Complex epistatic interactions between ELF3, PRR9, and PRR7 regulate the circadian clock and plant physiology

Circadian clocks are endogenous timekeeping mechanisms that coordinate internal physiological responses with the external environment. EARLY FLOWERING3 (ELF3), PSEUDO RESPONSE REGULATOR (PRR9), and PRR7 are essential components of the plant circadian clock and facilitate entrainment of the clock to internal and external stimuli. Previous studies have highlighted a critical role for ELF3 in repressing the expression of PRR9 and PRR7. However, the functional significance of activity in regulating circadian clock dynamics and plant development is unknown. To explore this regulatory dynamic further, we first employed mathematical modeling to simulate the effect of the prr9/prr7 mutation on the elf3 circadian phenotype. These simulations suggested that simultaneous mutations in prr9/prr7 could rescue the elf3 circadian arrhythmia. Following these simulations, we generated all Arabidopsis elf3/prr9/prr7 mutant combinations and investigated their circadian and developmental phenotypes. Although these assays could not replicate the results from the mathematical modeling, our results have revealed a complex epistatic relationship between ELF3 and PRR9/7 in regulating different aspects of plant development. ELF3 was essential for hypocotyl development under ambient and warm temperatures, while PRR9 was critical for root thermomorphogenesis. Finally, mutations in prr9 and prr7 rescued the photoperiod-insensitive flowering phenotype of the elf3 mutant. Together, our results highlight the importance of investigating the genetic relationship among plant circadian genes.

AraLeTA: An Arabidopsis leaf expression atlas across diurnal and developmental scales

Mature plant leaves are a composite of distinct cell types, including epidermal, mesophyll and vascular cells. Notably the proportion of these cells, and the relative transcript concentrations within different cell types, may change over time. While gene expression data at a single-cell level can provide cell-type-specific expression values, it is often too expensive to obtain this data for high-resolution time series. Although bulk RNA-seq can be performed in a high-resolution time series, RNA-seq using whole leaves measures average gene expression values across all cell types in each sample. In this study, we combined single-cell RNA-seq data with time-series data from whole leaves to assemble an atlas of cell-type-specific changes in gene expression over time for Arabidopsis (Arabidopsis thaliana). We inferred how the relative transcript concentrations of different cell types vary across diurnal and developmental time scales. Importantly, this analysis revealed three sub-groups of mesophyll cells with distinct temporal profiles of expression. Finally, we developed tissue-specific gene networks that form a community resource: An Arabidopsis Leaf Time-Dependent Atlas (AraLeTa). This allows users to extract gene networks that are confirmed by transcription factor binding data and specific to certain cell types at certain times of day and at certain developmental stages. AraLeTa is available at: https://regulatorynet.shinyapps.io/araleta/.

An early-morning gene network controlled by phytochromes and cryptochromes regulates photomorphogenesis pathways in Arabidopsis

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.

Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores

Some organizations such as 23andMe and the UK Biobank have large genomic databases that they re-use for multiple different genome-wide association studies. Even research studies that compile smaller genomic databases often utilize 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 by using a largely overlapping set of participants, 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 analyzing 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 single nucleotide polymorphisms 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.

What is quantitative plant biology?

Quantitative plant biology is an interdisciplinary field that builds on a long history of biomathematics and biophysics. Today, thanks to high spatiotemporal resolution tools and computational modelling, it sets a new standard in plant science. Acquired data, whether molecular, geometric or mechanical, are quantified, statistically assessed and integrated at multiple scales and across fields. They feed testable predictions that, in turn, guide further experimental tests. Quantitative features such as variability, noise, robustness, delays or feedback loops are included to account for the inner dynamics of plants and their interactions with the environment. Here, we present the main features of this ongoing revolution, through new questions around signalling networks, tissue topology, shape plasticity, biomechanics, bioenergetics, ecology and engineering. In the end, quantitative plant biology allows us to question and better understand our interactions with plants. In turn, this field opens the door to transdisciplinary projects with the society, notably through citizen science.

PAFway: pairwise associations between functional annotations in biological networks and pathways

Motivation

Large gene networks can be dense and difficult to interpret in a biologically meaningful way.

Results

Here, we introduce PAFway, which estimates pairwise associations between functional annotations in biological networks and pathways. It answers the biological question: do genes that have a specific function tend to regulate genes that have a different specific function? The results can be visualized as a heatmap or a network of biological functions. We apply this package to reveal associations between functional annotations in an Arabidopsis thaliana gene network.

Availability and implementation

PAFway is submitted to CRAN. Currently available here: https://github.com/ezer/PAFway.

Supplementary information

Supplementary data are available at Bioinformatics online.

AI for social good: unlocking the opportunity for positive impact

Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world's most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations' 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good.

The Evening Complex Establishes Repressive Chromatin Domains Via H2A.Z Deposition

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.

Data science for the scientific life cycle

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.

A mass participatory experiment provides a rich temporal profile of temperature response in spring onions

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.

Canonical and single-cell Hi-C reveal distinct chromatin interaction sub-networks of mammalian transcription factors

Background

Transcription factor (TF) binding to regulatory DNA sites is a key determinant of cell identity within multi-cellular organisms and has been studied extensively in relation to site affinity and chromatin modifications. There has been a strong focus on the inference of TF-gene regulatory networks and TF-TF physical interaction networks. Here, we present a third type of TF network, the spatial network of co-localized TF binding sites within the three-dimensional genome.

Results

Using published canonical Hi-C data and single-cell genome structures, we assess the spatial proximity of a genome-wide array of potential TF-TF co-localizations in human and mouse cell lines. For individual TFs, the abundance of occupied binding sites shows a positive correspondence with their clustering in three dimensions, and this is especially apparent for weak TF binding sites and at enhancer regions. An analysis between different TF proteins identifies significantly proximal pairs, which are enriched in reported physical interactions. Furthermore, clustering of different TFs based on proximity enrichment identifies two partially segregated co-localization sub-networks, involving different TFs in different cell types. Using data from both human lymphoblastoid cells and mouse embryonic stem cells, we find that these sub-networks are enriched within, but not exclusive to, different chromosome sub-compartments that have been identified previously in Hi-C data.

Conclusions

This suggests that the association of TFs within spatial networks is closely coupled to gene regulatory networks. This applies to both differentiated and undifferentiated cells and is a potential causal link between lineage-specific TF binding and chromosome sub-compartment segregation.

Electronic supplementary material

The online version of this article (10.1186/s13059-018-1558-2) contains supplementary material, which is available to authorized users.

The G-Box Transcriptional Regulatory Code in Arabidopsis

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.

The evening complex coordinates environmental and endogenous signals in Arabidopsis

Plants maximize their fitness by adjusting their growth and development in response to signals such as light and temperature. The circadian clock provides a mechanism for plants to anticipate events such as sunrise and adjust their transcriptional programmes. However, the underlying mechanisms by which plants coordinate environmental signals with endogenous pathways are not fully understood. Using RNA-sequencing and chromatin immunoprecipitation sequencing experiments, we show that the evening complex (EC) of the circadian clock plays a major role in directly coordinating the expression of hundreds of key regulators of photosynthesis, the circadian clock, phytohormone signalling, growth and response to the environment. We find that the ability of the EC to bind targets genome-wide depends on temperature. In addition, co-occurrence of phytochrome B (phyB) at multiple sites where the EC is bound provides a mechanism for integrating environmental information. Hence, our results show that the EC plays a central role in coordinating endogenous and environmental signals in Arabidopsis.

Phytochromes function as thermosensors in Arabidopsis

Plants are responsive to temperature, and some species can distinguish differences of 1°C. In Arabidopsis, warmer temperature accelerates flowering and increases elongation growth (thermomorphogenesis). However, the mechanisms of temperature perception are largely unknown. We describe a major thermosensory role for the phytochromes (red light receptors) during the night. Phytochrome null plants display a constitutive warm-temperature response, and consistent with this, we show in this background that the warm-temperature transcriptome becomes derepressed at low temperatures. We found that phytochrome B (phyB) directly associates with the promoters of key target genes in a temperature-dependent manner. The rate of phyB inactivation is proportional to temperature in the dark, enabling phytochromes to function as thermal timers that integrate temperature information over the course of the night.

Reliable scaling of position weight matrices for binding strength comparisons between transcription factors

Background

Scoring DNA sequences against Position Weight Matrices (PWMs) is a widely adopted method to identify putative transcription factor binding sites. While common bioinformatics tools produce scores that can reflect the binding strength between a specific transcription factor and the DNA, these scores are not directly comparable between different transcription factors. Other methods, including p-value associated approaches (Touzet H, Varré J-S. Efficient and accurate p-value computation for position weight matrices. Algorithms Mol Biol. 2007;2(1510.1186):1748–7188), provide more rigorous ways to identify potential binding sites, but their results are difficult to interpret in terms of binding energy, which is essential for the modeling of transcription factor binding dynamics and enhancer activities.

Results

Here, we provide two different ways to find the scaling parameter λ that allows us to infer binding energy from a PWM score. The first approach uses a PWM and background genomic sequence as input to estimate λ for a specific transcription factor, which we applied to show that λ distributions for different transcription factor families correspond with their DNA binding properties. Our second method can reliably convert λ between different PWMs of the same transcription factor, which allows us to directly compare PWMs that were generated by different approaches.

Conclusion

These two approaches provide computationally efficient ways to scale PWM scores and estimate the strength of transcription factor binding sites in quantitative studies of binding dynamics. Their results are consistent with each other and previous reports in most of cases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0666-1) contains supplementary material, which is available to authorized users.

Homotypic clusters of transcription factor binding sites: A model system for understanding the physical mechanics of gene expression

The organization of binding sites in cis-regulatory elements (CREs) can influence gene expression through a combination of physical mechanisms, ranging from direct interactions between TF molecules to DNA looping and transient chromatin interactions. The study of simple and common building blocks in promoters and other CREs allows us to dissect how all of these mechanisms work together. Many adjacent TF binding sites for the same TF species form homotypic clusters, and these CRE architecture building blocks serve as a prime candidate for understanding interacting transcriptional mechanisms. Homotypic clusters are prevalent in both bacterial and eukaryotic genomes, and are present in both promoters as well as more distal enhancer/silencer elements. Here, we review previous theoretical and experimental studies that show how the complexity (number of binding sites) and spatial organization (distance between sites and overall distance from transcription start sites) of homotypic clusters influence gene expression. In particular, we describe how homotypic clusters modulate the temporal dynamics of TF binding, a mechanism that can affect gene expression, but which has not yet been sufficiently characterized. We propose further experiments on homotypic clusters that would be useful in developing mechanistic models of gene expression.

Physical constraints determine the logic of bacterial promoter architectures

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.

Ureteroscopic management of upper tract transitional cell carcinoma in patients with normal contralateral kidneys

PURPOSE

The standard treatment for upper tract transitional cell carcinoma in patients with a normal contralateral kidney is nephroureterectomy with a bladder cuff or segmental ureterectomy. We evaluate whether ureteroscopic tumor resection with vigilant surveillance is a safe alternative in select patients.

MATERIALS AND METHODS

Patients with isolated upper tract filling defects on an excretory urogram and a normal contralateral kidney were diagnosed ureteroscopically with papillary low intermediate grade appearing transitional cell carcinoma. Biopsies of the lesions were obtained, and the tumors were treated with laser ablation or electrofulguration in the same sitting. Patients with cytopathological results of high grade transitional cell carcinoma underwent nephroureterectomy. Surveillance consisted of ureteroscopy every 3 months until tumor-free and ureteroscopy every 6 months thereafter.

RESULTS

Between 1989 and 1998, 23 patients with normal creatinine (mean 1.0, range 0.7 to 1.6) underwent ureteroscopic resection of unilateral upper tract transitional cell carcinoma. On initial biopsy 22 tumors were grade 1 or 2 and 1 was grade 2 to 3. After the primary tumor was treated 8 (35%) patients remained tumor-free and 15 (65%) had multiple recurrences, which were treated ureteroscopically. Mean followup was 35 months (range 8 to 103 months). All 23 patients are alive without evidence of disease progression. At last followup 4 patients (17%) had persistent disease, 4 (17%) elected to undergo nephroureterectomy and 15 (65%) are free of ipsilateral disease for a mean duration of 17 months (range 6 to 77).

CONCLUSIONS

Ureteroscopic treatment of focal low intermediate grade superficial upper tract transitional cell carcinoma is a safe alternative to nephroureterectomy in select patients when vigilant ureteroscopic followup is used.

p53 nuclear accumulation is not associated with decreased disease-free survival in patients with node positive transitional cell carcinoma of the bladder

PURPOSE

Although the majority of patients with node positive transitional cell carcinoma of the bladder have disease progression, a definitive subset is cured by surgery only. Nuclear accumulation of p53 has been associated with disease progression in patients with superficial transitional cell carcinoma and decreased survival in those with muscle invasive disease. We determined whether p53 status would predict survival in a cohort with nodal metastasis.

MATERIALS AND METHODS

We explored the comprehensive database of all 199 radical cystectomies performed at our institution between July 1988 and September 1999. The 59 patients in this database with node positive pathology comprise our study. We performed immunohistochemical analysis of specimens using the MAB1801 antibody with greater than 20% lymph node and primary tumor nucleus staining deemed positive. Additional covariates measured included patient age, sex, pathological disease stage, adjuvant chemotherapy and nodal stage. Disease-free survival curves were generated for the various covariates and compared using the log rank test. The Cox proportional hazards technique was used to determine covariate adjusted p53 survival.

RESULTS

In the cohort overall median disease-free survival was only 21 months, although 18% of patients were disease-free at 5 years. There was evidence of p53 nuclear accumulation in 54% of cases and complete agreement of nodal with bladder p53 nuclear accumulation. No significant baseline differences were noted in the covariates with respect to p53 nuclear accumulation. For stratum specific disease-free survival univariate and multivariate analyses revealed that only pathological stages p0-p2b versus p3-p4 (hazards ratio 2.86, p = 0.03), and nodal stages N2 versus N1 and N3 versus N1 (hazards ratio 3.84, p = 0.01 and hazards ratio 13.3, p = 0.0002, respectively) were significantly associated with prolonged disease-free survival, while p53 nuclear accumulation was not.

CONCLUSIONS

Despite credible evidence for p53 nuclear accumulation prognostication in patients with in situ and invasive transitional cell carcinoma, this marker is not predictive of disease-free survival in node positive disease.