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

Differential Regulation of Syngap1 Translation by FMRP Modulates eEF2 Mediated Response on NMDAR Activity

SYNGAP1, a Synaptic Ras-GTPase activating protein, regulates synapse maturation during a critical developmental window. Heterozygous mutation in SYNGAP1 (SYNGAP1-/+) has been shown to cause Intellectual Disability (ID) in children. Recent studies have provided evidence for altered neuronal protein synthesis in a mouse model of Syngap1-/+. However, the molecular mechanism behind the same is unclear. Here, we report the reduced expression of a known translation regulator, FMRP, during a specific developmental period in Syngap1-/+ mice. Our results demonstrate that FMRP interacts with and regulates the translation of Syngap1 mRNA. We further show reduced Fmr1 translation leads to decreased FMRP level during development in Syngap1-/+ which results in an increase in Syngap1 translation. These developmental changes are reflected in the altered response of eEF2 phosphorylation downstream of NMDA Receptor (NMDAR)-mediated signaling. In this study, we propose a cross-talk between FMRP and SYNGAP1 mediated signaling which can also explain the compensatory effect of impaired signaling observed in Syngap1-/+ mice

The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes

The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2,433 primary breast tumours that have copy number aberration (CNA), gene expression and long-term clinical follow-up data. We identify 40 mutation-driver (Mut-driver) genes, and determine associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assess the clonal states of Mut-driver mutations, and estimate levels of intra-tumour heterogeneity using mutant-allele fractions. Associations between PIK3CA mutations and reduced survival are identified in three subgroups of ER-positive cancer (defined by amplification of 17q23, 11q13-14 or 8q24). High levels of intra-tumour heterogeneity are in general associated with a worse outcome, but highly aggressive tumours with 11q13-14 amplification have low levels of intra-tumour heterogeneity. These results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies.The METABRIC project was funded by Cancer Research UK, the British Columbia Cancer Foundation and Canadian Breast Cancer Foundation BC/Yukon. This sequencing project was funded by CRUK grant C507/A16278 and Illumina UK performed all the sequencing. The authors also acknowledge the support of the University of Cambridge, Hutchinson Whampoa, the NIHR Cambridge Biomedical Research Centre, the Cambridge Experimental Cancer Medicine Centre, the Centre for Translational Genomics (CTAG) Vancouver and the BCCA Breast Cancer Outcomes Unit. We thank the Genomics, Histopathology, and Biorepository Core Facilities at the Cancer Research UK Cambridge Institute, and the Addenbrooke’s Human Research Tissue Bank (supported by the National Institute for Health Research Cambridge Biomedical Research Centre).This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms1147

Mechanistic Bayesian Networks for Integrating Knowledge and Data to Unravel Biological Complexity.

The determination of how protein interactions affect gene regulation is an important problem in systems biology. By identifying quantitative relationships between the interactome and transcriptome in complex pathologies, we can better characterize dysfunctional pathways, generate further hypotheses and identify potential targets for therapeutic interventions. This thesis develops methods and software for elucidating biological networks consistent with known mechanisms using Bayesian networks (BN) and high-throughput datasets in a novel methodology termed Mechanistic Bayesian networks (MBN). This thesis contributes new algorithms for data pre-processing and evaluating hidden variable models that are implemented in PEBL, an open-source library for MBN modeling with features unmatched by other software. Due to its ease of use and extensibility, PEBL allows one to run large, distributed analyses using cloud-computing platforms. MBN are used to identify the targets of the Sonic hedgehog signaling pathway that is implicated in development and cancer progression. The use of hidden variable models and the ability of BN to capture nonlinear, combinatorial and stochastic relationships identifies known and novel targets that are more biologically meaningful and outperforms other BN and non-BN methods. The approach developed is useful for identifying pathway targets, upstream regulators or, more generally, to identify additional components of partially-characterized topologies. MBN are next applied to identify subnetworks of the global interactome that govern gene expression during the epithelial-mesenchymal transition (EMT), a developmental process implicated in cancer metastasis. By modeling the effects of a protein interaction on downstream genes, a scoring metric was developed that quantifies the relevance of interactions to EMT. Application of the method to a cell-line lung cancer dataset identifies a core subnetwork that recapitulates EMT biology and makes predictions about protein interactions and their targets. Because the method does not rely on differential expression or the co-regulation assumptions, it is equally useful for microRNA-target, protein-DNA or mixed-interaction networks. The methods and software in this thesis are generally applicable to problems in elucidating interactions among variables using partially characterized knowledge and noisy high-dimensional datasets and furthers state-of-the-art BN methods by identifying results consistent with both known mechanisms and statistical relationships in data.Ph.D.BioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/84502/1/shahad_1.pd

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

Abstract 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.</p

RESEARCH REPORT Assessing the Functional Bias of Commercial Microarrays Using the Onto-Compare Database

Microarrays are at the center of a revolution in biotechnology, allowing researchers to screen tens of thousands of genes simultaneously. Typically, they have been used in exploratory research to help formulate hypotheses. In most cases, this phase is followed by a more focused, hypothesis-driven stage in which certain specific biological processes and pathways are thought to be involved. Since a single biological process can still involve hundreds of genes, microarrays are still the preferred approach as proven by the availability of focused arrays from several manufacturers. Because focused arrays from different manufacturers use different sets of genes, each array will represent any given regulatory pathway to a different extent. We argue that a functional analysis of the arrays available should be the most important criterion used in the array selection. We developed Onto-Compare as a database that can provide this functionality, based on the Gene Ontology Consortium nomenclature. We used this tool to compare several arrays focused on apoptosis, oncogenes, and tumor suppressors. We considered arrays from BD Biosciences Clontech, PerkinElmer, Sigma-Genosys, and SuperArray. We showed that among the oncogene arrays, the PerkinElmer MICROMAX™ Oncogene Microarray has a better representation of oncogenesis, protein phosphorylation, and negative control of cell proliferation. The comparison of the apoptosis arrays showed that most apoptosis-related biological processes are equally well represented on the arrays considered. However, functional categories such as immune response, cell-cell signaling, cell-surface receptor linked signal transduction, and interleukins are better represented on the Sigma-Genosys Panorama ™ human apoptosis array. At the same time, processes such as cell cycle control, oncogenesis, and negative control of cell proliferation are better represented on the BD Biosciences Clontech Atlas Select ™ human apoptosis array

Onto-Tools, the toolkit of the modern biologist: Onto-Express, Onto-Compare, Onto-Design and Onto-Translate

Onto-Tools is a set of four seamlessly integrated databases: Onto-Express, Onto-Compare, Onto-Design and Onto-Translate. Onto-Express is able to automatically translate lists of genes found to be differentially regulated in a given condition into functional profiles characterizing the impact of the condition studied upon various biological processes and pathways. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function and chromosome location. Statistical significance values are calculated for each category. Once the initial exploratory analysis identified a number of relevant biological processes, specific mechanisms of interactions can be hypothesized for the conditions studied. Currently, many commercial arrays are available for the investigation of specific mechanisms. Each such array is characterized by a biological bias determined by the extent to which the genes present on the array represent specific pathways. Onto-Compare is a tool that allows efficient comparisons of any sets of commercial or custom arrays. Using Onto-Compare, a researcher can determine quickly which array, or set of arrays, covers best the hypotheses studied. In many situations, no commercial arrays are available for specific biological mechanisms. Onto-Design is a tool that allows the user to select genes that represent given functional categories. Onto-Translate allows the user to translate easily lists of accession numbers, UniGene clusters and Affymetrix probes into one another. All tools above are seamlessly integrated. The Onto-Tools are available online at http://vortex.cs.wayne.edu/Projects.html

Light-activated cell identification and sorting (LACIS) for selection of edited clones on a nanofluidic device.

Despite improvements in the CRISPR molecular toolbox, identifying and purifying properly edited clones remains slow, laborious, and low-yield. Here, we establish a method to enable clonal isolation, selection, and expansion of properly edited cells, using OptoElectroPositioning technology for single-cell manipulation on a nanofluidic device. Briefly, after electroporation of primary T cells with CXCR4-targeting Cas9 ribonucleoproteins, single T cells are isolated on a chip and expanded into colonies. Phenotypic consequences of editing are rapidly assessed on-chip with cell-surface staining for CXCR4. Furthermore, individual colonies are identified based on their specific genotype. Each colony is split and sequentially exported for on-target sequencing and further off-chip clonal expansion of the validated clones. Using this method, single-clone editing efficiencies, including the rate of mono- and bi-allelic indels or precise nucleotide replacements, can be assessed within 10 days from Cas9 ribonucleoprotein introduction in cells