Low-variance RNAs identify Parkinson's disease molecular signature in blood.

The diagnosis of Parkinson's disease (PD) is usually not established until advanced neurodegeneration leads to clinically detectable symptoms. Previous blood PD transcriptome studies show low concordance, possibly resulting from the use of microarray technology, which has high measurement variation. The Leucine-rich repeat kinase 2 (LRRK2) G2019S mutation predisposes to PD. Using preclinical and clinical studies, we sought to develop a novel statistically motivated transcriptomic-based approach to identify a molecular signature in the blood of Ashkenazi Jewish PD patients, including LRRK2 mutation carriers. Using a digital gene expression platform to quantify 175 messenger RNA (mRNA) markers with low coefficients of variation (CV), we first compared whole-blood transcript levels in mouse models (1) overexpressing wild-type (WT) LRRK2, (2) overexpressing G2019S LRRK2, (3) lacking LRRK2 (knockout), and (4) and in WT controls. We then studied an Ashkenazi Jewish cohort of 34 symptomatic PD patients (both WT LRRK2 and G2019S LRRK2) and 32 asymptomatic controls. The expression profiles distinguished the four mouse groups with different genetic background. In patients, we detected significant differences in blood transcript levels both between individuals differing in LRRK2 genotype and between PD patients and controls. Discriminatory PD markers included genes associated with innate and adaptive immunity and inflammatory disease. Notably, gene expression patterns in levodopa-treated PD patients were significantly closer to those of healthy controls in a dose-dependent manner. We identify whole-blood mRNA signatures correlating with LRRK2 genotype and with PD disease state. This approach may provide insight into pathogenesis and a route to early disease detection.

Hybrid Bayesian-rank integration approach improves the predictive power of genomic dataset aggregation.

MOTIVATION: Modern molecular technologies allow the collection of large amounts of high-throughput data on the functional attributes of genes. Often multiple technologies and study designs are used to address the same biological question such as which genes are overexpressed in a specific disease state. Consequently, there is considerable interest in methods that can integrate across datasets to present a unified set of predictions. RESULTS: An important aspect of data integration is being able to account for the fact that datasets may differ in how accurately they capture the biological signal of interest. While many methods to address this problem exist, they always rely either on dataset internal statistics, which reflect data structure and not necessarily biological relevance, or external gold standards, which may not always be available. We present a new rank aggregation method for data integration that requires neither external standards nor internal statistics but relies on Bayesian reasoning to assess dataset relevance. We demonstrate that our method outperforms established techniques and significantly improves the predictive power of rank-based aggregations. We show that our method, which does not require an external gold standard, provides reliable estimates of dataset relevance and allows the same set of data to be integrated differently depending on the specific signal of interest. AVAILABILITY: The method is implemented in R and is freely available at http://www.pitt.edu/~mchikina/BIRRA/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Increasing consistency of disease biomarker prediction across datasets.

Microarray studies with human subjects often have limited sample sizes which hampers the ability to detect reliable biomarkers associated with disease and motivates the need to aggregate data across studies. However, human gene expression measurements may be influenced by many non-random factors such as genetics, sample preparations, and tissue heterogeneity. These factors can contribute to a lack of agreement among related studies, limiting the utility of their aggregation. We show that it is feasible to carry out an automatic correction of individual datasets to reduce the effect of such 'latent variables' (without prior knowledge of the variables) in such a way that datasets addressing the same condition show better agreement once each is corrected. We build our approach on the method of surrogate variable analysis but we demonstrate that the original algorithm is unsuitable for the analysis of human tissue samples that are mixtures of different cell types. We propose a modification to SVA that is crucial to obtaining the improvement in agreement that we observe. We develop our method on a compendium of multiple sclerosis data and verify it on an independent compendium of Parkinson's disease datasets. In both cases, we show that our method is able to improve agreement across varying study designs, platforms, and tissues. This approach has the potential for wide applicability to any field where lack of inter-study agreement has been a concern.

Homer1 alternative splicing is regulated by gonadotropin-releasing hormone and modulates gonadotropin gene expression.

Hypothalamic gonadotropin-releasing hormone (GnRH) plays a critical role in reproductive physiology by regulating follicle-stimulating hormone (FSH) and luteinizing hormone (LH) gene expression in the pituitary. Analysis of gonadotrope deep-sequencing data identified a global regulation of pre-mRNA splicing by GnRH. Homer1, a gene encoding a postsynaptic density scaffolding protein, was selected for further study. Homer1 expresses a short splice form, Homer1a, and more-abundant long transcripts Homer1b/c. GnRH induced a modest increase in Homer1b/c expression and a dramatic increase in the Homer1a splice form. G protein knockdown studies suggested that the Homer1 induction, but not the regulated splicing, was Gαq/11 dependent. Phosphorylation of the splicing regulator SRp20 was found to be induced by GnRH. SRp20 depletion attenuated the GnRH-induced increase in the Homer1a-to-Homer1b/c ratio and modulated the effects of GnRH on FSHß and LHß expression. Homer1 gene knockdown resulted in increased GnRH-induced FSHß and LHß transcript levels. Furthermore, splice-form-specific reduction of Homer1b/c increased both FSHß and LHß mRNA induction, whereas reduction of Homer1a had the opposite effect on FSHß induction. These results indicate that the regulation of Homer1 splicing by GnRH contributes to gonadotropin gene control.

An effective statistical evaluation of ChIPseq dataset similarity.

MOTIVATION: ChIPseq is rapidly becoming a common technique for investigating protein-DNA interactions. However, results from individual experiments provide a limited understanding of chromatin structure, as various chromatin factors cooperate in complex ways to orchestrate transcription. In order to quantify chromtain interactions, it is thus necessary to devise a robust similarity metric applicable to ChIPseq data. Unfortunately, moving past simple overlap calculations to give statistically rigorous comparisons of ChIPseq datasets often involves arbitrary choices of distance metrics, with significance being estimated by computationally intensive permutation tests whose statistical power may be sensitive to non-biological experimental and post-processing variation. RESULTS: We show that it is in fact possible to compare ChIPseq datasets through the efficient computation of exact P-values for proximity. Our method is insensitive to non-biological variation in datasets such as peak width, and can rigorously model peak location biases by evaluating similarity conditioned on a restricted set of genomic regions (such as mappable genome or promoter regions). Applying our method to the well-studied dataset of Chen et al. (2008), we elucidate novel interactions which conform well with our biological understanding. By comparing ChIPseq data in an asymmetric way, we are able to observe clear interaction differences between cofactors such as p300 and factors that bind DNA directly. AVAILABILITY: Source code is available for download at http://sonorus.princeton.edu/IntervalStats/IntervalStats.tar.gz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate quantification of functional analogy among close homologs.

Correctly evaluating functional similarities among homologous proteins is necessary for accurate transfer of experimental knowledge from one organism to another, and is of particular importance for the development of animal models of human disease. While the fact that sequence similarity implies functional similarity is a fundamental paradigm of molecular biology, sequence comparison does not directly assess the extent to which two proteins participate in the same biological processes, and has limited utility for analyzing families with several parologous members. Nevertheless, we show that it is possible to provide a cross-organism functional similarity measure in an unbiased way through the exclusive use of high-throughput gene-expression data. Our methodology is based on probabilistic cross-species mapping of functionally analogous proteins based on Bayesian integrative analysis of gene expression compendia. We demonstrate that even among closely related genes, our method is able to predict functionally analogous homolog pairs better than relying on sequence comparison alone. We also demonstrate that the landscape of functional similarity is often complex and that definitive "functional orthologs" do not always exist. Even in these cases, our method and the online interface we provide are designed to allow detailed exploration of sources of inferred functional similarity that can be evaluated by the user.

Global prediction of tissue-specific gene expression and context-dependent gene networks in Caenorhabditis elegans.

Tissue-specific gene expression plays a fundamental role in metazoan biology and is an important aspect of many complex diseases. Nevertheless, an organism-wide map of tissue-specific expression remains elusive due to difficulty in obtaining these data experimentally. Here, we leveraged existing whole-animal Caenorhabditis elegans microarray data representing diverse conditions and developmental stages to generate accurate predictions of tissue-specific gene expression and experimentally validated these predictions. These patterns of tissue-specific expression are more accurate than existing high-throughput experimental studies for nearly all tissues; they also complement existing experiments by addressing tissue-specific expression present at particular developmental stages and in small tissues. We used these predictions to address several experimentally challenging questions, including the identification of tissue-specific transcriptional motifs and the discovery of potential miRNA regulation specific to particular tissues. We also investigate the role of tissue context in gene function through tissue-specific functional interaction networks. To our knowledge, this is the first study producing high-accuracy predictions of tissue-specific expression and interactions for a metazoan organism based on whole-animal data.

The Sleipnir library for computational functional genomics.

MOTIVATION: Biological data generation has accelerated to the point where hundreds or thousands of whole-genome datasets of various types are available for many model organisms. This wealth of data can lead to valuable biological insights when analyzed in an integrated manner, but the computational challenge of managing such large data collections is substantial. In order to mine these data efficiently, it is necessary to develop methods that use storage, memory and processing resources carefully. RESULTS: The Sleipnir C++ library implements a variety of machine learning and data manipulation algorithms with a focus on heterogeneous data integration and efficiency for very large biological data collections. Sleipnir allows microarray processing, functional ontology mining, clustering, Bayesian learning and inference and support vector machine tasks to be performed for heterogeneous data on scales not previously practical. In addition to the library, which can easily be integrated into new computational systems, prebuilt tools are provided to perform a variety of common tasks. Many tools are multithreaded for parallelization in desktop or high-throughput computing environments, and most tasks can be performed in minutes for hundreds of datasets using a standard personal computer. AVAILABILITY: Source code (C++) and documentation are available at http://function.princeton.edu/sleipnir and compiled binaries are available from the authors on request.

Role of Domain IV/S4 outermost arginines in gating of T-type calcium channels.

The role of the outermost three charged residues of Domain IV/S4 in controlling gating of Ca(v)3.2 was investigated using single substitutions of each arginine with glutamine, cysteine, histidine, and lysine in a Flp-In-293 cell line, in which expression levels could be compared. Channel density, based on gating charge measurements, was ~125,000 channels/cell (10 fC/pF), except for R2Q and R3C, which expressed at lower levels. Channels substituted at Arg-1715 (R1C, R1Q, R1H) demonstrated such modest changes that a role in voltage sensing could not be determined. Arg-1718 (R2) made a contribution to activation voltage sensing, and the channel was sensitive to the geometry of side-chain substitutions at this position. Arg-1721 (R3) substitutions produced complex kinetic changes that together suggested that geometry made a larger contribution than charge. Current decay at positive potentials (O-->I) exponentially approached a constant value for all mutants except R2K channels, which were biphasically dependent on potential. R2K channels also displayed slowed deactivation with reduced voltage dependence despite near control values for conductance. Voltage-dependent accessibility of R to C mutants, evaluated with intracellularly and extracellularly applied methanthiosulfonate (MTS) reagents, showed that both R2 and R3 were exposed only when cells were depolarized, although it was not necessary for channels to open. Together, the data indicate that Domain IV/S4 is an activation domain and is not involved in inactivation from the open state.