Assessing the evolution of research topics in a biological field using plant science as an example.

Scientific advances due to conceptual or technological innovations can be revealed by examining how research topics have evolved. But such topical evolution is difficult to uncover and quantify because of the large body of literature and the need for expert knowledge in a wide range of areas in a field. Using plant biology as an example, we used machine learning and language models to classify plant science citations into topics representing interconnected, evolving subfields. The changes in prevalence of topical records over the last 50 years reflect shifts in major research trends and recent radiation of new topics, as well as turnover of model species and vastly different plant science research trajectories among countries. Our approaches readily summarize the topical diversity and evolution of a scientific field with hundreds of thousands of relevant papers, and they can be applied broadly to other fields.

Evolution and diversification of the ACT-like domain associated with plant basic helix-loop-helix transcription factors.

Basic helix-loop-helix (bHLH) proteins are one of the largest families of transcription factor (TF) in eukaryotes, and ~30% of all flowering plants' bHLH TFs contain the aspartate kinase, chorismate mutase, and TyrA (ACT)-like domain at variable distances C-terminal from the bHLH. However, the evolutionary history and functional consequences of the bHLH/ACT-like domain association remain unknown. Here, we show that this domain association is unique to the plantae kingdom with green algae (chlorophytes) harboring a small number of bHLH genes with variable frequency of ACT-like domain's presence. bHLH-associated ACT-like domains form a monophyletic group, indicating a common origin. Indeed, phylogenetic analysis results suggest that the association of ACT-like and bHLH domains occurred early in Plantae by recruitment of an ACT-like domain in a common ancestor with widely distributed ACT DOMAIN REPEAT (ACR) genes by an ancestral bHLH gene. We determined the functional significance of this association by showing that Chlamydomonas reinhardtii ACT-like domains mediate homodimer formation and negatively affect DNA binding of the associated bHLH domains. We show that, while ACT-like domains have experienced faster selection than the associated bHLH domain, their rates of evolution are strongly and positively correlated, suggesting that the evolution of the ACT-like domains was constrained by the bHLH domains. This study proposes an evolutionary trajectory for the association of ACT-like and bHLH domains with the experimental characterization of the functional consequence in the regulation of plant-specific processes, highlighting the impacts of functional domain coevolution.

Temporal regulation of cold transcriptional response in switchgrass.

Switchgrass low-land ecotypes have significantly higher biomass but lower cold tolerance compared to up-land ecotypes. Understanding the molecular mechanisms underlying cold response, including the ones at transcriptional level, can contribute to improving tolerance of high-yield switchgrass under chilling and freezing environmental conditions. Here, by analyzing an existing switchgrass transcriptome dataset, the temporal cis-regulatory basis of switchgrass transcriptional response to cold is dissected computationally. We found that the number of cold-responsive genes and enriched Gene Ontology terms increased as duration of cold treatment increased from 30 min to 24 hours, suggesting an amplified response/cascading effect in cold-responsive gene expression. To identify genomic sequences likely important for regulating cold response, machine learning models predictive of cold response were established using k-mer sequences enriched in the genic and flanking regions of cold-responsive genes but not non-responsive genes. These k-mers, referred to as putative cis-regulatory elements (pCREs) are likely regulatory sequences of cold response in switchgrass. There are in total 655 pCREs where 54 are important in all cold treatment time points. Consistent with this, eight of 35 known cold-responsive CREs were similar to top-ranked pCREs in the models and only these eight were important for predicting temporal cold response. More importantly, most of the top-ranked pCREs were novel sequences in cold regulation. Our findings suggest additional sequence elements important for cold-responsive regulation previously not known that warrant further studies.

Evolutionary analysis of the LORELEI gene family in plants reveals regulatory subfunctionalization.

A signaling complex comprising members of the LORELEI (LRE)-LIKE GPI-anchored protein (LLG) and Catharanthus roseus RECEPTOR-LIKE KINASE 1-LIKE (CrRLK1L) families perceive RAPID ALKALINIZATION FACTOR (RALF) peptides and regulate growth, reproduction, immunity, and stress responses in Arabidopsis (Arabidopsis thaliana). Genes encoding these proteins are members of multigene families in most angiosperms and could generate thousands of signaling complex variants. However, the links between expansion of these gene families and the functional diversification of this critical signaling complex as well as the evolutionary factors underlying the maintenance of gene duplicates remain unknown. Here, we investigated LLG gene family evolution by sampling land plant genomes and explored the function and expression of angiosperm LLGs. We found that LLG diversity within major land plant lineages is primarily due to lineage-specific duplication events, and that these duplications occurred both early in the history of these lineages and more recently. Our complementation and expression analyses showed that expression divergence (i.e. regulatory subfunctionalization), rather than functional divergence, explains the retention of LLG paralogs. Interestingly, all but one monocot and all eudicot species examined had an LLG copy with preferential expression in male reproductive tissues, while the other duplicate copies showed highest levels of expression in female or vegetative tissues. The single LLG copy in Amborella trichopoda is expressed vastly higher in male compared to in female reproductive or vegetative tissues. We propose that expression divergence plays an important role in retention of LLG duplicates in angiosperms.

Regulators of early maize leaf development inferred from transcriptomes of laser capture microdissection (LCM)-isolated embryonic leaf cells.

The superior photosynthetic efficiency of C4 leaves over C3 leaves is owing to their unique Kranz anatomy, in which the vein is surrounded by one layer of bundle sheath (BS) cells and one layer of mesophyll (M) cells. Kranz anatomy development starts from three contiguous ground meristem (GM) cells, but its regulators and underlying molecular mechanism are largely unknown. To identify the regulators, we obtained the transcriptomes of 11 maize embryonic leaf cell types from five stages of pre-Kranz cells starting from median GM cells and six stages of pre-M cells starting from undifferentiated cells. Principal component and clustering analyses of transcriptomic data revealed rapid pre-Kranz cell differentiation in the first two stages but slow differentiation in the last three stages, suggesting early Kranz cell fate determination. In contrast, pre-M cells exhibit a more prolonged transcriptional differentiation process. Differential gene expression and coexpression analyses identified gene coexpression modules, one of which included 3 auxin transporter and 18 transcription factor (TF) genes, including known regulators of Kranz anatomy and/or vascular development. In situ hybridization of 11 TF genes validated their expression in early Kranz development. We determined the binding motifs of 15 TFs, predicted TF target gene relationships among the 18 TF and 3 auxin transporter genes, and validated 67 predictions by electrophoresis mobility shift assay. From these data, we constructed a gene regulatory network for Kranz development. Our study sheds light on the regulation of early maize leaf development and provides candidate leaf development regulators for future study.

High-throughput measurement of plant fitness traits with an object detection method using Faster R-CNN.

Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection-based algorithm yielded near perfect seed counts (r2 = 0.9996) and highly accurate fruit counts (r2 = 0.980). Comparing seed counts for wild-type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.

Computational prediction of plant metabolic pathways.

Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. Here, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.

Modeling temporal and hormonal regulation of plant transcriptional response to wounding.

Plants respond to wounding stress by changing gene expression patterns and inducing the production of hormones including jasmonic acid. This wounding transcriptional response activates specialized metabolism pathways such as the glucosinolate pathways in Arabidopsis thaliana. While the regulatory factors and sequences controlling a subset of wound-response genes are known, it remains unclear how wound response is regulated globally. Here, we how these responses are regulated by incorporating putative cis-regulatory elements, known transcription factor binding sites, in vitro DNA affinity purification sequencing, and DNase I hypersensitive sites to predict genes with different wound-response patterns using machine learning. We observed that regulatory sites and regions of open chromatin differed between genes upregulated at early and late wounding time-points as well as between genes induced by jasmonic acid and those not induced. Expanding on what we currently know, we identified cis-elements that improved model predictions of expression clusters over known binding sites. Using a combination of genome editing, in vitro DNA-binding assays, and transient expression assays using native and mutated cis-regulatory elements, we experimentally validated four of the predicted elements, three of which were not previously known to function in wound-response regulation. Our study provides a global model predictive of wound response and identifies new regulatory sequences important for wounding without requiring prior knowledge of the transcriptional regulators.

The SEEL motif and members of the MYB-related REVEILLE transcription factor family are important for the expression of LORELEI in the synergid cells of the Arabidopsis female gametophyte.

Synergid cells in the micropylar end of the female gametophyte are required for critical cell-cell signaling interactions between the pollen tube and the ovule that precede double fertilization and seed formation in flowering plants. LORELEI (LRE) encodes a putative GPI-anchored protein that is expressed primarily in the synergid cells, and together with FERONIA, a receptor-like kinase, it controls pollen tube reception by the receptive synergid cell. Still, how LRE expression is controlled in synergid cells remains poorly characterized. We identified candidate cis-regulatory elements enriched in LRE and other synergid cell-expressed genes. One of the candidate motifs ('TAATATCT') in the LRE promoter was an uncharacterized variant of the Evening Element motif that we named as the Short Evening Element-like (SEEL) motif. Deletion or point mutations in the SEEL motif of the LRE promoter resulted in decreased reporter expression in synergid cells, demonstrating that the SEEL motif is important for expression of LRE in synergid cells. Additionally, we found that LRE expression is decreased in the loss of function mutants of REVEILLE (RVE) transcription factors, which are clock genes known to bind the SEEL and other closely related motifs. We propose that RVE transcription factors regulate LRE expression in synergid cells by binding to the SEEL motif in the LRE promoter. Identification of cis-regulatory elements and transcription factors involved in the expression of LRE will serve as a foundation to characterize the gene regulatory networks in synergid cells.

Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling.

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.

Predictive Models of Genetic Redundancy in Arabidopsis thaliana.

Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

Optimising the use of gene expression data to predict plant metabolic pathway memberships.

Plant metabolites from diverse pathways are important for plant survival, human nutrition and medicine. The pathway memberships of most plant enzyme genes are unknown. While co-expression is useful for assigning genes to pathways, expression correlation may exist only under specific spatiotemporal and conditional contexts. Utilising > 600 tomato (Solanum lycopersicum) expression data combinations, three strategies for predicting memberships in 85 pathways were explored. Optimal predictions for different pathways require distinct data combinations indicative of pathway functions. Naive prediction (i.e. identifying pathways with the most similarly expressed genes) is error prone. In 52 pathways, unsupervised learning performed better than supervised approaches, possibly due to limited training data availability. Using gene-to-pathway expression similarities led to prediction models that outperformed those based simply on expression levels. Using 36 experimental validated genes, the pathway-best model prediction accuracy is 58.3%, significantly better compared with that for predicting annotated genes without experimental evidence (37.0%) or random guess (1.2%), demonstrating the importance of data quality. Our study highlights the need to extensively explore expression-based features and prediction strategies to maximise the accuracy of metabolic pathway membership assignment. The prediction framework outlined here can be applied to other species and serves as a baseline model for future comparisons.

Contrasting transcriptional responses to Fusarium virguliforme colonization in symptomatic and asymptomatic hosts.

The broad host range of Fusarium virguliforme represents a unique comparative system to identify and define differentially induced responses between an asymptomatic monocot host, maize (Zea mays), and a symptomatic eudicot host, soybean (Glycine max). Using a temporal, comparative transcriptome-based approach, we observed that early gene expression profiles of root tissue from infected maize suggest that pathogen tolerance coincides with the rapid induction of senescence dampening transcriptional regulators, including ANACs (Arabidopsis thaliana NAM/ATAF/CUC protein) and Ethylene-Responsive Factors. In contrast, the expression of senescence-associated processes in soybean was coincident with the appearance of disease symptom development, suggesting pathogen-induced senescence as a key pathway driving pathogen susceptibility in soybean. Based on the analyses described herein, we posit that root senescence is a primary contributing factor underlying colonization and disease progression in symptomatic versus asymptomatic host-fungal interactions. This process also supports the lifestyle and virulence of F. virguliforme during biotrophy to necrotrophy transitions. Further support for this hypothesis lies in comprehensive co-expression and comparative transcriptome analyses, and in total, supports the emerging concept of necrotrophy-activated senescence. We propose that F. virguliforme conditions an environment within symptomatic hosts, which favors susceptibility through transcriptomic reprogramming, and as described herein, the induction of pathways associated with senescence during the necrotrophic stage of fungal development.

Impact of short-read sequencing on the misassembly of a plant genome.

BACKGROUND: Availability of plant genome sequences has led to significant advances. However, with few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues that could significantly impact any downstream analysis of plant genomes. In tomato for example, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. RESULTS: To understand what the causes may be for such uneven coverage, we first established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have higher simple sequence repeat and tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available tomato long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially misassembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a predictive model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. CONCLUSIONS: Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to plant genome misassembly when using short reads and the generality of these causes and factors should be tested further in other species.

Within- and cross-species predictions of plant specialized metabolism genes using transfer learning.

Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. Plant models like Arabidopsis thaliana have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from A. thaliana is transferred to predict gene functions in cultivated tomato with fewer experimentally annotated genes. The first tomato SM/GM prediction model using only tomato data performs well (F-measure = 0.74, compared with 0.5 for random and 1.0 for perfect predictions), but from manually curating 88 SM/GM genes, we found many mis-predicted entries were likely mis-annotated. When the SM/GM prediction models built with A. thaliana data were used to filter out genes where the A. thaliana-based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (F-measure = 0.92). Our study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one.

Maize ANT1 modulates vascular development, chloroplast development, photosynthesis, and plant growth.

Arabidopsis AINTEGUMENTA (ANT), an AP2 transcription factor, is known to control plant growth and floral organogenesis. In this study, our transcriptome analysis and in situ hybridization assays of maize embryonic leaves suggested that maize ANT1 (ZmANT1) regulates vascular development. To better understand ANT1 functions, we determined the binding motif of ZmANT1 and then showed that ZmANT1 binds the promoters of millet SCR1, GNC, and AN3, which are key regulators of Kranz anatomy, chloroplast development, and plant growth, respectively. We generated a mutant with a single-codon deletion and two frameshift mutants of the ANT1 ortholog in the C4 millet Setaria viridis by the CRISPR/Cas9 technique. The two frameshift mutants displayed reduced photosynthesis efficiency and growth rate, smaller leaves, and lower grain yields than wild-type (WT) plants. Moreover, their leaves sporadically exhibited distorted Kranz anatomy and vein spacing. Conducting transcriptomic analysis of developing leaves in the WT and the three mutants we identified differentially expressed genes (DEGs) in the two frameshift mutant lines and found many down-regulated DEGs enriched in photosynthesis, heme, tetrapyrrole binding, and antioxidant activity. In addition, we predicted many target genes of ZmANT1 and chose 13 of them to confirm binding of ZmANT1 to their promoters. Based on the above observations, we proposed a model for ANT1 regulation of cell proliferation and leaf growth, vascular and vein development, chloroplast development, and photosynthesis through its target genes. Our study revealed biological roles of ANT1 in several developmental processes beyond its known roles in plant growth and floral organogenesis.

Evolution of a plant gene cluster in Solanaceae and emergence of metabolic diversity.

Plants produce phylogenetically and spatially restricted, as well as structurally diverse specialized metabolites via multistep metabolic pathways. Hallmarks of specialized metabolic evolution include enzymatic promiscuity and recruitment of primary metabolic enzymes and examples of genomic clustering of pathway genes. Solanaceae glandular trichomes produce defensive acylsugars, with sidechains that vary in length across the family. We describe a tomato gene cluster on chromosome 7 involved in medium chain acylsugar accumulation due to trichome specific acyl-CoA synthetase and enoyl-CoA hydratase genes. This cluster co-localizes with a tomato steroidal alkaloid gene cluster and is syntenic to a chromosome 12 region containing another acylsugar pathway gene. We reconstructed the evolutionary events leading to this gene cluster and found that its phylogenetic distribution correlates with medium chain acylsugar accumulation across the Solanaceae. This work reveals insights into the dynamics behind gene cluster evolution and cell-type specific metabolite diversity.

Plants produce a vast variety of different molecules known as secondary or specialized metabolites to attract pollinating insects, such as bees, or protect themselves against herbivores and pests. The secondary metabolites are made from simple building blocks that are readily available in plants, including amino acids, fatty acids and sugars. Different species of plant, and even different parts of the same plant, produce their own sets of secondary metabolites. For example, the hairs on the surface of tomatoes and other members of the nightshade family of plants make metabolites known as acylsugars. These chemicals deter herbivores and pests from damaging the plants. To make acylsugars, the plants attach long chains known as fatty acyl groups to molecules of sugar, such as sucrose. Some members of the nightshade family produce acylsugars with longer chains than others. In particular, acylsugars with long chains are only found in tomatoes and other closely-related species. It remained unclear how the nightshade family evolved to produce acylsugars with chains of different lengths. To address this question, Fan et al. used genetic and biochemical approaches to study tomato plants and other members of the nightshade family. The experiments identified two genes known as AACS and AECH in tomatoes that produce acylsugars with long chains. These two genes originated from the genes of older enzymes that metabolize fatty acids ­ the building blocks of fats ­ in plant cells. Unlike the older genes, AACS and AECH were only active at the tips of the hairs on the plant's surface. Fan et al. then investigated the evolutionary relationship between 11 members of the nightshade family and two other plant species. This revealed that AACS and AECH emerged in the nightshade family around the same time that longer chains of acylsugars started appearing. These findings provide insights into how plants evolved to be able to produce a variety of secondary metabolites that may protect them from a broader range of pests. The gene cluster identified in this work could be used to engineer other species of crop plants to start producing acylsugars as natural pesticides.

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.

COST1 regulates autophagy to control plant drought tolerance.

Plants balance their competing requirements for growth and stress tolerance via a sophisticated regulatory circuitry that controls responses to the external environments. We have identified a plant-specific gene, COST1 (constitutively stressed 1), that is required for normal plant growth but negatively regulates drought resistance by influencing the autophagy pathway. An Arabidopsis thaliana cost1 mutant has decreased growth and increased drought tolerance, together with constitutive autophagy and increased expression of drought-response genes, while overexpression of COST1 confers drought hypersensitivity and reduced autophagy. The COST1 protein is degraded upon plant dehydration, and this degradation is reduced upon treatment with inhibitors of the 26S proteasome or autophagy pathways. The drought resistance of a cost1 mutant is dependent on an active autophagy pathway, but independent of other known drought signaling pathways, indicating that COST1 acts through regulation of autophagy. In addition, COST1 colocalizes to autophagosomes with the autophagosome marker ATG8e and the autophagy adaptor NBR1, and affects the level of ATG8e protein through physical interaction with ATG8e, indicating a pivotal role in direct regulation of autophagy. We propose a model in which COST1 represses autophagy under optimal conditions, thus allowing plant growth. Under drought, COST1 is degraded, enabling activation of autophagy and suppression of growth to enhance drought tolerance. Our research places COST1 as an important regulator controlling the balance between growth and stress responses via the direct regulation of autophagy.