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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.

Iron (Fe) is a key cofactor in many cellular redox processes, including respiration and photosynthesis. Plant Fe deficiency (-Fe) activates a complex regulatory network which coordinates root Fe uptake and distribution to sink tissues, while avoiding over-accumulation of Fe and other metals to toxic levels. In Arabidopsis (Arabidopsis thaliana), FIT (FER-LIKE FE DEFICIENCY-INDUCED TRANSCRIPTION FACTOR), a bHLH transcription factor (TF), is required for up-regulation of root Fe acquisition genes. However, other root and shoot -Fe-induced genes involved in Fe allocation and signaling are FIT-independent. The cis-regulatory code, i.e. the cis-regulatory elements (CREs) and their combinations that regulate plant -Fe-responses, remains largely elusive. Using Arabidopsis genome and transcriptome data, we identified over 100 putative CREs (pCREs) that were predictive of -Fe-induced up-regulation of genes in root tissue. We used large-scale in vitro TF binding data, association with FIT-dependent or FIT-independent co-expression clusters, positional bias, and evolutionary conservation to assess pCRE properties and possible functions. In addition to bHLH and MYB TFs, also B3, NAC, bZIP, and TCP TFs might be important regulators for -Fe responses. Our approach uncovered IDE1 (Iron Deficiency-responsive Element 1), a -Fe response CRE in grass species, to be conserved in regulating genes for biosynthesis of Fe-chelating compounds also in Arabidopsis. Our findings provide a comprehensive source of cis-regulatory information for -Fe-responsive genes, that advances our mechanistic understanding and informs future efforts in engineering plants with more efficient Fe uptake or transport systems.One sentence summary>100 putative cis-regulatory elements robustly predict Arabidopsis root Fe deficiency-responses in computational models, and shed light on the mechanisms of transcriptional regulation.

The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e., ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e., feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.

BackgroundSingle-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease.ResultsWhile previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches.ConclusionWe provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

Plants are exposed to a variety of environmental conditions, and their ability to respond to environmental variation depends on the proper regulation of gene expression in an organ-, tissue-, and cell type-specific manner. Although our knowledge of how stress responses are regulated is accumulating, a genome-wide model of how plant transcription factors (TFs) and cis-regulatory elements control spatially specific stress response has yet to emerge. Using Arabidopsis (Arabidopsis thaliana) as a model, we identified a set of 1,894 putative cis-regulatory elements (pCREs) that are associated with high-salinity (salt) up-regulated genes in the root or the shoot. We used these pCREs to develop computational models that can better predict salt up-regulated genes in the root and shoot compared with models based on known TF binding motifs. In addition, we incorporated TF binding sites identified via large-scale in vitro assays, chromatin accessibility, evolutionary conservation, and pCRE combinatorial relationships in machine learning models and found that only consideration of pCRE combinations led to better performance in salt up-regulation prediction in the root and shoot. Our results suggest that the plant organ transcriptional response to high salinity is regulated by a core set of pCREs and provide a genome-wide view of the cis-regulatory code of plant spatial transcriptional responses to environmental stress.

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, 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, 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.

Extensive transcriptional activity occurring in intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To address this, we evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions (ITRs) in four Poaceae species. Among 43,301 ITRs across the four species, 34,460 (80%) are species-specific. ITRs found across species tend to be more divergent in expression and have more recent duplicates compared to annotated genes. To assess if ITRs are functional (under selection), machine learning models were established in Oryza sativa (rice) that could accurately distinguish between phenotype genes and pseudogenes (area under curve-receiver operating characteristic = 0.94). Based on the models, 584 (8%) and 4391 (61%) rice ITRs are classified as likely functional and nonfunctional with high confidence, respectively. ITRs with conserved expression and ancient retained duplicates, features that were not part of the model, are frequently classified as likely-functional, suggesting these characteristics could serve as pragmatic rules of thumb for identifying candidate sequences likely to be under selection. This study also provides a framework to identify novel genes using comparative transcriptomic data to improve genome annotation that is fundamental for connecting genotype to phenotype in crop and model systems.

The usefulness of Genomic Prediction (GP) in crop and livestock breeding programs has led to efforts to develop new and improved GP approaches including non-linear algorithm, such as artificial neural networks (ANN) (i.e. deep learning) and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of GP datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and five non-linear algorithms, including ANNs. First, we found that hyperparameter selection was critical for all non-linear algorithms and that feature selection prior to model training was necessary for ANNs when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple GP algorithms (i.e. ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits than that of linear algorithms. Although ANNs did not perform best for any trait, we identified strategies (i.e. feature selection, seeded starting weights) that boosted their performance near the level of other algorithms. These results, together with the fact that even small improvements in GP performance could accumulate into large genetic gains over the course of a breeding program, highlights the importance of algorithm selection for the prediction of trait values.

The ability to predict traits from genome-wide sequence information (i.e., genomic prediction) has improved our understanding of the genetic basis of complex traits and transformed breeding practices. Transcriptome data may also be useful for genomic prediction. However, it remains unclear how well transcript levels can predict traits, particularly when traits are scored at different development stages. Using maize (Zea mays) genetic markers and transcript levels from seedlings to predict mature plant traits, we found that transcript and genetic marker models have similar performance. When the transcripts and genetic markers with the greatest weights (i.e., the most important) in those models were used in one joint model, performance increased. Furthermore, genetic markers important for predictions were not close to or identified as regulatory variants for important transcripts. These findings demonstrate that transcript levels are useful for predicting traits and that their predictive power is not simply due to genetic variation in the transcribed genomic regions. Finally, genetic marker models identified only 1 of 14 benchmark flowering-time genes, while transcript models identified 5. These data highlight that, in addition to being useful for genomic prediction, transcriptome data can provide a link between traits and variation that cannot be readily captured at the sequence level.

Background Transcription factors (TFs) play a key role in regulating plant development and response to environmental stimuli. While most genes revert to single copy after whole genome duplication (WGD) event, transcription factors are retained at a significantly higher rate. Little is known about how TF duplicates have diverged in their expression and regulation, the answer to which may contribute to a better understanding of the elevated retention rate among TFs. Results Here we assessed what features may explain differences in the retention of TF duplicates and other genes using Arabidopsis thaliana as a model. We integrated 34 expression, sequence, and conservation features to build a linear model for predicting the extent of duplicate retention following WGD events among TFs and 19 groups of genes with other functions. We found that TFs was the least well predicted, demonstrating the features of TFs are substantially deviated from duplicate genes in other function groups. Consistent with this, the evolution of TF expression patterns and cis-regulatory cites favors the partitioning of ancestral states among the resulting duplicates: one “ancestral” TF duplicate retains most ancestral expression and cis-regulatory sites, while the “non-ancestral” duplicate is enriched for novel regulatory sites. By modeling the retention of ancestral expression and cis-regulatory states in duplicate pairs using a system of differential equations, we found that TF duplicate pairs in a partitioned state are preferentially maintained. Conclusions These TF duplicates with asymmetrically partitioned ancestral states are likely maintained because one copy retains ancestral functions while the other, at least in some cases, acquires novel cis-regulatory sites that may be important for novel, adaptive traits. Electronic supplementary material The online version of this article (10.1186/s12862-019-1398-z) contains supplementary material, which is available to authorized users.

The Lyme disease spirochete Borrelia burgdorferi is unique among bacteria in its large number of lipoproteins that are encoded by a small, exceptionally fragmented, and predominantly linear genome. Peripherally anchored in either the inner or outer membrane and facing either the periplasm or the external environment, these lipoproteins assume varied roles. A prominent subset of lipoproteins functioning as the apparent linchpins of the enzootic tick-vertebrate infection cycle have been explored as vaccine targets. Yet, most of the B. burgdorferi lipoproteome has remained uncharacterized. Here, we comprehensively and conclusively localize the B. burgdorferi lipoproteome by applying established protein localization assays to a newly generated epitope-tagged lipoprotein expression library and by validating the obtained individual protein localization results using a sensitive global mass spectrometry approach. The derived consensus localization data indicate that 86 of the 125 analyzed lipoproteins encoded by B. burgdorferi are secreted to the bacterial surface. Thirty-one of the remaining 39 periplasmic lipoproteins are retained in the inner membrane, with only 8 lipoproteins being anchored in the periplasmic leaflet of the outer membrane. The localization of 10 lipoproteins was further defined or revised, and 52 surface and 23 periplasmic lipoproteins were newly localized. Cross-referencing prior studies revealed that the borrelial surface lipoproteome contributing to the host-pathogen interface is encoded predominantly by plasmids. Conversely, periplasmic lipoproteins are encoded mainly by chromosomal loci. These studies close a gap in our understanding of the functional lipoproteome of an important human pathogen and set the stage for more in-depth studies of thus-far-neglected spirochetal lipoproteins. IMPORTANCE The small and exceptionally fragmented genome of the Lyme disease spirochete Borrelia burgdorferi encodes over 120 lipoproteins. Studies in the field have predominantly focused on a relatively small number of surface lipoproteins that play important roles in the transmission and pathogenesis of this global human pathogen. Yet, a comprehensive spatial assessment of the entire borrelial lipoproteome has been missing. The current study newly identifies 52 surface and 23 periplasmic lipoproteins. Overall, two-thirds of the B. burgdorferi lipoproteins localize to the surface, while outer membrane lipoproteins facing the periplasm are rare. This analysis underscores the dominant contribution of lipoproteins to the spirochete's rather complex and adaptable host-pathogen interface, and it encourages further functional exploration of its lipoproteome.