Plant innate immune response: qualitative and quantitative resistance

The article reviews hierarchies of regulatory and resistance metabolite and protein biosynthetic genes induced in plants following pathogen perception to produce resistance metabolites and proteins that directly suppress pathogen development in plants. Their deployment in improving crop plant resistance against microbial stress is based on genome editing tools. Based on genome editing, resistance genes (R) can be identified in germplasm collections and replaced in commercial cultivars to improve plant resistance. The hierarchy of R genes with significant trait/resistance effect can be identified in world germplasm collections and used to replace non-functional genes in commercial cultivars to improve resistance

Gene discovery and genome editing to develop cisgenic crops with improved resistance against pathogen infection

Includes French abstractThe review discusses disease resistance in plants, the improvement of crop plants through genome editing to produce cisgenic cultivars, technologies available for gene discovery, novel mechanisms of resistance, and related regulatory issues. The new cultivars developed from these techniques are broadly grouped as: (i) cisgenic crops, where a gene from a sexually compatible species is replaced or introduced; and (ii) transgenic crops, where a gene from a sexually incompatible plant is replaced or introduced. Precise replacement of non-functional genes (r) with functional R genes using genome editing tools, similar to backcross breeding, can significantly improve resistance in commercial crops

Integrated Metabolo-Transcriptomics Reveals Fusarium Head Blight Candidate Resistance Genes in Wheat QTL-Fhb2

<div><p>Background</p><p>Fusarium head blight (FHB) caused by <i>Fusarium graminearum</i> not only causes severe losses in yield, but also reduces quality of wheat grain by accumulating mycotoxins. Breeding for host plant resistance is considered as the best strategy to manage FHB. Resistance in wheat to FHB is quantitative in nature, involving cumulative effects of many genes governing resistance. The poor understanding of genetics and lack of precise phenotyping has hindered the development of FHB resistant cultivars. Though more than 100 QTLs imparting FHB resistance have been reported, none discovered the specific genes localized within the QTL region, nor the underlying mechanisms of resistance.</p><p>Findings</p><p>In our study recombinant inbred lines (RILs) carrying resistant (R-RIL) and susceptible (S-RIL) alleles of QTL-Fhb2 were subjected to metabolome and transcriptome profiling to discover the candidate genes. Metabolome profiling detected a higher abundance of metabolites belonging to phenylpropanoid, lignin, glycerophospholipid, flavonoid, fatty acid, and terpenoid biosynthetic pathways in R-RIL than in S-RIL. Transcriptome analysis revealed up-regulation of several receptor kinases, transcription factors, signaling, mycotoxin detoxification and resistance related genes. The dissection of QTL-Fhb2 using flanking marker sequences, integrating metabolomic and transcriptomic datasets, identified 4-Coumarate: CoA ligase (<i>4CL</i>), callose synthase (<i>CS</i>), basic Helix Loop Helix (<i>bHLH041</i>) transcription factor, glutathione S-transferase (<i>GST</i>), ABC transporter-4 (<i>ABC4</i>) and cinnamyl alcohol dehydrogenase (<i>CAD</i>) as putative resistance genes localized within the QTL-Fhb2 region.</p><p>Conclusion</p><p>Some of the identified genes within the QTL region are associated with structural resistance through cell wall reinforcement, reducing the spread of pathogen through rachis within a spike and few other genes that detoxify DON, the virulence factor, thus eventually reducing disease severity. In conclusion, we report that the wheat resistance QTL-Fhb2 is associated with high rachis resistance through additive resistance effects of genes, based on cell wall enforcement and detoxification of DON. Following further functional characterization and validation, these resistance genes can be used to replace the genes in susceptible commercial cultivars, if nonfunctional, based on genome editing to improve FHB resistance.</p></div

Differentially expressed transcripts in RILs.

<p>(A). Venn diagram showing number of transcripts detected as up and down-regulated (<i>P</i> < 0.01) in R-RIL and S-RIL, 48 hours post <i>Fusarium graminearum</i> inoculation. (B). Heat Maps showing differentially expressed transcripts in R-RIL and S-RIL upon <i>Fusarium graminearum</i> and mock inoculations. They show transcripts differentially expressed transcripts between resistant mock (RM) and resistant pathogen (RP) and between susceptible mock (SM) and susceptible pathogen (SP), respectively.</p

Quantitative Real time PCR (qRT-PCR) showing transcript abundances of selected genes at 48 h post <i>F</i>. <i>graminearum</i> and water inoculation.

<p>The relative transcript abundance were calculated compared to mock treatments and for transcripts which were only expressed in pathogen treated treatments, susceptible pathogen was used to compare the abundance in resistant pathogen. RP = Resistant pathogen, RM = Resistant mock, SP = Susceptible pathogen and SM = Susceptible mock. PAL-Phenylammonia lyase, CHS = chalcone synthase, 4Cl = 4 coumarate CoA-ligase, GST = Glutathione S-transferase, bHLH041 = basic helix loop helix transcription factor, ABC4 = ABC transporter4.</p

Wheat chromosome 6B, depicting the physical location of QTL-Fhb2 (marked red), on short arm of the chromosome 6B, flanked by two SSR markers GWM-133 and GWM-644 (marked blue).

<p>The genes localized within the QTL locus were identified using metabolo-transcriptomics approach, which are shown within two arrow marks, marked purple in text. The corresponding gene Ids are given in parenthesis.</p

Hypothetical model for FHB resistance in wheat line carrying resistant alleles of R genes in QTL-Fhb2.

<p>Based on our findings we propose that the QTL-Fhb2 imparts high rachis resistance through combined effects of cell wall reinforcement and DON detoxification.</p

Phenotyping of RILs.

<p>(A). Spike and rachis of R-RIL and S-RIL, 21 dpi with <i>F</i>. <i>graminearum</i> spore suspension. A single alternate pair of spikelets in a spike was inoculated; black arrows indicate the site of inoculation. The spike and rachis in R-RIL shows only necrotic spots or diseased symptoms limited to the inoculated spikelet, while in S-RIL both spikelet and rachis are entirely diseased. (B). Proportion of Spikelets Diseased (PSD). A single pair of spikelets of a spike was inoculated in both the RILs and the proportion of spikelets diseased was recorded at 3 days intervals until 21 days, from which the PSD was calculated. The bar graph shows high PSD in S-RIL as compared to R-RIL. (C). The bar graph shows area under disease progress curve (AUDPC) calculated from PSD, significantly higher in S-RIL. (D) and (E). Fungal Biomass quantification in RILs. Three alternate pair of spikelets were inoculated with <i>F</i>. <i>graminearum</i> spore suspension and samples were collected at 7 dpi. The total genomic DNA was extracted and the relative gene copy number of <i>Tri6</i> was estimated using 2^−ΔΔC_T method. (D). Shows the relative gene copy number of <i>Tri6</i> in rachis tissues; (E). Shows the relative gene copy number of <i>Tri6</i> in spikelet tissues. In both graphs, the gene copy number of <i>Tri6</i> is significantly higher in S-RIL as compared to R-RIL.</p

Differentially expressed transcripts in R-RIL and S-RIL upon <i>Fusarium graminearum</i> and mock inoculation at 48 hpi.

<p>The differential expressions are in log₂FC values for the resistant genotype and in FPKM values following pathogen inoculation. The up-regulated transcripts were classified according to their biosynthetic pathways. The transcripts localized within QTL-Fhb2 are marked with an asterisk (*).</p

Classification of metabolites detected at 72 hours post <i>Fusarium graminearum</i> and water inoculations.

<p>Resistant Related Induced (RRI) and Resistant Related Constitutive (RRC) metabolites identified in the study were classified according to their chemical groups. (A) Pie chart shows RRI and (B) shows RRC metabolites classified into various chemical groups.</p