Wenbin Guo

Mr. Wenbin Guo

orcid

Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 May 2018 in The Plant Cell
Verified
Journal Article
Open access
PDF file
CCCristiane P.G. Calixto
Cristiane P.G. Calixto
WGWenbin Guo
Wenbin Guo
AJAllan B. James
Allan B. James
NTNikoleta A. Tzioutziou
Nikoleta A. Tzioutziou
JEJuan Carlos Entizne
Juan Carlos Entizne
JBJohn Brown
John Brown
Plants have adapted to tolerate and survive constantly changing environmental conditions by re-programming 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 which alongside transcription modulate transcriptome reprogramming that together govern the physiological and survival responses of plants to low temperature.

13 November 2024 in Nature
Verified
Journal Article
Open access
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MJMurukarthick Jayakodi
Murukarthick Jayakodi
QLQiongxian Lu
Qiongxian Lu
HPHélène Pidon
Hélène Pidon
MRM. Timothy Rabanus-Wallace
M. Timothy Rabanus-Wallace
MBMicha Bayer
Micha Bayer
NSNils Stein
Nils Stein
Pangenomes are collections of annotated genome sequences of multiple individuals of a species1. The structural variants uncovered by these datasets are a major asset to genetic analysis in crop plants2. Here we report a pangenome of barley comprising long-read sequence assemblies of 76 wild and domesticated genomes and short-read sequence data of 1,315 genotypes. An expanded catalogue of sequence variation in the crop includes structurally complex loci that are rich in gene copy number variation. To demonstrate the utility of the pangenome, we focus on four loci involved in disease resistance, plant architecture, nutrient release and trichome development. Novel allelic variation at a powdery mildew resistance locus and population-specific copy number gains in a regulator of vegetative branching were found. Expansion of a family of starch-cleaving enzymes in elite malting barleys was linked to shifts in enzymatic activity in micro-malting trials. Deletion of an enhancer motif is likely to change the developmental trajectory of the hairy appendages on barley grains. Our findings indicate that allelic diversity at structurally complex loci may have helped crop plants to adapt to new selective regimes in agricultural ecosystems.

19 December 2020 in RNA Biology
Verified
Journal Article
Open access
WGWenbin Guo
Wenbin Guo
NTNikoleta A Tzioutziou
Nikoleta A Tzioutziou
GSGordon Stephen
Gordon Stephen
IMIain Milne
Iain Milne
CCCristiane Pg Calixto
Cristiane Pg Calixto
RZRunxuan Zhang
Runxuan Zhang
RNA-sequencing (RNA-seq) analysis of gene expression and alternative splicing should be routine and robust but is often a bottleneck for biologists because of different and complex analysis programs and reliance on specialized bioinformatics skills. We have developed the ‘3D RNA-seq’ App, an R shiny App and web-based pipeline for the comprehensive analysis of RNA-seq data from any organism. It represents an easy-to-use, flexible and powerful tool for analysis of both gene and transcript-level gene expression to identify differential gene/transcript expression, differential alternative splicing and differential transcript usage (3D) as well as isoform switching from RNA-seq data. 3D RNA-seq integrates state-of-the-art differential expression analysis tools and adopts best practice for RNA-seq analysis. The program is designed to be run by biologists with minimal bioinformatics experience (or by bioinformaticians) allowing lab scientists to analyse their RNA-seq data. It achieves this by operating through a user-friendly graphical interface which automates the data flow through the programs in the pipeline. The comprehensive analysis performed by 3D RNA-seq is extremely rapid and accurate, can handle complex experimental designs, allows user setting of statistical parameters, visualizes the results through graphics and tables, and generates publication quality figures such as heat-maps, expression profiles and GO enrichment plots. The utility of 3D RNA-seq is illustrated by analysis of data from a time-series of cold-treated Arabidopsis plants and from dexamethasone-treated male and female mouse cortex and hypothalamus data identifying dexamethasone-induced sex- and brain region-specific differential gene expression and alternative splicing.

3 February 2025 in Nature Genetics
Verified
Journal Article
Open access
PDF file
WGWenbin Guo
Wenbin Guo
MSMiriam Schreiber
Miriam Schreiber
VMVanda B. Marosi
Vanda B. Marosi
PBpaolo bagnaresi
paolo bagnaresi
MJMorten Egevang Jørgensen
Morten Egevang Jørgensen
RWRobbie Waugh
Robbie Waugh
A pan-transcriptome describes the transcriptional and post-transcriptional consequences of genome diversity from multiple individuals within a species. We developed a barley pan-transcriptome using 20 inbred genotypes representing domesticated barley diversity by generating and analyzing short- and long-read RNA-sequencing datasets from multiple tissues. To overcome single reference bias in transcript quantification, we constructed genotype-specific reference transcript datasets (RTDs) and integrated these into a linear pan-genome framework to create a pan-RTD, allowing transcript categorization as core, shell or cloud. Focusing on the core (expressed in all genotypes), we observed significant transcript abundance variation among tissues and between genotypes driven partly by RNA processing, gene copy number, structural rearrangements and conservation of promotor motifs. Network analyses revealed conserved co-expression module::tissue correlations and frequent functional diversification. To complement the pan-transcriptome, we constructed a comprehensive cultivar (cv.) Morex gene-expression atlas and illustrate how these combined datasets can be used to guide biological inquiry.

1 November 2020 in New Phytologist
Verified
Journal Article
Closed access
PDF file
IJIbtissam Jabre
Ibtissam Jabre
Saurabh ChaudharySaurabh Chaudhary
Saurabh Chaudhary
WGWenbin Guo
Wenbin Guo
MKMaria Kalyna
Maria Kalyna
A.S.N.. ReddyA.S.N.. Reddy
A.S.N.. Reddy
NSNaeem Syed
Naeem Syed

14 July 2020 in The Plant Cell
Verified
Journal Article
Open access
PDF file
VRVivek K. Raxwal
Vivek K. Raxwal
CSCraig Simpson
Craig Simpson
JGJiradet Gloggnitzer
Jiradet Gloggnitzer
JEJuan Carlos Entinze
Juan Carlos Entinze
WGWenbin Guo
Wenbin Guo
KRKarel Riha
Karel Riha
The RNA helicase and ATPase UPF1 affects transcriptome homeostasis and gene expression through nonsense-mediated RNA decay, splicing, and translation in Arabido