A Guard Cell Abscisic Acid (ABA) Network Model That Captures the Stomatal Resting State

Inference of a Boolean Network From Causal Logic Implications

Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model. Boolean modeling, the simplest discrete dynamic modeling framework for biological networks, has proven its value in recapitulating experimental results and making predictions. A first step and major roadblock to the widespread use of Boolean networks in biology is the laborious network inference and construction process. Here we present a streamlined network inference method that combines the discovery of a parsimonious network structure and the identification of Boolean functions that determine the dynamics of the system. This inference method is based on a causal logic analysis method that associates a logic type (sufficient or necessary) to node-pair relationships (whether promoting or inhibitory). We use the causal logic framework to assimilate indirect information obtained from perturbation experiments and infer relationships that have not yet been documented experimentally. We apply this inference method to a well-studied process of hormone signaling in plants, the signaling underlying abscisic acid (ABA)—induced stomatal closure. Applying the causal logic inference method significantly reduces the manual work typically required for network and Boolean model construction. The inferred model agrees with the manually curated model. We also test this method by re-inferring a network representing epithelial to mesenchymal transition based on a subset of the information that was initially used to construct the model. We find that the inference method performs well for various likely scenarios of inference input information. We conclude that our method is an effective approach toward inference of biological networks and can become an efficient step in the iterative process between experiments and computations.

pystablemotifs: Python library for attractor identification and control in Boolean networks

SUMMARY

pystablemotifs is a Python 3 library for analyzing Boolean networks. Its non-heuristic and exhaustive attractor identification algorithm was previously presented in Rozum et al. (2021). Here, we illustrate its performance improvements over similar methods and discuss how it uses outputs of the attractor identification process to drive a system to one of its attractors from any initial state. We implement six attractor control algorithms, five of which are new in this work. By design, these algorithms can return different control strategies, allowing for synergistic use. We also give a brief overview of the other tools implemented in pystablemotifs.

AVAILABILITY AND IMPLEMENTATION

The source code is on GitHub at https://github.com/jcrozum/pystablemotifs/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Signatures of natural selection in a foundation tree along Mediterranean climatic gradients

Temperature and precipitation regimes are rapidly changing, resulting in forest dieback and extinction events, particularly in Mediterranean‐type climates (MTC). Forest management that enhance forests’ resilience is urgently required, however adaptation to climates in heterogeneous landscapes with multiple selection pressures is complex. For widespread trees in MTC we hypothesized that: patterns of local adaptation are associated with climate; precipitation is a stronger factor of adaptation than temperature; functionally related genes show similar signatures of adaptation; and adaptive variants are independently sorting across the landscape. We sampled 28 populations across the geographic distribution of Eucalyptus marginata (jarrah), in South‐west Western Australia, and obtained 13,534 independent single nucleotide polymorphic (SNP) markers across the genome. Three genotype‐association analyses that employ different ways of correcting population structure were used to identify putatively adapted SNPs associated with independent climate variables. While overall levels of population differentiation were low (F_ST = 0.04), environmental association analyses found a total of 2336 unique SNPs associated with temperature and precipitation variables, with 1440 SNPs annotated to genic regions. Considerable allelic turnover was identified for SNPs associated with temperature seasonality and mean precipitation of the warmest quarter, suggesting that both temperature and precipitation are important factors in adaptation. SNPs with similar gene functions had analogous allelic turnover along climate gradients, while SNPs among temperature and precipitation variables had uncorrelated patterns of adaptation. These contrasting patterns provide evidence that there may be standing genomic variation adapted to current climate gradients, providing the basis for adaptive management strategies to bolster forest resilience in the future.

Boolean modelling in plant biology

Signalling and genetic networks underlie most biological processes and are often complex, containing many highly connected components. Modelling these networks can provide insight into mechanisms but is challenging given that rate parameters are often not well defined. Boolean modelling, in which components can only take on a binary value with connections encoded by logic equations, is able to circumvent some of these challenges, and has emerged as a viable tool to probe these complex networks. In this review, we will give an overview of Boolean modelling, with a specific emphasis on its use in plant biology. We review how Boolean modelling can be used to describe biological networks and then discuss examples of its applications in plant genetics and plant signalling.

Boolink: a graphical interface for open access Boolean network simulations and use in guard cell CO2 signaling

Signaling networks are at the heart of almost all biological processes. Most of these networks contain large number of components, and often either the connections between these components are not known or the rate equations that govern the dynamics of soluble signaling components are not quantified. This uncertainty in network topology and parameters can make it challenging to formulate detailed mathematical models. Boolean networks, in which all components are either on or off, have emerged as viable alternatives to detailed mathematical models that contain rate constants and other parameters. Therefore, open-source platforms of Boolean models for community use are desirable. Here, we present Boolink, a freely available graphical user interface that allows users to easily construct and analyze existing Boolean networks. Boolink can be applied to any Boolean network. We demonstrate its application using a previously published network for abscisic acid (ABA)-driven stomatal closure in Arabidopsis spp. (Arabidopsis thaliana). We also show how Boolink can be used to generate testable predictions by extending the network to include CO2 regulation of stomatal movements. Predictions of the model were experimentally tested, and the model was iteratively modified based on experiments showing that ABA effectively closes Arabidopsis stomata at near-zero CO2 concentrations (1.5-ppm CO2). Thus, Boolink enables public generation and the use of existing Boolean models, including the prior developed ABA signaling model with added CO2 signaling components.

Deep dive into CO2-dependent molecular mechanisms driving stomatal responses in plants

Recent advances are revealing mechanisms mediating CO₂-regulated stomatal movements in Arabidopsis, stomatal architecture and stomatal movements in grasses, and the long-term impact of CO₂ on growth.

Influence of the Rhizobacterium Rhodobacter sphaeroides KE149 and Biochar on Waterlogging Stress Tolerance in Glycine max L

In the context of the current climate change and increasing population scenarios, waterlogging stress in plants represents a global threat to sustainable agriculture production. Plant-growth-promoting rhizobacteria and biochar have been widely reported to mitigate the effects of several abiotic stresses. Hence, in the present study, we examined the effect of the rhizobacterium Rhodobacter sphaeroides KE149 and biochar on soybean plants subjected to sufficient water supply and waterlogging stress conditions. Our results revealed that KE149 and biochar inoculation significantly improved plant morphological attributes, such as root length, shoot length, and fresh biomass. The biochemical analysis results showed that the two treatments determined a significant drop in the levels of endogenous phytohormones (such as abscisic acid) under normal conditions, which were considerably enhanced under waterlogging stress. However, the jasmonic acid content increased with the application of biochar and KE149 under normal conditions, and it considerably decreased under waterlogging stress. Moreover, proline, methionine, and aspartic acid were significantly increased, whereas the phenolic and flavonoid contents were reduced with the application of the two treatments under waterlogging stress. These results suggest that the application of KE149 and biochar can be a safe biological tool with which to improve the physiology and productivity of soybean plants exposed to waterlogging stress.

Optogenetic control of the guard cell membrane potential and stomatal movement by the light-gated anion channel GtACR1

Guard cells control the aperture of plant stomata, which are crucial for global fluxes of CO₂ and water. In turn, guard cell anion channels are seen as key players for stomatal closure, but is activation of these channels sufficient to limit plant water loss? To answer this open question, we used an optogenetic approach based on the light-gated anion channelrhodopsin 1 (GtACR1). In tobacco guard cells that express GtACR1, blue- and green-light pulses elicit Cl^- and NO₃ ^- currents of -1 to -2 nA. The anion currents depolarize the plasma membrane by 60 to 80 mV, which causes opening of voltage-gated K⁺ channels and the extrusion of K⁺ As a result, continuous stimulation with green light leads to loss of guard cell turgor and closure of stomata at conditions that provoke stomatal opening in wild type. GtACR1 optogenetics thus provides unequivocal evidence that opening of anion channels is sufficient to close stomata.

Anno genominis XX: 20 years of Arabidopsis genomics

Twenty years ago, the Arabidopsis thaliana genome sequence was published. This was an important moment as it was the first sequenced plant genome and explicitly brought plant science into the genomics era. At the time, this was not only an outstanding technological achievement, but it was characterized by a superb global collaboration. The Arabidopsis genome was the seed for plant genomic research. Here, we review the development of numerous resources based on the genome that have enabled discoveries across plant species, which has enhanced our understanding of how plants function and interact with their environments.