Positive Regulators of the HSR.

<p>Positive Regulators of the HSR.</p

Genome-wide RNAi screen for HSR regulators.

<p>(A–D) Nomarski and fluorescent images corresponding to the <i>phsp70::gfp</i> reporter strain. (A) Control animals show little reporter expression and only faint autofluorescence of the intestine. (B) Reporter induction in animals exposed to heat shock at 33°C for 1 hour. (C) RNAi knockdown of <i>hsf-1</i> decreases induction of the reporter by heat shock. (D) RNAi knockdown of <i>hsp-1</i> causes constitutive induction of the reporter in the absence of heat shock. The scale bar corresponds to 100 µm. (E–F) Quantitation of the effects on endogenous <i>hsp70</i> and <i>hsp-16.2</i> genes using qRT-PCR. (E) HSR positive regulators normalized to the heat shocked empty vector control. (F) HSR negative regulators normalized to empty vector control. Averages are from at least three biological replicates and error bars represent SEM.</p

HSR regulatory network model.

<p>Each HSR regulator, denoted by common terminology, is indicated as a box and is grouped according to its presence in a multi-subunit complex or functional pathway (i.e., the proteasome or secretory pathway). Positive or negative effects on HSR regulation are indicated by either a green arrow or red line respectively. Positive regulators are further separated from negative regulators by grey shading in the background. At the center of the network, HSF1 integrates signals from the various regulators and establishes a coordinated HSR.</p

Network analysis of HSR regulators.

<p>Shown is a network with HSR negative regulator genes depicted as nodes and interactions as edges. Node shape denotes grouping corresponding to a community detection algorithm based on the structure of the interaction network. Node color corresponds to the tissue-specific <i>phsp70::gfp</i> reporter induction. Cartoons of worms depicting the tissue specificity appear next to nodes containing those colors.</p

Tissue-selective patterns for multiple HSR reporters.

<p>I = Intestine, M = Muscle, • = Induction, ○ = No Induction.</p

Epistasis analysis of HSR regulators.

<p>The effects of HSR positive regulator knockdown on induction of the reporter by negative HSR regulator knockdown were measured using the <i>phsp70::gfp</i> reporter. (A) Images showing the results from double RNAi with each positive regulator and the negative regulator <i>hsp-1</i>. In each case, knockdown of the positive regulator decreased reporter fluorescence compared to knockdown of <i>hsp-1</i> alone. (B) Quantitation of the effects of HSR positive regulator knockdown using RNAi on induction of endogenous HSR genes by the HSR negative regulator <i>T24H7.2</i> mutant reveals that the positive regulators are epistatic to <i>T24H7.2</i>.</p

Reproduction is sensitive to chronic temperature changes.

<p>The average number of eggs laid by an individual hermaphrodite is substantially lower at 28°C (compared to ∼300 at 20°C), and is nearly zero at 30°C (A). In contrast, at 30°C, animals exhibit considerably milder effects on motility and viability (B).</p

Fitting the model to experimental data.

<p>Because the reproductive dynamics are strongly temperature dependent, we let the three model parameters vary as exponential functions of temperature (A–C). As expected, all parameters increased with temperature. Red circles represent the estimated parameters values for the three temperatures used to train the model. Constraining model parameters yielded close fits to experimental observations, represented by dots ±1 standard deviation (D). Model predictions (solid lines) ±1 standard deviation (dashed lines) are shown for comparison.</p

Predicting the dynamics of <i>C. elegans</i> reproduction.

<p>Predicted egg-laying trajectories (sold lines are median predictions; dashed lines are ±1 standard deviation) for animals shifted to 23, 28, and 30°C quantitatively capture the experimental data (dots; ±1 standard deviation).</p

More complicated models do not offer an improved description of the system.

<p>Explicitly accounting for oocyte development (blue) is nearly indistinguishable from the quasi-steady-state approximation (red) (A). Including a discrete state for dead oocytes (B) complicates the model, but leads to a description (Equation 6) that is mathematically equivalent to the parsimonious model (Equation 4).</p