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 SPECIFIC GOALS

This proposal aims to identify the extent of HR-impacting variation that exists naturally in maize and then clone at least 4 genes with the ability to suppress or enhance the HR response. An innovative idea will be used to unveil diversity of HR in maize.

 OBJECTIVE 1: Survey Maize Diversity Lines (MDLs) for Rp1-D21-Mediated HR Diversity

As noted earlier, the MDLs consist of 302 diverse inbreds that have been compiled to capture ~85% of the diversity present in public sector maize breeding programs worldwide . This germplasm set is comprised of current breeding lines as well as historically important lines from both temperate and tropical programs, including 8 popcorn and 7 sweetcorn lines with genetically distinct breeding histories [61]. Because these lines have also been characterized with regard to their population structure and linkage disequilibrium they represent a high-resolution platform for QTL dissection and association mapping. A subset of 25 of these MDLs, called the “founder” or core set, represent ~65% of the worldwide diversity in a more manageable number of lines. As described in detail later (objective 3), each of these ‘founders’ were crossed with B73 generate 25 separate recombinant inbred line (RIL) populations of 200 RILs. The 5000 RILs thus developed have been named NAM, for nested association mapping.

As mentioned earlier, we have identified a great deal of HR-impacting diversity in the founder MDLs (Table 1). Balint-Kurti and colleagues have developed  an extensive dataset on the resistance of the full 302 line population to three necrotrophic diseases (southern leaf blight, northern leaf blight and gray leaf spot) and have observed a large amount of phenotypic variation in disease resistance, well beyond that displayed amongst the 25 lines constituting the core set. Johal has assessed the population for resistance to common rust and has observed several lines with high levels of susceptibility including one which even allows rust to sporulate on leaf sheaths, which to our knowledge has never been reported before (Figure 6). This implies that the larger population of 302 lines may contain significant diversity for innate immunity traits.


To characterize MDLs with respect to HR diversity, they will each be crossed with Rp1-D21 heterozygotes in both the H95 and the B73 backgrounds. There are three reasons for crossing with Rp1-D21 in both backgrounds. First, the HR phenotype of Rp1-D21 F1s in certain MDL x Rp1- D21(H95) crosses is so severe that they die before reaching anthesis. As a result, they cannot be propagated and used for further genetic studies. Since B73 has a suppressive effect on the Rp1-D21 phenotype, Rp1-D21(B73) crosses will allow us to recover Rp1-D21 crossed to these MDLs. Second, including both B73 and H95 backgrounds in these crosses may provide a more complete understanding of the effect of each MDL on the Rp1-D21 phenotype as in one case it will be interacting with a genome that has an enhancing effect on the Rp1-D21 phenotype (H95) and in the other it will be interacting with a genome that has a suppressive effect (B73). It is well known that different QTL can have different effects in different backgrounds [70]. Thus, crossing with B73 may reveal additional QTL that will be missed if crossed only to H95, and vice versa. Several of the MDL lines are very early flowering while several others flower very late. We will try to obtain crosses with each of these lines by planting delays as appropriate and by attempting some of the crosses in the winter nursery, where shorter day lengths tend to reduce disparities in flowering time. Nevertheless, it is likely that it will not be possible to make all the crosses.

Crossing each MDL with lines heterozygous rather than homozygous for Rp1-D21 allows both mutant and wild type siblings to be compared side by side, thereby providing an ideal way to obtaining the height ratio between mutants and WT siblings.

 F1 Phenotypic Surveys:

To survey F1 progeny from crosses between each MDL and our two Rp1-D21 heterozygotes, families of 15 F1 kernels will be planted in a complete randomized block design with two replications per location at two locations (Indiana and N. Carolina) in each of two years and will be assessed for the following phenotypic parameters.

 
  1. The overall severity of the HR phenotype using the 1-10 scale described earlier.

  2. Height ratio of mutants compared to wt siblings after anthesis. This is an easy measurement to make and keeping the comparison within families serves to cancel out differences that may arise among different F1 crosses because of different levels of hybrid vigor.

  3. The date of lesion initiation. Thus far, we have only seen HR lesions forming on Rp1-D21 seedlings after they are at least 2 weeks old. What prevents earlier expression of Rp1-D21-mediated HR and is there genetic variation for this aspect of the HR? Given the extent of diversity we have seen in the MDLs, it is likely that we will see variation in the timing of lesion initiation. If this timing becomes earlier, Rp1-D21 plants may die as soon as they emerge, or may not germinate altogether. We will observe these seedlings at daily intervals once they emerge and then every few days after 3 weeks of age to document this parameter rigorously.

  4. The nature of HR lesions. Are they discrete or spreading? Are they necrotic,
     
    chlorotic, or of some other hue? We have observed a variety of lesion types previously in different backgrounds (Fig. 7).


    Since this is an important aspect of the HR response, we will document the Rp1-D21 lesion phenotypes in detail. In this effort we will use both visual observation and computational image analysis approaches. For image analysis we will expand our existing collaboration with the group of Dr. Chi-Ren Shyu (see letter of collaboration) who have extensive expertise in the image analysis of complex leaf phenotypes in maize.

  5. Electrolyte loss. This can be done fairly easily and generates high quality data for QTL and association studies. This type of assay is a standard assay used for quantitative analysis of cellular response and damage caused by events such as the hypersensitive response [71,72]. We will measure electrolytes using leaf punches from equivalent leaves and leaf areas.

The F1 survey will begin to tell us about the extent of variation in the maize germplasm with regard to natural genes/alleles capable of impacting HR. The information at this stage reveals, on a genome-wide scale, the loss of any recessive interactors present in either H95 or B73 and the presence of any dominant interactors in the MDLs. In addition, this step is also important for association studies that we will undertake as part of fine mapping and cloning Hrml1 (in Objective 2) and other HR-modulating loci (Objective 3).

 

F2 Surveys:

We will also advance each F1 cross to an F2. However, in this case rather than selfing the F1, we need to cross F1 Rp1-D21 mutants with nomutant siblings. There are two important reasons for this approach. First, many Rp1-D21 F1 plants produce pollen, but no ear. Second, by always scoring Rp1-D21 heterozygotes, the variation we observe cannot be due to changes in copy number of the Rp1-D21 allele.

 

F2 progeny (40 plants for each family) will be planted at both the Indiana and North Carolina locations, and they will be surveyed for all of the parameters listed above except the height ratio between Rp1-D21 plants and nonmutant sibs. Plant height will be harder to interpret meaningfully in the F2 because a large number of modifiers controlling plant height will be segregating in these plants that we were able to ignore because they were uniformly heterozygous in the F1.

 

We have three goals in generating these F2 populations and surveying them for diversity in HR. The first is that such F2 populations are needed to search for recessive suppressors or enhancers of the HR response. Second, F1s by themselves may be misleading with regard to the gene action underlying a trait. For example, if a particular MDL has both an enhancer and a suppressor of HR, then the mutant plants in the F1 of this cross might show little change in phenotype because the suppressor and the enhancer cancel each other out. However, in an F2 population, we will be able to distinguish these Hrml genes as they segregate away from each other and their individual effects are revealed (assuming they are not completely linked). Third, these F2 populations may facilitate high-resolution mapping and/or validation of Hrml loci that will be identified under objectives 2 and 3.

    

 OBJECTIVE 2: Clone and Characterize Hrml1

Hrml1 was mapped using only 174 RILs of the available 302 IBM lines. We have now generated F1 crosses between Rp1-D21(H95) and ~100 of the remaining IBM lines (giving us a total mapping population of >270 F1 families). In 2008, this expanded population will be phenotyped in the field in Indiana and North Carolina (two replications, complete randomized block design). Combining these data with additional markers from the region underlying Hrml1, we anticipate localizing this QTL to a <3cM region.

We will further improve the precision with which Hrml1 is mapped by exploiting the NAM population and its extraordinary potential to define the genetic architecture of quantitative traits in maize. The design of the NAM population takes advantage of both recent and ancient recombination events in the same population, allowing linkage mapping and association studies to be integrated [52]. All of the NAM components, the common parent (CP), which is B73, 25 founders, and 5000 RILs have been genotyped with 1536 B73-specific rare SNP loci (common parent specific (CPS) markers). The 26 founders (but not the RILs) are also being genotyped to an extremely high density (at least 1 million SNPs) [52]. To fine map a QTL, it is first identified by conventional association between chromosomal segments and phenotype in the 5000 lines. Fine-mapping to within a gene or few genes can then be achieved by projecting the expected SNPs in each of the RILs derived from the dense genotyping of the parents.

To identify the gene underlying Hrml1, we will also take advantage of additional NAM RILs, which we plan to phenotype for Rp1-D21 (objective 3). With all these data , combined with the fact that Mo17 and the IBM population are also being integrated with the NAM population the above mentioned CPS markers (Holland, personal communication), we should be able to locate Hrml1 to an extremely precise region on chromosome 10 by NAM analysis.

 Identifying the gene underlying Hrml1

 

Our fine-mapping should reduce the number of candidate genes that might underlie the Hrml1 phenotype to at most ~20, but likely less. We will then focus on sorting through these genes, taking advantage of the soon-to-be available B73 genomic sequence to identify potential candidates similar to known genes. Further prioritization will be based on

  1. Any prior knowledge about the functions of genes in the interval
  2. Expression analysis using RT-PCR of replicated, growth chamber-derived samples (genes in the region that are transcribed differentially in Rp1- D21 vs. nonmutant leaves will be prioritized)
  3. Limited sequencing as needed (genes that show potential amino acid sequence polymorphisms between the suppressing B73 and H95 parents will be prioritized)

 

Validation of the Hrml1 gene will be accomplished as follows. We will start by using the wellcharacterized, EMS-mutagenized B73 TILLING populations that are already at Purdue [8,9]. We will identify non-silent point mutations in the TILLING populations and cross these mutants to Rp1- D21(H95), looking for failure to suppress the HR phenotype. Silent alleles of the same gene will serve as controls. We will also sequence our candidate gene in the 302 lines of the association mapping population and use these data combined with the phenotypic data for the MDL x Rp1-D21(H95) F1 families generated under objective 1 to perform association mapping to validate a positive association between the Rp1-D21 phenotype and specific alleles of our candidate gene.

 

As an alternative, we will use directed mutagenesis to generate loss-of-function alleles of Hrml1. Rp1-D21 is being introgressed into B73 for this purpose. After the fifth backcross, mutant plants (these are heterozygous for Rp1-D21) will be selfed to generate Rp1-D21 homozygotes in the B73 background. These homozygotes will be propagated either by self-pollination where possible—recall, Rp1-D21 homozygotes often make no ear (see above)-- or by sib mating with Rp1-D21 heterozygotes. In the latter case they will segregate 1:1 along with Rp1-D21 heterozygotes. To knock out Hrml1 by EMS, pollen from Rp1-D21 homozygotes will be collected, treated with EMS according to established protocols, and used to pollinate ears of H95. We will generate at least 5,000 M1 seed, which will be planted and screened for plants that have a more severe (so, less suppressed) Rp1-D21 phenotype than the rest of the M1 population. These mutants will be sampled for DNA and RNA, and propagated if possible. The candidate gene will be sequenced from a maximum of 5 of these mutants and compared with the WT gene(s) from B73. Polymorphisms between the mutant vs WT genes will serve to validate that mutant gene as the Hrml1 gene. As indexed populations of transposon-mutagenized B73 become publicly available we will screen these resources as well; however, existing resources are not in this uniform background as of this writing.


 

In the event that we have difficulty getting enough pollen from Rp1-D21(B73) homozygotes to carry out an effective mutagenesis, we will use pollen from Rp1-D21(B73) heterozygotes for treatment instead. This approach will require that we generate twice as many M1 seed to identify the same number of mutants as with the homozygous pollen because half of the M1 progeny will not carry the Rp1-D21 allele and will, therefore, be uninformative.

 

 

Once completed, this work will demonstrate and provide methodology for the isolation of QTL from throughout the B73 genome, creating a powerful functional genomics pipeline from an existing, invaluable and rapidly improving resource.

 

 Objective 3: Evaluate NAM RIL Populations for Additional Hrml genes/QTL and Clone at Least Three of them

A key goal of this proposal is to broaden the methodology described in the first two objectives and demonstrate that collections of naturally diverse germplasm can be mined for networks of interacting genes. As a proof of this principle, we will identify additional Hrml genes/QTL present in diverse maize inbreds using additional NAM RIL populations. The NAM population will inevitably become a standard mapping set in the maize community, the mapping of disease resistance response phenotypes on this population increases its value, enabling the genetic architecture of other traits that are investigated with this population (e.g. other stress responses) to be compared and contrasted with the architecture of the disease resistance response.

As mentioned earlier, many lines of the core set of MDLs exhibit extensive diversity for Rp1-D21 mediated HR (Table 1). Some of these inbreds significantly suppressed the HR phenotype of Rp1-D21, while others enhanced it and still others had minimal impact (at least in the F1 generation). To make optimal use of our resources for discovering additional Hrml genes, we will first cross Rp1-D21(H95) heterozygotes with the complete RIL populations derived from NAM founders that show either suppressor or enhancer effects in the F1 (or the F2 in Objective 1 (see above)). Based on our data so far from the F1 of these crosses, the first three sets of 200 will be those derived from crosses between B73 and B97, CML333, and Tzi8, respectively, all of which markedly suppress HR when crossed with Rp1- D21(H95) (Table 1). The second set will be those derived from crosses between B73 and M162W, NC350 and TX303, all of which significantly enhanced Rp1-D21 mediated HR. A third, control set of three complete RIL populations will be those derived from crossing B73 with Ki3, IL14H, and HP301, all of which caused little or no change in HR when crossed with Rp1-D21(H95). To take advantage of the full power of the NAM population for cloning genes underlying additional Hrmls, we will also cross 100 RILs from each of the remaining 16 populations to Rp1-D21(H95). The total number of RILs that we will phenotype thus amounts to 3400. 

The resulting F1 progeny will be evaluated for the HR phenotypes using the lesion severity scale of 1 to 10 and the height ratio of mutant to nonmutant siblings, both described earlier. Using the publicly available genotypic data for the NAM population we will perform Nested Association Mapping (in collaboration with ED Buckler, Cornell, and Dr. Jim Holland, NCSU, see attached letters), as well as conventional QTL mapping to estimate the location of Hrml genes segregating in these populations. We will analyze these data by both including and excluding RILs that inherit Hrml1 from B73, which will be roughly half of them. The advantage of doing this kind of differential analysis is that it will allow us to find QTL regardless of whether they are masked by Hrml1 or not. 

As in Objective 2, candidate gene approaches will then be used to identify these genes precisely and validate them. Validation for the suppressing Hrmls will be done by generating the TILLING knockout alleles of these genes as described above. Validation of the enhancer loci will be carried out by using the cloned Hrml allele to transform maize at the NSF-funded Maize Transformation facility at Iowa State University. The transgenic line will then be crossed with Rp1-D21(H95). Both sets of genes will also be validated by association analysis as described in objective 2 above.

 Major Outcomes


  1. A comprehensive screen for natural genes/alleles that contribute to the HR response. Maize is especially well suited for this screen because, being an outbred species, maize is likely to retain much higher levels of allelic diversity than self-pollinated species.
  2. A proof-of-concept for MAGIC, which we believe can be used to mine and exploit natural variation underlying any trait, thereby providing new opportunities to gain insights into the networks of genes and mechanisms that underlie all traits agricultural and scientific importance in plants. The aims of this project are thus perfectly aligned with the program goals of research supported by the PGRP.