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| <imgcaption figure_5|Microdissection of root tissues. A-D Cutting of the root tissues by microdissection, the tissues are extracted successively from the inside to the outside. A) Paraffin section before cutting. B) After cutting the stele + endodermis. C) After cutting the cortex. D) After cutting the external tissues, epidermis/exodermis and sclerenchyma. E-F ddRTPCR of specific markers. E) Expression of OsSHR1 in the stele, cortex and external tissues. F) Expression of 5NG4 in the stele, cortex and external tissues. G) Expression of a constitutive gene expressed in all three tissues, Exp'. The positive droplets, materializing the presence of transcript are in green, the negative droplets in grey. The red bar represents the detection threshold. These results demonstrate that there is no cross-contamination between the microdissected tissues. H) and I) Expression profiles of RboH family genes during cortex maturation. Y-axis, number of RNAseq readings per bank obtained after normalization, in the division zone (CO-ZD), elongation zone (CO-ZE) and mature zone (CO-ZM). The standard error bar corresponds to four biological replicates.>{{:figure_5.png}}</imgcaption> | <imgcaption figure_5|Microdissection of root tissues. A-D Cutting of the root tissues by microdissection, the tissues are extracted successively from the inside to the outside. A) Paraffin section before cutting. B) After cutting the stele + endodermis. C) After cutting the cortex. D) After cutting the external tissues, epidermis/exodermis and sclerenchyma. E-F ddRTPCR of specific markers. E) Expression of OsSHR1 in the stele, cortex and external tissues. F) Expression of 5NG4 in the stele, cortex and external tissues. G) Expression of a constitutive gene expressed in all three tissues, Exp'. The positive droplets, materializing the presence of transcript are in green, the negative droplets in grey. The red bar represents the detection threshold. These results demonstrate that there is no cross-contamination between the microdissected tissues. H) and I) Expression profiles of RboH family genes during cortex maturation. Y-axis, number of RNAseq readings per bank obtained after normalization, in the division zone (CO-ZD), elongation zone (CO-ZE) and mature zone (CO-ZM). The standard error bar corresponds to four biological replicates.>{{project:figure_5.png}}</imgcaption> |
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| Using this technological development, we constructed and sequenced 36 RNAseq libraries for 3 tissues, the first 3 developmental steps and four biological repetitions to identify the expression profiles of the different stages of differentiation of aerenchyma. To verify that our RNAseq data capture the main steps of cortex differentiation into aerenchyma, we analyzed the expression of genes of the RboH family **(Figure 1H, I)**. Genes of this family are involved in cell death in rice and their expression increases during submergence (Yamauchi T. et al., 2017). Five genes of this family have a significant increase in expression during cortex maturation **(Figure 1H, I)**, confirming that our data allows us to monitor cortex differentiation into aerenchyma. The identification and classification of possible influential regulators of aerenchyma need to aggregate information from other sources and databases including but not limited to orthologs, gene family classification to explore GRN in a practical way. | Using this technological development, we constructed and sequenced 36 RNAseq libraries for 3 tissues, the first 3 developmental steps and four biological repetitions to identify the expression profiles of the different stages of differentiation of aerenchyma. To verify that our RNAseq data capture the main steps of cortex differentiation into aerenchyma, we analyzed the expression of genes of the RboH family **(Figure 1H, I)**. Genes of this family are involved in cell death in rice and their expression increases during submergence (Yamauchi T. et al., 2017). Five genes of this family have a significant increase in expression during cortex maturation **(Figure 1H, I)**, confirming that our data allows us to monitor cortex differentiation into aerenchyma. The identification and classification of possible influential regulators of aerenchyma need to aggregate information from other sources and databases including but not limited to orthologs, gene family classification to explore GRN in a practical way. |
| The next step after the reconstruction of the GRN is to identify the most relevant regulators in a set comprising several hundreds of co-regulated genes. Network visualization has shifted its focus from simple network models, to more complex ones with many unresolved challenges (von Landesberger et al., 2011), such as dynamic networks (Beck et al., 2017), compound networks, multivariate networks (Kerren et al., 2014) and multilayer networks (Kivelä et al., 2014; McGee et al., 2019). Navigating and comparing reconstructed networks is very difficult, especially when more than two networks are involved. Recent visualization techniques aim to ease such comparisons. For instance, hive plots are used to extract qualitative and quantitative information from these networks and compare them with each other (Krzywinski et al., 2012); their usefulness has been demonstrated with a use case about disease gene networks, among others (Krzywinski et al., 2012). The hive plot is a network representation which places the nodes belonging to distinct subnetwork on distinct axes, while the connections are depicted as curves between these nodes. Several axes can be represented in a star pattern** (Figure 2)**. | The next step after the reconstruction of the GRN is to identify the most relevant regulators in a set comprising several hundreds of co-regulated genes. Network visualization has shifted its focus from simple network models, to more complex ones with many unresolved challenges (von Landesberger et al., 2011), such as dynamic networks (Beck et al., 2017), compound networks, multivariate networks (Kerren et al., 2014) and multilayer networks (Kivelä et al., 2014; McGee et al., 2019). Navigating and comparing reconstructed networks is very difficult, especially when more than two networks are involved. Recent visualization techniques aim to ease such comparisons. For instance, hive plots are used to extract qualitative and quantitative information from these networks and compare them with each other (Krzywinski et al., 2012); their usefulness has been demonstrated with a use case about disease gene networks, among others (Krzywinski et al., 2012). The hive plot is a network representation which places the nodes belonging to distinct subnetwork on distinct axes, while the connections are depicted as curves between these nodes. Several axes can be represented in a star pattern** (Figure 2)**. |
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| {{:project:figure_6.png?direct|}} <imgcaption figure_6| The hive plots: visualize and compare gene networks. In this hypothetical example, two networks of transcription factors and their effectors are compared for two stages of cortex differentiation into aerenchyma, A) the elongation and B) initiation of cell death stages. Top are hypothetical, but classical representation of gene networks. Bottom are hive plots generated using gene network data. The 3 axes correspond to: a list of 4 effector genes involved in cell death (PCD genes); the most influential transcriptional factors on these effectors (most influential) on a second axis; the transcription factors most connected to these effectors on the last axis (most connected). The last two axes are ranked. With the help of the hives plots, we can directly compare the two networks and immediately visualize there is a shift between the TF1 (red color) and TF2 (green color) effect during the initiation stage of cell death. This suggest that TF1 is the earliest master regulator of PCD, initiating the cell death process while TF2 become dominant during the cell death initiation step itself. The axes can be arbitrary determined by a user to explore and compare gene networks in almost any way, while direct comparison of entire gene networks seems difficult and most of the time impossible. Curves represent network edges>{{:figure_6.png}}</imgcaption> | {{:project:figure_6.png?direct|}} <imgcaption figure_6| The hive plots: visualize and compare gene networks. In this hypothetical example, two networks of transcription factors and their effectors are compared for two stages of cortex differentiation into aerenchyma, A) the elongation and B) initiation of cell death stages. Top are hypothetical, but classical representation of gene networks. Bottom are hive plots generated using gene network data. The 3 axes correspond to: a list of 4 effector genes involved in cell death (PCD genes); the most influential transcriptional factors on these effectors (most influential) on a second axis; the transcription factors most connected to these effectors on the last axis (most connected). The last two axes are ranked. With the help of the hives plots, we can directly compare the two networks and immediately visualize there is a shift between the TF1 (red color) and TF2 (green color) effect during the initiation stage of cell death. This suggest that TF1 is the earliest master regulator of PCD, initiating the cell death process while TF2 become dominant during the cell death initiation step itself. The axes can be arbitrary determined by a user to explore and compare gene networks in almost any way, while direct comparison of entire gene networks seems difficult and most of the time impossible. Curves represent network edges>{{project:figure_6.png}}</imgcaption> |
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| <imgcaption figure_7|Activation and repression of a reporter gene, VENUS YFP, by the scaffold system. A) CRISPR RNA scaffold system: principe (from Zalatan 2015). CRISPR RNA scaffold-based allows simultaneous activation or repression of independent gene targets. sgRNA molecules are extended with aptamer recognize by RNA-binding proteins that are fused to functional effectors, either activator or repressor. This approach allows simultaneous activation and repression of several genes in the same time on the same cell. B) Illustration of a sorting of protoplasts transformed by a p35::VENUS:NLS construct. On the left, in green, non-fluorescent untransformed protoplasts, on the left transformed protoplasts. The average fluorescence of the positive protoplasts is used as a proxy for the activation or repression efficiency in C) and D). C) Induction of a VENUS YFP by the scaffold, fusion and SAM systems (combination of scaffold and fusion system). The VENUS promoter comprises 7 teto sequences recognized by the sgRNA used in the three systems. The graph in the upper left corner represents the activation fold of the VENUS by each system. The bottom graph represents the percentage of activation compared to the strong and constitutive p35:VENUS promoter. The best system achieves an induction equivalent to 36% of a 35S promoter. D) Repression of a 35S promoter coupled to a VENUS YFP by all three systems. The graph represents the percentage of repression achieved by each system. The best system achieves nearly 40% repression of a strong promoter>{{:figure_7.png}}</imgcaption> | <imgcaption figure_7|Activation and repression of a reporter gene, VENUS YFP, by the scaffold system. A) CRISPR RNA scaffold system: principe (from Zalatan 2015). CRISPR RNA scaffold-based allows simultaneous activation or repression of independent gene targets. sgRNA molecules are extended with aptamer recognize by RNA-binding proteins that are fused to functional effectors, either activator or repressor. This approach allows simultaneous activation and repression of several genes in the same time on the same cell. B) Illustration of a sorting of protoplasts transformed by a p35::VENUS:NLS construct. On the left, in green, non-fluorescent untransformed protoplasts, on the left transformed protoplasts. The average fluorescence of the positive protoplasts is used as a proxy for the activation or repression efficiency in C) and D). C) Induction of a VENUS YFP by the scaffold, fusion and SAM systems (combination of scaffold and fusion system). The VENUS promoter comprises 7 teto sequences recognized by the sgRNA used in the three systems. The graph in the upper left corner represents the activation fold of the VENUS by each system. The bottom graph represents the percentage of activation compared to the strong and constitutive p35:VENUS promoter. The best system achieves an induction equivalent to 36% of a 35S promoter. D) Repression of a 35S promoter coupled to a VENUS YFP by all three systems. The graph represents the percentage of repression achieved by each system. The best system achieves nearly 40% repression of a strong promoter>{{project:figure_7.png}}</imgcaption> |
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