Transcriptional dynamics elicited by a short pulse of notch activation involves feed-forward regulation by E(spl)/Hes genes.

Dynamic activity of signaling pathways, such as Notch, is vital to achieve correct development and homeostasis. However, most studies assess output many hours or days after initiation of signaling, once the outcome has been consolidated. Here we analyze genome-wide changes in transcript levels, binding of the Notch pathway transcription factor, CSL [Suppressor of Hairless, Su(H), in Drosophila], and RNA Polymerase II (Pol II) immediately following a short pulse of Notch stimulation. A total of 154 genes showed significant differential expression (DE) over time, and their expression profiles stratified into 14 clusters based on the timing, magnitude, and direction of DE. E(spl) genes were the most rapidly upregulated, with Su(H), Pol II, and transcript levels increasing within 5-10 minutes. Other genes had a more delayed response, the timing of which was largely unaffected by more prolonged Notch activation. Neither Su(H) binding nor poised Pol II could fully explain the differences between profiles. Instead, our data indicate that regulatory interactions, driven by the early-responding E(spl)bHLH genes, are required. Proposed cross-regulatory relationships were validated in vivo and in cell culture, supporting the view that feed-forward repression by E(spl)bHLH/Hes shapes the response of late-responding genes. Based on these data, we propose a model in which Hes genes are responsible for co-ordinating the Notch response of a wide spectrum of other targets, explaining the critical functions these key regulators play in many developmental and disease contexts

Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities

Visual Literacy Intervention for Improving Undergraduate Student Critical Thinking of Global Sustainability Issues

The promotion of global sustainability within environmental science courses requires a paradigm switch from knowledge-based teaching to teaching that stimulates higher-order cognitive skills. Non-major undergraduate science courses, such as environmental science, promote critical thinking in students in order to improve the uptake of scientific information and develop the rational decision making used to make more informed decisions. Science, engineering, technology and mathematics (STEM) courses rely extensively on visuals in lectures, readings and homework to improve knowledge. However, undergraduate students do not automatically acquire visual literacy and a lack of intervention from instructors could be limiting academic success. In this study, a visual literacy intervention was developed and tested in the face-to-face (FTF) and online sections of an undergraduate non-major Introduction to Environmental Science course. The intervention was designed to test and improve visual literacy at three levels: (1) elementary—identifying values; (2) intermediate—identifying trends; and (3) advanced—using the data to make projections or conclusions. Students demonstrated a significant difference in their ability to answer elementary and advanced visual literacy questions in both course sections in the pre-test and post-test. Students in the face-to-face course had significantly higher exam scores and higher median assessment scores compared to sections without a visual literacy intervention. The online section did not show significant improvements in visual literacy or academic success due to a lack of reinforcement of visual literacy following the initial intervention. The visual literacy intervention shows promising results in improving student academic success and should be considered for implementation in other general education STEM courses

Patterns of Su(H) and Pol II recruitment.

<p>A: Table showing, for each cluster, the proportion of genes with Su(H) binding within 10 kb [ # Su(H)] and with each Pol II state (Pol II: AU, AP, P and UB as described in main text). In cases where individual transcripts of a gene had different Pol II states, the gene was assigned a state as follows: AU>AP>P>UB. Conditions where >30% of genes are ascribed to a particular class are indicated in bold. B–D: Enrichment for Su(H) (blue) and Pol II (red) across the <i>W/hid</i> (B), <i>CG4398</i> (C) and <i>hairy</i> (D) genes at different time points (min) after Notch activation (Su(H) 0.5–4.5, Pol II 0–4.7 fold enrichment on a log₂ scale).</p

Transient activation of Notch and classification of transcripts according to Pol II class.

<p>A: Schematic outlining the experimental strategy. Arrows indicate the time-points at which data were collected. B: Levels of N^icd that co-immunoprecipitated with Su(H) after a 5 min pulse of Notch activation (Nact) using EDTA treatment. C: Graph shows a quantification of N^icd levels relative to Su(H) from B, normalised to 0 min. D: Representative genomic regions, gene models are indicated in black. Red graphs represent enrichment with anti-pSer2pSer5-Pol II relative to total input (0–0.47 fold enrichment on a log₂ scale). Pol II binding classes, AP (active poised), P (poised), UB (unbound) and AU (active uniform) are illustrated. A ratio of log₂(max)/log₂(median)≥2 cut-off was used to distinguish AP from AU (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003162#pgen.1003162.s010" target="_blank">Text S1</a>). E: Relationship between log₂ absolute expression levels at 0 min and Pol II class at 0 min. RNA expression levels were approximated, up to a constant, by the spot intensity levels (logC₀). The four Pol II classes have significantly different mean logC₀ (ANOVA p value<2.2e-16). All pairs of classes, except for AU and AP, have significantly different means (pair-wise one-sided two-sample t test p values<3e-15).</p

Evidence for cross-regulatory relationships between genes in different clusters.

<p>A: mRNA expression levels of the indicated genes in Notch activated cells relative to controls (log₂) in untreated (grey) and cycloheximide treated (black) cells at the times indicated. Cells were exposed to cycloheximide for 60 min prior to Notch activation at 0 min as well as during the timecourse. B,C: Expression of Hairy in the wing imaginal disc pouch from control (B, <i>ptc-Gal4 ; UAS-lacZ</i>) and HLHmβ-VP16 (C, <i>ptc-Gal4</i> ; <i>UAS-HLHmβ-VP16</i>), arrows indicate the stripe of HLHmβ-VP16 expression where Hairy is induced. D,E: Expression of Hairy in muscle progenitors (brackets) from control wing imaginal discs (D, <i>1151-Gal4</i>; <i>UAS-lacZ</i>) and from those expressing HLHmβ (E, <i>1151-Gal4</i>; <i>UAS-HLHmβ</i>). Hairy is reduced in the muscle progenitors (brackets) but not in neighbouring epithelial cells (asterisks). F,G: Quantification of expression levels of Hairy (F) and <i>edl-GFP</i> (G) in muscle progenitors expressing β-galactosidase (con), HLHmβ (mβ) or HLHmβ-VP16 (mβ-VP16). Average pixel intensities from a defined region within the expression domain were measured using ImageJ and normalized relative to background levels from a comparable region in the same discs, >5 discs per genotype. Error bars indicate standard error of the mean. Asterisks indicate that results are significantly different from control (p≤0.05; using an unpaired, 2-tailed student T-test). H: Fold change of the indicated mRNAs in cells treated with dsRNA against <i>hairy</i>, <i>edl</i> or <i>btn</i> in comparison to controls (no RNAi). RNA levels were reduced by 65%, 71% and 61% for <i>hairy</i>, <i>edl</i> and <i>btn</i> respectively. These experiments were performed in the basal state (no Notch activation). Bars represent the average of three biological replicates and error bars indicate standard error of the mean. I: Log₂ fold changes in mRNA levels of <i>hairy</i> (<i>h</i>, brown) <i>hibris</i> (<i>hbs</i>, green) and <i>HLHm</i>β (<i>m</i>β, blue) from the microarray study. Scale for <i>hbs</i> and <i>h</i> is indicated by left axis and for <i>m</i>β, which had larger fold changes, by right axis. J: Summary model of the feed-forward regulatory relationships, dotted line indicates that the direct regulation of <i>hbs</i> (cluster 3) by Notch signaling has not been directly tested here, although <i>hbs</i> and other genes in cluster 3 exhibit Su(H) binding, which implies that at least some undergo direct Notch regulation.</p

Rapid and transient recruitment of Su(H) and Pol II to genes in the <i>E(spl)</i> complex.

<p>A: Enrichment for Su(H) (blue) and Pol II (red) across the <i>E(spl)</i> complex at different time points (min) after Notch activation (Su(H) 0.5–4.5, Pol II 0–4.7 fold enrichment on a log₂ scale). Cluster 1 genes: <i>mβ</i> (orange) <i>m3</i> (brown) and <i>m7</i> (yellow) have poised Pol II at 0 min, <i>m6</i> (light blue), <i>mα</i> (dark blue) and <i>m2</i> (mid blue) have no Pol II present at 0 min. Cluster 2 genes: <i>mδ</i> (purple) and <i>mγ</i>(pink) have no Pol II present at 0 mins. Pol II is recruited at all expressed genes by 10 mins. Su(H) occupancy increases after Notch activation at all loci. B: Log₂ fold changes in mRNA levels for the indicated genes at different times (min) after Notch activation.</p

Different temporal profiles in response to Notch activation.

<p>Examples of 6 clusters of genes that exhibit different temporal expression profiles in Notch activated cells. Left graphs: profiles for all the genes in the cluster, coloured line represents the mean profile. Right graphs: profiles for a single gene from each cluster as indicated; Error bars indicate standard error of the mean from four replicates. Vertical axes for all graphs indicate median M values. Cluster types are indicated to the right of the graphs.</p

Differential sensitivities to the dose of Notch activation.

<p>A: Comparison of fold changes in mRNA expression levels (log₂) from 5 and 30 min Notch activation regimes. Symbols indicate fold-change at 30 min time-point after commencing activation (EDTA) treatment, where colours represent cluster assignments according to the legend in B. Dashed line represents the expected trend if each treatment produced the same response; solid line indicates the line of best fit from the data (regression coefficient = 0.84, r2 = 0.71). B: Ratio of the fold change in mRNA levels at 30 min, with a 30 versus 5 min treatment for the indicated genes. Higher bars indicate greater sensitivity to the differences in the activation regime. Colours indicate cluster assignments as in the legend. C: Levels of N^icd that co-immunoprecipitate with Su(H) under continuous treatment for the times indicated (compare with <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003162#pgen-1003162-g001" target="_blank">Figure 1A</a>). D: Fold changes for the indicated mRNAs at the times indicated (red lines: 5 min activation, blue lines: continuous activation). T = 0 corresponds to the time at which the activation regime commenced. Error bars indicate standard error of the mean from four biological replicates.</p

Transcriptional Dynamics Elicited by a Short Pulse of Notch Activation Involves Feed-Forward Regulation by E(spl)/Hes Genes

Dynamic activity of signaling pathways, such as Notch, is vital to achieve correct development and homeostasis. However, most studies assess output many hours or days after initiation of signaling, once the outcome has been consolidated. Here we analyze genome-wide changes in transcript levels, binding of the Notch pathway transcription factor, CSL [Suppressor of Hairless, Su(H), in Drosophila], and RNA Polymerase II (Pol II) immediately following a short pulse of Notch stimulation. A total of 154 genes showed significant differential expression (DE) over time, and their expression profiles stratified into 14 clusters based on the timing, magnitude, and direction of DE. E(spl) genes were the most rapidly upregulated, with Su(H), Pol II, and transcript levels increasing within 5–10 minutes. Other genes had a more delayed response, the timing of which was largely unaffected by more prolonged Notch activation. Neither Su(H) binding nor poised Pol II could fully explain the differences between profiles. Instead, our data indicate that regulatory interactions, driven by the early-responding E(spl)bHLH genes, are required. Proposed cross-regulatory relationships were validated in vivo and in cell culture, supporting the view that feed-forward repression by E(spl)bHLH/Hes shapes the response of late-responding genes. Based on these data, we propose a model in which Hes genes are responsible for co-ordinating the Notch response of a wide spectrum of other targets, explaining the critical functions these key regulators play in many developmental and disease contexts