Transcriptional regulation of a pineapple polyphenol oxidase gene and its relationship to blackheart.

Two genes encoding polyphenol oxidase (PPO) were isolated from pineapple (Ananas comosus[L.] Merr. cv. Smooth Cayenne). Sequence analyses showed that both contained a single intron and encoded typical chloroplast-localized PPO proteins, the sequences of which corresponded to two pineapple PPO cDNAs, PINPPO1 and PINPPO2, recently described by Stewart et al. (2001). Southern blot analyses suggested that pineapple contained only two PPO genes. Analysis of expression of PINPPO1 promoter GUS fusion constructs showed this promoter had a low basal activity and was cold- and wound-inducible, consistent with known mRNA expression profiles. Striking homologies to gibberellin response complexes (GARC) were observed in sequences of both the PINPPO1 and PINPPO2 promoters. Transient assays in mature pineapple fruit and stable expression in transgenic tobacco showed that PINPPO1 promoter-GUS fusions were indeed gibberellin (GA) responsive. A role for the element within the putative GARCs in mediating GA-responsiveness of the PINPPO1 promoter was confirmed by mutational analysis. PINPPO2 was also shown to be GA-responsive by RT-PCR analysis. Mutant PINPPO1 promoter-GUS fusion constructs, which were no longer GA-inducible, showed a delayed response to cold induction in pineapple fruit in transient assays, suggesting a role for GA in blackheart development. This was supported by observations that exogenous GA3 treatment induced blackheart in the absence of chilling. Sequences showing homology to GARCs are also present in some PPO promoters in tomato, suggesting that GA regulates PPO expression in diverse species.

Characterization of disease resistance gene candidates of the nucleotide binding site (NBS) type from banana and correlation of a transcriptional polymorphism with resistance to Fusarium oxysporum f.sp. cubense race 4

Most plant disease resistance (R) genes encode proteins with a nucleotide binding site and leucine-rich repeat structure (NBS-LRR). In this study, degenerate primers were used to amplify genomic NBS-type sequences from wild banana (Musa acuminata ssp. malaccensis) plants resistant to the fungal pathogen Fusarium oxysporum formae specialis (f. sp.) cubense (FOC) race 4. Five different classes of NBS-type sequences were identified and designated as resistance gene candidates (RGCs). The deduced amino acid sequences of the RGCs revealed the presence of motifs characteristic of the majority of known plant NBS-LRR resistance genes. Structural and phylogenetic analyses grouped the banana RGCs within the non-TIR (homology to Toll/interleukin-1 receptors) subclass of NBS sequences. Southern hybridization showed that each banana RGC is present in low copy number. The expression of the RGCs was assessed by RT-PCR in leaf and root tissues of plants resistant or susceptible to FOC race 4. RGC1, 3 and 5 showed a constitutive expression profile in both resistant and susceptible plants whereas no expression was detected for RGC4. Interestingly, RGC2 expression was found to be associated only to FOC race 4 resistant lines. This finding could assist in the identification of a FOC race 4 resistance gene.

Development of a model based on Bayesian networks to estimate the probability of sheep lice presence at shearing

This paper describes the development of a model, based on Bayesian networks, to estimate the likelihood that sheep flocks are infested with lice at shearing and to assist farm managers or advisers to assess whether or not to apply a lousicide treatment. The risk of lice comes from three main sources: (i) lice may have been present at the previous shearing and not eradicated; (ii) lice may have been introduced with purchased sheep; and (iii) lice may have entered with strays. A Bayesian network is used to assess the probability of each of these events independently and combine them for an overall assessment. Rubbing is a common indicator of lice but there are other causes too. If rubbing has been observed, an additional Bayesian network is used to assess the probability that lice are the cause. The presence or absence of rubbing and its possible cause are combined with these networks to improve the overall risk assessment.