Cristiane P. G. Calixto

Exome sequencing of geographically diverse barley landraces and wild relatives gives insights into environmental adaptation

After domestication, during a process of widespread range extension, barley adapted to a broad spectrum of agricultural environments. To explore how the barley genome responded to the environmental challenges it encountered, we sequenced the exomes of a collection of 267 georeferenced landraces and wild accessions. A combination of genome-wide analyses showed that patterns of variation have been strongly shaped by geography and that variant-by-environment associations for individual genes are prominent in our data set. We observed significant correlations of days to heading (flowering) and height with seasonal temperature and dryness variables in common garden experiments, suggesting that these traits were major drivers of environmental adaptation in the sampled germplasm. A detailed analysis of known flowering-associated genes showed that many contain extensive sequence variation and that patterns of single- and multiple-gene haplotypes exhibit strong geographical structuring. This variation appears to have substantially contributed to range-wide ecogeographical adaptation, but many factors key to regional success remain unidentified.

A high quality Arabidopsis transcriptome for accurate transcript-level analysis of alternative splicing

Abstract Alternative splicing generates multiple transcript and protein isoforms from the same gene and thus is important in gene expression regulation. To date, RNA-sequencing (RNA-seq) is the standard method for quantifying changes in alternative splicing on a genome-wide scale. Understanding the current limitations of RNA-seq is crucial for reliable analysis and the lack of high quality, comprehensive transcriptomes for most species, including model organisms such as Arabidopsis, is a major constraint in accurate quantification of transcript isoforms. To address this, we designed a novel pipeline with stringent filters and assembled a comprehensive Reference Transcript Dataset for Arabidopsis (AtRTD2) containing 82,190 non-redundant transcripts from 34 212 genes. Extensive experimental validation showed that AtRTD2 and its modified version, AtRTD2-QUASI, for use in Quantification of Alternatively Spliced Isoforms, outperform other available transcriptomes in RNA-seq analysis. This strategy can be implemented in other species to build a pipeline for transcript-level expression and alternative splicing analyses.

Evolutionary Relationships Among Barley and Arabidopsis Core Circadian Clock and Clock-Associated Genes

The circadian clock regulates a multitude of plant developmental and metabolic processes. In crop species, it contributes significantly to plant performance and productivity and to the adaptation and geographical range over which crops can be grown. To understand the clock in barley and how it relates to the components in the Arabidopsis thaliana clock, we have performed a systematic analysis of core circadian clock and clock-associated genes in barley, Arabidopsis and another eight species including tomato, potato, a range of monocotyledonous species and the moss, Physcomitrella patens. We have identified orthologues and paralogues of Arabidopsis genes which are conserved in all species, monocot/dicot differences, species-specific differences and variation in gene copy number (e.g. gene duplications among the various species). We propose that the common ancestor of barley and Arabidopsis had two-thirds of the key clock components identified in Arabidopsis prior to the separation of the monocot/dicot groups. After this separation, multiple independent gene duplication events took place in both monocot and dicot ancestors.

AtRTD – a comprehensive reference transcript dataset resource for accurate quantification of transcript‐specific expression in Arabidopsis thaliana

Summary RNA‐sequencing (RNA‐seq) allows global gene expression analysis at the individual transcript level. Accurate quantification of transcript variants generated by alternative splicing (AS) remains a challenge. We have developed a comprehensive, nonredundant Arabidopsis reference transcript dataset (AtRTD) containing over 74 000 transcripts for use with algorithms to quantify AS transcript isoforms in RNA‐seq. The AtRTD was formed by merging transcripts from TAIR10 and novel transcripts identified in an AS discovery project. We have estimated transcript abundance in RNA‐seq data using the transcriptome‐based alignment‐free programmes Sailfish and Salmon and have validated quantification of splicing ratios from RNA‐seq by high resolution reverse transcription polymerase chain reaction (HR RT‐PCR). Good correlations between splicing ratios from RNA‐seq and HR RT‐PCR were obtained demonstrating the accuracy of abundances calculated for individual transcripts in RNA‐seq. The AtRTD is a resource that will have immediate utility in analysing Arabidopsis RNA‐seq data to quantify differential transcript abundance and expression.

High-quality reference transcript datasets hold the key to transcript-specific RNA-sequencing analysis in plants.

525 I. 525 II. 526 III. 527 IV. 527 V. 529 VI. 529 529 References 529 SUMMARY: Re-programming of the transcriptome involves both transcription and alternative splicing (AS). Some genes are regulated only at the AS level with no change in expression at the gene level. AS data must be incorporated as an essential aspect of the regulation of gene expression. RNA-sequencing (RNA-seq) can deliver both transcriptional and AS information, but accurate methods to analyse the added complexity in RNA-seq data are needed. The construction of a comprehensive reference transcript dataset (RTD) for a specific plant species, variety or accession, from all available sequence data, will immediately allow more robust analysis of RNA-seq data. RTDs will continually evolve and improve, a process that will be more efficient if resources across a community are shared and pooled.

Alternative Splicing of Barley Clock Genes in Response to Low Temperature

Alternative splicing (AS) is a regulated mechanism that generates multiple transcripts from individual genes. It is widespread in eukaryotic genomes and provides an effective way to control gene expression. At low temperatures, AS regulates Arabidopsis clock genes through dynamic changes in the levels of productive mRNAs. We examined AS in barley clock genes to assess whether temperature-dependent AS responses also occur in a monocotyledonous crop species. We identify changes in AS of various barley core clock genes including the barley orthologues of Arabidopsis AtLHY and AtPRR7 which showed the most pronounced AS changes in response to low temperature. The AS events modulate the levels of functional and translatable mRNAs, and potentially protein levels, upon transition to cold. There is some conservation of AS events and/or splicing behaviour of clock genes between Arabidopsis and barley. In addition, novel temperature-dependent AS of the core clock gene HvPPD-H1 (a major determinant of photoperiod response and AtPRR7 orthologue) is conserved in monocots. HvPPD-H1 showed a rapid, temperature-sensitive isoform switch which resulted in changes in abundance of AS variants encoding different protein isoforms. This novel layer of low temperature control of clock gene expression, observed in two very different species, will help our understanding of plant adaptation to different environments and ultimately offer a new range of targets for plant improvement.

Rapid and dynamic alternative splicing impacts the Arabidopsis cold response transcriptome

Alternative splicing is a major contributor to reprogramming of the transcriptome in response to cold. Plants have adapted to tolerate and survive constantly changing environmental conditions by reprogramming gene expression The dynamics of the contribution of alternative splicing (AS) to stress responses are unknown. RNA-sequencing of a time-series of Arabidopsis thaliana plants exposed to cold determines the timing of significant AS changes. This shows a massive and rapid AS response with coincident waves of transcriptional and AS activity occurring in the first few hours of temperature reduction and further AS throughout the cold. In particular, hundreds of genes showed changes in expression due to rapidly occurring AS in response to cold (“early AS” genes); these included numerous novel cold-responsive transcription factors and splicing factors/RNA binding proteins regulated only by AS. The speed and sensitivity to small temperature changes of AS of some of these genes suggest that fine-tuning expression via AS pathways contributes to the thermo-plasticity of expression. Four early AS splicing regulatory genes have been shown previously to be required for freezing tolerance and acclimation; we provide evidence of a fifth gene, U2B”-LIKE. Such factors likely drive cascades of AS of downstream genes that, alongside transcription, modulate transcriptome reprogramming that together govern the physiological and survival responses of plants to low temperature.

TSIS: an R package to infer alternative splicing isoform switches for time-series data

Summary An alternative splicing isoform switch is where a pair of transcript isoforms reverse their relative expression abundances in response to external or internal stimuli. Although computational methods are available to study differential alternative splicing, few tools for detection of isoform switches exist and these are based on pairwise comparisons. Here, we provide the TSIS R package, which is the first tool for detecting significant transcript isoform switches in time‐series data. The main steps of TSIS are to search for the isoform switch points in the time‐series, characterize the switches and filter the results with user input parameters. All the functions are integrated into a Shiny App for ease of implementation of the analysis. Availability and implementation The TSIS package is available on GitHub: https://github.com/wyguo/TSIS. Contact runxuan.zhang@hutton.ac.uk

Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size

BackgroundCo-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required. Thus, the performances of co-expression network analysis methods on large co-expression networks consisting of a few thousand nodes, with only a small number of profiles with a single perturbation, which more accurately reflect normal experimental conditions, are generally uncharacterized and unknown.MethodsWe proposed a novel network inference methods based on Relevance Low order Partial Correlation (RLowPC). RLowPC method uses a two-step approach to select on the high-confidence edges first by reducing the search space by only picking the top ranked genes from an intial partial correlation analysis and, then computes the partial correlations in the confined search space by only removing the linear dependencies from the shared neighbours, largely ignoring the genes showing lower association.ResultsWe selected six co-expression-based methods with good performance in evaluation studies from the literature: Partial correlation, PCIT, ARACNE, MRNET, MRNETB and CLR. The evaluation of these methods was carried out on simulated time-series data with various network sizes ranging from 100 to 3000 nodes. Simulation results show low precision and recall for all of the above methods for large networks with a small number of expression profiles. We improved the inference significantly by refinement of the top weighted edges in the pre-inferred partial correlation networks using RLowPC. We found improved performance by partitioning large networks into smaller co-expressed modules when assessing the method performance within these modules.ConclusionsThe evaluation results show that current methods suffer from low precision and recall for large co-expression networks where only a small number of profiles are available. The proposed RLowPC method effectively reduces the indirect edges predicted as regulatory relationships and increases the precision of top ranked predictions. Partitioning large networks into smaller highly co-expressed modules also helps to improve the performance of network inference methods.The RLowPC R package for network construction, refinement and evaluation is available at GitHub: https://github.com/wyguo/RLowPC.

AtRTD2: A Reference Transcript Dataset for accurate quantification of alternative splicing and expression changes in Arabidopsis thaliana RNA-seq data

Background Alternative splicing is the major post-transcriptional mechanism by which gene expression is regulated and affects a wide range of processes and responses in most eukaryotic organisms. RNA-sequencing (RNA-seq) can generate genome-wide quantification of individual transcript isoforms to identify changes in expression and alternative splicing. RNA-seq is an essential modern tool but its ability to accurately quantify transcript isoforms depends on the diversity, completeness and quality of the transcript information. Results We have developed a new Reference Transcript Dataset for Arabidopsis (AtRTD2) for RNA-seq analysis containing over 82k non-redundant transcripts, whereby 74,194 transcripts originate from 27,667 protein-coding genes. A total of 13,524 protein-coding genes have at least one alternatively spliced transcript in AtRTD2 such that about 60% of the 22,453 protein-coding, intron-containing genes in Arabidopsis undergo alternative splicing. More than 600 putative U12 introns were identified in more than 2,000 transcripts. AtRTD2 was generated from transcript assemblies of ca. 8.5 billion pairs of reads from 285 RNA-seq data sets obtained from 129 RNA-seq libraries and merged along with the previous version, AtRTD, and Araport11 transcript assemblies. AtRTD2 increases the diversity of transcripts and through application of stringent filters represents the most extensive and accurate transcript collection for Arabidopsis to date. We have demonstrated a generally good correlation of alternative splicing ratios from RNA-seq data analysed by Salmon and experimental data from high resolution RT-PCR. However, we have observed inaccurate quantification of transcript isoforms for genes with multiple transcripts which have variation in the lengths of their UTRs. This variation is not effectively corrected in RNA-seq analysis programmes and will therefore impact RNA-seq analyses generally. To address this, we have tested different genome-wide modifications of AtRTD2 to improve transcript quantification and alternative splicing analysis. As a result, we release AtRTD2-QUASI specifically for use in Quantification of Alternatively Spliced Isoforms and demonstrate that it out-performs other available transcriptomes for RNA-seq analysis. Conclusions We have generated a new transcriptome resource for RNA-seq analyses in Arabidopsis (AtRTD2) designed to address quantification of different isoforms and alternative splicing in gene expression studies. Experimental validation of alternative splicing changes identified inaccuracies in transcript quantification due to UTR length variation. To solve this problem, we also release a modified reference transcriptome, AtRTD2-QUASI for quantification of transcript isoforms, which shows high correlation with experimental data.