The cell cycle program of polypeptide labeling in Chlamydomonas reinhardtii

The cell cycle program of polypeptide labeling in syndhronous cultures of wild-type Chlamydomonas reinhardtii was analyzed by pulse-labeling cells with 35SO4 = or [3H]arginine at different cell cycle stages. Nearly 100 labeled membrane and soluble polypeptides were resolved and studied using one-dimensional sodium dodecyl sulfate (SDS)- polyacrylamide gel electrophoresis. The labeling experiments produced the following results. (a) Total 35SO4 = and [3H]arginine incorporation rates varied independently throughout the cell cycle. 35SO4 = incorporation was highest in the mid-light phase, while [3H]arginine incorporation peaked in the dark phase just before cell division. (b) The relative labeling rate for 20 of 100 polypeptides showed significant fluctuations (3-12 fold) during the cell cycle. The remaining polypeptides were labeled at a rate commensurate with total 35SO4 = or [3H]arginine incorporation. The polypeptides that showed significant fluctuations in relative labeling rates served as markers to identify cell cycle stages. (c) The effects of illumination conditions on the apparent cell cycle stage-specific labeling of polypeptides were tested. Shifting light-grown asynchronous cells to the dark had an immediate and pronounced effect on the pattern of polypeptide labeling, but shifting dark-phase syndhronous cells to the light had little effect. The apparent cell cycle variations in the labeling of ribulose 1,5-biphosphate (RUBP)-carboxylase were strongly influenced by illumination effects. (d) Pulse-chase experiments with light-grown asynchronous cells revealed little turnover or inter- conversion of labeled polypeptides within one cell generation, meaning that major polypeptides, whether labeled in a stage-specific manner or not, do not appear transiently in the cell cycle of actively dividing, light-grown cells. The cell cycle program of labeling was used to analyze effects of a temperature-sensitive cycle blocked (cb) mutant. A synchronous culture of ts10001 was shifted to restrictive temperature before its block point to prevent it from dividing. The mutant continued its cell cycle program of polypeptide labeling for over a cell generation, despite its inability to divide

A Cis-Regulatory Map of the Drosophila Genome

Systematic annotation of gene regulatory elements is a major challenge in genome science. Direct mapping of chromatin modification marks and transcriptional factor binding sites genome-wide1, 2 has successfully identified specific subtypes of regulatory elements3. In Drosophila several pioneering studies have provided genome-wide identification of Polycomb response elements4, chromatin states5, transcription factor binding sites6, 7, 8, 9, RNA polymerase II regulation8 and insulator elements10; however, comprehensive annotation of the regulatory genome remains a significant challenge. Here we describe results from the modENCODE cis-regulatory annotation project. We produced a map of the Drosophila melanogaster regulatory genome on the basis of more than 300 chromatin immunoprecipitation data sets for eight chromatin features, five histone deacetylases and thirty-eight site-specific transcription factors at different stages of development. Using these data we inferred more than 20,000 candidate regulatory elements and validated a subset of predictions for promoters, enhancers and insulators in vivo. We identified also nearly 2,000 genomic regions of dense transcription factor binding associated with chromatin activity and accessibility. We discovered hundreds of new transcription factor co-binding relationships and defined a transcription factor network with over 800 potential regulatory relationships

A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis

Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns

Repetitive sequence transcripts in development

Interspersed repetitive sequences are represented widely in animal cell nuclear RNAs, in the poly(A) RNA stored in eggs and in some mRNAs. Their expression is developmentally modulated. Although the genomic location of repetitive sequences may change rapidly during evolution, the patterns of their transcription suggest a variety of possible functions

Repetitive sequence transcripts in development

Interspersed repetitive sequences are represented widely in animal cell nuclear RNAs, in the poly(A) RNA stored in eggs and in some mRNAs. Their expression is developmentally modulated. Although the genomic location of repetitive sequences may change rapidly during evolution, the patterns of their transcription suggest a variety of possible functions

Role of Architecture in the Function and Specificity of Two Notch-Regulated Transcriptional Enhancer Modules

<div><p>In <em>Drosophila melanogaster</em>, <em>cis</em>-regulatory modules that are activated by the Notch cell–cell signaling pathway all contain two types of transcription factor binding sites: those for the pathway's transducing factor Suppressor of Hairless [Su(H)] and those for one or more tissue- or cell type–specific factors called “local activators.” The use of different “Su(H) plus local activator” motif combinations, or codes, is critical to ensure that only the correct subset of the broadly utilized Notch pathway's target genes are activated in each developmental context. However, much less is known about the role of enhancer “architecture”—the number, order, spacing, and orientation of its component transcription factor binding motifs—in determining the module's specificity. Here we investigate the relationship between architecture and function for two Notch-regulated enhancers with spatially distinct activities, each of which includes five high-affinity Su(H) sites. We find that the first, which is active specifically in the socket cells of external sensory organs, is largely resistant to perturbations of its architecture. By contrast, the second enhancer, active in the “non-SOP” cells of the proneural clusters from which neural precursors arise, is sensitive to even simple rearrangements of its transcription factor binding sites, responding with both loss of normal specificity and striking ectopic activity. Thus, diverse cryptic specificities can be inherent in an enhancer's particular combination of transcription factor binding motifs. We propose that for certain types of enhancer, architecture plays an essential role in determining specificity, not only by permitting factor–factor synergies necessary to generate the desired activity, but also by preventing other activator synergies that would otherwise lead to unwanted specificities.</p> </div

Lateral inhibition: Two modes of non-autonomous negative autoregulation by <i>neuralized</i>

<div><p>Developmental patterning involves the progressive subdivision of tissue into different cell types by invoking different genetic programs. In particular, cell-cell signaling is a universally deployed means of specifying distinct cell fates in adjacent cells. For this mechanism to be effective, it is essential that an asymmetry be established in the signaling and responding capacities of the participating cells. Here we focus on the regulatory mechanisms underlying the role of the <i>neuralized</i> gene and its protein product in establishing and maintaining asymmetry of signaling through the Notch pathway. The context is the classical process of “lateral inhibition” within <i>Drosophila</i> proneural clusters, which is responsible for distinguishing the sensory organ precursor (SOP) and non-SOP fates among adjacent cells. We find that <i>neur</i> is directly regulated in proneural clusters by both proneural transcriptional activators and <i>Enhancer of split</i> basic helix-loop-helix repressors (bHLH-Rs), via two separate cis-regulatory modules within the <i>neur</i> locus. We show that this bHLH-R regulation is required to prevent the early, pre-SOP expression of <i>neur</i> from being maintained in a subset of non-SOPs following SOP specification. Lastly, we demonstrate that Neur activity in the SOP is required to inhibit, in a cell non-autonomous manner, both <i>neur</i> expression and Neur function in non-SOPs, thus helping to secure the robust establishment of distinct cell identities within the developing proneural cluster.</p></div

Forcing persistent non-SOP expression of <i>neur</i> causes both loss and gain of SOPs.

<p>(A) Quantification of macrochaete gain and loss on the dorsal head and thorax of flies of the genotypes indicated at right. Error bars represent SEM. (B and C) Scutellar bristle positions in 24 hr APF nota of the indicated genotypes, stained with anti-Cut antibody, show loss of the SOP with <i>neur</i> misexpression. (D-I) Uniform expression of <i>UAS-neur</i> driven by <i>tub-GAL4</i> in <i>neur</i> mutant clones using the MARCM system in either a wing imaginal disc (D-F) or a 12 hr APF notum (G-I). GFP (green in F and I) marks the territories of <i>tub>neur</i> expression; anti-Sens antibody signal (magenta in F and I) marks SOPs. Brackets in D and F mark SOP loss at the region of overlap between <i>tub>neur</i> activity and the wing margin. Sens-positive cells boxed in D are in a different focal plane from the GFP-expressing cells.</p

Through N signaling, proneural-dependent SOP expression of Neur promotes the inhibition of both <i>neur</i> transcription and Neur function in non-SOP cells.

<p>Proneural proteins activate <i>neur</i> transcription both directly, via binding sites in the neur4D and neur1B enhancers, and indirectly by activating expression of other positive regulators of <i>neur</i> in the SOP. <i>neur</i>-dependent N signaling, combined with proneural factor activity, non-autonomously promotes expression of both <i>E(spl)</i> bHLH-Rs and BFMs in non-SOP cells. The bHLH-Rs repress further transcription of <i>neur</i> directly, through binding motifs in neur4D and neur1B, and similarly inhibit the expression of other SOP-specific targets. The BFMs bind Neur and block its interaction with Dl, preventing non-SOP cells from sending an effective N signal back to the SOP.</p