Imaging the living plant cell: From probes to quantification.

At the center of cell biology is our ability to image the cell and its various components, either in isolation or within an organism. Given its importance, biological imaging has emerged as a field of its own, which is inherently highly interdisciplinary. Indeed, biologists rely on physicists and engineers to build new microscopes and imaging techniques, chemists to develop better imaging probes, and mathematicians and computer scientists for image analysis and quantification. Live imaging collectively involves all the techniques aimed at imaging live samples. It is a rapidly evolving field, with countless new techniques, probes, and dyes being continuously developed. Some of these new methods or reagents are readily amenable to image plant samples, while others are not and require specific modifications for the plant field. Here, we review some recent advances in live imaging of plant cells. In particular, we discuss the solutions that plant biologists use to live image membrane-bound organelles, cytoskeleton components, hormones, and the mechanical properties of cells or tissues. We not only consider the imaging techniques per se, but also how the construction of new fluorescent probes and analysis pipelines are driving the field of plant cell biology.

Patterns of differential expression by association in omic data using a new measure based on ensemble learning.

The ongoing development of high-throughput technologies is allowing the simultaneous monitoring of the expression levels for hundreds or thousands of biological inputs with the proliferation of what has been coined as omic data sources. One relevant issue when analyzing such data sources is concerned with the detection of differential expression across two experimental conditions, clinical status or two classes of a biological outcome. While a great deal of univariate data analysis approaches have been developed to address the issue, strategies for assessing interaction patterns of differential expression are scarce in the literature and have been limited to ad hoc solutions. This paper contributes to the problem by exploiting the facilities of an ensemble learning algorithm like random forests to propose a measure that assesses the differential expression explained by the interaction of the omic variables so subtle biological patterns may be uncovered as a result. The out of bag error rate, which is an estimate of the predictive accuracy of a random forests classifier, is used as a by-product to propose a new measure that assesses interaction patterns of differential expression. Its performance is studied in synthetic scenarios and it is also applied to real studies on SARS-CoV-2 and colon cancer data where it uncovers associations that remain undetected by other methods. Our proposal is aimed at providing a novel approach that may help the experts in biomedical and life sciences to unravel insightful interaction patterns that may decipher the molecular mechanisms underlying biological and clinical outcomes.

Auxin response factors are keys to the many auxin doors.

In plants, most developmental programs depend on the action of auxin. The best described model of the auxin signaling pathway, which explains most, but not all, of the auxin transcriptional responses, relies on a de-repression mechanism. The auxin/indole-3-acetic acid repressors (Aux/IAAs) interact with the auxin response factors (ARFs), the transcription factors of the auxin signaling pathway, leading to repression of the ARF-controlled genes. Auxin induces Aux/IAA degradation, releases ARFs and activates transcription. However, this elegant model is not suitable for all ARFs. Indeed, in Arabidopsis, which has 22 ARFs, only five of them fit into the model since they are the ones able to interact with Aux/IAAs. The remaining 17 have a limited capacity to interact with the repressors, and their mechanisms of action are still unclear. The differential interactions between ARF and Aux/IAA proteins constitute one of many examples of the biochemical and structural diversification of ARFs that affect their action and therefore affect auxin transcriptional responses. A deeper understanding of the structural properties of ARFs is fundamental to obtaining a better explanation of the action of auxin in plants.

Evolution of the Auxin Response Factors from charophyte ancestors.

Auxin is a major developmental regulator in plants and the acquisition of a transcriptional response to auxin likely contributed to developmental innovations at the time of water-to-land transition. Auxin Response Factors (ARFs) Transcription Factors (TFs) that mediate auxin-dependent transcriptional changes are divided into A, B and C evolutive classes in land plants. The origin and nature of the first ARF proteins in algae is still debated. Here, we identify the most 'ancient' ARF homologue to date in the early divergent charophyte algae Chlorokybus atmophyticus, CaARF. Structural modelling combined with biochemical studies showed that CaARF already shares many features with modern ARFs: it is capable of oligomerization, interacts with the TOPLESS co-repressor and specifically binds Auxin Response Elements as dimer. In addition, CaARF possesses a DNA-binding specificity that differs from class A and B ARFs and that was maintained in class C ARF along plants evolution. Phylogenetic evidence together with CaARF biochemical properties indicate that the different classes of ARFs likely arose from an ancestral proto-ARF protein with class C-like features. The foundation of auxin signalling would have thus happened from a pre-existing hormone-independent transcriptional regulation together with the emergence of a functional hormone perception complex.

Crystal structure of the transcriptional repressor DdrO: insight into the metalloprotease/repressor-controlled radiation response in Deinococcus.

Exposure to harmful conditions such as radiation and desiccation induce oxidative stress and DNA damage. In radiation-resistant Deinococcus bacteria, the radiation/desiccation response is controlled by two proteins: the XRE family transcriptional repressor DdrO and the COG2856 metalloprotease IrrE. The latter cleaves and inactivates DdrO. Here, we report the biochemical characterization and crystal structure of DdrO, which is the first structure of a XRE protein targeted by a COG2856 protein. DdrO is composed of two domains that fold independently and are separated by a flexible linker. The N-terminal domain corresponds to the DNA-binding domain. The C-terminal domain, containing three alpha helices arranged in a novel fold, is required for DdrO dimerization. Cleavage by IrrE occurs in the loop between the last two helices of DdrO and abolishes dimerization and DNA binding. The cleavage site is hidden in the DdrO dimer structure, indicating that IrrE cleaves DdrO monomers or that the interaction with IrrE induces a structural change rendering accessible the cleavage site. Predicted COG2856/XRE regulatory protein pairs are found in many bacteria, and available data suggest two different molecular mechanisms for stress-induced gene expression: COG2856 protein-mediated cleavage or inhibition of oligomerization without cleavage of the XRE repressor.

Structure of the Arabidopsis TOPLESS corepressor provides insight into the evolution of transcriptional repression.

Transcriptional repression involves a class of proteins called corepressors that link transcription factors to chromatin remodeling complexes. In plants such as Arabidopsis thaliana, the most prominent corepressor is TOPLESS (TPL), which plays a key role in hormone signaling and development. Here we present the crystallographic structure of the Arabidopsis TPL N-terminal region comprising the LisH and CTLH (C-terminal to LisH) domains and a newly identified third region, which corresponds to a CRA domain. Comparing the structure of TPL with the mammalian TBL1, which shares a similar domain structure and performs a parallel corepressor function, revealed that the plant TPLs have evolved a new tetramerization interface and unique and highly conserved surface for interaction with repressors. Using site-directed mutagenesis, we validated those surfaces in vitro and in vivo and showed that TPL tetramerization and repressor binding are interdependent. Our results illustrate how evolution used a common set of protein domains to create a diversity of corepressors, achieving similar properties with different molecular solutions.

Shining light on plant hormones with genetically encoded biosensors.

Signalling molecules are produced, degraded, modified and transported throughout the development of higher organisms. Understanding their mode of action implies understanding these dynamics in vivo and in real time. Genetically encoded biosensors are being more and more used as tools to 'follow' signalling molecules and their responses inside an organism. This is the case for plants, where important progresses have been made in the development of such biosensors. Here, we summarize the main genetically encoded biosensors built for plant hormones, constructed using diverse components and steps of their signalling pathways.

Decoding the Auxin Matrix: Auxin Biology Through the Eye of the Computer.

The plant hormone auxin is certainly the most studied developmental regulator in plants. The many functions of auxin during development, from the embryo to the root and shoot construction, are mediated by an ever-growing collection of molecular regulators, with an overwhelming degree of both ubiquity and complexity that we are still far from fully understanding and that biological experiments alone cannot grasp. In this review, we discuss how bioinformatics and computational modeling approaches have helped in recent years to explore this complexity and to push the frontiers of our understanding of auxin biology. We focus on how analysis of massive amounts of genomic data and construction of computational models to simulate auxin-regulated processes at different scales have complemented wet experiments to increase the understanding of how auxin acts in the nucleus to regulate transcription and how auxin movement between cells regulates development at the tissular scale.

Contrasted evolutionary trajectories of plant transcription factors.

Because of their prominent roles in plant development, transcription factors (TF) play central roles as drivers of innovation in the evolution of the green lineage (viridiplantae). The advent of massive sequencing combined with comparative genetics/genomics allows a rigorous investigation of how TF families have contributed to plant diversification from charophyte algae to bryophytes to angiosperms. Here, we review recent progress on TF family reconstruction and the identification of distantly related TFs present throughout the evolutionary timeline from algae to angiosperms. These data provide examples of contrasting evolutionary trajectories of TF families and illustrate how conserved TFs adopt diverse roles over the course of evolution.

Temporal integration of auxin information for the regulation of patterning.

Positional information is essential for coordinating the development of multicellular organisms. In plants, positional information provided by the hormone auxin regulates rhythmic organ production at the shoot apex, but the spatio-temporal dynamics of auxin gradients is unknown. We used quantitative imaging to demonstrate that auxin carries high-definition graded information not only in space but also in time. We show that, during organogenesis, temporal patterns of auxin arise from rhythmic centrifugal waves of high auxin travelling through the tissue faster than growth. We further demonstrate that temporal integration of auxin concentration is required to trigger the auxin-dependent transcription associated with organogenesis. This provides a mechanism to temporally differentiate sites of organ initiation and exemplifies how spatio-temporal positional information can be used to create rhythmicity.

Plants, like animals and many other multicellular organisms, control their body architecture by creating organized patterns of cells. These patterns are generally defined by signal molecules whose levels differ across the tissue and change over time. This tells the cells where they are located in the tissue and therefore helps them know what tasks to perform. A plant hormone called auxin is one such signal molecule and it controls when and where plants produce new leaves and flowers. Over time, this process gives rise to the dashing arrangements of spiraling organs exhibited by many plant species. The leaves and flowers form from a relatively small group of cells at the tip of a growing stem known as the shoot apical meristem. Auxin accumulates at precise locations within the shoot apical meristem before cells activate the genes required to make a new leaf or flower. However, the precise role of auxin in forming these new organs remained unclear because the tools to observe the process in enough detail were lacking. Galvan-Ampudia, Cerutti et al. have now developed new microscopy and computational approaches to observe auxin in a small plant known as Arabidopsis thaliana. This showed that dozens of shoot apical meristems exhibited very similar patterns of auxin. Images taken over a period of several hours showed that the locations where auxin accumulated were not fixed on a group of cells but instead shifted away from the center of the shoot apical meristems faster than the tissue grew. This suggested the cells experience rapidly changing levels of auxin. Further experiments revealed that the cells needed to be exposed to a high level of auxin over time to activate genes required to form an organ. This mechanism sheds a new light on how auxin regulates when and where plants make new leaves and flowers. The tools developed by Galvan-Ampudia, Cerutti et al. could be used to study the role of auxin in other plant tissues, and to investigate how plants regulate the response to other plant hormones.

Capturing Auxin Response Factors Syntax Using DNA Binding Models.

Auxin is a key hormone performing a wealth of functions throughout the life cycle of plants. It acts largely by regulating genes at the transcriptional level through a family of transcription factors called auxin response factors (ARFs). Even though all ARF monomers analyzed so far bind a similar DNA sequence, there is evidence that ARFs differ in their target genomic regions and regulated genes. Here, we report the use of position weight matrices (PWMs) to model ARF DNA binding specificity based on published DNA affinity purification sequencing (DAP-seq) data. We found that the genome binding of two ARFs (ARF2 and ARF5/Monopteros [MP]) differ largely because these two factors have different preferred ARF binding site (ARFbs) arrangements (orientation and spacing). We illustrated why PWMs are more versatile to reliably identify ARFbs than the widely used consensus sequences and demonstrated their power with biochemical experiments in the identification of the regulatory regions of IAA19, an well-characterized auxin-responsive gene. Finally, we combined gene regulation by auxin with ARF-bound regions and identified specific ARFbs configurations that are over-represented in auxin-upregulated genes, thus deciphering the ARFbs syntax functional for regulation. Our study provides a general method to exploit the potential of genome-wide DNA binding assays and to decode gene regulation.