Terpenoids are the largest and most diverse class of plant-specialized metabolites, which function in diverse physiological processes during plant development. In the biosynthesis of plant terpenoids, short-chain prenyltransferases (SC-PTs), together with terpene synthases (TPSs), play critical roles in determining terpenoid diversity. SC-PTs biosynthesize prenyl pyrophosphates with different chain lengths, and these compounds are the direct precursors of terpenoids. Arabidopsis thaliana possesses a subgroup of SC-PTs whose functions are not clearly known. In this study, we focus on 10 geranylgeranyl pyrophosphate synthase-like [GGPPSL] proteins, which are commonly thought to produce GGPP [C20]. We found that a subset of members of the Arabidopsis GGPPSL gene family have undergone neo-functionalization: GGPPSL6, 7, 9, and 10 mainly have geranylfarnesyl pyrophosphate synthase activity (C25; renamed AtGFPPS1, 2, 3, and 4), and GGPPSL8 produces even longer chain prenyl pyrophosphate (≥ C30; renamed polyprenyl pyrophosphate synthase 2, AtPPPS2). By solving the crystal structures of AtGFPPS2, AtPPPS2, and AtGGPPS11, we reveal the product chain-length determination mechanism of SC-PTs and interpret it as a "three floors" model. Using this model, we identified a novel GFPPS clade distributed in Brassicaceae plants and found that the GFPPS gene typically occurs in tandem with a gene encoding a TPS, forming a GFPPS-TPS gene cluster.
When assessing a correlation between two exposure or biological marker variables, one sometimes encounters the problem of indeterminate values for one of the variables due to an assay detection limit. In this event, investigators often report correlation coefficients computed after removing the pairs involving non-detectable values, or after substituting some small constant for those values. These ad hoc practices can lead to bias in both point and confidence interval estimates of the true correlation coefficient. To address this issue, we consider two parametric techniques for estimating the correlation in the presence of left censoring for one of the variables. The first is a maximum likelihood approach, and the second is an adaptation of multiple imputation motivated primarily by potential benefits in confidence interval coverage. Both of the estimators studied reduce to the standard Pearson's correlation coefficient in the event of no censoring, and hence are valid in cases where this measure would be appropriate for the complete data. We assess these approaches empirically and contrast them with ad hoc methods for estimating the correlation between cervicovaginal human immunodeficiency virus (HIV) viral load measurements and CD4+ lymphocyte counts from HIV positive women enrolled in a clinical trial conducted in Bangkok, Thailand.
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