Jet-lagged plants enable researchers to understand human health problems - here's how
Anyone who has experienced jet lag can relate to the disastrous consequences of a thrown-off biological clock.
A recent study led by the University of Edinburgh researchers indicates that plants also have problems when their clocks are out of sync.
By creating an intricate computational model, researchers have taken a huge step toward creating the first digital plant while simultaneously resolving one of the longest-running puzzles in plant science: the function of the biological clock.
The research was published in Plants on May 30.
Flying from New York to the UK every day
Researchers found that altering a plant's biological clock affects its growth by producing a perpetually jet-lagged plant comparable to daily flights from New York to the UK. The scientists also developed a computer model of the "jet-lagged" plant that could precisely forecast the effects on growth and identify the affected biochemical pathways.
With the exception of single-celled microorganisms, sophisticated, multicellular digital organisms have rarely been created, and development constitutes a significant step in that direction.
As stated in the Phys, every plant has a biological clock, a molecular time-keeping system that detects environmental changes and prepares the plant for transitions from nightfall to dawn and season to season. Although each plant cell appears to have its own clock that controls approximately 30 percent of its genes, nothing is known about their significance in plant growth.
An extensively researched plant species called Arabidopsis thaliana's clock genes were altered, and researchers looked into the ramifications of these changes.
The team was able to examine whether clock genes were involved in the plant's nightly release of sugar held in starch, which supports their growth, thanks to the clock-mutant plants.
They developed a Framework model
They also developed a Framework model for these clock mutants, which merged mathematical models of clock gene activity with metabolic and physiological models.
The outcomes showed that the Framework model successfully predicted that the slow release of sugars from starch during the night in the clock mutants was the cause of their growth is slowed.
This accomplishment is similar to comprehending a human health syndrome produced by a genetic alteration that affects several physiological pathways discreetly.
"The success of the Framework model shows that we can understand subtle effects at the whole-plant level, in this case, just from changing the timing of gene expression. By 'understand' we mean to explain and predict," said Professor Andrew Millar of the University of Edinburgh's School of Biological Sciences.
"Not all details of this model will transfer to crop species, but it extends the 'proofs of principle' for informing crop improvement at the molecular level."
Predicting a multicellular organism’s phenotype quantitatively from its genotype is challenging, as genetic effects must propagate across scales. Circadian clocks are intracellular regulators that control temporal gene expression patterns and hence metabolism, physiology and behaviour. Here we explain and predict canonical phenotypes of circadian timing in a multicellular, model organism. We used diverse metabolic and physiological data to combine and extend mathematical models of rhythmic gene expression, photoperiod-dependent flowering, elongation growth and starch metabolism within a Framework Model for the vegetative growth of Arabidopsis thaliana, sharing the model and data files in a structured, public resource. The calibrated model predicted the effect of altered circadian timing upon each particular phenotype in clock-mutant plants under standard laboratory conditions. Altered night-time metabolism of stored starch accounted for most of the decrease in whole-plant biomass, as previously proposed. Mobilization of a secondary store of malate and fumarate was also mis-regulated, accounting for any remaining biomass defect. The three candidate mechanisms tested did not explain this organic acid accumulation. Our results link genotype through specific processes to higher-level phenotypes, formalizing our understanding of a subtle, pleiotropic syndrome at the whole-organism level, and validating the systems approach to understand complex traits starting from intracellular circuits.
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