Paired-end analysis of transcription start sites in Arabidopsis reveals plant-specific promoter signatures

LsRTDv1, a reference transcript dataset for accurate transcript-specific expression analysis in lettuce

Accurate quantification of gene and transcript-specific expression, with the underlying knowledge of precise transcript isoforms, is crucial to understanding many biological processes. Analysis of RNA sequencing data has benefited from the development of alignment-free algorithms which enhance the precision and speed of expression analysis. However, such algorithms require a reference transcriptome. Here we generate a reference transcript dataset (LsRTDv1) for lettuce (cv. Saladin), combining long- and short-read sequencing with publicly available transcriptome annotations, and filtering to keep only transcripts with high-confidence splice junctions and transcriptional start and end sites. LsRTDv1 identifies novel genes (mostly long non-coding RNAs) and increases the number of transcript isoforms per gene in the lettuce genome from 1.4 to 2.7. We show that LsRTDv1 significantly increases the mapping rate of RNA-seq data from a lettuce time-series experiment (mock- and Botrytis cinerea-inoculated) and enables detection of genes that are differentially alternatively spliced in response to infection as well as transcript-specific expression changes. LsRTDv1 is a valuable resource for investigation of transcriptional and alternative splicing regulation in lettuce.

A foundational large language model for edible plant genomes

Significant progress has been made in the field of plant genomics, as demonstrated by the increased use of high-throughput methodologies that enable the characterization of multiple genome-wide molecular phenotypes. These findings have provided valuable insights into plant traits and their underlying genetic mechanisms, particularly in model plant species. Nonetheless, effectively leveraging them to make accurate predictions represents a critical step in crop genomic improvement. We present AgroNT, a foundational large language model trained on genomes from 48 plant species with a predominant focus on crop species. We show that AgroNT can obtain state-of-the-art predictions for regulatory annotations, promoter/terminator strength, tissue-specific gene expression, and prioritize functional variants. We conduct a large-scale in silico saturation mutagenesis analysis on cassava to evaluate the regulatory impact of over 10 million mutations and provide their predicted effects as a resource for variant characterization. Finally, we propose the use of the diverse datasets compiled here as the Plants Genomic Benchmark (PGB), providing a comprehensive benchmark for deep learning-based methods in plant genomic research. The pre-trained AgroNT model is publicly available on HuggingFace at https://huggingface.co/InstaDeepAI/agro-nucleotide-transformer-1b  for future research purposes.

Alternative TSS use is widespread in Cryptococcus fungi in response to environmental cues and regulated genome-wide by the transcription factor Tur1

Alternative transcription start site (TSS) usage regulation has been identified as a major means of gene expression regulation in metazoans. However, in fungi, its impact remains elusive as its study has thus far been restricted to model yeasts. Here, we first re-analyzed TSS-seq data to define genuine TSS clusters in 2 species of pathogenic Cryptococcus. We identified 2 types of TSS clusters associated with specific DNA sequence motifs. Our analysis also revealed that alternative TSS usage regulation in response to environmental cues is widespread in Cryptococcus, altering gene expression and protein targeting. Importantly, we performed a forward genetic screen to identify a unique transcription factor (TF) named Tur1, which regulates alternative TSS (altTSS) usage genome-wide when cells switch from exponential phase to stationary phase. ChiP-Seq and DamID-Seq analyses suggest that at some loci, the role of Tur1 might be direct. Tur1 has been previously shown to be essential for virulence in C. neoformans. We demonstrated here that a tur1Δ mutant strain is more sensitive to superoxide stress and phagocytosed more efficiently by macrophages than the wild-type (WT) strain.

RNAirport: a deep neural network-based database characterizing representative gene models in plants

A 5'-leader, known initially as the 5'-untranslated region, contains multiple isoforms due to alternative splicing (aS) and alternative transcription start site (aTSS). Therefore, a representative 5'-leader is demanded to examine the embedded RNA regulatory elements in controlling translation efficiency. Here, we develop a ranking algorithm and a deep-learning model to annotate representative 5'-leaders for five plant species. We rank the intra-sample and inter-sample frequency of aS-mediated transcript isoforms using the Kruskal-Wallis test-based algorithm and identify the representative aS-5'-leader. To further assign a representative 5'-end, we train the deep-learning model 5'leaderP to learn aTSS-mediated 5'-end distribution patterns from cap-analysis gene expression data. The model accurately predicts the 5'-end, confirmed experimentally in Arabidopsis and rice. The representative 5'-leader-contained gene models and 5'leaderP can be accessed at RNAirport (http://www.rnairport.com/leader5P/). The Stage 1 annotation of 5'-leader records 5'-leader diversity and will pave the way to Ribo-Seq open-reading frame annotation, identical to the project recently initiated by human GENCODE.

FAM222A, Part of the BET-Regulated Basal Endothelial Transcriptome, Is a Novel Determinant of Endothelial Biology and Angiogenesis-Brief Report

BACKGROUND

BETs (bromodomain and extraterminal domain-containing epigenetic reader proteins), including BRD4 (bromodomain-containing protein 4), orchestrate transcriptional programs induced by pathogenic stimuli, as intensively studied in cardiovascular disease and elsewhere. In endothelial cells (ECs), BRD4 directs induced proinflammatory, proatherosclerotic transcriptional responses; BET inhibitors, like JQ1, repress these effects and decrease atherosclerosis. While BET effects in pathogenic conditions have prompted therapeutic BET inhibitor development, BET action under basal conditions, including ECs, has remained understudied. To understand BET action in basal endothelial transcriptional programs, we first analyzed EC RNA-Seq data in the absence versus presence of JQ1 before using BET regulation to identify novel determinants of EC biology and function.

METHODS

RNA-Seq datasets of human umbilical vein ECs without and with JQ1 treatment were analyzed. After identifying C12orf34, also known as FAM222A (family with sequence similarity 222 member A), as a previously unreported, basally expressed, potently JQ1-induced EC gene, FAM222A was studied in endothelial and angiogenic responses in vitro using small-interference RNA silencing and lentiviral overexpression, in vitro, ex vivo and in vivo, including aortic sprouting, matrigel plug assays, and murine neonatal oxygen-induced retinopathy.

RESULTS

Resting EC RNA-Seq data indicate BETs direct transcriptional programs underlying core endothelial properties including migration, proliferation, and angiogenesis. BET inhibition in resting ECs also significantly induced a subset of mRNAs, including FAM222A-a unique BRD4-regulated gene with no reported EC role. Silencing endothelial FAM222A significantly decreased cellular proliferation, migration, network formation, aorta sprouting, and Matrigel plug vascularization through coordinated modulation of VEGF (vascular endothelial growth factor) and NOTCH mediator expression in vitro, ex vivo, in vivo; lentiviral FAM222A overexpression had opposite effects. In vivo, siFAM222A significantly repressed retinal revascularization in neonatal murine oxygen-induced retinopathy through similar angiogenic signaling modulation.

CONCLUSIONS

BET control over the basal endothelial transcriptome includes FAM222A, a novel, BRD4-regulated, key determinant of endothelial biology and angiogenesis.

Transposable element-initiated enhancer-like elements generate the subgenome-biased spike specificity of polyploid wheat

Transposable elements (TEs) comprise ~85% of the common wheat genome, which are highly diverse among subgenomes, possibly contribute to polyploid plasticity, but the causality is only assumed. Here, by integrating data from gene expression cap analysis and epigenome profiling via hidden Markov model in common wheat, we detect a large proportion of enhancer-like elements (ELEs) derived from TEs producing nascent noncoding transcripts, namely ELE-RNAs, which are well indicative of the regulatory activity of ELEs. Quantifying ELE-RNA transcriptome across typical developmental stages reveals that TE-initiated ELE-RNAs are mainly from RLG_famc7.3 specifically expanded in subgenome A. Acquisition of spike-specific transcription factor binding likely confers spike-specific expression of RLG_famc7.3-initiated ELE-RNAs. Knockdown of RLG_famc7.3-initiated ELE-RNAs resulted in global downregulation of spike-specific genes and abnormal spike development. These findings link TE expansion to regulatory specificity and polyploid developmental plasticity, highlighting the functional impact of TE-driven regulatory innovation on polyploid evolution.

Causal role of a promoter polymorphism in natural variation of the Arabidopsis floral repressor gene FLC

Noncoding polymorphism frequently associates with phenotypic variation, but causation and mechanism are rarely established. Noncoding single-nucleotide polymorphisms (SNPs) characterize the major haplotypes of the Arabidopsis thaliana floral repressor gene FLOWERING LOCUS C (FLC). This noncoding polymorphism generates a range of FLC expression levels, determining the requirement for and the response to winter cold. The major adaptive determinant of these FLC haplotypes was shown to be the autumnal levels of FLC expression. Here, we investigate how noncoding SNPs influence FLC transcriptional output. We identify an upstream transcription start site (uTSS) cluster at FLC, whose usage is increased by an A variant at the promoter SNP-230. This variant is present in relatively few Arabidopsis accessions, with the majority containing G at this site. We demonstrate a causal role for the A variant at -230 in reduced FLC transcriptional output. The G variant upregulates FLC expression redundantly with the major transcriptional activator FRIGIDA (FRI). We demonstrate an additive interaction of SNP-230 with an intronic SNP+259, which also differentially influences uTSS usage. Combinatorial interactions between noncoding SNPs and transcriptional activators thus generate quantitative variation in FLC transcription that has facilitated the adaptation of Arabidopsis accessions to distinct climates.

In Silico Verification of Predicted Potential Promoter Sequences in the Rice (Oryza sativa) Genome

The exact identification of promoter sequences remains a serious problem in computational biology, as the promoter prediction algorithms under development continue to produce false-positive results. Therefore, to fully assess the validity of predicted sequences, it is necessary to perform a comprehensive test of their properties, such as the presence of downstream transcribed DNA regions behind them, or chromatin accessibility for transcription factor binding. In this paper, we examined the promoter sequences of chromosome 1 of the rice Oryza sativa genome from the Database of Potential Promoter Sequences predicted using a mathematical algorithm based on the derivation and calculation of statistically significant promoter classes. In this paper TATA motifs and cis-regulatory elements were identified in the predicted promoter sequences. We also verified the presence of potential transcription start sites near the predicted promoters by analyzing CAGE-seq data. We searched for unannotated transcripts behind the predicted sequences by de novo assembling transcripts from RNA-seq data. We also examined chromatin accessibility in the region of the predicted promoters by analyzing ATAC-seq data. As a result of this work, we identified the predicted sequences that are most likely to be promoters for further experimental validation in an in vivo or in vitro system.

Challenges and advances towards the rational design of microalgal synthetic promoters in Chlamydomonas reinhardtii

Microalgae hold enormous potential to provide a safe and sustainable source of high-value compounds, acting as carbon-fixing biofactories that could help to mitigate rapidly progressing climate change. Bioengineering microalgal strains will be key to optimizing and modifying their metabolic outputs, and to render them competitive with established industrial biotechnology hosts, such as bacteria or yeast. To achieve this, precise and tuneable control over transgene expression will be essential, which would require the development and rational design of synthetic promoters as a key strategy. Among green microalgae, Chlamydomonas reinhardtii represents the reference species for bioengineering and synthetic biology; however, the repertoire of functional synthetic promoters for this species, and for microalgae generally, is limited in comparison to other commercial chassis, emphasizing the need to expand the current microalgal gene expression toolbox. Here, we discuss state-of-the-art promoter analyses, and highlight areas of research required to advance synthetic promoter development in C. reinhardtii. In particular, we exemplify high-throughput studies performed in other model systems that could be applicable to microalgae, and propose novel approaches to interrogating algal promoters. We lastly outline the major limitations hindering microalgal promoter development, while providing novel suggestions and perspectives for how to overcome them.

Methodology for Constructing a Knowledgebase for Plant Gene Regulation Information

The amount of biological data is growing at a rapid pace as many high-throughput omics technologies and data pipelines are developed. This is resulting in the growth of databases for DNA and protein sequences, gene expression, protein accumulation, structural, and localization information. The diversity and multi-omics nature of such bioinformatic data requires well-designed databases for flexible organization and presentation. Besides general-purpose online bioinformatic databases, users need narrowly focused online databases to quickly access a meaningful collection of related data for their research. Here, we describe the methodology used to implement a plant gene regulatory knowledgebase, with data, query, and tool features, as well as the ability to expand to accommodate future datasets. We exemplify this methodology for the GRASSIUS knowledgebase, but it is applicable to developing and updating similar plant gene regulatory knowledgebases. GRASSIUS organizes and presents gene regulatory data from grass species with a central focus on maize (Zea mays). The main class of data presented include not only the families of transcription factors (TFs) and co-regulators (CRs) but also protein-DNA interaction data, where available.

Plant microProteins: Small but powerful modulators of plant development

MicroProteins (miPs) are small and single-domain containing proteins of less than 20 kDa. This domain allows microProteins to interact with compatible domains of evolutionary-related proteins and fine-tuning the key physiological pathways in several organisms. Since the first report of a microProtein in mice, numerous microProteins have been identified in plants by computational approaches. However, only a few candidates have been functionally characterized, primarily in Arabidopsis. The recent success of synthetic microProteins in modulating physiological activities in crops makes these proteins interesting candidates for crop engineering. Here, we comprehensively summarise the synthesis, mode of action, and functional roles of microProteins in plants. We also discuss different approaches used to identify plant microProteins. Additionally, we discuss novel approaches to design synthetic microProteins that can be used to target proteins regulating plant growth and development. We finally highlight the prospects and challenges of utilizing microProteins in future crop improvement programs.

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MicroProteins (miPs) are small-sized proteins with a molecular weight of 5–20 kDa

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MiPs can be detected through multiomics and computational approaches

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MiPs are crucial regulators of plant growth and development

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MiPs as condensates, synthetic miPs, and limitations

Performance of abiotic stress-inducible synthetic promoters in genetically engineered hybrid poplar (Populus tremula × Populus alba)

Abiotic stresses can cause significant damage to plants. For sustainable bioenergy crop production, it is critical to generate resistant crops to such stress. Engineering promoters to control the precise expression of stress resistance genes is a very effective way to address the problem. Here we developed stably transformed Populus tremula × Populus alba hybrid poplar (INRA 717-1B4) containing one-of-six synthetic drought stress-inducible promoters (SDs; SD9-1, SD9-2, SD9-3, SD13-1, SD18-1, and SD18-3) identified previously by transient transformation assays. We screened green fluorescent protein (GFP) induction in poplar under osmotic stress conditions. Of six transgenic lines containing synthetic promoter, three lines (SD18-1, 9-2, and 9-3) had significant GFP expression in both salt and osmotic stress treatments. Each synthetic promoter employed heptamerized repeats of specific and short cis-regulatory elements (7 repeats of 7-8 bases). To verify whether the repeats of longer sequences can improve osmotic stress responsiveness, a transgenic poplar containing the synthetic promoter of the heptamerized entire SD9 motif (20 bases, containing all partial SD9 motifs) was generated and measured for GFP induction under osmotic stress. The heptamerized entire SD9 motif did not result in higher GFP expression than the shorter promoters consisting of heptamerized SD9-1, 9-2, and 9-3 (partial SD9) motifs. This result indicates that shorter synthetic promoters (~50 bp) can be used for versatile control of gene expression in transgenic poplar. These synthetic promoters will be useful tools to engineer stress-resilient bioenergy tree crops in the future.

High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica

The Reference Transcriptomic Dataset (RTD) is an accurate and comprehensive collection of transcripts originating from a given organism. It holds the key to precise transcript quantification and downstream analysis of differential expressions and regulations. Currently, transcriptome annotations for most crop plants are far from complete. For example, Oryza sativa indica (O. sativa indica) is reported to have 40,759 transcripts in the Ensembl database without alternative transcript isoforms and alternative splicing (AS) events. To generate a high-quality RTD, we conducted RNA sequencing of rice leaf samples collected at various time points during Rhizoctonia solani infection. The obtained reads were analyzed by adopting the recently developed computational analysis pipeline to assemble the RTD with increased transcript and AS diversity for O. sativa indica (IndicaRTD). After stringent quality filtering, the newly constructed transcriptome annotation was comprised of 122,968 non-redundant transcripts from 53,695 genes. This study identified many novel transcripts compared to Ensembl deposited data that are important for regulating molecular and physiological processes in the plant system. Currently, the assembled IndicaRTD must allow fast quantification of transcript and gene expression with high precision.

BaRTv2: a highly resolved barley reference transcriptome for accurate transcript-specific RNA-seq quantification

Accurate characterisation of splice junctions (SJs) as well as transcription start and end sites in reference transcriptomes allows precise quantification of transcripts from RNA-seq data, and enables detailed investigations of transcriptional and post-transcriptional regulation. Using novel computational methods and a combination of PacBio Iso-seq and Illumina short-read sequences from 20 diverse tissues and conditions, we generated a comprehensive and highly resolved barley reference transcript dataset from the European 2-row spring barley cultivar Barke (BaRTv2.18). Stringent and thorough filtering was carried out to maintain the quality and accuracy of the SJs and transcript start and end sites. BaRTv2.18 shows increased transcript diversity and completeness compared with an earlier version, BaRTv1.0. The accuracy of transcript level quantification, SJs and transcript start and end sites have been validated extensively using parallel technologies and analysis, including high-resolution reverse transcriptase-polymerase chain reaction and 5'-RACE. BaRTv2.18 contains 39 434 genes and 148 260 transcripts, representing the most comprehensive and resolved reference transcriptome in barley to date. It provides an important and high-quality resource for advanced transcriptomic analyses, including both transcriptional and post-transcriptional regulation, with exceptional resolution and precision.

A high-resolution single-molecule sequencing-based Arabidopsis transcriptome using novel methods of Iso-seq analysis

BACKGROUND

Accurate and comprehensive annotation of transcript sequences is essential for transcript quantification and differential gene and transcript expression analysis. Single-molecule long-read sequencing technologies provide improved integrity of transcript structures including alternative splicing, and transcription start and polyadenylation sites. However, accuracy is significantly affected by sequencing errors, mRNA degradation, or incomplete cDNA synthesis.

RESULTS

We present a new and comprehensive Arabidopsis thaliana Reference Transcript Dataset 3 (AtRTD3). AtRTD3 contains over 169,000 transcripts-twice that of the best current Arabidopsis transcriptome and including over 1500 novel genes. Seventy-eight percent of transcripts are from Iso-seq with accurately defined splice junctions and transcription start and end sites. We develop novel methods to determine splice junctions and transcription start and end sites accurately. Mismatch profiles around splice junctions provide a powerful feature to distinguish correct splice junctions and remove false splice junctions. Stratified approaches identify high-confidence transcription start and end sites and remove fragmentary transcripts due to degradation. AtRTD3 is a major improvement over existing transcriptomes as demonstrated by analysis of an Arabidopsis cold response RNA-seq time-series. AtRTD3 provides higher resolution of transcript expression profiling and identifies cold-induced differential transcription start and polyadenylation site usage.

CONCLUSIONS

AtRTD3 is the most comprehensive Arabidopsis transcriptome currently. It improves the precision of differential gene and transcript expression, differential alternative splicing, and transcription start/end site usage analysis from RNA-seq data. The novel methods for identifying accurate splice junctions and transcription start/end sites are widely applicable and will improve single-molecule sequencing analysis from any species.

BLSSpeller to discover novel regulatory motifs in maize

With the decreasing cost of sequencing and availability of larger numbers of sequenced genomes, comparative genomics is becoming increasingly attractive to complement experimental techniques for the task of transcription factor (TF) binding site identification. In this study, we redesigned BLSSpeller, a motif discovery algorithm, to cope with larger sequence datasets. BLSSpeller was used to identify novel motifs in Zea mays in a comparative genomics setting with 16 monocot lineages. We discovered 61 motifs of which 20 matched previously described motif models in Arabidopsis. In addition, novel, yet uncharacterized motifs were detected, several of which are supported by available sequence-based and/or functional data. Instances of the predicted motifs were enriched around transcription start sites and contained signatures of selection. Moreover, the enrichment of the predicted motif instances in open chromatin and TF binding sites indicates their functionality, supported by the fact that genes carrying instances of these motifs were often found to be co-expressed and/or enriched in similar GO functions. Overall, our study unveiled several novel candidate motifs that might help our understanding of the genotype to phenotype association in crops.

A native promoter–gene fusion created by CRISPR/Cas9‐mediated genomic deletion offers a transgene‐free method to drive oil accumulation in leaves

Achieving gain‐of‐function phenotypes without inserting foreign DNA is an important challenge for plant biotechnologists. Here, we show that a gene can be brought under the control of a promoter from an upstream gene by deleting the intervening genomic sequence using dual‐guide CRISPR/Cas9. We fuse the promoter of a nonessential photosynthesis‐related gene to DIACYLGLYCEROL ACYLTRANSFERASE 2 (DGAT2) in the lipase‐deficient sugar‐dependent 1 mutant of Arabidopsis thaliana to drive ectopic oil accumulation in leaves. DGAT2 expression is enhanced more than 20‐fold and the triacylglycerol content increases by around 30‐fold. This deletion strategy offers a transgene‐free route to engineering traits that rely on transcriptional gain‐of‐function, such as producing high lipid forage to increase the productivity and sustainability of ruminant farming.

Sequence-based evaluation of promoter context for prediction of transcription start sites in Arabidopsis and rice

Genes are transcribed from transcription start sites (TSSs), and their position in a genome is strictly controlled to avoid mis-expression of undesired regions. In this study, we designed and developed a methodology for the evaluation of promoter context, which detects proximal promoter regions from − 200 to − 60 bp relative to a TSS, in Arabidopsis and rice genomes. The method positively evaluates spacer sequences and Regulatory Element Groups, but not core promoter elements like TATA boxes, and is able to predict the position of a TSS within a width of 200 bp. An important feature of the evaluation/prediction method is its independence of the core promoter elements, which was demonstrated by successful prediction of all the TATA, GA, and coreless types of promoters without notable differences in the accuracy of prediction. The positive relationship identified between the evaluation scores and gene expression levels suggests that this method is useful for the evaluation of promoter maturity.

R-loops at microRNA encoding loci promote co-transcriptional processing of pri-miRNAs in plants

In most organisms, the maturation of nascent RNAs is coupled to transcription. Unlike in animals, the RNA polymerase II (RNAPII) transcribes microRNA genes (MIRNAs) as long and structurally variable pri-miRNAs in plants. Current evidence suggests that the miRNA biogenesis complex assembly initiates early during the transcription of pri-miRNAs in plants. However, it is unknown whether miRNA processing occurs co-transcriptionally. Here, we used native elongating transcript sequencing data and imaging techniques to demonstrate that plant miRNA biogenesis occurs coupled to transcription. We found that the entire biogenesis occurs co-transcriptionally for pri-miRNAs processed from the loop of the hairpin but requires a second nucleoplasmic step for those processed from the base. Furthermore, we found that co- and post-transcriptional miRNA processing mechanisms co-exist for most miRNAs in a dynamic balance. Notably, we discovered that R-loops, formed near the transcription start site region of MIRNAs, promote co-transcriptional pri-miRNA processing. Furthermore, our results suggest the neofunctionalization of co-transcriptionally processed miRNAs, boosting countless regulatory scenarios.

Genome-Wide Prediction of Transcription Start Sites in Conifers

The identification of promoters is an essential step in the genome annotation process, providing a framework for gene regulatory networks and their role in transcription regulation. Despite considerable advances in the high-throughput determination of transcription start sites (TSSs) and transcription factor binding sites (TFBSs), experimental methods are still time-consuming and expensive. Instead, several computational approaches have been developed to provide fast and reliable means for predicting the location of TSSs and regulatory motifs on a genome-wide scale. Numerous studies have been carried out on the regulatory elements of mammalian genomes, but plant promoters, especially in gymnosperms, have been left out of the limelight and, therefore, have been poorly investigated. The aim of this study was to enhance and expand the existing genome annotations using computational approaches for genome-wide prediction of TSSs in the four conifer species: loblolly pine, white spruce, Norway spruce, and Siberian larch. Our pipeline will be useful for TSS predictions in other genomes, especially for draft assemblies, where reliable TSS predictions are not usually available. We also explored some of the features of the nucleotide composition of the predicted promoters and compared the GC properties of conifer genes with model monocot and dicot plants. Here, we demonstrate that even incomplete genome assemblies and partial annotations can be a reliable starting point for TSS annotation. The results of the TSS prediction in four conifer species have been deposited in the Persephone genome browser, which allows smooth visualization and is optimized for large data sets. This work provides the initial basis for future experimental validation and the study of the regulatory regions to understand gene regulation in gymnosperms.

Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction

Promoters are crucial regulatory DNA regions for gene transcriptional activation. Rapid advances in next-generation sequencing technologies have accelerated the accumulation of genome sequences, providing increased training data to inform computational approaches for both prokaryotic and eukaryotic promoter prediction. However, it remains a significant challenge to accurately identify species-specific promoter sequences using computational approaches. To advance computational support for promoter prediction, in this study, we curated 58 comprehensive, up-to-date, benchmark datasets for 7 different species (i.e. Escherichia coli, Bacillus subtilis, Homo sapiens, Mus musculus, Arabidopsis thaliana, Zea mays and Drosophila melanogaster) to assist the research community to assess the relative functionality of alternative approaches and support future research on both prokaryotic and eukaryotic promoters. We revisited 106 predictors published since 2000 for promoter identification (40 for prokaryotic promoter, 61 for eukaryotic promoter, and 5 for both). We systematically evaluated their training datasets, computational methodologies, calculated features, performance and software usability. On the basis of these benchmark datasets, we benchmarked 19 predictors with functioning webservers/local tools and assessed their prediction performance. We found that deep learning and traditional machine learning-based approaches generally outperformed scoring function-based approaches. Taken together, the curated benchmark dataset repository and the benchmarking analysis in this study serve to inform the design and implementation of computational approaches for promoter prediction and facilitate more rigorous comparison of new techniques in the future.

TSSFinder-fast and accurate ab initio prediction of the core promoter in eukaryotic genomes

Promoter annotation is an important task in the analysis of a genome. One of the main challenges for this task is locating the border between the promoter region and the transcribing region of the gene, the transcription start site (TSS). The TSS is the reference point to delimit the DNA sequence responsible for the assembly of the transcribing complex. As the same gene can have more than one TSS, so to delimit the promoter region, it is important to locate the closest TSS to the site of the beginning of the translation. This paper presents TSSFinder, a new software for the prediction of the TSS signal of eukaryotic genes that is significantly more accurate than other available software. We currently are the only application to offer pre-trained models for six different eukaryotic organisms: Arabidopsis thaliana, Drosophila melanogaster, Gallus gallus, Homo sapiens, Oryza sativa and Saccharomyces cerevisiae. Additionally, our software can be easily customized for specific organisms using only 125 DNA sequences with a validated TSS signal and corresponding genomic locations as a training set. TSSFinder is a valuable new tool for the annotation of genomes. TSSFinder source code and docker container can be downloaded from http://tssfinder.github.io. Alternatively, TSSFinder is also available as a web service at http://sucest-fun.org/wsapp/tssfinder/.

Expression Elements Derived From Plant Sequences Provide Effective Gene Expression Regulation and New Opportunities for Plant Biotechnology Traits

Plant biotechnology traits provide a means to increase crop yields, manage weeds and pests, and sustainably contribute to addressing the needs of a growing population. One of the key challenges in developing new traits for plant biotechnology is the availability of expression elements for efficacious and predictable transgene regulation. Recent advances in genomics, transcriptomics, and computational tools have enabled the generation of new expression elements in a variety of model organisms. In this study, new expression element sequences were computationally generated for use in crops, starting from native Arabidopsis and maize sequences. These elements include promoters, 5' untranslated regions (5' UTRs), introns, and 3' UTRs. The expression elements were demonstrated to drive effective transgene expression in stably transformed soybean plants across multiple tissues types and developmental stages. The expressed transcripts were characterized to demonstrate the molecular function of these expression elements. The data show that the promoters precisely initiate transcripts, the introns are effectively spliced, and the 3' UTRs enable predictable processing of transcript 3' ends. Overall, our results indicate that these new expression elements can recapitulate key functional properties of natural sequences and provide opportunities for optimizing the expression of genes in future plant biotechnology traits.

Cryptic promoter activation occurs by at least two different mechanisms in the Arabidopsis genome

In gene-trap screening of plant genomes, promoterless reporter constructs are often expressed without trapping of annotated gene promoters. The molecular basis of this phenomenon, which has been interpreted as the trapping of cryptic promoters, is poorly understood. Here, we found that cryptic promoter activation occurs by at least two different mechanisms using Arabidopsis gene-trap lines in which a firefly luciferase (LUC) open reading frame (ORF) without an apparent promoter sequence was expressed from intergenic regions: one mechanism is 'cryptic promoter capturing', in which the LUC ORF captured pre-existing promoter-like chromatin marked by H3K4me3 and H2A.Z, and the other is 'promoter de novo origination', in which the promoter chromatin was newly formed near the 5' end of the inserted LUC ORF. The latter finding raises a question as to how the inserted LUC ORF sequence is involved in this phenomenon. To examine this, we performed a model experiment with chimeric LUC genes in transgenic plants. Using Arabidopsis psaH1 promoter-LUC constructs, we found that the functional core promoter region, where transcription start sites (TSSs) occur, cannot simply be determined by the upstream nor core promoter sequences; rather, its positioning proximal to the inserted LUC ORF sequence was more critical. This result suggests that the insertion of the coding sequence alters the local distribution of TSSs in the plant genome. The possible impact of the two types of cryptic promoter activation mechanisms on plant genome evolution and endosymbiotic gene transfer is discussed.

Global approaches for profiling transcription initiation

Transcription start site (TSS) selection influences transcript stability and translation as well as protein sequence. Alternative TSS usage is pervasive in organismal development, is a major contributor to transcript isoform diversity in humans, and is frequently observed in human diseases including cancer. In this review, we discuss the breadth of techniques that have been used to globally profile TSSs and the resulting insights into gene regulation, as well as future prospects in this area of inquiry.

Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters

Targeted engineering of plant gene expression holds great promise for ensuring food security and for producing biopharmaceuticals in plants. However, this engineering requires thorough knowledge of cis-regulatory elements to precisely control either endogenous or introduced genes. To generate this knowledge, we used a massively parallel reporter assay to measure the activity of nearly complete sets of promoters from Arabidopsis, maize and sorghum. We demonstrate that core promoter elements-notably the TATA box-as well as promoter GC content and promoter-proximal transcription factor binding sites influence promoter strength. By performing the experiments in two assay systems, leaves of the dicot tobacco and protoplasts of the monocot maize, we detect species-specific differences in the contributions of GC content and transcription factors to promoter strength. Using these observations, we built computational models to predict promoter strength in both assay systems, allowing us to design highly active promoters comparable in activity to the viral 35S minimal promoter. Our results establish a promising experimental approach to optimize native promoter elements and generate synthetic ones with desirable features.

Prediction of Rice Transcription Start Sites Using TransPrise: A Novel Machine Learning Approach

As the interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper, we present TransPrise-an efficient deep learning tool for predicting positions of eukaryotic transcription start sites. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise with the TSSPlant approach for well-annotated genome of Oryza sativa. Using a computer with a graphics processing unit, the run time of TransPrise is 250 min on a genome of 374 Mb long.We provide the full basis for the comparison and encourage users to freely access a set of our computational tools to facilitate and streamline their own analyses. The ready-to-use Docker image with all the necessary packages, models, and code as well as the source code of the TransPrise algorithm are available at http://compubioverne.group/ . The source code is ready to use and to be customized to predict TSS in any eukaryotic organism.

A Synthetic Strong and Constitutive Promoter Derived from the Stellaria media pro-SmAMP1 and pro-SmAMP2 Promoters for Effective Transgene Expression in Plants

Synthetic promoters are vital for genetic engineering-based strategies for crop improvement, but effective methodologies for their creation and systematic testing are lacking. We report here on the comparative analysis of the promoters pro-SmAMP1 and pro-SmAMP2 from Stellaria media ANTIMICROBIAL PEPTIDE1 (AMP1) and ANTIMICROBIAL PEPTIDE2 (AMP2). These promoters are more effective than the well-known Cauliflower mosaic virus 35S promoter. Although these promoters share about 94% identity, the pro-SmAMP1 promoter demonstrated stronger transient expression of a reporter gene in Agrobacterium infiltration of Nicotiana benthamiana leaves, while the pro-SmAMP2 promoter was more effective for the selection of transgenic tobacco (Nicotiana tabacum) cells when driving a selectable marker. Using the cap analysis of gene expression method, we detected no differences in the structure of the transcription start sites for either promoter in transgenic plants. For both promoters, we used fine-scale deletion analysis to identify 160 bp-long sequences that retain the unique properties of each promoter. With the use of chimeric promoters and directed mutagenesis, we demonstrated that the superiority of the pro-SmAMP1 promoter for Agrobacterium-mediated infiltration is caused by the proline-inducible ACTCAT cis-element strictly positioned relative to the TATA box in the core promoter. Surprisingly, the ACTCAT cis-element not only activated but also suppressed the efficiency of the pro-SmAMP1 promoter under proline stress. The absence of the ACTCAT cis-element and CAANNNNATC motif (negative regulator) in the pro-SmAMP2 promoter provided a more constitutive gene expression profile and better selection of transgenic cells on selective medium. We created a new synthetic promoter that enjoys high effectiveness both in transient expression and in selection of transgenic cells. Intact promoters with differing properties and high degrees of sequence identity may thus be used as a basis for the creation of new synthetic promoters for precise and coordinated gene expression.

Parallel global profiling of plant TOR dynamics reveals a conserved role for LARP1 in translation

Target of rapamycin (TOR) is a protein kinase that coordinates eukaryotic metabolism. In mammals, TOR specifically promotes translation of ribosomal protein (RP) mRNAs when amino acids are available to support protein synthesis. The mechanisms controlling translation downstream from TOR remain contested, however, and are largely unexplored in plants. To define these mechanisms in plants, we globally profiled the plant TOR-regulated transcriptome, translatome, proteome, and phosphoproteome. We found that TOR regulates ribosome biogenesis in plants at multiple levels, but through mechanisms that do not directly depend on 5' oligopyrimidine tract motifs (5'TOPs) found in mammalian RP mRNAs. We then show that the TOR-LARP1-5'TOP signaling axis is conserved in plants and regulates expression of a core set of eukaryotic 5'TOP mRNAs, as well as new, plant-specific 5'TOP mRNAs. Our study illuminates ancestral roles of the TOR-LARP1-5'TOP metabolic regulatory network and provides evolutionary context for ongoing debates about the molecular function of LARP1.

Epigenetic regulation of spurious transcription initiation in Arabidopsis

In plants, epigenetic regulation is critical for silencing transposons and maintaining proper gene expression. However, its impact on the genome-wide transcription initiation landscape remains elusive. By conducting a genome-wide analysis of transcription start sites (TSSs) using cap analysis of gene expression (CAGE) sequencing, we show that thousands of TSSs are exclusively activated in various epigenetic mutants of Arabidopsis thaliana and referred to as cryptic TSSs. Many have not been identified in previous studies, of which up to 65% are contributed by transposons. They possess similar genetic features to regular TSSs and their activation is strongly associated with the ectopic recruitment of RNAPII machinery. The activation of cryptic TSSs significantly alters transcription of nearby TSSs, including those of genes important for development and stress responses. Our study, therefore, sheds light on the role of epigenetic regulation in maintaining proper gene functions in plants by suppressing transcription from cryptic TSSs.

Epigenetic regulation can silence transposons and maintain gene expression. Here the authors survey Arabidopsis mutants defective in epigenetic regulation and show ectopic activation of thousands of cryptic TSSs and altered expression of nearby genes demonstrating the importance of suppressing spurious transcription.

Simple and efficient profiling of transcription initiation and transcript levels with STRIPE-seq

Accurate mapping of transcription start sites (TSSs) is key for understanding transcriptional regulation. However, current protocols for genome-wide TSS profiling are laborious and/or expensive. We present Survey of TRanscription Initiation at Promoter Elements with high-throughput sequencing (STRIPE-seq), a simple, rapid, and cost-effective protocol for sequencing capped RNA 5' ends from as little as 50 ng total RNA. Including depletion of uncapped RNA and reaction cleanups, a STRIPE-seq library can be constructed in about 5 h. We show application of STRIPE-seq to TSS profiling in yeast and human cells and show that it can also be effectively used for quantification of transcript levels and analysis of differential gene expression. In conjunction with our ready-to-use computational workflows, STRIPE-seq is a straightforward, efficient means by which to probe the landscape of transcriptional initiation.

Characterization of Arabidopsis thaliana Promoter Bidirectionality and Antisense RNAs by Inactivation of Nuclear RNA Decay Pathways

In animals, RNA polymerase II initiates transcription bidirectionally from gene promoters to produce pre-mRNAs on the forward strand and promoter upstream transcripts (PROMPTs) on the reverse strand. PROMPTs are degraded by the nuclear exosome. Previous studies based on nascent RNA approaches concluded that Arabidopsis (Arabidopsis thaliana) does not produce PROMPTs. Here, we used steady-state RNA sequencing in mutants defective in nuclear RNA decay including the exosome to reassess the existence of Arabidopsis PROMPTs. While they are rare, we identified ∼100 cases of exosome-sensitive PROMPTs in Arabidopsis. Such PROMPTs are sources of small interfering RNAs in exosome-deficient mutants, perhaps explaining why plants have evolved mechanisms to suppress PROMPTs. In addition, we found ∼200 long, unspliced and exosome-sensitive antisense RNAs that arise from transcription start sites within parts of the genome encoding 3'-untranslated regions on the sense strand. The previously characterized noncoding RNA that regulates expression of the key seed dormancy regulator, DELAY OF GERMINATION1, is a typical representative of this class of RNAs. Transcription factor genes are overrepresented among loci with exosome-sensitive antisense RNAs, suggesting a potential for widespread control of gene expression via this class of noncoding RNAs. Lastly, we assess the use of alternative promoters in Arabidopsis and compare the accuracy of existing TSS annotations.

Transcript isoform sequencing reveals widespread promoter-proximal transcriptional termination in Arabidopsis

RNA polymerase II (RNAPII) transcription converts the DNA sequence of a single gene into multiple transcript isoforms that may carry alternative functions. Gene isoforms result from variable transcription start sites (TSSs) at the beginning and polyadenylation sites (PASs) at the end of transcripts. How alternative TSSs relate to variable PASs is poorly understood. Here, we identify both ends of RNA molecules in Arabidopsis thaliana by transcription isoform sequencing (TIF-seq) and report four transcript isoforms per expressed gene. While intragenic initiation represents a large source of regulated isoform diversity, we observe that ~14% of expressed genes generate relatively unstable short promoter-proximal RNAs (sppRNAs) from nascent transcript cleavage and polyadenylation shortly after initiation. The location of sppRNAs correlates with the position of promoter-proximal RNAPII stalling, indicating that large pools of promoter-stalled RNAPII may engage in transcriptional termination. We propose that promoter-proximal RNAPII stalling-linked to premature transcriptional termination may represent a checkpoint that governs plant gene expression.

Metabolic Labeling of RNAs Uncovers Hidden Features and Dynamics of the Arabidopsis Transcriptome

Transcriptome analysis by RNA sequencing (RNA-seq) has become an indispensable research tool in modern plant biology. Virtually all RNA-seq studies provide a snapshot of the steady state transcriptome, which contains valuable information about RNA populations at a given time but lacks information about the dynamics of RNA synthesis and degradation. Only a few specialized sequencing techniques, such as global run-on sequencing, have been used to provide information about RNA synthesis rates in plants. Here, we demonstrate that RNA labeling with the modified, nontoxic uridine analog 5-ethynyl uridine (5-EU) in Arabidopsis (Arabidopsis thaliana) seedlings provides insight into plant transcriptome dynamics. Pulse labeling with 5-EU revealed nascent and unstable RNAs, RNA processing intermediates generated by splicing, and chloroplast RNAs. Pulse-chase experiments with 5-EU allowed us to determine RNA stabilities without the need for chemical transcription inhibitors such as actinomycin and cordycepin. Inhibitor-free, genome-wide analysis of polyadenylated RNA stability via 5-EU pulse-chase experiments revealed RNAs with shorter half-lives than those reported after chemical inhibition of transcription. In summary, our results indicate that the Arabidopsis nascent transcriptome contains unstable RNAs and RNA processing intermediates and suggest that polyadenylated RNAs have low stability in plants. Our technique lays the foundation for easy, affordable, nascent transcriptome analysis and inhibitor-free analysis of RNA stability in plants.

Analyses of microRNA166 gene structure, expression, and function during the early stage of somatic embryogenesis in Dimocarpus longan Lour

MicroRNA166 (miR166) contributes to post-transcriptional regulation by binding the mRNAs of HD-ZIP III genes, which affects plant growth and development. The structural characteristics, expression, and functions of miR166 genes during the early somatic embryogenesis stage in Dimocarpus longan remain unknown. We isolated the transcripts of pri-miR166 S78 with two transcription initiation sites (TSSs) and pri-miR166 S338 with one TSS. These sequences contain potential smORFs and encode different miRNA peptides (miPEPs). Additionally, their promoters contain cis-acting elements responsive to diverse stimuli. The pre-miR166 S78 and pre-miR166 S338 expression levels were up-regulated in response to 2,4-D, abscisic acid, and ethylene. Although the expression patterns induced by hormones were similar, there were differences in the extent of the response, with pre-miR166 S338 more responsive than pre-miR166 S78. Thus, miRNA transcription and maturation are not simply linearly correlated. Moreover, pre-miR166 S78 and pre-miR166 S338 expression levels were down-regulated, whereas ATHB15 (target gene) expression was up-regulated, from the longan embryonic callus to the globular embryo stages. These results are indicative of a negative regulatory relationship between miR166 and ATHB15 during the early somatic embryogenesis stage in longan. At the same stages, miR166a.2-agomir, miR166a.2-antagomir, and miPEP166 S338 increased or decreased the expression of miR166a.2 and ATHB15, but with no consistent patterns or linear synchronization, from which we've found some reasons for it.

Identification of transcription factors that bind to the 5'-UTR of the barley PHO2 gene

In barley and other higher plants, phosphate homeostasis is maintained by a regulatory network involving the PHO2 (PHOSPHATE2) encoding ubiquitin-conjugating (UBC) E2 enzyme, the PHR1 (PHOSPHATE STARVATION RESPONSE 1) transcription factor (TF), IPS1 (INDUCED BYPHOSPHATESTARVATION1) RNA, and miR399. During phosphate ion (Pi) deprivation, PHR1 positively regulates MIR399 expression, after transcription and processing mature miR399 guides the Ago protein to the 5'-UTR of PHO2 transcripts. Non-coding IPS1 RNA is highly expressed during Pi starvation, and the sequestration of miR399 molecules protects PHO2 mRNA from complete degradation. Here, we reveal new cis- and trans-regulatory elements that are crucial for efficient PHO2 gene expression in barley. We found that the 5'-UTR of PHO2 contains two PHR1 binding sites (P1BSs) and one Pi-responsive PHO element. Using a yeast one-hybrid (Y1H) assay, we identified two candidate proteins that might mediate this transcriptional regulation: a barley PHR1 ortholog and a TF containing an uncharacterized MYB domain. Additional results classified this new potential TF as belonging to the APL (ALTERED PHLOEM DEVELOPMENT) protein family, and we observed its nuclear localization in barley protoplasts. Pi starvation induced the accumulation of barley APL transcripts in both the shoots and roots. Interestingly, the deletion of the P1BS motif from the first intron of the barley 5'-UTR led to a significant increase in the transcription of a downstream β-glucuronidase (GUS) reporter gene in tobacco leaves. Our work extends the current knowledge about putative cis- and trans-regulatory elements that may affect the expression of the barley PHO2 gene.

TransPrise: a novel machine learning approach for eukaryotic promoter prediction

As interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper we present TransPrise-an efficient deep learning tool for prediction of positions of eukaryotic transcription start sites. Our pipeline consists of two parts: the binary classifier operates the first, and if a sequence is classified as TSS-containing the regression step follows, where the precise location of TSS is being identified. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise classification and regression models with the TSSPlant approach for the well annotated genome of Oryza sativa. Using a computer equipped with a graphics processing unit, the run time of TransPrise is 250 minutes on a genome of 374 Mb long. The Matthews correlation coefficient value for TransPrise is 0.79, more than two times larger than the 0.31 for TSSPlant classification models. This represents a high level of prediction accuracy. Additionally, the mean absolute error for the regression model is 29.19 nt, allowing for accurate prediction of TSS location. TransPrise was also tested in Homo sapiens, where mean absolute error of the regression model was 47.986 nt. We provide the full basis for the comparison and encourage users to freely access a set of our computational tools to facilitate and streamline their own analyses. The ready-to-use Docker image with all necessary packages, models, code as well as the source code of the TransPrise algorithm are available at (http://compubioverne.group/). The source code is ready to use and customizable to predict TSS in any eukaryotic organism.

Multi-strategic RNA-seq analysis reveals a high-resolution transcriptional landscape in cotton

Cotton is an important natural fiber crop, however, its comprehensive and high-resolution gene map is lacking. Here we integrate four complementary high-throughput techniques, including Pacbio long read Iso-seq, strand-specific RNA-seq, CAGE-seq, and PolyA-seq, to systematically explore the transcription landscape across 16 tissues or different organ types in Gossypium arboreum. We devise a computational pipeline, named IGIA, to reconstruct accurate gene structures from the integrated data. Our results reveal a dynamic and diverse transcriptional map in cotton: tissue-specific gene expression, alternative usage of TSSs and polyadenylation sites, hotspot of alternative splicing, and transcriptional read-through. These regulated events affect many genes in various aspects such as gain or loss of functional RNA motifs and protein domains, fine-tuning of DNA binding activity, and co-regulation for genes in the same complex or pathway. The methods and findings provide valuable resources for further functional genomic studies such as understanding natural SNP variations for plant community.

CAGEfightR: analysis of 5'-end data using R/Bioconductor

BACKGROUND

5'-end sequencing assays, and Cap Analysis of Gene Expression (CAGE) in particular, have been instrumental in studying transcriptional regulation. 5'-end methods provide genome-wide maps of transcription start sites (TSSs) with base pair resolution. Because active enhancers often feature bidirectional TSSs, such data can also be used to predict enhancer candidates. The current availability of mature and comprehensive computational tools for the analysis of 5'-end data is limited, preventing efficient analysis of new and existing 5'-end data.

RESULTS

We present CAGEfightR, a framework for analysis of CAGE and other 5'-end data implemented as an R/Bioconductor-package. CAGEfightR can import data from BigWig files and allows for fast and memory efficient prediction and analysis of TSSs and enhancers. Downstream analyses include quantification, normalization, annotation with transcript and gene models, TSS shape statistics, linking TSSs to enhancers via co-expression, identification of enhancer clusters, and genome-browser style visualization. While built to analyze CAGE data, we demonstrate the utility of CAGEfightR in analyzing nascent RNA 5'-data (PRO-Cap). CAGEfightR is implemented using standard Bioconductor classes, making it easy to learn, use and combine with other Bioconductor packages, for example popular differential expression tools such as limma, DESeq2 and edgeR.

CONCLUSIONS

CAGEfightR provides a single, scalable and easy-to-use framework for comprehensive downstream analysis of 5'-end data. CAGEfightR is designed to be interoperable with other Bioconductor packages, thereby unlocking hundreds of mature transcriptomic analysis tools for 5'-end data. CAGEfightR is freely available via Bioconductor: bioconductor.org/packages/CAGEfightR .

Using RAMPAGE to Identify and Annotate Promoters in Insect Genomes

Application of Transcription Start Site (TSS) profiling technologies, coupled with large-scale next-generation sequencing (NGS) has yielded valuable insights into the location, structure, and activity of promoters across diverse metazoan model systems. In insects, TSS profiling has been used to characterize the promoter architecture of Drosophila melanogaster (Hoskins et al., Genome Res 21(2):182-192, 2011) and subsequently was employed to reveal widespread transposon-driven alternative promoter usage in the fruit fly (Batut et al., Genome Res 23:169-180, 2012).In this chapter we discuss the computational analysis of the experimental data derived from one of TSS profiling methods, RAMPAGE (RNA Annotation and Mapping of Promoters for Analysis of Gene Expression) that can be used for the precise, quantitative identification of promoters in insect genomes. We demonstrate this using the software tools GoRAMPAGE (Brendel and Raborn, GoRAMPAGE-A workflow for promoter detection by 5'-read mapping. https://github.com/BrendelGroup/GoRAMPAGE , 2016) and TSRchitect (Raborn and Brendel, TSRchitect: promoter identification from large-scale TSS profiling data. R Bioconductor package version 1.8.0 [Online]. Available: http://bioconductor.org/packages/release/bioc/html/TSRchitect.html , 2017), providing detailed instructions with the aim of taking the user from raw reads to processed results.

Machine learning approaches and their current application in plant molecular biology: A systematic review

Machine learning (ML) is a field of artificial intelligence that has rapidly emerged in molecular biology, thus allowing the exploitation of Big Data concepts in plant genomics. In this context, the main challenges are given in terms of how to analyze massive datasets and extract new knowledge in all levels of cellular systems research. In summary, ML techniques allow complex interactions to be inferred in several biological systems. Despite its potential, ML has been underused due to complex computational algorithms and definition terms. Therefore, a systematic review to disentangle ML approaches is relevant for plant scientists and has been considered in this study. We presented the main steps for ML development (from data selection to evaluation of classification/prediction models) with a respective discussion approaching functional genomics mainly in terms of pathogen effector genes in plant immunity. Additionally, we also considered how to access public source databases under an ML framework towards advancing plant molecular biology and introduced novel powerful tools, such as deep learning.

Analysis of Transcriptome and Epitranscriptome in Plants Using PacBio Iso-Seq and Nanopore-Based Direct RNA Sequencing

Nanopore sequencing from Oxford Nanopore Technologies (ONT) and Pacific BioSciences (PacBio) single-molecule real-time (SMRT) long-read isoform sequencing (Iso-Seq) are revolutionizing the way transcriptomes are analyzed. These methods offer many advantages over most widely used high-throughput short-read RNA sequencing (RNA-Seq) approaches and allow a comprehensive analysis of transcriptomes in identifying full-length splice isoforms and several other post-transcriptional events. In addition, direct RNA-Seq provides valuable information about RNA modifications, which are lost during the PCR amplification step in other methods. Here, we present a comprehensive summary of important applications of these technologies in plants, including identification of complex alternative splicing (AS), full-length splice variants, fusion transcripts, and alternative polyadenylation (APA) events. Furthermore, we discuss the impact of the newly developed nanopore direct RNA-Seq in advancing epitranscriptome research in plants. Additionally, we summarize computational tools for identifying and quantifying full-length isoforms and other co/post-transcriptional events and discussed some of the limitations with these methods. Sequencing of transcriptomes using these new single-molecule long-read methods will unravel many aspects of transcriptome complexity in unprecedented ways as compared to previous short-read sequencing approaches. Analysis of plant transcriptomes with these new powerful methods that require minimum sample processing is likely to become the norm and is expected to uncover novel co/post-transcriptional gene regulatory mechanisms that control biological outcomes during plant development and in response to various stresses.

Comprehensive profiling of the fission yeast transcription start site activity during stress and media response

Fission yeast, Schizosaccharomyces pombe, is an attractive model organism for transcriptional and chromatin biology research. Such research is contingent on accurate annotation of transcription start sites (TSSs). However, comprehensive genome-wide maps of TSSs and their usage across commonly applied laboratory conditions and treatments for S. pombe are lacking. To this end, we profiled TSS activity genome-wide in S. pombe cultures exposed to heat shock, nitrogen starvation, hydrogen peroxide and two commonly applied media, YES and EMM2, using Cap Analysis of Gene Expression (CAGE). CAGE-based annotation of TSSs is substantially more accurate than existing PomBase annotation; on average, CAGE TSSs fall 50-75 bp downstream of PomBase TSSs and co-localize with nucleosome boundaries. In contrast to higher eukaryotes, dispersed TSS distributions are not common in S. pombe. Our data recapitulate known S. pombe stress expression response patterns and identify stress- and media-responsive alternative TSSs. Notably, alteration of growth medium induces changes of similar magnitude as some stressors. We show a link between nucleosome occupancy and genetic variation, and that the proximal promoter region is genetically diverse between S. pombe strains. Our detailed TSS map constitutes a central resource for S. pombe gene regulation research.

Computational analysis of alternative splicing in plant genomes

Computational analyses play crucial roles in characterizing splicing isoforms in plant genomes. In this review, we provide a survey of computational tools used in recently published, genome-scale splicing analyses in plants. We summarize the commonly used software and pipelines for read mapping, isoform reconstruction, isoform quantification, and differential expression analysis. We also discuss methods for analyzing long reads and the strategies to combine long and short reads in identifying splicing isoforms. We review several tools for characterizing local splicing events, splicing graphs, coding potential, and visualizing splicing isoforms. We further discuss the procedures for identifying conserved splicing isoforms across plant species. Finally, we discuss the outlook of integrating other genomic data with splicing analyses to identify regulatory mechanisms of AS on genome-wide scale.

An Overview of Methodologies in Studying lncRNAs in the High-Throughput Era: When Acronyms ATTACK!

The discovery of pervasive transcription in eukaryotic genomes provided one of many surprising (and perhaps most surprising) findings of the genomic era and led to the uncovering of a large number of previously unstudied transcriptional events. This pervasive transcription leads to the production of large numbers of noncoding RNAs (ncRNAs) and thus opened the window to study these diverse, abundant transcripts of unclear relevance and unknown function. Since that discovery, recent advances in high-throughput sequencing technologies have identified a large collection of ncRNAs, from microRNAs to long noncoding RNAs (lncRNAs). Subsequent discoveries have shown that many lncRNAs play important roles in various eukaryotic processes; these discoveries have profoundly altered our understanding of the regulation of eukaryotic gene expression. Although the identification of ncRNAs has become a standard experimental approach, the functional characterization of these diverse ncRNAs remains a major challenge. In this chapter, we highlight recent progress in the methods to identify lncRNAs and the techniques to study the molecular function of these lncRNAs and the application of these techniques to the study of plant lncRNAs.

Natural variation in acyl editing is a determinant of seed storage oil composition

Seeds exhibit wide variation in the fatty acid composition of their storage oil. However, the genetic basis of this variation is only partially understood. Here we have used a multi-parent advanced generation inter-cross (MAGIC) population to study the genetic control of fatty acid chain length in Arabidopsis thaliana seed oil. We mapped four quantitative trait loci (QTL) for the quantity of the major very long chain fatty acid species 11-eicosenoic acid (20:1), using multiple QTL modelling. Surprisingly, the main-effect QTL does not coincide with FATTY ACID ELONGASE 1 and a parallel genome wide association study suggested that LYSOPHOSPHATIDYLCHOLINE ACYLTRANSFERASE 2 (LPCAT2) is a candidate for this QTL. Regression analysis also suggested that LPCAT2 expression and 20:1 content in seeds of the 19 MAGIC founder accessions are related. LPCAT is a key component of the Lands cycle; an acyl editing pathway that enables acyl-exchange between the acyl-Coenzyme A and phosphatidylcholine precursor pools used for microsomal fatty acid elongation and desaturation, respectively. We Mendelianised the main-effect QTL using biparental chromosome segment substitution lines and carried out complementation tests to show that a single cis-acting polymorphism in the LPCAT2 promoter causes the variation in seed 20:1 content, by altering the LPCAT2 expression level and total LPCAT activity in developing siliques. Our work establishes that oilseed species exhibit natural variation in the enzymic capacity for acyl editing and this contributes to the genetic control of storage oil composition.

NanoPARE: parallel analysis of RNA 5' ends from low-input RNA

Diverse RNA 5′ ends are generated through both transcriptional and post-transcriptional processes. These important modes of gene regulation often vary across cell types and can contribute to the diversification of transcriptomes and thus cellular differentiation. Therefore, the identification of primary and processed 5′ ends of RNAs is important for their functional characterization. Methods have been developed to profile either RNA 5′ ends from primary transcripts or the products of RNA degradation genome-wide. However, these approaches either require high amounts of starting RNA or are performed in the absence of paired gene-body mRNA-seq data. This limits current efforts in RNA 5′ end annotation to whole tissues and can prevent accurate RNA 5′ end classification due to biases in the data sets. To enable the accurate identification and precise classification of RNA 5′ ends from standard and low-input RNA, we developed a next-generation sequencing-based method called nanoPARE and associated software. By integrating RNA 5′ end information from nanoPARE with gene-body mRNA-seq data from the same RNA sample, our method enables the identification of transcription start sites at single-nucleotide resolution from single-cell levels of total RNA, as well as small RNA-mediated cleavage events from at least 10,000-fold less total RNA compared to conventional approaches. NanoPARE can therefore be used to accurately profile transcription start sites, noncapped RNA 5′ ends, and small RNA targeting events from individual tissue types. As a proof-of-principle, we utilized nanoPARE to improve Arabidopsis thaliana RNA 5′ end annotations and quantify microRNA-mediated cleavage events across five different flower tissues.