Putative cis-Regulatory Elements Predict Iron Deficiency Responses in Arabidopsis Roots

Nutri-cereal tissue-specific transcriptome atlas during development: Functional integration of gene expression to identify mineral uptake pathways in little millet (Panicum sumatrense)

Little millet (Panicum sumatrense Roth ex Roem. & Schult.) is an essential minor millet of southeast Asia and Africa's temperate and subtropical regions. The plant is stress-tolerant, has a short life cycle, and has a mineral-rich nutritional profile associated with unique health benefits. We report the developmental gene expression atlas of little millet (genotype JK-8) from ten tissues representing different stages of its life cycle, starting from seed germination and vegetative growth to panicle maturation. The developmental transcriptome atlas led to the identification of 342 827 transcripts. The BUSCO analysis and comparison with the transcriptomes of related species confirm that this study presents high-quality, in-depth coverage of the little millet transcriptome. In addition, the eFP browser generated here has a user-friendly interface, allowing interactive visualizations of tissue-specific gene expression. Using these data, we identified transcripts, the orthologs of which in Arabidopsis and rice are involved in nutrient acquisition, transport, and response pathways. The comparative analysis of the expression levels of these transcripts holds great potential for enhancing the mineral content in crops, particularly zinc and iron, to address the issue of "hidden hunger" and to attain nutritional security, making it a valuable asset for translational research.

The transcription factor bZIP44 cooperates with MYB10 and MYB72 to regulate the response of Arabidopsis thaliana to iron deficiency stress

Nicotianamine (NA) plays a crucial role in transporting metal ions, including iron (Fe), in plants; therefore, NICOTIANAMINE SYNTHASE (NAS) genes, which control NA synthesis, are tightly regulated at the transcriptional level. However, the transcriptional regulatory mechanisms of NAS genes require further investigations. In this study, we determined the role of bZIP44 in mediating plant response to Fe deficiency stress by conducting transformation experiments and assays. bZIP44 positively regulated the response of Arabidopsis to Fe deficiency stress by interacting with MYB10 and MYB72 to enhance their abilities to bind at NAS2 and NAS4 promoters, thereby increasing NAS2 and NAS4 transcriptional levels and promote NA synthesis. In summary, the transcription activities of bZIP44, MYB10, and MYB72 were induced in response to Fe deficiency stress, which enhanced the interaction between bZIP44 and MYB10 or MYB72 proteins, synergistically activated the transcriptional activity of NAS2 and NAS4, promoted NA synthesis, and improved Fe transport, thereby enhancing plant tolerance to Fe deficiency stress.

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

Cellular clarity: a logistic regression approach to identify root epidermal regulators of iron deficiency response

BACKGROUND

Plants respond to stress through highly tuned regulatory networks. While prior works identified master regulators of iron deficiency responses in A. thaliana from whole-root data, identifying regulators that act at the cellular level is critical to a more comprehensive understanding of iron homeostasis. Within the root epidermis complex molecular mechanisms that facilitate iron reduction and uptake from the rhizosphere are known to be regulated by bHLH transcriptional regulators. However, many questions remain about the regulatory mechanisms that control these responses, and how they may integrate with developmental processes within the epidermis. Here, we use transcriptional profiling to gain insight into root epidermis-specific regulatory processes.

RESULTS

Set comparisons of differentially expressed genes (DEGs) between whole root and epidermis transcript measurements identified differences in magnitude and timing of organ-level vs. epidermis-specific responses. Utilizing a unique sampling method combined with a mutual information metric across time-lagged and non-time-lagged windows, we identified relationships between clusters of functionally relevant differentially expressed genes suggesting that developmental regulatory processes may act upstream of well-known Fe-specific responses. By integrating static data (DNA motif information) with time-series transcriptomic data and employing machine learning approaches, specifically logistic regression models with LASSO, we also identified putative motifs that served as crucial features for predicting differentially expressed genes. Twenty-eight transcription factors (TFs) known to bind to these motifs were not differentially expressed, indicating that these TFs may be regulated post-transcriptionally or post-translationally. Notably, many of these TFs also play a role in root development and general stress response.

CONCLUSIONS

This work uncovered key differences in -Fe response identified using whole root data vs. cell-specific root epidermal data. Machine learning approaches combined with additional static data identified putative regulators of -Fe response that would not have been identified solely through transcriptomic profiles and reveal how developmental and general stress responses within the epidermis may act upstream of more specialized -Fe responses for Fe uptake.

Plant Promoters and Terminators for High-Precision Bioengineering

High-precision bioengineering and synthetic biology require fine-tuning gene expression at both transcriptional and posttranscriptional levels. Gene transcription is tightly regulated by promoters and terminators. Promoters determine the timing, tissues and cells, and levels of the expression of genes. Terminators mediate transcription termination of genes and affect mRNA levels posttranscriptionally, e.g., the 3'-end processing, stability, translation efficiency, and nuclear to cytoplasmic export of mRNAs. The promoter and terminator combination affects gene expression. In the present article, we review the function and features of plant core promoters, proximal and distal promoters, and terminators, and their effects on and benchmarking strategies for regulating gene expression.

Indole-3-acetaldoxime delays root iron-deficiency responses and modify auxin homeostasis in Medicago truncatula

Iron (Fe) is an essential plant micronutrient, being a major limiting growth factor in calcareous soils. To increase Fe uptake, plants induce lateral roots growth, the expression of a Fe(III)-chelate reductase (FCR), a Fe(II)-transporter and a H⁺-ATPase and the secretion of flavins. Furthermore, auxin hormone family is involved in the Fe-deficiency responses but the action mechanism remains elusive. In this work, we evaluated the effect of the auxin-precursor indole-3-acetaldoxime (IAOx) on hydroponically grown Medicago truncatula plants under different Fe conditions. Upon 4-days of Fe starvation, the pH of the nutrient solution decreased, while both the FCR activity and the presence of flavins increased. Exogenous IAOx increased lateral roots growth contributing to superroot phenotype, decreased chlorosis, and delayed up to 3-days the pH-decrease, the FCR-activity increase, and the presence of flavins, compared to Fe-deficient plants. Gene expression levels were in concordance with the physiological responses. RESULTS: showed that IAOx was immediately transformed to IAN in roots and shoots to maintain auxin homeostasis. IAOx plays an active role in iron homeostasis delaying symptoms and responses in Fe-deficient plants. We may speculate that IAOx or its derivatives remobilize Fe from root cells to alleviate Fe-deficiency. Overall, these results point out that the IAOx-derived phenotype may have advantages to overcome nutritional stresses.

Comparative transcriptomic and metabolite profiling reveals genotype-specific responses to Fe starvation in chickpea

Iron deficiency is a major nutritional stress that severely impacts crop productivity worldwide. However, molecular intricacies and subsequent physiological and metabolic changes in response to Fe starvation, especially in leguminous crops like chickpea, remain elusive. In the present study, we investigated physiological, transcriptional, and metabolic reprogramming in two chickpea genotypes (H6013 and L4958) with contrasting seed iron concentrations upon Fe deficiency. Our findings revealed that iron starvation affected growth and physiological parameters of both chickpea genotypes. Comparative transcriptome analysis led to the identification of differentially expressed genes between the genotypes related to strategy I uptake, metal ions transporters, reactive oxygen species-associated genes, transcription factors, and protein kinases that could mitigate Fe deficiency. Our gene correlation network discovered several putative candidate genes like CIPK25, CKX3, WRKY50, NAC29, MYB4, and PAP18, which could facilitate the investigation of the molecular rationale underlying Fe tolerance in chickpea. Furthermore, the metabolite analysis also illustrated the differential accumulation of organic acids, amino acids and other metabolites associated with Fe mobilization in chickpea genotypes. Overall, our study demonstrated the comparative transcriptional dynamics upon Fe starvation. The outcomes of the current endeavor will enable the development of Fe deficiency tolerant chickpea cultivars.

Plant Synthetic Promoters: Advancement and Prospective

Native/endogenous promoters have several fundamental limitations in terms of their size, Cis-elements distribution/patterning, and mode of induction, which is ultimately reflected in their insufficient transcriptional activity. Several customized synthetic promoters were designed and tested in plants during the past decade to circumvent such constraints. Such synthetic promoters have a built-in capacity to drive the expression of the foreign genes at their maximum amplitude in plant orthologous systems. The basic structure and function of the promoter has been discussed in this review, with emphasis on the role of the Cis-element in regulating gene expression. In addition to this, the necessity of synthetic promoters in the arena of plant biology has been highlighted. This review also provides explicit information on the two major approaches for developing plant-based synthetic promoters: the conventional approach (by utilizing the basic knowledge of promoter structure and Cis-trans interaction) and the advancement in gene editing technology. The success of plant genetic manipulation relies on the promoter efficiency and the expression level of the transgene. Therefore, advancements in the field of synthetic promoters has enormous potential in genetic engineering-mediated crop improvement.

Gene Regulatory Network Modeling Using Single-Cell Multi-Omics in Plants

Single-cell multi-omics technology can be applied to plant cells to characterize gene expression and open chromatin regions in individual cells. In this chapter, we describe a computational pipeline for the analysis of single-cell data to construct gene regulatory networks. The major steps of this pipeline include the following: (1) normalize and integrate scRNA-seq and scATAC-seq data (2) identify cluster maker genes (3) perform motif finding for selected marker genes, and (4) identify regulatory networks with machine learning. The pipeline has been tested using data from the model species Arabidopsis and is generally applicable to other plant and animal species to characterize regulatory networks using single-cell multi-omics data.

Prediction poverty levels of needy college students using RF-PCA model

Nowadays, poverty-stricken college students have become a special group among college students and occupied a higher proportion in it. How to accurately identify poverty levels of college students and provide funding is a new problem for universities. In this study, a novel model, which incorporated Random Forest with Principle Components Analysis (RF-PCA), is proposed to predict poverty levels of college students. To establish this model, we collect some useful information is to construct the datasets which include 4 classes of poverty levels and 21 features of poverty-stricken college students. Furthermore, the feature dimension reduction consists of two steps: the first step is to select the top 16 features with the ranking of feature, according to the Gini importance and Shapley Additive explanations (SHAP) values of features based on Random Forest (RF) model; the second step is to extract 11 dimensions by means of Principle Components Analysis (PCA). Subsequently, confusion metrics and receiver operating characteristic (ROC) curves are utilized to evaluate the promising performance of the proposed model. Especially the accuracy of the model achieves 78.61% . Finally, compared with seven states of the art classification algorithms, the proposed model achieves a higher prediction accuracy, which indicates that the results provide great potential to identify the poverty levels of college students.

The basic leucine zipper transcription factor OsbZIP83 and the glutaredoxins OsGRX6 and OsGRX9 facilitate rice iron utilization under the control of OsHRZ ubiquitin ligases

Under low iron availability, plants induce the expression of various genes for iron uptake and translocation. The rice (Oryza sativa) ubiquitin ligases OsHRZ1 and OsHRZ2 cause overall repression of these iron-related genes at the transcript level, but their protein-level regulation is unclear. We conducted a proteome analysis to identify key regulators whose abundance was regulated by OsHRZs at the protein level. In response to iron deficiency or OsHRZ knockdown, many genes showed differential regulation between the transcript and protein levels, including the TGA-type basic leucine zipper transcription factor OsbZIP83. We also identified two glutaredoxins, OsGRX6 and OsGRX9, as OsHRZ-interacting proteins in yeast and plant cells. OsGRX6 also interacted with OsbZIP83. Our in vitro degradation assay suggested that OsbZIP83, OsGRX6 and OsGRX9 proteins are subjected to 26S proteasome- and OsHRZ-dependent degradation. Proteome analysis and our in vitro degradation assay also suggested that OsbZIP83 protein was preferentially degraded under iron-deficient conditions in rice roots. Transgenic rice lines overexpressing OsGRX9 and OsbZIP83 showed improved tolerance to iron deficiency. Expression of iron-related genes was affected in the OsGRX9 and OsGRX6 knockdown lines, suggesting disturbed iron utilization and signaling. OsbZIP83 overexpression lines showed enhanced expression of OsYSL2 and OsNAS3, which are involved in internal iron translocation, in addition to OsGRX9 and genes related to phytoalexin biosynthesis and the salicylic acid pathway. The results suggest that OsbZIP83, OsGRX6 and OsGRX9 facilitate iron utilization downstream of the OsHRZ pathway.

Machine learning: its challenges and opportunities in plant system biology

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.

Strategies and Bottlenecks in Hexaploid Wheat to Mobilize Soil Iron to Grains

Our knowledge of iron (Fe) uptake and mobilization in plants is mainly based on Arabidopsis and rice. Although multiple players of Fe homeostasis have been elucidated, there is a significant gap in our understanding of crop species, such as wheat. It is, therefore, imperative not only to understand the different hurdles for Fe enrichment in tissues but also to address specifically the knowns/unknowns involved in the plausible mechanism of Fe sensing, signaling, transport, and subsequent storage in plants. In the present review, a unique perspective has been described in light of recent knowledge generated in wheat, an economically important crop. The strategies to boost efficient Fe uptake, transcriptional regulation, and long-distance mobilization in grains have been discussed, emphasizing recent biotechnological routes to load Fe in grains. This article also highlights the new elements of physiological and molecular genetics that underpin the mechanistic insight for the identified Fe-related genes and discusses the bottlenecks in unloading the Fe in grains. The information presented here will provide much-needed resources and directions to overcome challenges and design efficient strategies to enhance the Fe density in wheat grains.

Endophytic Fungus Phomopsis liquidambaris Enhances Fe Absorption in Peanuts by Reducing Hydrogen Peroxide

Iron (Fe) deficiency in alkaline calcium soil is a problem that needs to be solved urgently as Fe is an essential and commonly limiting nutrient for plants. Endophytic fungus, Phomopsis liquidambaris (P. liquidambaris), has been reported to promote Fe absorption in peanuts (Arachis hypogaea L.), however, the mechanisms remain unclear. Under prolonged Fe deficiency, an increase in hydrogen peroxide (H₂O₂) often triggers a series of signaling events and leads to the inhibition of Fe acquisition. The main purpose of this study was to explore whether and how the endophytic fungus P. liquidambaris promote Fe absorption in peanut through regulating H₂O₂ and assisting in resisting oxidative stress. In this study, we detected the Fe deficiency-induced transcription factor (FIT), Fe²⁺ transporter (IRT1), and ferric reduction oxidase 2 (FRO2) of peanuts, and confirmed that they were negatively related to Fe concentration. Similarly, FIT, IRT1, and FRO2 were also inhibited by H₂O₂. The addition of P. liquidambaris reduces H₂O₂ under Fe-deficiency with an increase in Fe content, while the exogenous addition of H₂O₂ further decreases it, and the addition of catalase (CAT) under Fe-deficiency reverses this phenomenon. Through transcriptome analysis, we proved that the expression of FIT, IRT1, FRO2 and CAT are consistent with our hypothesis, and P. liquidambaris has a stress-mitigating effect on peanuts mainly via CAT, glutathione peroxidase, and malondialdehyde. Our study proved the Fe-absorption promoting effect and stress mitigation effect of P. liquidambaris under Fe-deficiency in peanuts, and their combined usage may help peanuts grow better.

Prediction of conserved and variable heat and cold stress response in maize using cis-regulatory information

Changes in gene expression are important for responses to abiotic stress. Transcriptome profiling of heat- or cold-stressed maize genotypes identifies many changes in transcript abundance. We used comparisons of expression responses in multiple genotypes to identify alleles with variable responses to heat or cold stress and to distinguish examples of cis- or trans-regulatory variation for stress-responsive expression changes. We used motifs enriched near the transcription start sites (TSSs) for thermal stress-responsive genes to develop predictive models of gene expression responses. Prediction accuracies can be improved by focusing only on motifs within unmethylated regions near the TSS and vary for genes with different dynamic responses to stress. Models trained on expression responses in a single genotype and promoter sequences provided lower performance when applied to other genotypes but this could be improved by using models trained on data from all three genotypes tested. The analysis of genes with cis-regulatory variation provides evidence for structural variants that result in presence/absence of transcription factor binding sites in creating variable responses. This study provides insights into cis-regulatory motifs for heat- and cold-responsive gene expression and defines a framework for developing models to predict expression responses across multiple genotypes.

Transcriptional regulation in plants: Using omics data to crack the cis-regulatory code

Innovative omics technologies, advanced bioinformatics, and machine learning methods are rapidly becoming integral tools for plant functional genomics, with tremendous recent advances made in this field. In transcriptional regulation, an initial lag in the accumulation of plant omics data relative to that of animals stimulated the development of computational methods capable of extracting maximum information from the available data sets. Recent comprehensive studies of transcription factor-binding profiles in Arabidopsis and maize and the accumulation of uniformly processed omics data in public databases have brought plant biologists into the big leagues, with many cutting-edge methods available. Here, we summarize the state-of-the-art bioinformatics approaches used to predict or infer the cis-regulatory code behind transcriptional gene regulation, focusing on their plant research applications.

Physiological and molecular responses to combinatorial iron and phosphate deficiencies in hexaploid wheat seedlings

Iron (Fe) and phosphorus (P) are the essential mineral nutrients for plant growth and development. However, the molecular interaction of the Fe and P pathways in crops remained largely obscure. In this study, we provide a comprehensive physiological and molecular analysis of hexaploid wheat response to single (Fe, P) and its combinatorial deficiencies. Our data showed that inhibition of the primary root growth occurs in response to Fe deficiency; however, growth was rescued when combinatorial deficiencies occurred. Analysis of RNAseq revealed that distinct molecular rearrangements during combined deficiencies with predominance for genes related to metabolic pathways and secondary metabolite biosynthesis primarily include genes for UDP-glycosyltransferase, cytochrome-P450s, and glutathione metabolism. Interestingly, the Fe-responsive cis-regulatory elements in the roots in Fe stress conditions were enriched compared to the combined stress. Our metabolome data also revealed the accumulation of distinct metabolites such as amino-isobutyric acid, arabinonic acid, and aconitic acid in the combined stress environment. Overall, these results are essential in developing new strategies to improve the resilience of crops in limited nutrients.

Elucidation of Novel cis-Regulatory Elements and Promoter Structures Involved in Iron Excess Response Mechanisms in Rice Using a Bioinformatics Approach

Iron (Fe) excess is a major constraint on crop production in flooded acidic soils, particularly in rice cultivation. Under Fe excess, plants activate a complex mechanism and network regulating Fe exclusion by roots and isolation in various tissues. In rice, the transcription factors and cis-regulatory elements (CREs) that regulate Fe excess response mechanisms remain largely elusive. We previously reported comprehensive microarray analyses of several rice tissues in response to various levels of Fe excess stress. In this study, we further explored novel CREs and promoter structures in rice using bioinformatics approaches with this microarray data. We first performed network analyses to predict Fe excess-related CREs through the categorization of the gene expression patterns of Fe excess-responsive transcriptional regulons, and found four major expression clusters: Fe storage type, Fe chelator type, Fe uptake type, and WRKY and other co-expression type. Next, we explored CREs within these four clusters of gene expression types using a machine-learning method called microarray-associated motif analyzer (MAMA), which we previously established. Through a comprehensive bioinformatics approach, we identified a total of 560 CRE candidates extracted by MAMA analyses and 42 important conserved sequences of CREs directly related to the Fe excess response in various rice tissues. We explored several novel cis-elements as candidate Fe excess CREs including GCWGCWGC, CGACACGC, and Myb binding-like motifs. Based on the presence or absence of candidate CREs using MAMA and known PLACE CREs, we found that the Boruta-XGBoost model explained expression patterns with high accuracy of about 83%. Enriched sequences of both novel MAMA CREs and known PLACE CREs led to high accuracy expression patterns. We also found new roles of known CREs in the Fe excess response, including the DCEp2 motif, IDEF1-, Zinc Finger-, WRKY-, Myb-, AP2/ERF-, MADS- box-, bZIP and bHLH- binding sequence-containing motifs among Fe excess-responsive genes. In addition, we built a molecular model and promoter structures regulating Fe excess-responsive genes based on new finding CREs. Together, our findings about Fe excess-related CREs and conserved sequences will provide a comprehensive resource for discovery of genes and transcription factors involved in Fe excess-responsive pathways, clarification of the Fe excess response mechanism in rice, and future application of the promoter sequences to produce genotypes tolerant of Fe excess.

Evolutionarily conserved hierarchical gene regulatory networks for plant salt stress response

Plant cells constantly alter their gene expression profiles to respond to environmental fluctuations. These continuous adjustments are regulated by multi-hierarchical networks of transcription factors. To understand how such gene regulatory networks (GRNs) have stabilized evolutionarily while allowing for species-specific responses, we compare the GRNs underlying salt response in the early-diverging and late-diverging plants Marchantia polymorpha and Arabidopsis thaliana. Salt-responsive GRNs, constructed on the basis of the temporal transcriptional patterns in the two species, share common trans-regulators but exhibit an evolutionary divergence in cis-regulatory sequences and in the overall network sizes. In both species, WRKY-family transcription factors and their feedback loops serve as central nodes in salt-responsive GRNs. The divergent cis-regulatory sequences of WRKY-target genes are probably associated with the expansion in network size, linking salt stress to tissue-specific developmental and physiological responses. The WRKY modules and highly linked WRKY feedback loops have been preserved widely in other plants, including rice, while keeping their binding-motif sequences mutable. Together, the conserved trans-regulators and the quickly evolving cis-regulatory sequences allow salt-responsive GRNs to adapt over a long evolutionary timescale while maintaining some consistent regulatory structure. This strategy may benefit plants as they adapt to changing environments.

Interconnection of iron and osmotic stress signalling in plants: is FIT a regulatory hub to cross-connect abscisic acid responses?

Osmotic stresses, such as salinity and drought, have deleterious effects on uptake and translocation of essential mineral nutrients. Iron (Fe) is an important micronutrient that regulates many processes in plants. Plants have adopted various molecular and physiological strategies for Fe acquisition from soil and transport to and within plants. Dynamic Fe signalling in plants tightly regulates Fe uptake and homeostasis. In this way, Fe nutrition is adjusted to growth and stress conditions, and Fe deficiency-regulated transcription factors, such as FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT), act as regulatory hubs in these responses. Here, we review and analyse expression of the various components of the Fe signalling during osmotic stresses. We discuss common players in the Fe and osmotic stress signalling. Furthermore, this review focuses on exploring a novel and exciting direct connection of regulatory mechanisms of Fe intake and acquisition with ABA-mediated environmental stress cues, like salt/drought. We propose a model that discuss how environmental stress affects Fe uptake and acquisition and vice versa at molecular-physiological levels in plants.

Computational solutions for modeling and controlling plant response to abiotic stresses: a review with focus on iron deficiency

Computational solutions enable plant scientists to model protein-mediated stress responses and characterize novel gene functions that coordinate responses to a variety of abiotic stress conditions. Recently, density functional theory was used to study proteins active sites and elucidate enzyme conversion mechanisms involved in iron deficiency responsive signaling pathways. Computational approaches for protein homology modeling and the kinetic modeling of signaling pathways have also resolved the identity and function in proteins involved in iron deficiency signaling pathways. Significant changes in gene relationships under other stress conditions, such as heat or drought stress, have been recently identified using differential network analysis, suggesting that stress tolerance is achieved through asynchronous control. Moreover, the increasing development and use of statistical modeling and systematic modeling of transcriptomic data have provided significant insight into the gene regulatory mechanisms associated with abiotic stress responses. These types of in silico approaches have facilitated the plant science community's future goals of developing multi-scale models of responses to iron deficiency stress and other abiotic stress conditions.

metaRE R Package for Meta-Analysis of Transcriptome Data to Identify the cis-Regulatory Code behind the Transcriptional Reprogramming

At the molecular level, response to an external factor or an internal condition causes reprogramming of temporal and spatial transcription. When an organism undergoes physiological and/or morphological changes, several signaling pathways are activated simultaneously. Examples of such complex reactions are the response to temperature changes, dehydration, various biologically active substances, and others. A significant part of the regulatory ensemble in such complex reactions remains unidentified. We developed metaRE, an R package for the systematic search for cis-regulatory elements enriched in the promoters of the genes significantly changed their transcription in a complex reaction. metaRE mines multiple expression profiling datasets generated to test the same organism’s response and identifies simple and composite cis-regulatory elements systematically associated with differential expression of genes. Here, we showed metaRE performance for the identification of low-temperature-responsive cis-regulatory code in Arabidopsis thaliana and Danio rerio. MetaRE identified potential binding sites for known as well as unknown cold response regulators. A notable part of cis-elements was found in both searches discovering great conservation in low-temperature responses between plants and animals.

FIT, a regulatory hub for iron deficiency and stress signaling in roots, and FIT-dependent and -independent gene signatures

Iron (Fe) is vital for plant growth. Plants balance the beneficial and toxic effects of this micronutrient, and tightly control Fe uptake and allocation. Here, we review the role of the basic helix-loop-helix (bHLH) transcription factor FIT (FER-LIKE FE DEFICIENCY-INDUCED TRANSCRIPTION FACTOR) in Fe acquisition. FIT is not only essential, it is also a central regulatory hub in root cells to steer and adjust the rate of Fe uptake by the root in a changing environment. FIT regulates a subset of root Fe deficiency (-Fe) response genes. Based on a combination of co-expression network and FIT-dependent transcriptome analyses, we defined a set of FIT-dependent and FIT-independent gene expression signatures and co-expression clusters that encode specific functions in Fe regulation and Fe homeostasis. These gene signatures serve as markers to integrate novel regulatory factors and signals into the -Fe response cascade. FIT forms a complex with bHLH subgroup Ib transcription factors. Furthermore, it interacts with key regulators from different signaling pathways that either activate or inhibit FIT function to adjust Fe acquisition to growth and environmental constraints. Co-expression clusters and FIT protein interactions suggest a connection of -Fe with ABA responses and root cell elongation processes that can be explored in future studies.