Strigolactones spatially influence lateral root development through the cytokinin signaling network

Jiang, Lingxiang; Matthys, Cedrick; Márquez-García, Belén; Cuyper, Carolien De; Smet, Lien De; Keyser, Annick De; Boyer, François‐Didier; Beeckman, Tom; Depuydt, Stephen; Goormachtig, Sofie

doi:10.1093/jxb/erv478

Cited by 66 publications

(44 citation statements)

References 81 publications

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“…Interestingly, this carotenoidderived signal is generated in the differentiation zone just shootward of the oscillatory region, implying a non-cellautonomous action. More recently, strigolactone signaling, known to modulate auxin fluxes, was reported to influence prebranch site formation (Jiang et al, 2016). The data from Jiang et al (2016) further support the importance for auxin fluxes in lateral root priming.…”

Section: Experimental Data On Primingmentioning

confidence: 77%

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Periodic Lateral Root Priming: What Makes It Tick?

Laskowski

Tusscher

2017

Plant Cell

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Section: Experimental Data On Primingmentioning

confidence: 77%

“…More recently, strigolactone signaling, known to modulate auxin fluxes, was reported to influence prebranch site formation (Jiang et al, 2016). The data from Jiang et al (2016) further support the importance for auxin fluxes in lateral root priming.…”

Section: Experimental Data On Primingmentioning

confidence: 77%

Periodic Lateral Root Priming: What Makes It Tick?

Laskowski

Tusscher

2017

Plant Cell

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“…Indeed, the positive effect of rac ‐GR24 on root hair elongation depends on MAX2, but could not be backed with aberrant phenotypes in strigolactone biosynthesis mutants (Kapulnik et al ). Concerning lateral roots, under phosphate‐sufficient conditions, rac ‐GR24 negatively affects both lateral root outgrowth and priming in a MAX2‐dependent manner (Jiang et al ), whereas under phosphate‐limiting conditions, it has a positive effect on lateral root development (Ruyter‐Spira et al ). Accordingly, max2 mutants are characterized by an enhanced lateral root density, although the lateral root phenotypes of the strigolactone‐deficient mutants max3 and max4 are currently unclear (Kapulnik et al ; Koltai ; Ruyter‐Spira et al ).…”

Section: Strigolactones Versus Karrikin(‐like)s Let the Game Beginmentioning

confidence: 99%

Strigolactones, karrikins and beyond

Cuyper

Struk

Braem

et al. 2017

Plant Cell & Environment

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The plant hormones strigolactones are synthesized from carotenoids and signal via the α/β hydrolase DWARF 14 (D14) and the F-box protein MORE AXILLARY GROWTH 2 (MAX2). Karrikins, molecules produced upon fire, share MAX2 for signalling, but depend on the D14 paralog KARRIKIN INSENSITIVE 2 (KAI2) for perception with strong evidence that the MAX2-KAI2 protein complex might also recognize so far unknown plant-made karrikin-like molecules. Thus, the phenotypes of the max2 mutants are the complex consequence of a loss of both D14-dependent and KAI2-dependent signalling, hence, the reason why some biological roles, attributed to strigolactones based on max2 phenotypes, could never be observed in d14 or in the strigolactone-deficient max3 and max4 mutants. Moreover, the broadly used synthetic strigolactone analog rac-GR24 has been shown to mimic strigolactone as well as karrikin(-like) signals, providing an extra level of complexity in the distinction of the unique and common roles of both molecules in plant biology. Here, a critical overview is provided of the diverse biological processes regulated by strigolactones and/or karrikins. These two growth regulators are considered beyond their boundaries, and the importance of the yet unknown karrikin-like molecules is discussed as well.

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“…The same community also contained AHK3, ARR1 and ARR2. Those three members are known to regulate primary root meristem activity and senescence 26 . AHK4, AHK2 and ARR14 which have been shown to regulate shoot apical meristem activity were grouped in the same community 27 .…”

Section: Discussionmentioning

confidence: 99%

Ranking genome-wide correlation measurements improves microarray and RNA-seq based global and targeted co-expression networks

Liesecke

Daudu

Bernonville

et al. 2018

Preprint

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10Co-expression networks are essential tools to infer biological associations between gene products and predict gene annotation. Global networks can be analyzed at the transcriptome wide scale or after querying them with a set of guide genes to capture the transcriptional landscape of a given pathway in a process named Pathway Level Correlation (PLC). A critical step in network construction remains the definition of gene co-expression. In the present work, we compared how Pearson Correlation Coefficient (PCC), Spearman Correlation Coefficient (SCC), their respective ranked values (Highest Reciprocal Rank (HRR)), Mutual Information (MI) and Partial Correlations (PC) performed on global networks and PLCs. This evaluation was conducted on the model plant Arabidopsis thaliana using microarray and differently pre-processed RNA-seq datasets. We particularly evaluated how dataset x distance measurement combinations performed in 5 PLCs corresponding to 4 well described plant metabolic pathways (phenylpropanoid, carbohydrate, fatty acid and terpene metabolisms) and the cytokinin signaling pathway. Our present work highlights how PCC ranked with HRR is better suited for global network construction and PLC with microarray and RNA-seq data than other distance methods, especially to cluster genes in partitions similar to biological subpathways. 11 Introduction 12Constructing global gene co-expression networks is a popular approach to highlight transcriptional relationships (edges) 13 between genes (vertices). The 'Guilt-by-Association' (GBA) principle supposes that genes sharing similar functions are 14 preferentially connected and aims at predicting new functions for proteins by determining how their respective encoding 15 genes are co-expressed with others using a reference dataset containing known gene functions such as the Gene Ontology 16 (GO) 1 . Defining edges connecting genes remains a critical step in global co-expression network construction. Expression 17 data (microarray or RNA-seq) are used to construct expression matrices (genes x samples) and to calculate a distance or 18 a similarity for each possible gene pair. The resulting pairwise distance matrix is then thresholded to obtain an adjacency 19 matrix that discriminates relevant edges. Only edges with a distance below (or a similarity above) the set threshold are 20 considered significant and retained for network construction. The procedure is expected to remove non biologically relevant 21 gene associations while retaining the relevant ones and can be assessed with any reference dataset. Alternatively, guide gene sets 22 may be used to extract more human-readable information from large networks in a process named Pathway-Level Correlation 23 (PLC) 2-6 . This approach aims at capturing the best transcriptional associations of a gene set and at highlighting functional gene 24 groups such as known subpathways in this set. There are two types of approaches to determine transcriptional associations 25 of genes: those that are supervised and those that are unsu...

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Strigolactones spatially influence lateral root development through the cytokinin signaling network

Abstract: HighlightStrigolactones monitor lateral root development in a spatiotemporal manner by an interplay with cytokinin.

Cited by 66 publications

References 81 publications

Periodic Lateral Root Priming: What Makes It Tick?

Periodic Lateral Root Priming: What Makes It Tick?

Strigolactones, karrikins and beyond

Ranking genome-wide correlation measurements improves microarray and RNA-seq based global and targeted co-expression networks

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