CrebA increases secretory capacity through direct transcriptional regulation of the secretory machinery, a subset of secretory cargo, and other key regulators.

Specialization of many cells, including the acinar cells of the salivary glands and pancreas, milk-producing cells of mammary glands, mucus-secreting goblet cells, antibody-producing plasma cells, and cells that generate the dense extracellular matrices of bone and cartilage, requires scaling up both secretory machinery and cell-type specific secretory cargo. Using tissue-specific genome-scale analyses, we determine how increases in secretory capacity are coordinated with increases in secretory load in the Drosophila salivary gland (SG), an ideal model for gaining mechanistic insight into the functional specialization of secretory organs. Our findings show that CrebA, a bZIP transcription factor, directly binds genes encoding the core secretory machinery, including protein components of the signal recognition particle and receptor, ER cargo translocators, Cop I and Cop II vesicles, as well as the structural proteins and enzymes of these organelles. CrebA directly binds a subset of SG cargo genes and CrebA binds and boosts expression of Sage, a SG-specific transcription factor essential for cargo expression. To further enhance secretory output, CrebA binds and activates Xbp1 and Tudor-SN. Thus, CrebA directly upregulates the machinery of secretion and additional factors to increase overall secretory capacity in professional secretory cells; concomitant increases in cargo are achieved both directly and indirectly.

Ribbon regulates morphogenesis of the Drosophila embryonic salivary gland through transcriptional activation and repression.

Transcription factors affect spatiotemporal patterns of gene expression often regulating multiple aspects of tissue morphogenesis, including cell-type specification, cell proliferation, cell death, cell polarity, cell shape, cell arrangement and cell migration. In this work, we describe a distinct role for Ribbon (Rib) in controlling cell shape/volume increases during elongation of the Drosophila salivary gland (SG). Notably, the morphogenetic changes in rib mutants occurred without effects on general SG cell attributes such as specification, proliferation and apoptosis. Moreover, the changes in cell shape/volume in rib mutants occurred without compromising epithelial-specific morphological attributes such as apicobasal polarity and junctional integrity. To identify the genes regulated by Rib, we performed ChIP-seq analysis in embryos driving expression of GFP-tagged Rib specifically in the SGs. To learn if the Rib binding sites identified in the ChIP-seq analysis were linked to changes in gene expression, we performed microarray analysis comparing RNA samples from age-matched wild-type and rib null embryos. From the superposed ChIP-seq and microarray gene expression data, we identified 60 genomic sites bound by Rib likely to regulate SG-specific gene expression. We confirmed several of the identified Rib targets by qRT-pCR and/or in situ hybridization. Our results indicate that Rib regulates cell growth and tissue shape in the Drosophila salivary gland via a diverse array of targets through both transcriptional activation and repression. Furthermore, our results suggest that autoregulation of rib expression may be a key component of the SG morphogenetic gene network.

Protein function annotation with Structurally Aligned Local Sites of Activity (SALSAs).

BACKGROUND: The prediction of biochemical function from the 3D structure of a protein has proved to be much more difficult than was originally foreseen. A reliable method to test the likelihood of putative annotations and to predict function from structure would add tremendous value to structural genomics data. We report on a new method, Structurally Aligned Local Sites of Activity (SALSA), for the prediction of biochemical function based on a local structural match at the predicted catalytic or binding site. RESULTS: Implementation of the SALSA method is described. For the structural genomics protein PY01515 (PDB ID 2aqw) from Plasmodium yoelii, it is shown that the putative annotation, Orotidine 5'-monophosphate decarboxylase (OMPDC), is most likely correct. SALSA analysis of YP_001304206.1 (PDB ID 3h3l), a putative sugar hydrolase from Parabacteroides distasonis, shows that its active site does not bear close resemblance to any previously characterized member of its superfamily, the Concanavalin A-like lectins/glucanases. It is noted that three residues in the active site of the thermophilic beta-1,4-xylanase from Nonomuraea flexuosa (PDB ID 1m4w), Y78, E87, and E176, overlap with POOL-predicted residues of similar type, Y168, D153, and E232, in YP_001304206.1. The substrate recognition regions of the two proteins are rather different, suggesting that YP_001304206.1 is a new functional type within the superfamily. A structural genomics protein from Mycobacterium avium (PDB ID 3q1t) has been reported to be an enoyl-CoA hydratase (ECH), but SALSA analysis shows a poor match between the predicted residues for the SG protein and those of known ECHs. A better local structural match is obtained with Anabaena beta-diketone hydrolase (ABDH), a known ß-diketone hydrolase from Cyanobacterium anabaena (PDB ID 2j5s). This suggests that the reported ECH function of the SG protein is incorrect and that it is more likely a ß-diketone hydrolase. CONCLUSIONS: A local site match provides a more compelling function prediction than that obtainable from a simple 3D structure match. The present method can confirm putative annotations, identify misannotation, and in some cases suggest a more probable annotation.

Functional classification of protein structures by local structure matching in graph representation.

As a result of high-throughput protein structure initiatives, over 14,400 protein structures have been solved by Structural Genomics (SG) centers and participating research groups. While the totality of SG data represents a tremendous contribution to genomics and structural biology, reliable functional information for these proteins is generally lacking. Better functional predictions for SG proteins will add substantial value to the structural information already obtained. Our method described herein, Graph Representation of Active Sites for Prediction of Function (GRASP-Func), predicts quickly and accurately the biochemical function of proteins by representing residues at the predicted local active site as graphs rather than in Cartesian coordinates. We compare the GRASP-Func method to our previously reported method, Structurally Aligned Local Sites of Activity (SALSA), using the Ribulose Phosphate Binding Barrel (RPBB), 6-Hairpin Glycosidase (6-HG), and Concanavalin A-like Lectins/Glucanase (CAL/G) superfamilies as test cases. In each of the superfamilies, SALSA and the much faster method GRASP-Func yield similar correct classification of previously characterized proteins, providing a validated benchmark for the new method. In addition, we analyzed SG proteins using our SALSA and GRASP-Func methods to predict function. Forty-one SG proteins in the RPBB superfamily, nine SG proteins in the 6-HG superfamily, and one SG protein in the CAL/G superfamily were successfully classified into one of the functional families in their respective superfamily by both methods. This improved, faster, validated computational method can yield more reliable predictions of function that can be used for a wide variety of applications by the community.

DNA-binding sequence specificity of DUX4.

BACKGROUND: Misexpression of the double homeodomain transcription factor DUX4 results in facioscapulohumeral muscular dystrophy (FSHD). A DNA-binding consensus with two tandem TAAT motifs based on chromatin IP peaks has been discovered; however, the consensus has multiple variations (flavors) of unknown relative activity. In addition, not all peaks have this consensus, and the Pitx1 promoter, the first DUX4 target sequence mooted, has a different TAAT-rich sequence. Furthermore, it is not known whether and to what extent deviations from the consensus affect DNA-binding affinity and transcriptional activation potential. RESULTS: Here, we take both unbiased and consensus sequence-driven approaches to determine the DNA-binding specificity of DUX4 and its tolerance to mismatches at each site within its consensus sequence. We discover that the best binding and the greatest transcriptional activation are observed when the two TAAT motifs are separated by a C residue. The second TAAT motif in the consensus sequence is actually (T/C)AAT. We find that a T is preferred here. DUX4 has no transcriptional activity on "half-sites", i.e., those bearing only a single TAAT motif. We further find that DUX4 does not bind to the TAATTA motif in the Pitx1 promoter, that Pitx1 sequences have no competitive band shift activity, and that the Pitx1 sequence is transcriptionally inactive, calling into question PITX1 as a DUX4 target gene. Finally, by multimerizing binding sites, we find that DUX4 transcriptional activation demonstrates tremendous synergy and that at low DNA concentrations, at least two motifs are necessary to detect a transcriptional response. CONCLUSIONS: These studies illuminate the DNA-binding sequence preferences of DUX4.

Beyond antioxidant genes in the ancient Nrf2 regulatory network.

Nrf2, a basic leucine zipper transcription factor encoded by the gene NFE2L2, is a master regulator of the transcriptional response to oxidative stress. Nrf2 is structurally and functionally conserved from insects to humans, and it heterodimerizes with the small MAF transcription factors to bind a consensus DNA sequence (the antioxidant response element, or ARE) and regulate gene expression. We have used genome-wide chromatin immunoprecipitation and gene expression data to identify direct Nrf2 target genes in Drosophila and humans. These data have allowed us to construct the deeply conserved ancient Nrf2 regulatory network-target genes that are conserved from Drosophila to human. The ancient network consists of canonical antioxidant genes, as well as genes related to proteasomal pathways and metabolism and a number of less expected genes. We have also used enhancer reporter assays and electrophoretic mobility-shift assays to confirm Nrf2-mediated regulation of ARE activity at a number of these novel target genes. Interestingly, the ancient network also highlights a prominent negative feedback loop; this, combined with the finding that Nrf2-mediated regulatory output is tightly linked to the quality of the ARE it is targeting, suggests that precise regulation of nuclear Nrf2 concentration is necessary to achieve proper quantitative regulation of distinct gene sets. Together, these findings highlight the importance of balance in the Nrf2-ARE pathway and indicate that Nrf2-mediated regulation of xenobiotic metabolism, glucose metabolism, and proteostasis has been central to this pathway since its inception.

Functional classification of protein 3D structures from predicted local interaction sites.

A new approach to the functional classification of protein 3D structures is described with application to some examples from structural genomics. This approach is based on functional site prediction with THEMATICS and POOL. THEMATICS employs calculated electrostatic potentials of the query structure. POOL is a machine learning method that utilizes THEMATICS features and has been shown to predict accurate, precise, highly localized interaction sites. Extension to the functional classification of structural genomics proteins is now described. Predicted functionally important residues are structurally aligned with those of proteins with previously characterized biochemical functions. A 3D structure match at the predicted local functional site then serves as a more reliable predictor of biochemical function than an overall structure match. Annotation is confirmed for a structural genomics protein with the ribulose phosphate binding barrel (RPBB) fold. A putative glucoamylase from Bacteroides fragilis (PDB ID 3eu8) is shown to be in fact probably not a glucoamylase. Finally a structural genomics protein from Streptomyces coelicolor annotated as an enoyl-CoA hydratase (PDB ID 3g64) is shown to be misannotated. Its predicted active site does not match the well-characterized enoyl-CoA hydratases of similar structure but rather bears closer resemblance to those of a dehalogenase with similar fold.