Partially observed bipartite network analysis to identify predictive connections in transcriptional regulatory networks

<p>Abstract</p> <p>Background</p> <p>Messenger RNA expression is regulated by a complex interplay of different regulatory proteins. Unfortunately, directly measuring the individual activity of these regulatory proteins is difficult, leaving us with only the resulting gene expression pattern as a marker for the underlying regulatory network or regulator-gene associations. Furthermore, traditional methods to predict these regulator-gene associations do not define the relative importance of each association, leading to a large number of connections in the global regulatory network that, although true, are not useful.</p> <p>Results</p> <p>Here we present a Bayesian method that identifies which known transcriptional relationships in a regulatory network are consistent with a given body of static gene expression data by eliminating the non-relevant ones. The Partially Observed Bipartite Network (POBN) approach developed here is tested using <it>E. coli </it>expression data and a transcriptional regulatory network derived from RegulonDB. When the regulatory network for <it>E. coli </it>was integrated with 266 <it>E. coli </it>gene chip observations, POBN identified 93 out of 570 connections that were either inconsistent or not adequately supported by the expression data.</p> <p>Conclusion</p> <p>POBN provides a systematic way to integrate known transcriptional networks with observed gene expression data to better identify which transcriptional pathways are likely responsible for the observed gene expression pattern.</p

poolMC: Smart pooling of mRNA samples in microarray experiments

Background: Typically, pooling of mRNA samples in microarray experiments implies mixing mRNA from several biological-replicate samples before hybridization onto a microarray chip. Here we describe an alternative smart pooling strategy in which different samples, not necessarily biological replicates, are pooled in an information theoretic efficient way. Further, each sample is tested on multiple chips, but always in pools made up of different samples. The end goal is to exploit the compressibility of microarray data to reduce the number of chips used and increase the robustness to noise in measurements. Results: A theoretical framework to perform smart pooling of mRNA samples in microarray experiments was established and the software implementation of the pooling and decoding algorithms was developed in MATLAB. A proof-of-concept smart pooled experiment was performed using validated biological samples on commercially available gene chips. Conclusions: The theoretical developments and experimental demonstration in this paper provide a useful starting point to investigate smart pooling of mRNA samples in microarray experiments. Important conditions for its successful implementation include linearity of measurements, sparsity in data, and large experiment size.

Using mechanistic Bayesian networks to identify downstream targets of the Sonic Hedgehog pathway

Background: The topology of a biological pathway provides clues as to how a pathway operates, but rationally using this topology information with observed gene expression data remains a challenge. Results: We introduce a new general-purpose analytic method called Mechanistic Bayesian Networks (MBNs) that allows for the integration of gene expression data and known constraints within a signal or regulatory pathway to predict new downstream pathway targets. The MBN framework is implemented in an open-source Bayesian network learning package, the Python Environment for Bayesian Learning (PEBL). We demonstrate how MBNs can be used by modeling the early steps of the sonic hedgehog pathway using gene expression data from different developmental stages and genetic backgrounds in mouse. Using the MBN approach we are able to automatically identify many of the known downstream targets of the hedgehog pathway such as Gas1 and Gli1, along with a short list of likely targets such as Mig12. Conclusions: The MBN approach shown here can easily be extended to other pathways and data types to yield a more mechanistic framework for learning genetic regulatory models.Molecular and Cellular BiologyStem Cell and Regenerative Biolog

Time series gene expression profiling and temporal regulatory pathway analysis of BMP6 induced osteoblast differentiation and mineralization

Background: BMP6 mediated osteoblast differentiation plays a key role in skeletal development and bone disease. Unfortunately, the signaling pathways regulated by BMP6 are largely uncharacterized due to both a lack of data and the complexity of the response. Results: To better characterize the signaling pathways responsive to BMP6, we conducted a time series microarray study to track BMP6 induced osteoblast differentiation and mineralization. These temporal data were analyzed using a customized gene set analysis approach to identify temporally coherent sets of genes that act downstream of BMP6. Our analysis identified BMP6 regulation of previously reported pathways, such as the TGF-beta pathway. We also identified previously unknown connections between BMP6 and pathways such as Notch signaling and the MYB and BAF57 regulatory modules. In addition, we identify a super-network of pathways that are sequentially activated following BMP6 induction. Conclusion: In this work, we carried out a microarray-based temporal regulatory pathway analysis of BMP6 induced osteoblast differentiation and mineralization using GAGE method. This novel temporal analysis is more informative and powerful than the classical static pathway analysis in that: (1) it captures the interconnections between signaling pathways or functional modules and demonstrates the even higher level organization of molecular biological systems; (2) it describes the temporal perturbation patterns of each pathway or module and their dynamic roles in osteoblast differentiation. The same set of experimental and computational strategies employed in our work could be useful for studying other complex biological processes

GAGE: generally applicable gene set enrichment for pathway analysis

<p>Abstract</p> <p>Background</p> <p>Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.</p> <p>Results</p> <p>To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred.</p> <p>GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways–all of which are supported by the experimental literature.</p> <p>Conclusion</p> <p>GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from <url>http://sysbio.engin.umich.edu/~luow/downloads.php</url>.</p

Soluble epoxide hydrolase limits mechanical hyperalgesia during inflammation.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: Cytochrome-P450 (CYP450) epoxygenases metabolise arachidonic acid (AA) into four different biologically active epoxyeicosatrienoic acid (EET) regioisomers. Three of the EETs (i.e., 8,9-, 11,12- and 14,15-EET) are rapidly hydrolysed by the enzyme soluble epoxide hydrolase (sEH). Here, we investigated the role of sEH in nociceptive processing during peripheral inflammation. RESULTS: In dorsal root ganglia (DRG), we found that sEH is expressed in medium and large diameter neurofilament 200-positive neurons. Isolated DRG-neurons from sEH(-/-) mice showed higher EET and lower DHET levels. Upon AA stimulation, the largest changes in EET levels occurred in culture media, indicating both that cell associated EET concentrations quickly reach saturation and EET-hydrolyzing activity mostly effects extracellular EET signaling. In vivo, DRGs from sEH-deficient mice exhibited elevated 8,9-, 11,12- and 14,15-EET-levels. Interestingly, EET levels did not increase at the site of zymosan-induced inflammation. Cellular imaging experiments revealed direct calcium flux responses to 8,9-EET in a subpopulation of nociceptors. In addition, 8,9-EET sensitized AITC-induced calcium increases in DRG neurons and AITC-induced calcitonin gene related peptide (CGRP) release from sciatic nerve axons, indicating that 8,9-EET sensitizes TRPA1-expressing neurons, which are known to contribute to mechanical hyperalgesia. Supporting this, sEH(-/-) mice showed increased nociceptive responses to mechanical stimulation during zymosan-induced inflammation and 8,9-EET injection reduced mechanical thresholds in naive mice. CONCLUSION: Our results show that the sEH can regulate mechanical hyperalgesia during inflammation by inactivating 8,9-EET, which sensitizes TRPA1-expressing nociceptors. Therefore we suggest that influencing the CYP450 pathway, which is actually highly considered to treat cardiovascular diseases, may cause pain side effects.Peer Reviewe

Developing standards for reporting implementation studies of complex interventions (StaRI): a systematic review and e-Delphi

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited

Liberal market economies, business, and political finance: Britain under New Labour

The extent and nature of business financing of parties is an important feature of political finance. Britain’s transparent and permissive regulatory system provides an excellent opportunity to study business financing of parties. Business donations have been very important to the Conservative party over the last decade, and of only marginal importance to Labour. Unlike other Conservative contributors, business donors are more likely to contribute when the party is popular. In contrast to the previous period of Conservative government, the biggest British businesses tended to abstain from political finance under New Labour. However, their bias towards the Conservatives is affected by the party’s popularity and the closeness of an election. Britain shares the political importance of business financing of parties and its mixture of ideological and pragmatic motivations with other liberal market economies. However, in Britain the bias towards the right is much stronger and the role of big business more marginal

A Mechanistic Model of PCR for Accurate Quantification of Quantitative PCR Data

Background: Quantitative PCR (qPCR) is a workhorse laboratory technique for measuring the concentration of a target DNA sequence with high accuracy over a wide dynamic range. The gold standard method for estimating DNA concentrations via qPCR is quantification cycle (Cq) standard curve quantification, which requires the time- and labor-intensive construction of a Cq standard curve. In theory, the shape of a qPCR data curve can be used to directly quantify DNA concentration by fitting a model to data; however, current empirical model-based quantification methods are not as reliable as Cq standard curve quantification. Principal Findings: We have developed a two-parameter mass action kinetic model of PCR (MAK2) that can be fitted to qPCR data in order to quantify target concentration from a single qPCR assay. To compare the accuracy of MAK2-fitting to other qPCR quantification methods, we have applied quantification methods to qPCR dilution series data generated in three independent laboratories using different target sequences. Quantification accuracy was assessed by analyzing the reliability of concentration predictions for targets at known concentrations. Our results indicate that quantification by MAK2-fitting is as reliable as Cq standard curve quantification for a variety of DNA targets and a wide range of concentrations. Significance: We anticipate that MAK2 quantification will have a profound effect on the way qPCR experiments are designed and analyzed. In particular, MAK2 enables accurate quantification of portable qPCR assays with limited sampl