Advances in understanding obligate biotrophy in rust fungi.
Cécile LorrainKaren Cristine Gonçalves Dos SantosHugo GermainArnaud HeckerSebastien DuplessisPublished in: The New phytologist (2019)
Contents Summary 1190 I. Introduction 1190 II. Rust fungi: a diverse and serious threat to agriculture 1191 III. The different facets of rust life cycles and unresolved questions about their evolution 1191 IV. The biology of rust infection 1192 V. Rusts in the genomics era: the ever-expanding list of candidate effector genes 1195 VI. Functional characterization of rust effectors 1197 VII. Putting rusts to sleep: Pucciniales research outlooks 1201 Acknowledgements 1202 References 1202 SUMMARY: Rust fungi (Pucciniales) are the largest group of plant pathogens and represent one of the most devastating threats to agricultural crops worldwide. Despite the economic importance of these highly specialized pathogens, many aspects of their biology remain obscure, largely because rust fungi are obligate biotrophs. The rise of genomics and advances in high-throughput sequencing technology have presented new options for identifying candidate effector genes involved in pathogenicity mechanisms of rust fungi. Transcriptome analysis and integrated bioinformatics tools have led to the identification of key genetic determinants of host susceptibility to infection by rusts. Thousands of genes encoding secreted proteins highly expressed during host infection have been reported for different rust species, which represents significant potential towards understanding rust effector function. Recent high-throughput in planta expression screen approaches (effectoromics) have pushed the field ahead even further towards predicting high-priority effectors and identifying avirulence genes. These new insights into rust effector biology promise to inform future research and spur the development of effective and sustainable strategies for managing rust diseases.
Keyphrases
- high throughput
- genome wide
- dendritic cells
- regulatory t cells
- poor prognosis
- single cell
- physical activity
- bioinformatics analysis
- risk assessment
- gene expression
- cystic fibrosis
- gram negative
- long non coding rna
- transcription factor
- dna methylation
- staphylococcus aureus
- artificial intelligence
- big data
- deep learning
- biofilm formation
- pseudomonas aeruginosa
- genome wide identification