Parihar M , Bendelac-Kapon L , Gur M , Abbou T , Belorkar A , Achanta S , Kinberg K , Vadigepalli R , Fainsod A .

U01 EB023224 NIBIB NIH HHS

             retinoic acid receptor signaling pathway

GSE154407: NCBI
GSE154408: NCBI

	FIGURE 1. Efficient RA signaling robustness following transient physiological manipulation of RA levels. (A) Schematic depiction of the experimental design to directly challenge and study the robustness of RA signaling during embryogenesis. Timing and developmental stages are shown. (B) Principal Components 1 and 2 (PC1, PC2) of all six biological replicates studied by time series RNAseq. (C) Heatmap of gene expression of the top-100 positive and top-100 negative loadings corresponding to PC1 and PC2. A subset of the genes is highlighted based on the relevance to early developmental processes.
	FIGURE 2. Phenotypic and molecular robustness of the retinoic acid metabolic pathway. Retinoic acid levels were reduced in Xenopus laevis embryos by inhibition of the RALDH activity with DEAB or by CYP26A1 overexpression to render retinoic acid inactive. (A) Control embryo at st. 27. (B) Embryo injected with capped RNA (0.8 ng) encoding the CYP26A1 enzyme. (C) Embryo treated with DEAB (50 μM) from st. 8.5 until st. 27. (D) Embryo treated with DEAB and injected with cyp26a1 mRNA. To exemplify the induced developmental malformations, lines depicting the size of the head domain (blue) and the trunk (red) were drawn on the control embryo (A) and then copied unto the treated embryos (B–D). (E) Distribution of the developmental defect severity induced by the retinoic acid manipulations. Embryos were scored for the induction of moderate (B,C) or severe (D) phenotypes, or normal looking. (F–H) Gene expression changes as a result of retinoic acid manipulation. qPCR analysis of hoxa1.L (F), hoxa1.S (G), and dhrs3.L (H) relative expression levels as a result of the individual or combined retinoic acid manipulations. Statistical significance (Student’s t-test) was calculated compared to the combined treatment group. *p < 0.05; **p < 0.01; ****p < 0.0001; ns, not significant.
	FIGURE 3. Transient physiological manipulation of RA levels for robustness analysis. (A) Embryos were treated with increasing concentrations of all-trans RA from 1 nM to 1 μM. Treatments were initiated at st. 8.5 and RNA samples were collected at tailbud stages (st. 32–33) for phenotypic analysis. To exemplify the induced developmental malformations, lines depicting the size of the head domain (blue), the trunk (red), and the tail (purple), were drawn on the control embryo (-RA) and then copied unto the treated embryos (10–1,000 nM). (B) Distribution of the severity of the developmental defects induced by the retinoic acid manipulations. Treated embryos were scored for the induction of moderate (as in 25 or 50 nM) or severe phenotypes (as in 100 or 1,000 nM), or normal looking (as in -RA). (C–H) RA treated embryos were collected at early (st. 10.25) and late (st. 12) gastrula for analysis of the changes in expression level. The response of RA metabolic and target genes was studied by qPCR. (C) hoxb1 (D) cyp26a1 (E) dhrs3 (F) aldh1a2 (G) aldh1a3 (H) rdh10. Statistical significance (Student’s t-test) was calculated compared to the control group. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns, not significant.
	FIGURE 4. Comparative pattern analysis to uncover genes responding to opposing RA manipulations. (A) Genes were grouped into discretized expression patterns based on up-regulation (yellow), down-regulation (blue), or no change (gray) compared to the control sample at the same time point. The numbers below the patterns indicate the number of genes that show the corresponding expression pattern in RA vs. control or DEAB vs. control. Only 32 out of 81 theoretically possible (four time points, 3*3*3*3 = 81) dynamic patterns were exhibited by at least one gene and are included in the figure. (B) COMPACT matrix comparing gene expression changes due to RA and DEAB relative to control. The subset of 32 × 32 patterns with non-zero number of genes in either perturbation group are shown. The gene counts were grouped within related patterns based on the time of initial up- or down-regulation. Supplementary Data 1 contains a version of the COMPACT matrix shown with the gene identifiers corresponding to the counts. The different quadrants are labeled a to d. (C) Dynamic expression patterns of genes showing opposite changes in response to RA and DEAB treatments (quadrant b).
	FIGURE 5. Network-wide response to transient RA manipulations. (A) RA metabolic network components and RA target genes analyzed during early/mid gastrula. The homeolog (L&S) or singleton status of the gene(s) in the genome is denoted outside the box with the gene name. The gene names shown include a .L suffix to mark in cases where only one of the homeologs changed significantly in the RNAseq and HT-qPCR data. (B) Analysis of the expression change during the recovery period following the transient increase (RA) and decrease (DEAB) in RA levels. The hox gene and the RA metabolic network component changes were averaged from the RNAseq and HT-qPCR analyses. The black frame around the t = 0 h time point denotes the network-wide response at the end of the treatment. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; not labeled, not significant. (C,D) Mapping the differential expression data onto the RA network shown in panel (A) to highlight the differences in regulation of biosynthesis versus degradation of RA signal between RA and DEAB treatment groups.
	FIGURE 6. RA responsiveness and differential expression dynamics of select RA network genes and targets in different clutches. (A–F) Embryos were treated at midblastula (st.8.5) with increasing concentrations of RA (1–1,000 nM) or DEAB 1–50 μM) to generate a gradient of RA responses. At mid gastrula (st. 11) the changes in RA network genes was determined by qPCR. (A) hoxa1.L, hoxa1.S; (B) dhrs3.L, dhrs3.S; (C) cyp26a1.L, cyp26a1.S; (D) rdh10.L, rdh10.S; (E) aldh1a2.L, aldh1a2.S; (F) aldh1a3.L, aldh1a3.S. (G) Dynamic expression patterns of multiple RA network genes across the different clutches. The data was combined from RNAseq (clutches A-F) and HT-qPCR (clutches G-L). The clutches are ordered left to right based on the earliest time at which hoxa1 expression returned to the baseline levels.
	FIGURE 7. Trajectory analysis to compare the extent of individual clutch robustness based on hox expression. (A) 3-dimensional principal curve for the hox genes, showing projections of the sample points on the curve for (A, top) RA and (A, bottom) DEAB treatments. Principal curves for clutches C, J, and E are shown. The black star indicates the beginning of the curve for the distance measurement along the trajectory. Ranking of clutches is based on the net (absolute) normalized distance of treatment samples from the corresponding control sample for each time point. (B) Normalized expression shift profile calculated from the principal curve as the arc distance between the treatment and the corresponding control. Clutches are rank-ordered from lowest to highest net expression shift for hox genes in the RA group. Clutches A-F data from RNA-seq, clutches G-L data from HT-qPCR. (C) Distribution of clutches based on the trajectory determined robustness to RA and DEAB treatments relative to each other based on hox expression. The letters indicate the distinct clutches. (D) Heat map representation of gene expression changes of five additional clutches transiently treated with RA or DEAB. Samples were analyzed at the end of the pulse (t0) and 2 h in the chase (t2). Asterisks denote clutches with abnormal responses.
	FIGURE 8. Differential robustness to direction of RA change is related to the effectiveness of feedback regulatory action. (A,B) Comparative distribution of clutches based on the trajectory determined robustness to RA (A) and DEAB (B). The change of the hox genes as a function of the change in the RA network genes is plotted for each RA manipulation. The letters indicate the distinct clutches. (C) Schematic diagram of the robustness efficiency-efficacy matrix in which different quadrants indicate the relative level of feedback observed and robustness achieved. (D,E) Mapping the differential expression data of clutches A, E, and F onto the RA network shown in Figure 5A to highlight the heterogeneity of differential regulation of RA network components for clutches closely situated in the robustness efficiency matrix (A,B).
	FIGURE 9. Proposed model of the alternative regulatory schemes to achieve RA robustness. Schematic representation of two alternative scenarios in the decision tree to achieve robustness. These two scenarios are representative of the multiple permutations possible to achieve similar robustness outcomes. Each clutch follows a decision trajectory based on the direction of RA fluctuation and clutch-specific composition of RA network variants. In this scheme, uneven robustness arises when the regulatory feedback response of a clutch is optimized for one scenario over another. Our results revealed such a trade-off over distinct objectives of counteracting RA increase versus decrease.

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