Mechanism of tocopherol transfer by human α-tocopherol transfer protein (α-hTTP)

Vitamin E is a well known fat soluble chain breaking antioxidant. It is a general tenn used to describe a family of eight stereoisomers of tocopherols. Selective retention of a-tocopherol in the human circulation system is regulated by the a -Tocopherol Transfer Protein (a-TIP). Using a fluorescently labelled a-tocopherol (NBD-a-Toc) synthesized in our laboratory, a fluorescence resonance energy transfer (FRET) assay was developed to monitor the kinetics of ligand transfer by a-hTTP in lipid vesicles. Preliminary results implied that NBD-a-Toe simply diffused from 6-His-a-hTTP to acceptor membranes since the kinetics of transfer were not responsive to a variety of conditions tested. After a series of trouble shooting experiments, we identified a minor contaminant, E coli. outer membrane porin F (OmpF) that co-purified with 6-His-a-hTTP from the metal affinity column as the source of the problem. In order to completely avoid OmpF contamination, a GST -a-hTTP fusion protein was purified from a glutathione agarose column followed by an on-column thrombin digestion to remove the GST tag. We then demonstrated that a-hTTP utilizes a collisional mechanism to deliver its ligand. Furthennore, a higher rate of a-tocopherol transfer to small unilamellar vesicles (SUV s) versus large unilamellar vesicles (LUV s) indicated that transfer is sensitive to membrane curvature. These findings suggest that ahTTP mediated a-Toc transfer is dominated by the hydrophobic nature of a-hTTP and the packing density of phospholipid head groups within acceptor membranes. Based on the calculated free energy change (dG) when a protein is transferred from water to the lipid bilayer, a model was generated to predict the orientation of a-hTTP when it interacts with lipid membranes. Guided by this model, several hydrophobic residues expected to penetrate deeply into the bilayer hydrophobic core, were mutated to either aspartate or alanine. Utilizing dual polarization interferometry and size exclusion vesicle binding assays, we identified the key residues for membrane binding to be F 165, F 169 and 1202. In addition, the rates of ligand transfer of the u-TTP mutants were directly correlated to their membrane binding capabilities, indicating that membrane binding was likely the rate limiting step in u-TTP mediated transfer of u-Toc. The propensity of u-TTP for highly curved membrane provides a connection to its colocalization with u-Toc in late endosomes.

Identification and characterization of a Catharanthus roseus mutant altered in monoterpenoid indole alkaloid biosynthesis

The Madagascar periwinkle [Catharanthus roseus (L.) G. Don] is a commercially important horticultural flower species and is the only source for several pharmaceutically valuable monoterpenoid indole alkaloids (MIAs), including the powerful antihypertensive ajmalicine and the antineoplastic agents vincristine and vinblastine. While biosynthesis of MIA precursors has been elucidated, conversion of the common MIA precursor strictosidine to MIAs of different families, for example ajmalicine, catharanthine or vindoline, remains uncharacterized. Deglycosylation of strictosidine by the key enzyme Strictosidine beta-glucosidase (SGD) leads to a pool of uncharacterized reaction products that are diverted into the different MIA families, but the downstream reactions are uncharacterized. Screening of 3600 EMS (ethyl methane sulfonate) mutagenized C. roseus plants to identify mutants with altered MIA profiles yielded one plant with high ajmalicine, and low catharanthine and vindoline content. RNA sequencing and comparative bioinformatics of mutant and wildtype plants showed up-regulation of SGD and the transcriptional repressor Zinc finger Catharanthus transcription factor (ZCT1) in the mutant line. The increased SGD activity in mutants seems to yield a larger pool of uncharacterized SGD reaction products that are channeled away from catharanthine and vindoline towards biosynthesis of ajmalicine when compared to the wildtype. Further bioinformatic analyses, and crossings between mutant and wildtype suggest a transcription factor upstream of SGD and ZCT1 to be mutated, leading to up-regulation of Sgd and Zct1. The crossing experiments further show that biosynthesis of the different MIA families is differentially regulated and highly complex. Three new transcription factors were identified by bioinformatics that seem to be involved in the regulation of Zct1 and Sgd expression, leading to the high ajmalicine phenotype. Increased cathenamine reductase activity in the mutant converts the pool of SGD reaction products into ajmalicine and its stereoisomer tetrahydroalstonine. The stereochemistry of ajmalicine and tetrahydroalstonine biosynthesis in vivo and in vitro was further characterized. In addition, a new clade of perakine reductase-like enzymes was identified that reduces the SGD reaction product vallesiachotamine in a stereo-specific manner, characterizing one of the many reactions immediately downstream of SGD that determine the different MIA families. This study establishes that RNA sequencing and comparative bioinformatics, in combination with molecular and biochemical characterization, are valuable tools to determine the genetic basis for mutations that trigger phenotypes, and this approach can also be used for identification of new enzymes and transcription factors.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.