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bioRxiv2020

GWAS reveals the genetic complexity of fructan accumulation patterns in barley grain

We profiled the grain oligosaccharide content of 154 two-row spring barley genotypes and quantified 27 compounds, mainly fructans, that exhibited differential abundance. Clustering revealed two major profile groups where the ‘high’ set contained greater amounts of sugar monomers, sucrose and overall fructans, but lower fructosylraffinose. GWAS identified a significant association for the variability of two fructan types; neoseries-DP7 and inulin-DP9 which showed increased strength when a compound-ratio GWAS was applied. Gene models within this region included five fructan biosynthesis genes, of which three (fructan:fructan 1-fructosyltransferase, sucrose:sucrose 1-fructosyltransferase, and sucrose:fructan 6-fructosyltransferase) have already been described. The remaining two, 6(G)-fructosyltransferase and vacuolar invertase1 have not previously been linked to fructan biosynthesis in barley and showed expression patterns distinct from those of the other three genes, including exclusive expression of 6(G)-fructosyltransferase in outer grain tissues at the storage phase. From exome capture data several SNPs related to inulin- and neoseries-type fructan variability were identified in fructan:fructan 1-fructosyltransferase and 6(G)-fructosyltransferase genes Co-expression analyses uncovered potential regulators of fructan biosynthesis including transcription factors. Our results provide evidence for the distinct biosynthesis of neoseries-type fructans during barley grain maturation plus new gene candidates likely involved in the differential biosynthesis of the various fructan types.Highlight Grain fructan profiles in barley are more complex than previously expected and variations in a diversity panel relate to a genomic region where fructan biosynthesis genes cluster.Abbreviations1-FFTfructan:fructan 1-fructosyltransferase1-SSTsucrose:sucrose 1-fructosyltransferase6-SFTsucrose:fructan 6-fructosyltransferase6G-FFT6(G)-fructosyltransferaseDAPdays after pollinationDPdegree of polymerisationDMdry matterELSDevaporative light scattering detectionFDRfalse discovery rateFOSfructooligosaccharidesFPKMfragments per kilobase, per million mapped readsGWAgenome wide associationGWASgenome wide association studyHAIhours after imbibitionHPAEC–PADhigh pH anion exchange chromatography with pulsed amperometric detectionHPLChigh performance liquid chromatographyKPkestopentaoseKTkestotetraoseLCliquid chromatographyLDlinkage disequilibriumLODlogarithm of oddsMAFminimum allele frequencyMSmass spectrometryNGNeural GasNSneoseries-type fructanPprobability valuePEGpolyethylene glycolQTLquantitative trait lociRFOraffinose family oligosaccharidesRTretention timeSNPsingle nucleotide polymorphismsSPEsolid phase extractionTFAtrifluoroacetic acidTPMtranscripts per millionVI-1vacuolar invertase1View Full Text

plant biology↗

bioRxiv2020

'Macrobot' - an automated segmentation-based system for powdery mildew disease quantification

Plant diseases, as one of the perpetual problems in agriculture, is increasingly difficult to manage due to intensifying of the field production, global trafficking, reduction of genetic variability of crops, climatic changes-driven expansion of pests, redraw and loss of effectiveness of pesticides and rapid breakdown of the disease resistance in the field. The substantial progress in genomics of both plants and pathogens, achieved in the last decades has the potential to counteract this negative trend, however, only when the genomic data is supported by relevant phenotypic data that allows linking the genomic information to specific traits. In this respect, phenotyping is and will remain an essential element of any comprehensive functional genomics study. We have developed a set of methods and equipment and combined them into a "Macrophenomics pipeline". The pipeline has been optimized for the quantification of powdery mildew infection symptoms on wheat and barley but it can be adapted to other diseases and host plants. The Macrophenomics pipeline scores the visible disease symptoms, typically 5-7 days after inoculation (dai) in a highly automated manner. The system can precisely and reproducibly quantify the percentage of the infected leaf area with a throughput of the image acquisition module of up to 10 000 individual samples per day, making it appropriate for phenotyping of large germplasms collections and crossing populations.

plant biology↗