S8
Department of Vegetable Crops, University of California, Davis, CA 95616
Classical genetics has demonstrated that resistance genes tend to be
clustered in the genome. This led to the hypothesis that resistance genes
are members of multigene families encoding receptors with individual
members of these families having diverged to acquire novel recognition
specificities. The cloning of resistance genes for a variety of diseases
from diverse plant species is providing increasing support for this
hypothesis. The similarities between resistance genes has allowed the
facile identification of resistance gene candidates (RGCs) using PCR with
degenerate oligonucleotide primers to conserved domains. However, there
are hundreds of RGCs in each plant species and these tend to be organized
in clusters. Therefore it remains difficult to identify sequences that
encode an individual recognition specificity. Complementation and mutant
analysis are still required. It is currently unknown how many distinct
types of resistance genes there are in plants. The most common types are
receptor-like sequences containing a nucleotide binding domain and leucine
rich region (NBS-LRR). Sequence analysis indicates the presence of several
sub-families of varying complexity within the NBS-LRR type. Classical
genetics also indicated that clusters of resistance genes were unstable and
that unequal crossing over was involved in the generation of new
specificities. This led to the idea that clusters of resistance genes are
dynamic regions of the genome in which new specificities are frequently
being generated. Characterization of resistance gene clusters at the
molecular level confirms that unequal crossing-over can occur. However,
the rate of change at these loci seems to be slow. There is little
evidence for gene conversion homogenizing these multigene families.
Clusters of resistance genes seem to be storehouses of variation rather
than dynamic, rapidly-evolving pools of sequences. Genome rearrangements
may be instrumental in expressing cryptic recognition specificities as well
as generating specificities de novo.