- Award ID(s):
- 1638507
- PAR ID:
- 10295694
- Editor(s):
- Jez, Joseph M.; Topp, Christopher N.
- Date Published:
- Journal Name:
- Emerging Topics in Life Sciences
- Volume:
- 5
- Issue:
- 2
- ISSN:
- 2397-8554
- Page Range / eLocation ID:
- 249 to 260
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Abstract State-of-the-Art models of Root System Architecture (RSA) do not allow simulating root growth around rigid obstacles. Yet, the presence of obstacles can be highly disruptive to the root system. We grew wheat seedlings in sealed petri dishes without obstacle and in custom 3D-printed rhizoboxes containing obstacles. Time-lapse photography was used to reconstruct the wheat root morphology network. We used the reconstructed wheat root network without obstacle to calibrate an RSA model implemented in the R-SWMS software. The root network with obstacles allowed calibrating the parameters of a new function that models the influence of rigid obstacles on wheat root growth. Experimental results show that the presence of a rigid obstacle does not affect the growth rate of the wheat root axes, but that it does influence the root trajectory after the main axis has passed the obstacle. The growth recovery time, i.e. the time for the main root axis to recover its geotropism-driven growth, is proportional to the time during which the main axis grows along the obstacle. Qualitative and quantitative comparisons between experimental and numerical results show that the proposed model successfully simulates wheat RSA growth around obstacles. Our results suggest that wheat roots follow patterns that could inspire the design of adaptive engineering flow networks.more » « less
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Convergent evolution of root system architecture in two independently evolved lineages of weedy rice
Summary Root system architecture (
RSA ) is a critical aspect of plant growth and competitive ability. Here we used two independently evolved strains of weedy rice, a de‐domesticated form of rice, to study the evolution of weed‐associatedRSA traits and the extent to which they evolve through shared or different genetic mechanisms.We characterised 98 two‐dimensional and three‐dimensional
RSA traits in 671 plants representing parents and descendants of two recombinant inbred line populations derived from two weed × crop crosses. A random forest machine learning model was used to assess the degree to which root traits can predict genotype and the most diagnostic traits for doing so. We used quantitative trait locus (QTL) mapping to compare genetic architecture between the weed strains.The two weeds were distinguishable from the crop in similar and predictable ways, suggesting independent evolution of a ‘weedy’
RSA phenotype. Notably, comparativeQTL mapping revealed little evidence for shared underlying genetic mechanisms.Our findings suggest that despite the double bottlenecks of domestication and de‐domestication, weedy rice nonetheless shows genetic flexibility in the repeated evolution of weedy
RSA traits. Whereas the root growth of cultivated rice may facilitate interactions among neighbouring plants, the weedy rice phenotype may minimise below‐ground contact as a competitive strategy. -
GLO-Roots: an imaging platform enabling multidimensional characterization of soil-grown root systems
Root systems develop different root types that individually sense cues from their local environment and integrate this information with systemic signals. This complex multi-dimensional amalgam of inputs enables continuous adjustment of root growth rates, direction, and metabolic activity that define a dynamic physical network. Current methods for analyzing root biology balance physiological relevance with imaging capability. To bridge this divide, we developed an integrated-imaging system called Growth and Luminescence Observatory for Roots (GLO-Roots) that uses luminescence-based reporters to enable studies of root architecture and gene expression patterns in soil-grown, light-shielded roots. We have developed image analysis algorithms that allow the spatial integration of soil properties, gene expression, and root system architecture traits. We propose GLO-Roots as a system that has great utility in presenting environmental stimuli to roots in ways that evoke natural adaptive responses and in providing tools for studying the multi-dimensional nature of such processes.
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