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

The Cell Surface Membrane Proteins Cdo and Boc Are Components and Targets of the Hedgehog Signaling Pathway and Feedback Network in Mice

SummaryCdo and Boc encode cell surface Ig/fibronectin superfamily members linked to muscle differentiation. Data here indicate they are also targets and signaling components of the Sonic hedgehog (Shh) pathway. Although Cdo and Boc are generally negatively regulated by Hedgehog (HH) signaling, in the neural tube Cdo is expressed within the Shh-dependent floor plate while Boc expression lies within the dorsal limit of Shh signaling. Loss of Cdo results in a Shh dosage-dependent reduction of the floor plate. In contrast, ectopic expression of Boc or Cdo results in a Shh-dependent, cell autonomous promotion of ventral cell fates and a non-cell-autonomous ventral expansion of dorsal cell identities consistent with Shh sequestration. Cdo and Boc bind Shh through a high-affinity interaction with a specific fibronectin repeat that is essential for activity. We propose a model where Cdo and Boc enhance Shh signaling within its target field

Genomic characterization of Gli-activator targets in sonic hedgehog-mediated neural patterning

Sonic hedgehog (Shh) acts as a morphogen to mediate the specification of distinct cell identities in the ventral neural tube through a Gli-mediated (Gli1-3) transcriptional network. Identifying Gli targets in a systematic fashion is central to the understanding of the action of Shh. We examined this issue in differentiating neural progenitors in mouse. An epitope-tagged Gli-activator protein was used to directly isolate cis-regulatory sequences by chromatin immunoprecipitation (ChIP). ChIP products were then used to screen custom genomic tiling arrays of putative Hedgehog (Hh) targets predicted from transcriptional profiling studies, surveying 50-150 kb of non-transcribed sequence for each candidate. In addition to identifying expected Gli-target sites, the data predicted a number of unreported direct targets of Shh action. Transgenic analysis of binding regions in Nkx2.2, Nkx2.1 (Titf1) and Rab34 established these as direct Hh targets. These data also facilitated the generation of an algorithm that improved in silico predictions of Hh target genes. Together, these approaches provide significant new insights into both tissue-specific and general transcriptional targets in a crucial Shh-mediated patterning process

The Hedgehog-binding proteins Gas1 and Cdo cooperate to positively regulate Shh signaling during mouse development

Hedgehog (Hh) signaling is critical for patterning and growth during mammalian embryogenesis. Transcriptional profiling identified Growth-arrest-specific 1 (Gas1) as a general negative target of Shh signaling. Data presented here define Gas1 as a novel positive component of the Shh signaling cascade. Removal of Gas1 results in a Shh dose-dependent loss of cell identities in the ventral neural tube and facial and skeletal defects, also consistent with reduced Shh signaling. In contrast, ectopic Gas1 expression results in Shh-dependent cell-autonomous promotion of ventral cell identities. These properties mirror those of Cdo, an unrelated, cell surface Shh-binding protein. We show that Gas1 and Cdo cooperate to promote Shh signaling during neural tube patterning, craniofacial, and vertebral development. Overall, these data support a new paradigm in Shh signaling whereby positively acting ligand-binding components, which are initially expressed in responding tissues to promote signaling, are then down-regulated by active Hh signaling, thereby modulating responses to ligand input

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

Hedgehog signaling in the neural crest cells regulates the patterning and growth of facial primordia

Facial abnormalities in human SHH mutants have implicated the Hedgehog (Hh) pathway in craniofacial development, but early defects in mouse Shh mutants have precluded the experimental analysis of this phenotype. Here, we removed Hh-responsiveness specifically in neural crest cells (NCCs), the multipotent cell type that gives rise to much of the skeleton and connective tissue of the head. In these mutants, many of the NCC-derived skeletal and nonskeletal components are missing, but the NCC-derived neuronal cell types are unaffected. Although the initial formation of branchial arches (BAs) is normal, expression of several Fox genes, specific targets of Hh signaling in cranial NCCs, is lost in the mutant. The spatially restricted expression of Fox genes suggests that they may play an important role in BA patterning. Removing Hh signaling in NCCs also leads to increased apoptosis and decreased cell proliferation in the BAs, which results in facial truncation that is evident by embryonic day 11.5 (E11.5). Together, our results demonstrate that Hh signaling in NCCs is essential for normal patterning and growth of the face. Further, our analysis of Shh–Fox gene regulatory interactions leads us to propose that Fox genes mediate the action of Shh in facial development

Developmental regulation of DNA replication timing at the human β globin locus

The human β globin locus replicates late in most cell types, but becomes early replicating in erythroid cells. Using FISH to map DNA replication timing around the endogenous β globin locus and by applying a genetic approach in transgenic mice, we have demonstrated that both the late and early replication states are controlled by regulatory elements within the locus control region. These results also show that the pattern of replication timing is set up by mechanisms that work independently of gene transcription