Cis-Regulatory Code for Predicting Plant Cell-Type Transcriptional Response to High Salinity

Artificial intelligence in plant breeding

Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.

Identification of the ERF gene family of Mangifera indica and the defense response of MiERF4 to Xanthomonas campestris pv. mangiferaeindicae

An important regulatory role for ethylene-responsive transcription factors (ERFs) is in plant growth and development, stress response, and hormone signaling. However, AP2/ERF family genes in mango have not been systematically studied. In this study, a total of 113 AP2/ERF family genes were identified from the mango genome and phylogenetically classified into five subfamilies: AP2 (28 genes), DREB (42 genes), ERF (33 genes), RAV (6 genes), and Soloist (4 genes). Of these, the ERF family, in conjunction with Arabidopsis and rice, forms a phylogenetic tree divided into seven groups, five of which have MiERF members. Analysis of gene structure and cis-elements showed that each MiERF gene contains only one AP2 structural domain, and that MiERF genes contain a large number of cis-elements associated with hormone signaling and stress response. Collinearity tests revealed a high degree of homology between MiERFs and CsERFs. Tissue-specific and stress-responsive expression profiling revealed that MiERF genes are primarily involved in the regulation of reproductive growth and are differentially and positively expressed in response to external hormones and pathogenic bacteria. Physiological results from a gain-of-function analysis of MiERF4 transiently overexpressed in tobacco and mango showed that transient expression of MiERF4 resulted in decreased colony count and callose deposition, as well as varying degrees of response to hormonal signals such as ETH, JA, and SA. Thus, MiERF4 may be involved in the JA/ETH signaling pathway to enhance plant defense against pathogenic bacteria. This study provides a basis for further research on the function and regulation of MiERF genes and lays a foundation for the selection of disease-resistant genes in mango.

Cell-type-specific transcriptomics uncovers spatial regulatory networks in bioenergy sorghum stems

Bioenergy sorghum is a low-input, drought-resilient, deep-rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum's 4-5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about cell-type gene expression and regulation in stems was available to enable engineering. To obtain this information, laser capture microdissection was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type-specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell-type-specific gene regulatory networks (GRNs) revealed that unique transcription factor families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell-specific transcriptome data was combined with known secondary cell wall (SCW) networks to identify the GRNs that differentially activate SCW formation in vascular sclerenchyma and epidermal cells. The spatial transcriptomic dataset provides a valuable source of information about the function of different sorghum cell types and GRNs that will enable the engineering of bioenergy sorghum stems, and an interactive web application developed during this project will allow easy access and exploration of the data (https://mc-lab.shinyapps.io/lcm-dataset/).

Modeling alternative translation initiation sites in plants reveals evolutionarily conserved cis-regulatory codes in eukaryotes

mRNA translation relies on identifying translation initiation sites (TISs) in mRNAs. Alternative TISs are prevalent across plant transcriptomes, but the mechanisms for their recognition are unclear. Using ribosome profiling and machine learning, we developed models for predicting alternative TISs in the tomato (Solanum lycopersicum). Distinct feature sets were predictive of AUG and nonAUG TISs in 5' untranslated regions and coding sequences, including a novel CU-rich sequence that promoted plant TIS activity, a translational enhancer found across dicots and monocots, and humans and viruses. Our results elucidate the mechanistic and evolutionary basis of TIS recognition, whereby cis-regulatory RNA signatures affect start site selection. The TIS prediction model provides global estimates of TISs to discover neglected protein-coding genes across plant genomes. The prevalence of cis-regulatory signatures across plant species, humans, and viruses suggests their broad and critical roles in reprogramming the translational landscape.

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.

Machine learning in photosynthesis: Prospects on sustainable crop development

Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.

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.

Designing artificial synthetic promoters for accurate, smart, and versatile gene expression in plants

With the development of high-throughput biology techniques and artificial intelligence, it has become increasingly feasible to design and construct artificial biological parts, modules, circuits, and even whole systems. To overcome the limitations of native promoters in controlling gene expression, artificial promoter design aims to synthesize short, inducible, and conditionally controlled promoters to coordinate the expression of multiple genes in diverse plant metabolic and signaling pathways. Synthetic promoters are versatile and can drive gene expression accurately with smart responses; they show potential for enhancing desirable traits in crops, thereby improving crop yield, nutritional quality, and food security. This review first illustrates the importance of synthetic promoters, then introduces promoter architecture and thoroughly summarizes advances in synthetic promoter construction. Restrictions to the development of synthetic promoters and future applications of such promoters in synthetic plant biology and crop improvement are also discussed.

Cis-Regulation by NACs: A Promising Frontier in Wheat Crop Improvement

Crop traits are controlled by multiple genes; however, the complex spatio-temporal transcriptional behavior of genes cannot be fully understood without comprehending the role of transcription factors (TFs) and the underlying mechanisms of the binding interactions of their cis-regulatory elements. NAC belongs to one of the largest families of plant-specific TFs and has been associated with the regulation of many traits. This review provides insight into the cis-regulation of genes by wheat NACs (TaNACs) for the improvement in yield-related traits, including phytohormonal homeostasis, leaf senescence, seed traits improvement, root modulation, and biotic and abiotic stresses in wheat and other cereals. We also discussed the current potential, knowledge gaps, and prospects of TaNACs.

Integrating speed breeding with artificial intelligence for developing climate-smart crops

INTRODUCTION

In climate change, breeding crop plants with improved productivity, sustainability, and adaptability has become a daunting challenge to ensure global food security for the ever-growing global population. Correspondingly, climate-smart crops are also the need to regulate biomass production, which is imperative for the maintenance of ecosystem services worldwide. Since conventional breeding technologies for crop improvement are limited, time-consuming, and involve laborious selection processes to foster new and improved crop varieties. An urgent need is to accelerate the plant breeding cycle using artificial intelligence (AI) to depict plant responses to environmental perturbations in real-time.

MATERIALS AND METHODS

The review is a collection of authorized information from various sources such as journals, books, book chapters, technical bulletins, conference papers, and verified online contents.

CONCLUSIONS

Speed breeding has emerged as an essential strategy for accelerating the breeding cycles of crop plants by growing them under artificial light and temperature conditions. Furthermore, speed breeding can also integrate marker-assisted selection and cutting-edged gene-editing tools for early selection and manipulation of essential crops with superior agronomic traits. Scientists have recently applied next-generation AI to delve deeper into the complex biological and molecular mechanisms that govern plant functions under environmental cues. In addition, AIs can integrate, assimilate, and analyze complex OMICS data sets, an essential prerequisite for successful speed breeding protocol implementation to breed crop plants with superior yield and adaptability.

Cis-element amplified polymorphism (CEAP), a novel promoter- and gene-targeted molecular marker of plants

In this study, we selected eight cis-elements: AAAG, ACGTG, CCGA, ACTCAT, GGTCA, TATCC, TGAC and GATAA, which are closely related to plant growth and development, signal transduction and stress response. The CEAP primers were 18 nucleotides long and consisted of a central cis-element nucleotide core flanked by a filler sequence at the 5' end and di- or tri-nucleotides at the 3' end. A total of two hundred and twenty-four primers were developed, and the PCR procedure consisted of 5 cycles of low-temperature annealing and 35 subsequent cycles of annealing at 50°C. The PCR products are electrophoretically separated by 1.8-2.3% agarose. The polymorphism of the CEAP marker was amplified in eight mango (Mangifera indica L.) species. The results showed that the CEAP primers could amplify clear, repeatable bands in mango and combine at least four cis-elements from which a large number of bands were amplified and six highly polymorphic primers for each cis-element can reach an accurate clustering result. The results of CEAP marker assays compared with ISSR, CBDP and iPBS marker assays showed that CEAP marker was better than the other three markers in the number of fragment bands, H and I indexes. In addition, we also tested the CEAP markers in rice, tomato, potato, wax gourd, citrus and longan and the results showed that the CEAP marker assay could amplify clear polymorphic bands in different species. Our results indicate that the CEAP markers could be universally used in different species for genetic diversity analysis, relationship analysis, and marker-assisted selection for breeding.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-022-01212-5.

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.

Single-cell analysis of cis-regulatory elements

Plant tissues and organs are composed of functionally discrete cell types that are all defined by the same genome sequence. Cell-type variation in part arises from differential accessibility of cis-regulatory elements that encode the blueprints for transcriptional programs underlying cell identity and function. Owing to technical limitations, the role of cis-regulatory elements in cell identity maintenance, differentiation, and functional specialization has remained relatively unexplored in plant systems. Single-cell profiling has emerged as a powerful tool to circumvent these past obstacles by enabling unbiased charting of transcriptional and cis-regulatory states at the resolution of individual cells. Here, we review state-of-the-art single-cell approaches and analytical frameworks that have paved the way for establishing the link between cellular phenotypic variation and cis-regulatory mechanisms in plants.

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.

Persistence and plasticity in bacterial gene regulation

Organisms orchestrate cellular functions through transcription factor (TF) interactions with their target genes, although these regulatory relationships are largely unknown in most species. Here we report a high-throughput approach for characterizing TF-target gene interactions across species and its application to 354 TFs across 48 bacteria, generating 17,000 genome-wide binding maps. This dataset revealed themes of ancient conservation and rapid evolution of regulatory modules. We observed rewiring, where the TF sensing and regulatory role is maintained while the arrangement and identity of target genes diverges, in some cases encoding entirely new functions. We further integrated phenotypic information to define new functional regulatory modules and pathways. Finally, we identified 242 new TF DNA binding motifs, including a 70% increase of known Escherichia coli motifs and the first annotation in Pseudomonas simiae, revealing deep conservation in bacterial promoter architecture. Our method provides a versatile tool for functional characterization of genetic pathways in prokaryotes and eukaryotes.

Intergenic spaces: a new frontier to improving plant health

To more sustainably mitigate the impact of crop diseases on plant health and productivity, there is a need for broader spectrum, long-lasting resistance traits. Defense response (DR) genes, located throughout the genome, participate in cellular and system-wide defense mechanisms to stave off infection by diverse pathogens. This multigenic resistance avoids rapid evolution of a pathogen to overcome host resistance. DR genes reside within resistance-associated quantitative trait loci (QTL), and alleles of DR genes in resistant varieties are more active during pathogen attack relative to susceptible haplotypes. Differential expression of DR genes results from polymorphisms in their regulatory regions, that includes cis-regulatory elements such as transcription factor binding sites as well as features that influence epigenetic structural changes to modulate chromatin accessibility during infection. Many of these elements are found in clusters, known as cis-regulatory modules (CRMs), which are distributed throughout the host genome. Regulatory regions involved in plant-pathogen interactions may also contain pathogen effector binding elements that regulate DR gene expression, and that, when mutated, result in a change in the plants' response. We posit that CRMs and the multiple regulatory elements that comprise them are potential targets for marker-assisted breeding for broad-spectrum, durable disease resistance.

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.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

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.

Machine learning in plant science and plant breeding

Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

Network-based approaches for understanding gene regulation and function in plants

Expression reprogramming directed by transcription factors is a primary gene regulation underlying most aspects of the biology of any organism. Our views of how gene regulation is coordinated are dramatically changing thanks to the advent and constant improvement of high-throughput profiling and transcriptional network inference methods: from activities of individual genes to functional interactions across genes. These technical and analytical advances can reveal the topology of transcriptional networks in which hundreds of genes are hierarchically regulated by multiple transcription factors at systems level. Here we review the state of the art of experimental and computational methods used in plant biology research to obtain large-scale datasets and model transcriptional networks. Examples of direct use of these network models and perspectives on their limitations and future directions are also discussed.

The cis-regulatory codes of response to combined heat and drought stress in Arabidopsis thaliana

Plants respond to their environment by dynamically modulating gene expression. A powerful approach for understanding how these responses are regulated is to integrate information about cis-regulatory elements (CREs) into models called cis-regulatory codes. Transcriptional response to combined stress is typically not the sum of the responses to the individual stresses. However, cis-regulatory codes underlying combined stress response have not been established. Here we modeled transcriptional response to single and combined heat and drought stress in Arabidopsis thaliana. We grouped genes by their pattern of response (independent, antagonistic and synergistic) and trained machine learning models to predict their response using putative CREs (pCREs) as features (median F-measure = 0.64). We then developed a deep learning approach to integrate additional omics information (sequence conservation, chromatin accessibility and histone modification) into our models, improving performance by 6.2%. While pCREs important for predicting independent and antagonistic responses tended to resemble binding motifs of transcription factors associated with heat and/or drought stress, important synergistic pCREs resembled binding motifs of transcription factors not known to be associated with stress. These findings demonstrate how in silico approaches can improve our understanding of the complex codes regulating response to combined stress and help us identify prime targets for future characterization.

Opening the Black Box: Interpretable Machine Learning for Geneticists

Because of its ability to find complex patterns in high dimensional and heterogeneous data, machine learning (ML) has emerged as a critical tool for making sense of the growing amount of genetic and genomic data available. While the complexity of ML models is what makes them powerful, it also makes them difficult to interpret. Fortunately, efforts to develop approaches that make the inner workings of ML models understandable to humans have improved our ability to make novel biological insights. Here, we discuss the importance of interpretable ML, different strategies for interpreting ML models, and examples of how these strategies have been applied. Finally, we identify challenges and promising future directions for interpretable ML in genetics and genomics.

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

Plant iron deficiency (-Fe) activates a complex regulatory network that coordinates root Fe uptake and distribution to sink tissues. In Arabidopsis (Arabidopsis thaliana), FER-LIKE FE DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT), a basic helix-loop-helix (bHLH) transcription factor (TF), regulates root Fe acquisition genes. Many other -Fe-induced genes are FIT independent, and instead regulated by other bHLH TFs and by yet unknown TFs. The cis-regulatory code, that is, the cis-regulatory elements (CREs) and their combinations that regulate plant -Fe-responses, remains largely elusive. Using Arabidopsis root transcriptome data and coexpression clustering, we identified over 100 putative CREs (pCREs) that predicted -Fe-induced gene expression in computational models. To assess pCRE properties and possible functions, we used large-scale in vitro TF binding data, positional bias, and evolutionary conservation. As one example, our approach uncovered pCREs resembling IDE1 (iron deficiency-responsive element 1), a known grass -Fe response CRE. Arabidopsis IDE1-likes were associated with FIT-dependent gene expression, more specifically with biosynthesis of Fe-chelating compounds. Thus, IDE1 seems to be conserved in grass and nongrass species. Our pCREs matched among others in vitro binding sites of B3, NAC, bZIP, and TCP TFs, which might be regulators of -Fe responses. Altogether, our findings provide a comprehensive source of cis-regulatory information for -Fe-responsive genes that advance our mechanistic understanding and inform future efforts in engineering plants with more efficient Fe uptake or transport systems.