Corrigendum: Bipartite networks represent causality better than simple networks: Evidence, algorithms, and applications.

[This corrects the article DOI: 10.3389/fgene.2024.1371607.].

Phylogenetically diverse wild plant species use common biochemical strategies to thrive in the Atacama Desert.

The best ideotypes are under mounting pressure due to increased aridity. Understanding the conserved molecular mechanisms that evolve in wild plants adapted to harsh environments is crucial in developing new strategies for agriculture. Yet our knowledge of such mechanisms in wild species is scant. We performed metabolic pathway reconstruction using transcriptome information from 32 Atacama and phylogenetically related species that do not live in Atacama (sister species). We analyzed reaction enrichment to understand the commonalities and differences of Atacama plants. To gain insights into the mechanisms that ensure survival, we compared expressed gene isoform numbers and gene expression patterns between the annotated biochemical reactions from 32 Atacama and sister species. We found biochemical convergences characterized by reactions enriched in at least 50% of the Atacama species, pointing to potential advantages against drought and nitrogen starvation, for instance. These findings suggest that the adaptation in the Atacama Desert may result in part from shared genetic legacies governing the expression of key metabolic pathways to face harsh conditions. Enriched reactions corresponded to ubiquitous compounds common to extreme and agronomic species and were congruent with our previous metabolomic analyses. Convergent adaptive traits offer promising candidates for improving abiotic stress resilience in crop species.

Nitrogen sensing and regulatory networks: it's about time and space.

A plant's response to external and internal nitrogen signals/status relies on sensing and signaling mechanisms that operate across spatial and temporal dimensions. From a comprehensive systems biology perspective, this involves integrating nitrogen responses in different cell types and over long distances to ensure organ coordination in real time and yield practical applications. In this prospective review, we focus on novel aspects of nitrogen (N) sensing/signaling uncovered using temporal and spatial systems biology approaches, largely in the model Arabidopsis. The temporal aspects span: transcriptional responses to N-dose mediated by Michaelis-Menten kinetics, the role of the master NLP7 transcription factor as a nitrate sensor, its nitrate-dependent TF nuclear retention, its "hit-and-run" mode of target gene regulation, and temporal transcriptional cascade identified by "network walking." Spatial aspects of N-sensing/signaling have been uncovered in cell type-specific studies in roots and in root-to-shoot communication. We explore new approaches using single-cell sequencing data, trajectory inference, and pseudotime analysis as well as machine learning and artificial intelligence approaches. Finally, unveiling the mechanisms underlying the spatial dynamics of nitrogen sensing/signaling networks across species from model to crop could pave the way for translational studies to improve nitrogen-use efficiency in crops. Such outcomes could potentially reduce the detrimental effects of excessive fertilizer usage on groundwater pollution and greenhouse gas emissions.

Philip N. Benfey (1953-2023).

A "ring" master of plant development and cellular genomics.

The genome of the Wollemi pine, a critically endangered "living fossil" unchanged since the Cretaceous, reveals extensive ancient transposon activity.

We present the genome of the living fossil, Wollemia nobilis, a southern hemisphere conifer morphologically unchanged since the Cretaceous. Presumed extinct until rediscovery in 1994, the Wollemi pine is critically endangered with less than 60 wild adults threatened by intensifying bushfires in the Blue Mountains of Australia. The 12 Gb genome is among the most contiguous large plant genomes assembled, with extremely low heterozygosity and unusual abundance of DNA transposons. Reduced representation and genome re-sequencing of individuals confirms a relictual population since the last major glacial/drying period in Australia, 120 ky BP. Small RNA and methylome sequencing reveal conservation of ancient silencing mechanisms despite the presence of thousands of active and abundant transposons, including some transferred horizontally to conifers from arthropods in the Jurassic. A retrotransposon burst 8-6 my BP coincided with population decline, possibly as an adaptation enhancing epigenetic diversity. Wollemia, like other conifers, is susceptible to Phytophthora, and a suite of defense genes, similar to those in loblolly pine, are targeted for silencing by sRNAs in leaves. The genome provides insight into the earliest seed plants, while enabling conservation efforts.

DamID-seq: A Genome-Wide DNA Methylation Method that Captures Both Transient and Stable TF-DNA Interactions in Plant Cells.

Capturing the dynamic and transient interactions of a transcription factor (TF) with its genome-wide targets whose regulation leads to plants' adaptation to their changing environment is a major technical challenge. This is a widespread problem with biochemical methods such as chromatin immunoprecipitation-sequencing (ChIP-seq) which are biased towards capturing stable TF-target gene interactions. Herein, we describe how DNA adenine methyltransferase identification and sequencing (DamID-seq) can be used to capture both transient and stable TF-target interactions by DNA methylation. The DamID technique uses a TF protein fused to a DNA adenine methyltransferase (Dam) from E. coli. When expressed in a plant cell, the Dam-TF fusion protein will methylate adenine (A) bases near the sites of TF-DNA interactions. In this way, DamID results in a permanent, stable DNA methylation mark on TF-target gene promoters, even if the target gene is only transiently "touched" by the Dam-TF fusion protein. Here we provide a step-by-step protocol to perform DamID-seq experiments in isolated plant cells for any Dam-TF fusion protein of interest. We also provide information that will enable researchers to analyze DamID-seq data to identify TF-binding sites in the genome. Our protocol includes instructions for vector cloning of the Dam-TF fusion proteins, plant cell protoplast transfections, DamID preps, library preparation, and sequencing data analysis. The protocol outlined in this chapter is performed in Arabidopsis thaliana, however, the DamID-seq workflow developed in this guide is broadly applicable to other plants and organisms.

Building High-Confidence Gene Regulatory Networks by Integrating Validated TF-Target Gene Interactions Using ConnecTF.

Many methods are now available to identify or predict the target genes of transcription factors (TFs) in plants. These include experimental approaches such as in vivo or in vitro TF-target gene-binding assays and various methods for identifying regulated targets in mutants, transgenics, or isolated plant cells. In addition, computational approaches are used to infer TF-target gene interactions from the regulatory elements or gene expression changes across treatments. While each of these approaches has now been applied to a large number of TFs from many species, each method has its own limitations which necessitates that multiple data types are integrated to build the most accurate representation of the gene regulatory networks operating in plants. To make the analyses of TF-target interaction datasets available to the broader research community, we have developed the ConnecTF web platform ( https://connectf.org/ ). In this chapter, we describe how ConnecTF can be used to integrate validated and predicted TF-target gene interactions in order to dissect the regulatory role of TFs in developmental and stress response pathways. Using as our examples KN1 and RA1, two well-characterized maize TFs involved in developing floral tissue, we demonstrate how ConnecTF can be used to (1) compare the target genes between TFs, (2) identify direct vs. indirect targets by combining TF-binding and TF-regulation datasets, (3) chart and visualize network paths between TFs and their downstream targets, and (4) prune inferred user networks for high-confidence predicted interactions using validated TF-target gene data. Finally, we provide instructions for setting up a private version of ConnecTF that enables research groups to store and analyze their own TF-target gene interaction datasets.

EnsInfer: a simple ensemble approach to network inference outperforms any single method.

This study evaluates both a variety of existing base causal inference methods and a variety of ensemble methods. We show that: (i) base network inference methods vary in their performance across different datasets, so a method that works poorly on one dataset may work well on another; (ii) a non-homogeneous ensemble method in the form of a Naive Bayes classifier leads overall to as good or better results than using the best single base method or any other ensemble method; (iii) for the best results, the ensemble method should integrate all methods that satisfy a statistical test of normality on training data. The resulting ensemble model EnsInfer easily integrates all kinds of RNA-seq data as well as new and existing inference methods. The paper categorizes and reviews state-of-the-art underlying methods, describes the EnsInfer ensemble approach in detail, and presents experimental results. The source code and data used will be made available to the community upon publication.

The TARGET System: Rapid Identification of Direct Targets of Transcription Factors by Gene Regulation in Plant Cells.

The TARGET system allows for the rapid identification of direct regulated gene targets of transcription factors (TFs). It employs the transient transformation of plant protoplasts with inducible nuclear entry of the TF and subsequent transcriptomic and/or ChIP-seq analysis. The ability to separate direct TF-target gene regulatory interactions from indirect downstream responses and the significantly shorter amount of time required to perform the assay, compared to the generation of transgenics, make this plant cell-based approach a valuable tool for a higher throughput approach to identify the genome-wide targets of multiple TFs, to build validated transcriptional networks in plants. Here, we describe the use of the TARGET system in Arabidopsis seedling root protoplasts to map the gene regulatory network downstream of transcription factors-of-interest.

Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice.

Nitrogen (N) and Water (W) - two resources critical for crop productivity - are becoming increasingly limited in soils globally. To address this issue, we aim to uncover the gene regulatory networks (GRNs) that regulate nitrogen use efficiency (NUE) - as a function of water availability - in Oryza sativa, a staple for 3.5 billion people. In this study, we infer and validate GRNs that correlate with rice NUE phenotypes affected by N-by-W availability in the field. We did this by exploiting RNA-seq and crop phenotype data from 19 rice varieties grown in a 2x2 N-by-W matrix in the field. First, to identify gene-to-NUE field phenotypes, we analyzed these datasets using weighted gene co-expression network analysis (WGCNA). This identified two network modules ("skyblue" & "grey60") highly correlated with NUE grain yield (NUEg). Next, we focused on 90 TFs contained in these two NUEg modules and predicted their genome-wide targets using the N-and/or-W response datasets using a random forest network inference approach (GENIE3). Next, to validate the GENIE3 TF→target gene predictions, we performed Precision/Recall Analysis (AUPR) using nine datasets for three TFs validated in planta. This analysis sets a precision threshold of 0.31, used to "prune" the GENIE3 network for high-confidence TF→target gene edges, comprising 88 TFs and 5,716 N-and/or-W response genes. Next, we ranked these 88 TFs based on their significant influence on NUEg target genes responsive to N and/or W signaling. This resulted in a list of 18 prioritized TFs that regulate 551 NUEg target genes responsive to N and/or W signals. We validated the direct regulated targets of two of these candidate NUEg TFs in a plant cell-based TF assay called TARGET, for which we also had in planta data for comparison. Gene ontology analysis revealed that 6/18 NUEg TFs - OsbZIP23 (LOC_Os02g52780), Oshox22 (LOC_Os04g45810), LOB39 (LOC_Os03g41330), Oshox13 (LOC_Os03g08960), LOC_Os11g38870, and LOC_Os06g14670 - regulate genes annotated for N and/or W signaling. Our results show that OsbZIP23 and Oshox22, known regulators of drought tolerance, also coordinate W-responses with NUEg. This validated network can aid in developing/breeding rice with improved yield on marginal, low N-input, drought-prone soils.

Spatiotemporal analysis identifies ABF2 and ABF3 as key hubs of endodermal response to nitrate.

Nitrate is a nutrient and a potent signal that impacts global gene expression in plants. However, the regulatory factors controlling temporal and cell type-specific nitrate responses remain largely unknown. We assayed nitrate-responsive transcriptome changes in five major root cell types of the Arabidopsis thaliana root as a function of time. We found that gene-expression response to nitrate is dynamic and highly localized and predicted cell type-specific transcription factor (TF)-target interactions. Among cell types, the endodermis stands out as having the largest and most connected nitrate-regulatory gene network. ABF2 and ABF3 are major hubs for transcriptional responses in the endodermis cell layer. We experimentally validated TF-target interactions for ABF2 and ABF3 by chromatin immunoprecipitation followed by sequencing and a cell-based system to detect TF regulation genome-wide. Validated targets of ABF2 and ABF3 account for more than 50% of the nitrate-responsive transcriptome in the endodermis. Moreover, ABF2 and ABF3 are involved in nitrate-induced lateral root growth. Our approach offers an unprecedented spatiotemporal resolution of the root response to nitrate and identifies important components of cell-specific gene regulatory networks.

The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues.

As sessile organisms, plants are finely tuned to respond dynamically to developmental, circadian and environmental cues. Genome-wide studies investigating these types of cues have uncovered the intrinsically different ways they can impact gene expression over time. Recent advances in single-cell sequencing and time-based bioinformatic algorithms are now beginning to reveal the dynamics of these time-based responses within individual cells and plant tissues. Here, we review what these techniques have revealed about the spatiotemporal nature of gene regulation, paying particular attention to the three distinct ways in which plant tissues are time sensitive. (i) First, we discuss how studying plant cell identity can reveal developmental trajectories hidden in pseudotime. (ii) Next, we present evidence that indicates that plant cell types keep their own local time through tissue-specific regulation of the circadian clock. (iii) Finally, we review what determines the speed of environmental signaling responses, and how they can be contingent on developmental and circadian time. By these means, this review sheds light on how these different scales of time-based responses can act with tissue and cell-type specificity to elicit changes in whole plant systems.

Plant ecological genomics at the limits of life in the Atacama Desert.

The Atacama Desert in Chile-hyperarid and with high-ultraviolet irradiance levels-is one of the harshest environments on Earth. Yet, dozens of species grow there, including Atacama-endemic plants. Herein, we establish the Talabre-Lejía transect (TLT) in the Atacama as an unparalleled natural laboratory to study plant adaptation to extreme environmental conditions. We characterized climate, soil, plant, and soil-microbe diversity at 22 sites (every 100 m of altitude) along the TLT over a 10-y period. We quantified drought, nutrient deficiencies, large diurnal temperature oscillations, and pH gradients that define three distinct vegetational belts along the altitudinal cline. We deep-sequenced transcriptomes of 32 dominant plant species spanning the major plant clades, and assessed soil microbes by metabarcoding sequencing. The top-expressed genes in the 32 Atacama species are enriched in stress responses, metabolism, and energy production. Moreover, their root-associated soils are enriched in growth-promoting bacteria, including nitrogen fixers. To identify genes associated with plant adaptation to harsh environments, we compared 32 Atacama species with the 32 closest sequenced species, comprising 70 taxa and 1,686,950 proteins. To perform phylogenomic reconstruction, we concatenated 15,972 ortholog groups into a supermatrix of 8,599,764 amino acids. Using two codon-based methods, we identified 265 candidate positively selected genes (PSGs) in the Atacama plants, 64% of which are located in Pfam domains, supporting their functional relevance. For 59/184 PSGs with an Arabidopsis ortholog, we uncovered functional evidence linking them to plant resilience. As some Atacama plants are closely related to staple crops, these candidate PSGs are a "genetic goldmine" to engineer crop resilience to face climate change.

Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.

Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine.

GARP transcription factors repress Arabidopsis nitrogen starvation response via ROS-dependent and -independent pathways.

Plants need to cope with strong variations of nitrogen availability in the soil. Although many molecular players are being discovered concerning how plants perceive NO3- provision, it is less clear how plants recognize a lack of nitrogen. Following nitrogen removal, plants activate their nitrogen starvation response (NSR), which is characterized by the activation of very high-affinity nitrate transport systems (NRT2.4 and NRT2.5) and other sentinel genes involved in N remobilization such as GDH3. Using a combination of functional genomics via transcription factor perturbation and molecular physiology studies, we show that the transcription factors belonging to the HHO subfamily are important regulators of NSR through two potential mechanisms. First, HHOs directly repress the high-affinity nitrate transporters, NRT2.4 and NRT2.5. hho mutants display increased high-affinity nitrate transport activity, opening up promising perspectives for biotechnological applications. Second, we show that reactive oxygen species (ROS) are important to control NSR in wild-type plants and that HRS1 and HHO1 overexpressors and mutants are affected in their ROS content, defining a potential feed-forward branch of the signaling pathway. Taken together, our results define the relationships of two types of molecular players controlling the NSR, namely ROS and the HHO transcription factors. This work (i) up opens perspectives on a poorly understood nutrient-related signaling pathway and (ii) defines targets for molecular breeding of plants with enhanced NO3- uptake.

Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks.

All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets-at both the local and genome-wide levels-and how they are used to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology.

ConnecTF: A platform to integrate transcription factor-gene interactions and validate regulatory networks.

Deciphering gene regulatory networks (GRNs) is both a promise and challenge of systems biology. The promise lies in identifying key transcription factors (TFs) that enable an organism to react to changes in its environment. The challenge lies in validating GRNs that involve hundreds of TFs with hundreds of thousands of interactions with their genome-wide targets experimentally determined by high-throughput sequencing. To address this challenge, we developed ConnecTF, a species-independent, web-based platform that integrates genome-wide studies of TF-target binding, TF-target regulation, and other TF-centric omic datasets and uses these to build and refine validated or inferred GRNs. We demonstrate the functionality of ConnecTF by showing how integration within and across TF-target datasets uncovers biological insights. Case study 1 uses integration of TF-target gene regulation and binding datasets to uncover TF mode-of-action and identify potential TF partners for 14 TFs in abscisic acid signaling. Case study 2 demonstrates how genome-wide TF-target data and automated functions in ConnecTF are used in precision/recall analysis and pruning of an inferred GRN for nitrogen signaling. Case study 3 uses ConnecTF to chart a network path from NLP7, a master TF in nitrogen signaling, to direct secondary TF2s and to its indirect targets in a Network Walking approach. The public version of ConnecTF (https://ConnecTF.org) contains 3,738,278 TF-target interactions for 423 TFs in Arabidopsis, 839,210 TF-target interactions for 139 TFs in maize (Zea mays), and 293,094 TF-target interactions for 26 TFs in rice (Oryza sativa). The database and tools in ConnecTF will advance the exploration of GRNs in plant systems biology applications for model and crop species.

Author Correction: OutPredict: multiple datasets can improve prediction of expression and inference of causality.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Nutrient dose-responsive transcriptome changes driven by Michaelis-Menten kinetics underlie plant growth rates.

An increase in nutrient dose leads to proportional increases in crop biomass and agricultural yield. However, the molecular underpinnings of this nutrient dose-response are largely unknown. To investigate, we assayed changes in the Arabidopsis root transcriptome to different doses of nitrogen (N)-a key plant nutrient-as a function of time. By these means, we found that rate changes of genome-wide transcript levels in response to N-dose could be explained by a simple kinetic principle: the Michaelis-Menten (MM) model. Fitting the MM model allowed us to estimate the maximum rate of transcript change (Vmax), as well as the N-dose at which one-half of Vmax was achieved (Km) for 1,153 N-dose-responsive genes. Since transcription factors (TFs) can act in part as the catalytic agents that determine the rates of transcript change, we investigated their role in regulating N-dose-responsive MM-modeled genes. We found that altering the abundance of TGA1, an early N-responsive TF, perturbed the maximum rates of N-dose transcriptomic responses (Vmax), Km, as well as the rate of N-dose-responsive plant growth. We experimentally validated that MM-modeled N-dose-responsive genes included both direct and indirect TGA1 targets, using a root cell TF assay to detect TF binding and/or TF regulation genome-wide. Taken together, our results support a molecular mechanism of transcriptional control that allows an increase in N-dose to lead to a proportional change in the rate of genome-wide expression and plant growth.