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Predicting Specificities Under the Non-self Gametophytic Self-Incompatibility Recognition Model

Authors: Jorge Vieira; Jorge Vieira; Sara Rocha; Sara Rocha; Noé Vázquez; Noé Vázquez; Hugo López-Fernández; +12 Authors

Predicting Specificities Under the Non-self Gametophytic Self-Incompatibility Recognition Model

Abstract

Non-self gametophytic self-incompatibility (GSI) recognition system is characterized by the presence of multiple F-box genes tandemly located in the S-locus, that regulate pollen specificity. This reproductive barrier is present in Solanaceae, Plantaginacea and Maleae (Rosaceae), but only in Petunia functional assays have been performed to get insight on how this recognition mechanism works. In this system, each of the encoded S-pollen proteins (called SLFs in Solanaceae and Plantaginaceae /SFBBs in Maleae) recognizes and interacts with a sub-set of non-self S-pistil proteins, called S-RNases, mediating their ubiquitination and degradation. In Petunia there are 17 SLF genes per S-haplotype, making impossible to determine experimentally each SLF specificity. Moreover, domain -swapping experiments are unlikely to be performed in large scale to determine S-pollen and S-pistil specificities. Phylogenetic analyses of the Petunia SLFs and those from two Solanum genomes, suggest that diversification of SLFs predate the two genera separation. Here we first identify putative SLF genes from nine Solanum and 10 Nicotiana genomes to determine how many gene lineages are present in the three genera, and the rate of origin of new SLF gene lineages. The use of multiple genomes per genera precludes the effect of incompleteness of the genome at the S-locus. The similar number of gene lineages in the three genera implies a comparable effective population size for these species, and number of specificities. The rate of origin of new specificities is one per 10 million years. Moreover, here we determine the amino acids positions under positive selection, those involved in SLF specificity recognition, using 10 Petunia S-haplotypes with more than 11 SLF genes. These 16 amino acid positions account for the differences of self-incompatible (SI) behavior described in the literature. When SLF and S-RNase proteins are divided according to the SI behavior, and the positively selected amino acids classified according to hydrophobicity, charge, polarity and size, we identified fixed differences between SI groups. According to the in silico 3D structure of the two proteins these amino acid positions interact. Therefore, this methodology can be used to infer SLF/S-RNase specificity recognition.

Country
Portugal
Keywords

BDBM, Plant culture, Plant Science, Specificity recognition, self-incompatibility, SB1-1110, Self-incompatibility, Positive selection, positive selection, SLFs, S-RNase, Solanaceae, specificity recognition

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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Average
Top 10%
Green
gold