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Daphniphyllum alkaloids are structurally diverse nitrogen-containing compounds with polycyclic, stereochemically rich carbon skeletons. Understanding how plants biosynthesise these compounds may lead to greater access to allow exploration of bioactivities; however, very little is known about their biosynthetic origins. Here, we integrated metabolomics approaches to map alkaloid distribution across Daphniphyllum macropodum plants and tissues. We generated a novel untargeted metabolomics workflow to highlight trends in alkaloid distribution across tissues, using a holistic approach that does not rely on ambiguous peak annotations. Both liquid-chromatography-mass spectrometry and mass-spectrometry imaging analyses independently revealed that alkaloids have a pattern of spatial distribution based on their skeletal subtypes. The distinct alkaloid subtype localisation suggests the biosynthetic pathway is controlled spatially with intermediates transported from the phloem to the epidermis where they undergo additional derivatization. This study sets the stage for the future work on Daphniphyllum alkaloid biosynthesis and highlights how integrating different metabolomics strategies can reveal valuable insights on these compounds’ distribution within the plant.

Some organisations like 23andMe and the UK Biobank have large genomic databases that they re-use for multiple different genome-wide association studies (GWAS). Even research studies that compile smaller genomic databases often utilise these databases to investigate many related traits. It is common for the study to report a genetic risk score (GRS) model for each trait within the publication. Here we show that under some circumstances, these GRS models can be used to recover the genetic variants of individuals in these genomic databases—a reconstruction attack. In particular, if two GRS models are trained using a largely overlapping set of participants, then it is often possible to determine the genotype for each of the individuals who were used to train one GRS model, but not the other. We demonstrate this theoretically and experimentally by analysing the Cornell Dog Genome database. The accuracy of our reconstruction attack depends on how accurately we can estimate the rate of co-occurrence of pairs of SNPs within the private database, so if this aggregate information is ever released, it would drastically reduce the security of a private genomic database. Caution should be applied when using the same database for multiple analysis, especially when a small number of individuals are included or excluded from one part of the study.

Light perception at dawn plays a key role in coordinating multiple molecular processes and in entraining the plant circadian clock. The Arabidopsis mutant lacking the main photoreceptors, however, still shows clock entrainment, indicating that the integration of light into the morning transcriptome is not well understood. In this study, we performed a high-resolution RNA-sequencing time-series experiment, sampling every 2 min beginning at dawn. In parallel experiments, we perturbed temperature, the circadian clock, photoreceptor signaling, and chloroplast-derived light signaling. We used these data to infer a gene network that describes the gene expression dynamics after light stimulus in the morning, and then validated key edges. By sampling time points at high density, we are able to identify three light- and temperature-sensitive bursts of transcription factor activity, one of which lasts for only about 8 min. Phytochrome and cryptochrome mutants cause a delay in the transcriptional bursts at dawn, and completely remove a burst of expression in key photomorphogenesis genes (HY5 and BBX family). Our complete network is available online (http://www-users.york.ac.uk/∼de656/dawnBurst/dawnBurst.html). Taken together, our results show that phytochrome and cryptochrome signaling is required for fine-tuning the dawn transcriptional response to light, but separate pathways can robustly activate much of the program in their absence.

The Evening Complex (EC) is a core component of the Arabidopsis (Arabidopsis thaliana) circadian clock, which represses target gene expression at the end of the day and integrates temperature information to coordinate environmental and endogenous signals. Here we show that the EC induces repressive chromatin structure to regulate the evening transcriptome. The EC component ELF3 directly interacts with a protein from the SWI2/SNF2-RELATED (SWR1) complex to control deposition of H2A.Z-nucleosomes at the EC target genes. SWR1 components display circadian oscillation in gene expression with a peak at dusk. In turn, SWR1 is required for the circadian clockwork, as defects in SWR1 activity alter morning-expressed genes. The EC-SWR1 complex binds to the loci of the core clock genes PSEUDO-RESPONSE REGULATOR7 (PRR7) and PRR9 and catalyzes deposition of nucleosomes containing the histone variant H2A.Z coincident with the repression of these genes at dusk. This provides a mechanism by which the circadian clock temporally establishes repressive chromatin domains to shape oscillatory gene expression around dusk.

The decision of whether to grow and proliferate or to restrict growth and develop resilience to stress is a key biological trade-off. In plants, constitutive growth results in increased sensitivity to environmental stress1,2. The underlying mechanisms controlling this decision are however not well understood. We used temperature as a cue to discover regulators of this process in plants, as it both enhances growth and development rates within a specific range and is also a stress at extremes. We found that the conserved chromatin-associated protein DEK plays a central role in balancing the response between growth and arrest in Arabidopsis, and it does this via H2A.Z-nucleosomes. DEK target genes show two distinct categories of chromatin architecture based on the distribution of H2A.Z in +1 nucleosome and gene body, and these predict induction or repression by DEK. We show that these chromatin signatures of DEK target genes are conserved in human cells, suggesting that DEK may act through an evolutionarily conserved mechanism to control the balance between growth and arrest in plants and animals.

Data science can be incorporated into every stage of a scientific study. Here we describe how data science can be used to generate hypotheses, to design experiments, to perform experiments, and to analyse data. We also present our vision for how data science techniques will be an integral part of the laboratory of the future.

Plants modulate their growth rates based on the environmental signals; however, it is difficult to experimentally test how natural temperature and light fluctuations affect growth, since realistic outdoor environments are difficult to replicate in controlled laboratory conditions, and it is expensive to conduct experiments in many environmentally diverse regions. In partnership with BBC Terrific Scientific, over 50 primary schools from around the UK grew spring onions outside of hydroponic growth chambers that they constructed. Over 2 weeks, students measured the height of the spring onions daily, while the hourly temperature and visibility data were determined for each school based on the UK Meteorological Office data. This rich time series data allowed us to model how plants integrate temperature and light signals to determine how much to grow, using techniques from functional data analysis. We determined that under nutrient‐poor hydroponic conditions, growth of spring onion is sensitive to even a few degrees change in temperature, and is most correlated with warm nighttime temperatures, high temperatures at the start of the experiment, and light exposure near the end of the experiment. We show that scientists can leverage schools to conduct experiments that leverage natural environmental variability to develop complex models of plant‐environment interactions.

MotivationThe design of an experiment influences both what a researcher can measure, as well as how much confidence can be placed in the results. As such, it is vitally important that experimental design decisions do not systematically bias research outcomes. At the same time, making optimal design decisions can produce results leading to statistically stronger conclusions. Deciding where and when to sample are among the most critical aspects of many experimental designs; for example, we might have to choose the time points at which to measure some quantity in a time series experiment. Choosing times which are too far apart could result in missing short bursts of activity. On the other hand, there may be time points which provide very little information regarding the overall behaviour of the quantity in question.ResultsIn this study, we design a survey to analyse how biologists use previous research outcomes to inform their decisions about which time points to sample in subsequent experiments. We then determine how the choice of time points affects the type of perturbations in gene expression that can be observed. Finally, we present our main result: NITPicker, a computational strategy for selecting optimal time points (or spatial points along a single axis), that eliminates some of the biases caused by human decision-making while maximising information about the shape of the underlying curves, utilising ideas from the field of functional data analysis.AvailabilityNITPicker is available on GIThub (https://github.com/ezer/NITPicker).

Summary Small increases in ambient temperature can elicit striking effects on plant architecture, collectively termed thermomorphogenesis [1]. In Arabidopsis thaliana, these include marked stem elongation and leaf elevation, responses that have been predicted to enhance leaf cooling [2, 3, 4, 5]. Thermomorphogenesis requires increased auxin biosynthesis, mediated by the bHLH transcription factor PHYTOCHROME-INTERACTING FACTOR 4 (PIF4) [6, 7, 8], and enhanced stability of the auxin co-receptor TIR1, involving HEAT SHOCK PROTEIN 90 (HSP90) [9]. High-temperature-mediated hypocotyl elongation additionally involves localized changes in auxin metabolism, mediated by the indole-3-acetic acid (IAA)-amido synthetase Gretchen Hagen 3 (GH3).17 [10]. Here we show that ultraviolet-B light (UV-B) perceived by the photoreceptor UV RESISTANCE LOCUS 8 (UVR8) [11] strongly attenuates thermomorphogenesis via multiple mechanisms inhibiting PIF4 activity. Suppression of thermomorphogenesis involves UVR8 and CONSTITUTIVELY PHOTOMORPHOGENIC 1 (COP1)-mediated repression of PIF4 transcript accumulation, reducing PIF4 abundance. UV-B also stabilizes the bHLH protein LONG HYPOCOTYL IN FAR RED (HFR1), which can bind to and inhibit PIF4 function. Collectively, our results demonstrate complex crosstalk between UV-B and high-temperature signaling. As plants grown in sunlight would most likely experience concomitant elevations in UV-B and ambient temperature, elucidating how these pathways are integrated is of key importance to the understanding of plant development in natural environments., Highlights • UVR8 activity inhibits auxin signaling and stem elongation at high temperature • UVR8 acting with COP1 suppresses transcript abundance of the bHLH factor PIF4 • UV-B-mediated degradation of PIF4 is temperature dependent • UV-B stabilizes the bHLH factor HFR1, which can bind to and inhibit PIF4 function, Hayes et al. show that low-dose UV-B, perceived by the UVR8 photoreceptor, is a potent inhibitor of high-temperature-induced stem elongation. This provides plants with an important braking mechanism in bright sunlight, preventing excessive elongation growth that could lead to stem lodging and critical reductions in root and leaf biomass.

Plants have significantly more transcription factor (TF) families than animals and fungi, and plant TF families tend to contain more genes; these expansions are linked to adaptation to environmental stressors. Many TF family members bind to similar or identical sequence motifs, such as G-boxes (CACGTG), so it is difficult to predict regulatory relationships. We determined that the flanking sequences near G-boxes help determine in vitro specificity but that this is insufficient to predict the transcription pattern of genes near G-boxes. Therefore, we constructed a gene regulatory network that identifies the set of bZIPs and bHLHs that are most predictive of the expression of genes downstream of perfect G-boxes. This network accurately predicts transcriptional patterns and reconstructs known regulatory subnetworks. Finally, we present Ara-BOX-cis (araboxcis.org), a Web site that provides interactive visualizations of the G-box regulatory network, a useful resource for generating predictions for gene regulatory relations.

Plants have significantly more transcription factor (TF) families than animals and fungi, and plant TF families tend to contain more genes—these expansions are linked to adaptation to environmental stressors (1, 2). Many TF family members bind to similar or identical sequence motifs, such as G-boxes (CACGTG), so it is difficult to predict regulatory relationships. We determine that the flanking sequences near G-boxes help determine in vitro specificity, but that this is insufficient to predict the transcription pattern of genes near G-boxes. Therefore, we construct a gene regulatory network that identifies the set of bZIPs and bHLHs that are most predictive of the gene expression of genes downstream of perfect G-boxes. This network accurately predicts transcriptional patterns and reconstructs known regulatory subnetworks. Finally, we present Ara-BOX-cis (araboxcis.org), a website that provides interactive visualisations of the G-box regulatory network, a useful resource for generating predictions for gene regulatory relations.

Combining heat and light responses Plants integrate a variety of environmental signals to regulate growth patterns. Legris et al. and Jung et al. analyzed how the quality of light is interpreted through ambient temperature to regulate transcription and growth (see the Perspective by Halliday and Davis). The phytochromes responsible for reading the ratio of red to far-red light were also responsive to the small shifts in temperature that occur when dusk falls or when shade from neighboring plants cools the soil. Science , this issue p. 897 , p. 886 ; see also p. 832

Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete pipeline for the analysis of single cell qPCR data that uses the mathematics behind bursty expression to develop more accurate and robust algorithms for analyzing the origin of heterogeneity in experimental samples, specifically an algorithm for clustering cells by their bursting behavior (Simulated Annealing for Bursty Expression Clustering, SABEC) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells (Estimation of Parameter changes in Kinetics, EPiK). We applied these methods to hematopoiesis, including a new single cell dataset in which transcription factors (TFs) involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated. We could identify two unique sub-populations within a seemingly homogenous group of hematopoietic stem cells. In addition, we could predict regulatory mechanisms controlling the expression levels of eighteen key hematopoietic transcription factors throughout differentiation. Detailed information about gene regulatory mechanisms can therefore be obtained simply from high throughput single cell gene expression data, which should be widely applicable given the rapid expansion of single cell genomics., Author Summary Many genes are expressed in bursts, which can contribute to cell-to-cell variability. We construct a pipeline for analyzing single cell gene expression data that uses the mathematics behind bursty expression. This pipeline includes one algorithm for clustering cells (Simulated Annealing for Bursty Expression Clustering, SABEC) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells (Estimation of Parameter changes in Kinetics, EPiK). We applied these methods to blood development, including a new single cell dataset in which TFs involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated.

To predict in vivo occupancy of a transcription factor (TF), current models consider only the immediate genomic context of a putative binding site (BS) – impact of the site’s spatial chromatin context is not known. Using clusters of spatially proximal enhancers, or archipelagos, and DNase footprints to quantify TF occupancy, we report for the first time an emergent group-level effect on occupancy, whereby BS within an archipelago experience greater in vivo occupancy than comparable BS outside archipelagos, i.e. BS not in spatial proximity with other homotypic BS. This occupancy boost is tissue-specific and scales robustly with the total number of BS, or enhancers, for the TF in the archipelago. Interestingly, enhancers within an archipelago are non-uniformly impacted by the occupancy boost; specifically, archipelago enhancers that are enriched for BS corresponding to degenerate motifs exhibit the greatest occupancy boost, as well as the highest overall accessibility, evolutionary selection, and expression at neighboring gene loci. Strikingly, archipelago-wide activity scales with expression of TFs with degenerate, but not specific, motifs. We explain these results through biophysical modelling, which suggests that spatially proximal homotypic BS facilitate TF diffusion, and induce boosts in local TF concentration and occupancy. Together, we demonstrate for the first time cooperativity among genomically distal homotypic BS that is contingent upon their spatial proximity, consistent with a TF diffusion model. Through leveraging of three-dimensional chromatin structure and TF availability, weak archipelago binding sites crowdsource their occupancy as well as context specificity, with coordinated switch-like effect on overall archipelago activity.

Site-specific transcription factors (TFs) bind to their target sites on the DNA, where they regulate the rate at which genes are transcribed. Bacterial TFs undergo facilitated diffusion (a combination of 3D diffusion around and 1D random walk on the DNA) when searching for their target sites. Using computer simulations of this search process, we show that the organization of the binding sites, in conjunction with TF copy number and binding site affinity, plays an important role in determining not only the steady state of promoter occupancy, but also the order at which TFs bind. These effects can be captured by facilitated diffusion-based models, but not by standard thermodynamics. We show that the spacing of binding sites encodes complex logic, which can be derived from combinations of three basic building blocks: switches, barriers and clusters, whose response alone and in higher orders of organization we characterize in detail. Effective promoter organizations are commonly found in the E. coli genome and are highly conserved between strains. This will allow studies of gene regulation at a previously unprecedented level of detail, where our framework can create testable hypothesis of promoter logic.