BAD2matrix: Phylogenomic matrix concatenation, indel coding, and more.

Premise: Common steps in phylogenomic matrix production include biological sequence concatenation, morphological data concatenation, insertion/deletion (indel) coding, gene content (presence/absence) coding, removing uninformative characters for parsimony analysis, recording with reduced amino acid alphabets, and occupancy filtering. Existing software does not accomplish these tasks on a phylogenomic scale using a single program.

Methods And Results: BAD2matrix is a Python script that performs the above-mentioned steps in phylogenomic matrix construction for DNA or amino acid sequences as well as morphological data.

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

[This corrects the article DOI: 10.3389/fgene.2024.

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.

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.

Philip N. Benfey (1953-2023).

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

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.

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 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, , 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.

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.

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.

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.

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 root as a function of time.

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.

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.

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.

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.

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.

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.

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 root transcriptome to different doses of nitrogen (N)-a key plant nutrient-as a function of time.

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

The ability to accurately predict the causal relationships from transcription factors to genes would greatly enhance our understanding of transcriptional dynamics. This could lead to applications in which one or more transcription factors could be manipulated to effect a change in genes leading to the enhancement of some desired trait. Here we present a method called OutPredict that constructs a model for each gene based on time series (and other) data and that predicts gene's expression in a previously unseen subsequent time point.

SDG8 Potentiates the Sustainable Transcriptional Induction of the Genes and During Plant Defense Response.

Post-translational covalent modifications of histones play important roles in modulating chromatin structure and are involved in the control of multiple developmental processes in plants. Here we provide insight into the contribution of the histone lysine methyltransferase SET DOMAIN GROUP 8 (SDG8), implicated in histone H3 lysine 36 trimethylation (H3K36me3), in connection with RNA polymerase II (RNAPII) to enhance immunity. We showed that even if the loss-of-function mutant, defective in H3K36 methylation, displayed a higher sensitivity to different strains of the bacterial pathogen , effector-triggered immunity (ETI) still operated, but less efficiently than in the wild-type (WT) plants.

Transient genome-wide interactions of the master transcription factor NLP7 initiate a rapid nitrogen-response cascade.

Dynamic reprogramming of gene regulatory networks (GRNs) enables organisms to rapidly respond to environmental perturbation. However, the underlying transient interactions between transcription factors (TFs) and genome-wide targets typically elude biochemical detection. Here, we capture both stable and transient TF-target interactions genome-wide within minutes after controlled TF nuclear import using time-series chromatin immunoprecipitation (ChIP-seq) and/or DNA adenine methyltransferase identification (DamID-seq).

A balancing act: how plants integrate nitrogen and water signals.

Nitrogen (N) and water (W) are crucial inputs for plant survival as well as costly resources for agriculture. Given their importance, the molecular mechanisms that plants rely on to signal changes in either N or W status have been under intense scrutiny. However, how plants sense and respond to the combination of N and W signals at the molecular level has received scant attention.