Control of Neuroendocrine Cell Physiology by a Single Transcription Factor, Drosophila Basic Helix Loop Helix Regulator DIMMED

Neuroendocrine cells feature a large capacity for the processing, accumulation and regulated release of bioactive peptides and peptide hormones. The ultrastructural correlate of this regulated secretory pathway is a specialized organelle, called a dense core vesicle: DCV). DCVs are typically larger than conventional, small synaptic vesicles. Past work has identified intrinsic DCV proteins: non-cargo proteins, like the processing enzyme, carboxypeptidase) or ancillary ones that play a role in DCV trafficking and exocytosis: like CAPS, the Ca2+-dependent activator protein for secretion). Currently, there is a lack of understanding of the developmental and physiological mechanisms that permit neurosecretory cells to coordinate and scale the regulated secretory pathway. In this context, the basic helix-loop-helix transcription factor dimmed: dimm) is especially important in the fruit fly Drosophila, but it is not involved in neuroendocrine cell fate determination. Neuroendocrine cells require DIMM to accumulate, and process large amounts of secretory peptides, but DIMM does not target individual neuropeptide-encoding genes. Instead, we show that DIMM supports the complete resolution of NE-specific cellular properties by organizing the cellular machinery required to support a highly active RSP. The mouse orthologue Mist1 likewise plays a role in supporting the RSP of serous exocrine cells. This thesis has three goals. First, I evaluated a set of putative DIMM targets obtained by another scientist in the lab, and ask whether or not these are direct targets of this transcription factor. To accomplish this, I use in vivo chromatin immunoprecipitation: ChIP) followed by measuring DIMM binding to putative DIMM enhancers by quantitative Polymerase Chain Reaction: qPCR). This work is described in Chapter 2. Secondly, I extend DIMM ChIP analysis to identify direct DIMM transcriptional targets on a genome-wide level in vivo in adult neurons. This was done by DIMM chromatin immunoprecipitation coupled to tiling microarrays: ChIP-chip), and also applying Fluorescence Activated Cell Sorting: FACS) and deep sequencing: RNA-seq) to define the transcriptome of DIMM neuroendocrine cells, as described in Chapter 3. I then integrate the ChIP-chip and RNA-seq datasets to provide new viewpoints on how DIMM is used to coordinate and appropriately scale the RSP in NE cells. The intersection of the RNA-Seq and ChIP-chip data presents a list of genes that is likely to mediate the bulk of the transcriptional output of DIMM - i.e., its molecular mechanism . In order to conduct a functional assay and thus validate the list of intersected genes, I conducted a behavioral genetic screen. DIMM-expressing cells have previously been shown to regulate sleep amount in flies. I conducted an RNA interference-based screen, in which expression of individual DIMM target genes was knocked down in DIMM neurons and the effects of this manipulation on sleep were quantified. This in vivo validation provides an important filter with which to ascribe single gene functions and gives further insights into the general mechanisms by which DIMM operates

Calculating Valid Domains for BDD-Based Interactive Configuration

In these notes we formally describe the functionality of Calculating Valid Domains from the BDD representing the solution space of valid configurations. The formalization is largely based on the CLab configuration framework

Interactive Cost Configuration Over Decision Diagrams

Abstract In many AI domains such as product configuration, a user should interactively specify a solution that must satisfy a set of constraints. In such scenarios, offline compilation of feasible solutions into a tractable representation is an important approach to delivering efficient backtrack-free user interaction online. In particular, binary decision diagrams (BDDs) have been successfully used as a compilation target for product and service configuration. In this paper we discuss how to extend BDD-based configuration to scenarios involving cost functions which express user preferences. We first show that an efficient, robust and easy to implement extension is possible if the cost function is additive, and feasible solutions are represented using multi-valued decision diagrams (MDDs). We also discuss the effect on MDD size if the cost function is non-additive or if it is encoded explicitly into MDD. We then discuss interactive configuration in the presence of multiple cost functions. We prove that even in its simplest form, multiple-cost configuration is NP-hard in the input MDD. However, for solving two-cost configuration we develop a pseudo-polynomial scheme and a fully polynomial approximation scheme. The applicability of our approach is demonstrated through experiments over real-world configuration models and product-catalogue datasets. Response times are generally within a fraction of a second even for very large instances

Genome-wide features of neuroendocrine regulation in Drosophila by the basic helix-loop-helix transcription factor DIMMED.

Neuroendocrine (NE) cells use large dense core vesi-cles (LDCVs) to traffic, process, store and secrete neuropeptide hormones through the regulated secre-tory pathway. The dimmed (DIMM) basic helix-loop-helix transcription factor of Drosophila controls the level of regulated secretory activity in NE cells. To pursue its mechanisms, we have performed two in-dependent genome-wide analyses of DIMM’s activi-ties: (i) in vivo chromatin immunoprecipitation (ChIP) to define genomic sites of DIMM occupancy and (ii) deep sequencing of purified DIMM neurons to char-acterize their transcriptional profile. By this com-bined approach, we showed that DIMM binds to con-served E-boxes in enhancers of 212 genes whose expression is enriched in DIMM-expressing NE cells. DIMM binds preferentially to certain E-boxes within first introns of specific gene isoforms. Statistical ma-chine learning revealed that flanking regions of puta-tive DIMM binding sites contribute to its DNA binding specificity. DIMM’s transcriptional repertoire features at least 20 LDCV constituents. In addition, DIMM no-tably targets the pro-secretory transcription factor, creb-A, but significantly, DIMM does not target any neuropeptide genes. DIMM therefore prescribes the scale of secretory activity in NE neurons, by a sys-tematic control of both proximal and distal points in the regulated secretory pathway