Biology subjects

Stitt, M.

Publications and source records attributed to Stitt, M..

4 recordsLinked to original sources

bioRxiv2018

Kingdom-wide comparison reveals conserved diurnal gene expression in Archaeplastida

Plants have adapted to the diurnal light-dark cycle by establishing elaborate transcriptional programs that coordinate innumerable metabolic, physiological, and developmental responses to the external environment. These transcriptional programs have been studied in only a few species, and their function and conservation across algae and plants is currently unknown. We performed a comparative transcriptome analysis of the diurnal cycle of nine members of Archaeplastida, and we observed that, despite large phylogenetic distances and dramatic differences in morphology and lifestyle, diurnal transcriptional programs of these organisms are similar. However, the establishment of multicellularity coincided with the uncoupling of cell division from the diurnal cycle and decreased diurnal control of the expression of the biological pathways. Hence, our study provides evidence for the universality of diurnal gene expression and elucidates its evolutionary history among different photosynthetic eukaryotes.

evolutionary biology↗

bioRxiv2017

Photoperiodic control of the Arabidopsis proteome reveals a translational coincidence mechanism

Plants respond to seasonal cues, such as the photoperiod, to adapt to current conditions and to prepare for environmental changes in the season to come. To assess photoperiodic responses at the protein level, we quantified the proteome of the model plant Arabidopsis thaliana by mass spectrometry across four photoperiods. This revealed coordinated changes of abundance in proteins of photosynthesis, primary and secondary metabolism, including pigment biosynthesis, consistent with higher metabolic activity in long photoperiods. Higher translation rates in the daytime than the night likely contribute to these changes via rhythmic changes in RNA abundance. Photoperiodic control of protein levels might be greatest only if high translation rates coincide with high transcript levels in some photoperiods. We term this proposed mechanism {acute}translational coincidence{acute}, mathematically model its components, and demonstrate its effect on the Arabidopsis proteome. Datasets from a green alga and a cyanobacterium suggest that translational coincidence contributes to seasonal control of the proteome in many phototrophic organisms. This may explain why many transcripts but not their cognate proteins exhibit diurnal rhythms.

plant biology↗

bioRxiv2017

Omics-Based Hybrid Prediction In Maize

Accurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction (WGP) has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream \"omics\" data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of \"omics\" data. Here, we evaluate several \"omics\" predictors -- genomic, transcriptomic and metabolic data -- measured on parent lines at early developmental stages, and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream \"omics\" data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding.\n\nKey messageComplementing genomic data with other \"omics\" predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits.\n\nConflict of InterestThe authors declare that they have no conflict of interest.

genetics↗

bioRxiv2017

Linking circadian time to growth rate quantitatively via carbon metabolism

Predicting a multicellular organisms 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. Mobilisation of a secondary store of malate and fumarate was also mis-regulated, accounting for any remaining biomass defect. We test three candidate mechanisms for the accumulation of these organic acids. Our results link genotype through specific processes to higher-level phenotypes, formalising 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. This work updates the first biorXiv version, February 2017, https://doi.org/10.1101/105437, with an expanded description and additional analysis of the same core data sets and the same FMv2 model, summary tables and supporting, follow-on data from three further studies with further collaborators. This biorXiv revision constitutes the second version of this report.

systems biology↗