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- Member of: ASU Regents' Professors Open Access Works
- Member of: Glen Canyon Dam Adaptive Management Program Administrative History
Methods: Using next-generation sequence data, we assembled the plastid genome of saguaro cactus and probed the nuclear genome for transferred plastid genes and functionally related nuclear genes. We combined these results with available data across Cactaceae and seed plants more broadly to infer the history of gene loss and to assess the strength of phylogenetic association between gene loss and loss of the inverted repeat (IR).
Key results: The saguaro plastid genome is the smallest known for an obligately photosynthetic angiosperm (∼113 kb), having lost the IR and plastid ndh genes. This loss supports a statistically strong association across seed plants between the loss of ndh genes and the loss of the IR. Many nonplastid copies of plastid ndh genes were found in the nuclear genome, but none had intact reading frames; nor did three related nuclear-encoded subunits. However, nuclear pgr5, which functions in a partially redundant pathway, was intact.
Conclusions: The existence of an alternative pathway redundant with the function of the plastid NADH dehydrogenase-like complex (NDH) complex may permit loss of the plastid ndh gene suite in photoautotrophs like saguaro. Loss of these genes may be a recurring mechanism for overall plastid genome size reduction, especially in combination with loss of the IR.
Background:
Many pharmaceutical drugs are known to be ineffective or have negative side effects in a substantial proportion of patients. Genomic advances are revealing that some non-synonymous single nucleotide variants (nsSNVs) may cause differences in drug efficacy and side effects. Therefore, it is desirable to evaluate nsSNVs of interest in their ability to modulate the drug response.
Results:
We found that the available data on the link between drug response and nsSNV is rather modest. There were only 31 distinct drug response-altering (DR-altering) and 43 distinct drug response-neutral (DR-neutral) nsSNVs in the whole Pharmacogenomics Knowledge Base (PharmGKB). However, even with this modest dataset, it was clear that existing bioinformatics tools have difficulties in correctly predicting the known DR-altering and DR-neutral nsSNVs. They exhibited an overall accuracy of less than 50%, which was not better than random diagnosis. We found that the underlying problem is the markedly different evolutionary properties between positions harboring nsSNVs linked to drug responses and those observed for inherited diseases. To solve this problem, we developed a new diagnosis method, Drug-EvoD, which was trained on the evolutionary properties of nsSNVs associated with drug responses in a sparse learning framework. Drug-EvoD achieves a TPR of 84% and a TNR of 53%, with a balanced accuracy of 69%, which improves upon other methods significantly.
Conclusions:
The new tool will enable researchers to computationally identify nsSNVs that may affect drug responses. However, much larger training and testing datasets are needed to develop more reliable and accurate tools.
