Plant Genomes in a Changing Environment 2019 (II)

Exploring and utilization of rice resources with broad-spectrum resistance against blast disease (Xuwei Chen, Sichuan University, China)

He speaks about their sampling effort to identify alleles in rice germplasm that confer resistance to blast disease. A survey of 3K sequenced rice genomes discovered through GWAS an allele in cultivar Digu with MAF=0.10.  It is a SNP in the promoter of a Zinc Finger TF. The results are published in https://www.ncbi.nlm.nih.gov/pubmed/28666113 . He then moves on to their work on transcription factor IPA1 (Ideal Plant Architecture) , that represses improductive tillers and enhances immune responses. Again, the selected allele (ipa1-1D) carries a mutation that breaks a miRNA site. The results can be found in https://www.ncbi.nlm.nih.gov/pubmed/30190406

Do environmental changes induce retrotransposon expression in plants? (Flavia Mascagni, University of Pisa, Italy)

She is conducting work to determine to what extent retrotransposons (RTs) are expressed in response to environmental changes in sunflower. Upon treatment with hormones and chemicals, they observe higher expression in the leaf than in the root, with some genotypes more prone than others. Overall they found 134 differentially expressed RTs. Then they used a similar approach in poplar, again using public cDNA libraries. Some genotypes are more prone than others to show RT expression in response to treatment. In both species, of the few differentially expressed RTs, most belong to the Copia superfamily.

Functional genomics of European hazel (Corylus avellana L.) to address an emerging, destructive powdery mildew pathogen (Stuart Lucas, Sabanci University, Turkey)

For their search of alleles conferring resistance they have completed a genome assembly yielding 11 scaffolds (370Mb) for a predicted size of 380Mb. They are now annotating MLO and NLR genes. As for MLO genes, 5 clustered copies are good candidates for disease resistance. For NLR they are using long-reads to sequence end-to-end copies, on a pool of 363 genes with little overlap across populations.

Natural genetic variation in the response of Arabidopsis to Plasmodiophora brassicae infection (William Truman, IPG PAS Poznan, Poland)

He describes this obligate pathogen protest (clubroot) that affects a wide range of Brassica crops. Some of their previous results are at http://www.plantcell.org/content/30/12/3058 . They are testing candidate resistant alleles in Arabidopsis thaliana ecotypes. Some are being further studied in Y2H assays.

Daniele Filiault, Gregor Mendel Institute, Austria

She describes her Arabidopsis thaliana experiments in a latitudinal gradient from Germany to Sweden. The measure survival and slug susceptibility and observe local adaptation, with germplasms from S latitudes doing badly when planted up North. They then do GWAS and separate intra-specific and Genus-specific variants.

Pathogen-informed strategies for sustainable broad-spectrum resistance in crops (Bart Thomma, University of Wageningen, The Netherlands)

He talks about how we can learn from pathogen molecules to obtain resistant crops. He shows this video of tomato fungal pathogen Verticillium dahlie: https://vimeo.com/222178738 . He then shows haplotypes of different isolates of the pan-genome and refers to https://genome.cshlp.org/content/early/2016/07/12/gr.204974.116 and https://onlinelibrary.wiley.com/doi/full/10.1111/mec.15168 . Each isolate has 10% lineage-specific non-core genes and they are apparently more conserved across species of the Gens than core genes. This could be due to horizontal transfer (unlikely), selection (unlikely) or reduced error replicons (Hi-C experiments suggest co-localized in nucleous, unmethylated, enriched in TE, etc). Their most recent manuscript is https://www.biorxiv.org/content/10.1101/528729v1 . The find that a single effector gene in the fungus is responsible for pathogenicity, and when removed infection does not occur/progress. Conversely, when transformed into non-pathogenic species of the genus they now cause a disease in tomato.

He ends with another story, where they have seen that the fungus produces an antimicrobial protein (VdAve1, is that an antibiotic?) that alters the plant root microbiome and ultimately facilitate infection.

Beyond single genes: receptor networks underpin plant immunity (Sophien Kamoun, The Sainsbury Laboratory, UK)

Most plants are resistant to most pathogens, they have a very efficient immune system with Pattern recognition receptors (PRR) and NLR receptors. Pathogens secrete effectors to modulate plant defenses (https://www.ncbi.nlm.nih.gov/pubmed/23223409). Together, plant and pathogens coevolve and drive diversification. The NLR diversification is much larger in plants than in mammals (human vs muse, tomato vs coffee, 100Myr). In fact, ultimately, pathogens alter plant genomes (gene-for-gene model). He proposes to move from the single gene paradigm to the immune network, incorporating redundancy, evolvability, robustness and epistasis (https://www.ncbi.nlm.nih.gov/pubmed/29930125).  

Plant NLRs are typically made of three domains: [CC|CCR|TIR]NB-ARC-LRR. They form resistosome complexes that integrate in the membrane (https://www.ncbi.nlm.nih.gov/pubmed/30948527). These genes cluster in the genome (https://www.pnas.org/content/114/30/8113). This would be the most ancestral network, found in chr5 of sugar beet and conserved in other plants (such as tomato?):

A fifth of monocot/dicot NLR N-termina share a conserved MADA motif (MADAxVSFxVxKLxxLLxxEx, https://www.biorxiv.org/content/10.1101/693291v1). The CC domain diversified and became non-functional in many cases.

Using data science to understand plant gene regulation (Daphne Ezer, University of York, UK)

She starts by asking how do we know that our experiments are relevant in the real world? We need to correct for confounding variables and always put the data in its context, right? For instance, for bulk RNA-seq you must sync plants/treatments to make sure you are comparing tissues of the same age, same circadian point and tissue ratio. She has developed tools for these tasks, such as https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2717-5

Structure, stability and phenotypic relevance of DNA methylation in Thlaspi arvense natural populations (Dario Galanti, University of Tubingen, Germany)

He talks about his PhD project, which is concerned about heritable methylation as a function of location of origin, and how that affects phenotypes. He is working with pennycress (Thlaspi arvense). Populations sampled across Europe. I'll see if we can load that genome in Ensembl.

Nanopore Direct RNA Sequencing Maps the Arabidopsis m6A Epitranscriptome (Matthew Parker, University of Dundee, UK)

He starts by enumerating the theoretical advantages of sequencing native RNA directly, instead of sequencing cDNA. They are using it in several projects. The error rate is 5-8% which is not a problem for polyAs, but it is for short exon annotation and intron boundaries. In those cases they still use Illumina to correct the long reads.

Improving gene regulatory network inference from ATAC-Seq data using an ensemble motif mapping approach (Marc Jones, VIB / Ghent University, Belgium)

This talk complements yesterday’s talk by focusing on ATAC-Seq. They use ATAC read depth to restrict genome regions where known motifs can be scanned to discover relvant cis regulation.

No genome required: Finding genetic variants associated with plant phenotypes without complete genome information (Yoav Voichek, Max Plank Institute for Developmental Biology, Germany)

This talk complements yesterday’s but with a focus on the biology and the comparison between GWAS based on SNPs and kmers. He shows a Venn plot to show that noth approaches miss have a large intersection. However, there are some SNPs associated not found with kmers and also the converse (structural variants, regions missing in reference, etc). He is asked how this would work with heterozygous genomes.