonical (non-R132H) IDH1/2 Mut oligodendroglioma. A loss of H3K27me3 was observed in 36/40 (90%) of IDH1-R132H Mut oligodendroglioma. In contrast, loss of H3K27me3 was never seen in IDH1-R132L or IDH2-mutated 1p/19q codeleted oligodendrogliomas. IDH Mut astrocytoma, IDH Wt astrocytoma and GBM showed preserved nuclear staining in 87%, 94%, and 91% of cases, respectively. A high recursive partitioning model predicted probability score (0.9835) indicated that the loss of H3K27me3 is frequent to IDH1-R132H Mut oligodendroglioma. Our results demonstrate H3K27me3 immunohistochemical evaluation to be a cost-effective and reliable method for defining 1p/19q codeletion along with IDH1-R132H and ATRX immunostaining, even in the absence of 1p/19q testing.

Origin Sample Prediction and Spatial Modeling of Antimicrobial Resistance in Metagenomic Sequencing Data.

The steady elaboration of the Metagenomic and Metadesign of Subways and Urban Biomes (MetaSUB) international consortium project raises important new questions about the origin, variation, and antimicrobial resistance of the collected samples. CAMDA (Critical Assessment of Massive Data Analysis, http://camda.info/) forum organizes annual challenges where different bioinformatics and statistical approaches are tested on samples collected around the world for bacterial classification and prediction of geographical origin. This work proposes a method which not only predicts the locations of unknown samples, but also estimates the relative risk of antimicrobial resistance through spatial modeling. We introduce a new component in the standard analysis as we apply a Bayesian spatial convolution model which accounts for spatial structure of the data as defined by the longitude and latitude of the samples and assess the relative risk of antimicrobial resistance taxa across regions which is relevant to public health. We can then use the estimated relative risk as a new measure for antimicrobial resistance. We also compare the performance of several machine learning methods, such as Gradient Boosting Machine, Random Forest, and Neural Network to predict the geographical origin of the mystery samples. All three methods show consistent results with some superiority of Random Forest classifier. In our future work we can consider a broader class of spatial models and incorporate covariates related to the environment and climate profiles of the samples to achieve more reliable estimation of the relative risk related to antimicrobial resistance.

CbGRiTS: cerebellar gene regulation in time and space.

The mammalian CNS is one of the most complex biological systems to understand at the molecular level. The temporal information from time series transcriptome analysis can serve as a potent source of associative information between developmental processes and regulatory genes. Here, we introduce a new transcriptome database called, Cerebellar Gene Regulation in Time and Space (CbGRiTS). This dataset is populated with transcriptome data across embryonic and postnatal development from two standard mouse strains, C57BL/6J and DBA/2J, several recombinant inbred lines and cerebellar mutant strains. Users can evaluate expression profiles across cerebellar development in a deep time series with graphical interfaces for data exploration and link-out to anatomical expression databases. We present three analytical approaches that take advantage of specific aspects of the time series for transcriptome analysis. We demonstrate the use of CbGRiTS dataset as a community resource to explore patterns of gene expression and develop hypotheses concerning gene regulatory networks in brain development.

Unraveling inflammatory responses using systems genetics and gene-environment interactions in macrophages.

Many common diseases have an important inflammatory component mediated in part by macrophages. Here we used a systems genetics strategy to examine the role of common genetic variation in macrophage responses to inflammatory stimuli. We examined genome-wide transcript levels in macrophages from 92 strains of the Hybrid Mouse Diversity Panel. We exposed macrophages to control media, bacterial lipopolysaccharide (LPS), or oxidized phospholipids. We performed association mapping under each condition and identified several thousand expression quantitative trait loci (eQTL), gene-by-environment interactions, and eQTL "hot spots" that specifically control LPS responses. We used siRNA knockdown of candidate genes to validate an eQTL hot spot in chromosome 8 and identified the gene 2310061C15Rik as a regulator of inflammatory responses in macrophages. We have created a public database where the data presented here can be used as a resource for understanding many common inflammatory traits that are modeled in the mouse and for the dissection of regulatory relationships between genes.

FDR-FET: an optimizing gene set enrichment analysis method.

Gene set enrichment analysis for analyzing large profiling and screening experiments can reveal unifying biological schemes based on previously accumulated knowledge represented as "gene sets". Most of the existing implementations use a fixed fold-change or P value cutoff to generate regulated gene lists. However, the threshold selection in most cases is arbitrary, and has a significant effect on the test outcome and interpretation of the experiment. We developed a new gene set enrichment analysis method, ie, FDR-FET, which dynamically optimizes the threshold choice and improves the sensitivity and selectivity of gene set enrichment analysis. The procedure translates experimental results into a series of regulated gene lists at multiple false discovery rate (FDR) cutoffs, and computes the P value of the overrepresentation of a gene set using a Fisher's exact test (FET) in each of these gene lists. The lowest P value is retained to represent the significance of the gene set. We also implemented improved methods to define a more relevant global reference set for the FET. We demonstrate the validity of the method using a published microarray study of three protease inhibitors of the human immunodeficiency virus and compare the results with those from other popular gene set enrichment analysis algorithms. Our results show that combining FDR with multiple cutoffs allows us to control the error while retaining genes that increase information content. We conclude that FDR-FET can selectively identify significant affected biological processes. Our method can be used for any user-generated gene list in the area of transcriptome, proteome, and other biological and scientific applications.

Comparative analysis of proteome and transcriptome variation in mouse.

The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

Sex-specific gene expression in the BXD mouse liver.

Differences in clinical phenotypes between the sexes are well documented and have their roots in differential gene expression. While sex has a major effect on gene expression, transcription is also influenced by complex interactions between individual genetic variation and environmental stimuli. In this study, we sought to understand how genetic variation affects sex-related differences in liver gene expression by performing genetic mapping of genomewide liver mRNA expression data in a genetically defined population of naive male and female mice from C57BL/6J, DBA/2J, B6D2F1, and 37 C57BL/6J x DBA/2J (BXD) recombinant inbred strains. As expected, we found that many genes important to xenobiotic metabolism and other important pathways exhibit sexually dimorphic expression. We also performed gene expression quantitative trait locus mapping in this panel and report that the most significant loci that appear to regulate a larger number of genes than expected by chance are largely sex independent. Importantly, we found that the degree of correlation within gene expression networks differs substantially between the sexes. Finally, we compare our results to a recently released human liver gene expression data set and report on important similarities in sexually dimorphic liver gene expression between mouse and human. This study enhances our understanding of sex differences at the genome level and between species, as well as increasing our knowledge of the molecular underpinnings of sex differences in responses to xenobiotics.

Phospholipidosis as a function of basicity, lipophilicity, and volume of distribution of compounds.

Drug-induced phospholipidosis (PLD) is an adaptive histologic alteration that is seen with various marketed drugs and often encountered during drug development. Various in silico and in vitro cell-based methods have been developed to predict the PLD-inducing potential of compounds. These methods rely on the inherent physicochemical properties of the molecule and, as such, tend to overpredict compounds as PLD inducers. Recognizing that the distribution of compounds into tissues or tissue accumulation is likely a key factor in PLD induction, in addition to key physicochemical properties, we developed a model to predict PLD in vivo using the measures of basicity (pK(a)), lipophilicity (ClogP), and volume of distribution (V(d)). Using sets of PLD inducers and noninducers, we demonstrate improved concordance with this method. Furthermore, we propose a screening paradigm that includes a combination of various methods to predict the in vivo PLD-inducing potential of compounds, which may be especially useful in lead identification and optimization processes in drug discovery.

Systems genetics analysis of gene-by-environment interactions in human cells.

Gene by environment (GxE) interactions are clearly important in many human diseases, but they have proven to be difficult to study on a molecular level. We report genetic analysis of thousands of transcript abundance traits in human primary endothelial cell (EC) lines in response to proinflammatory oxidized phospholipids implicated in cardiovascular disease. Of the 59 most regulated transcripts, approximately one-third showed evidence of GxE interactions. The interactions resulted primarily from effects of distal-, trans-acting loci, but a striking example of a local-GxE interaction was also observed for FGD6. Some of the distal interactions were validated by siRNA knockdown experiments, including a locus involved in the regulation of multiple transcripts involved in the ER stress pathway. Our findings add to the understanding of the overall architecture of complex human traits and are consistent with the possibility that GxE interactions are responsible, in part, for the failure of association studies to more fully explain common disease variation.

A high-resolution association mapping panel for the dissection of complex traits in mice.

Systems genetics relies on common genetic variants to elucidate biologic networks contributing to complex disease-related phenotypes. Mice are ideal model organisms for such approaches, but linkage analysis has been only modestly successful due to low mapping resolution. Association analysis in mice has the potential of much better resolution, but it is confounded by population structure and inadequate power to map traits that explain less than 10% of the variance, typical of mouse quantitative trait loci (QTL). We report a novel strategy for association mapping that combines classic inbred strains for mapping resolution and recombinant inbred strains for mapping power. Using a mixed model algorithm to correct for population structure, we validate the approach by mapping over 2500 cis-expression QTL with a resolution an order of magnitude narrower than traditional QTL analysis. We also report the fine mapping of metabolic traits such as plasma lipids. This resource, termed the Hybrid Mouse Diversity Panel, makes possible the integration of multiple data sets and should prove useful for systems-based approaches to complex traits and studies of gene-by-environment interactions.

Reconstructing generalized logical networks of transcriptional regulation in mouse brain from temporal gene expression data.

Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol are used for modeling. Genes from major neuronal pathways are identified as putative components of the alcohol response mechanism. Nine of these genes have associations with alcohol reported in literature. Several other potentially relevant genes, compatible with independent results from literature mining, may play a role in the response to alcohol. Additional, previously unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.