Hitting pause on the cell cycle

Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice

Plants have evolved various mechanisms to adapt to adverse environmental stresses, such as the modulation of gene expression. Expression of stress-responsive genes is controlled by specific regulators, including transcription factors (TFs), that bind to sequence-specific binding sites, representing key components of cis-regulatory elements and regulatory networks. Our understanding of the underlying regulatory code remains, however, incomplete. Recent studies have shown that, by training machine learning (ML) algorithms on genomic sequence features, it is possible to predict which genes will transcriptionally respond to a specific stress. By identifying the most important features for gene expression prediction, these trained ML models allow, in theory, to further elucidate the regulatory code underlying the transcriptional response to abiotic stress. Here, we trained random forest ML models to predict gene expression in rice (Oryza sativa) in response to heat or drought stress. Apart from thoroughly assessing model performance and robustness across various input training data, the importance of promoter and gene body sequence features to train ML models was evaluated. The use of enriched promoter oligomers, complementing known TF binding sites, allowed us to gain novel insights in DNA motifs contributing to the stress regulatory code. By comparing genomic feature importance scores for drought and heat stress over time, general and stress-specific genomic features contributing to the performance of the learned models and their temporal variation were identified. This study provides a solid foundation to build and interpret ML models accurately predicting transcriptional responses and enables novel insights in biological sequence features that are important for abiotic stress responses.

Morphological, physiological, and transcriptional responses of the freshwater diatom Fragilaria crotonensis to elevated pH conditions

Harmful algal blooms (HABs) caused by the toxin-producing cyanobacteria Microcystis spp., can increase water column pH. While the effect(s) of these basified conditions on the bloom formers are a high research priority, how these pH shifts affect other biota remains understudied. Recently, it was shown these high pH levels decrease growth and Si deposition rates in the freshwater diatom Fragilaria crotonensis and natural Lake Erie (Canada-US) diatom populations. However, the physiological mechanisms and transcriptional responses of diatoms associated with these observations remain to be documented. Here, we examined F. crotonensis with a set of morphological, physiological, and transcriptomic tools to identify cellular responses to high pH. We suggest 2 potential mechanisms that may contribute to morphological and physiological pH effects observed in F. crotonensis. Moreover, we identified a significant upregulation of mobile genetic elements in the F. crotonensis genome which appear to be an extreme transcriptional response to this abiotic stress to enhance cellular evolution rates-a process we have termed "genomic roulette." We discuss the ecological and biogeochemical effects high pH conditions impose on fresh waters and suggest a means by which freshwater diatoms such as F. crotonensis may evade high pH stress to survive in a "basified" future.

An insight into the mechanism of DNA damage response in plants- role of SUPPRESSOR OF GAMMA RESPONSE 1: An overview

Because of their sessile lifestyle, plants are inescapably exposed to various kinds of environmental stresses throughout their lifetime. Therefore, to regulate their growth and development, plants constantly monitor the environmental signals and respond appropriately. However, these environmental stress factors, along with some endogenous metabolites, generated in response to environmental stress factors often induce various forms of DNA damage in plants and thus promote genome instability. To maintain the genomic integrity, plants have developed an extensive, sophisticated and coordinated cellular signaling mechanism known as DNA damage response or DDR. DDR evokes a signaling process which initiates with the sensing of DNA damage and followed by the subsequent activation of downstream pathways in many directions to repair and eliminate the harmful effects of DNA damages. SUPPRESSOR OF GAMMA RESPONSE 1 (SOG1), one of the newly identified components of DDR in plant genome, appears to play central role in this signaling network. SOG1 is a member of NAC [NO APICAL MERISTEM (NAM), ARABIDOPSIS TRANSCRIPTION ACTIVATION FACTOR (ATAF), CUP-SHAPED COTYLEDON (CUC)] domain family of transcription factors and involved in a diverse array of function in plants, encompassing transcriptional response to DNA damage, cell cycle checkpoint functions, ATAXIA-TELANGIECTASIA-MUTATED (ATM) or ATAXIA TELANGIECTASIA AND RAD3-RELATED (ATR) mediated activation of DNA damage response and repair, functioning in programmed cell death and regulation of induction of endoreduplication. Although most of the functional studies on SOG1 have been reported in Arabidopsis, some recent reports have indicated diverse functions of SOG1 in various other plant species, including Glycine max, Medicago truncatula, Sorghum bicolour, Oryza sativa and Zea mays, respectively. The remarkable functional diversity shown by SOG1 protein indicates its multitasking capacity. In this review, we integrate information mainly related to functional aspects of SOG1 in the context of DDR in plants. Considering the important role of SOG1 in DDR and its functional diversity, in-depth functional study of this crucial regulatory protein can provide further potential information on genome stability maintenance mechanism in plants in the context of changing environmental condition.