The identification and characterization of inter- and intra-species genetic diversity derived from retrotransposons in humans

Retrotransposons, which used to be considered as “junk DNA”, have begun to reveal their immense value to genome evolution and human biology due to recent studies. They consist of at least ~45% of the human genome and are more or less the same in other mammalian genomes. Retrotransposon elements (REs) are known to affect the human genome through many different mechanisms, such as generating insertion mutations, genomic instability, and alteration in gene expression. Previous studies have suggested several RE subfamilies, such as Alu, L1, SVA and LTR, are currently active in the human genome, and they are an important source of genetic diversity between human and other primates, as well as among humans. Although several groups had used Retrotransposon Insertion Polymorphisms (RIPs) as markers in studying primate evolutionary history, no study specifically focused on identifying Human-Specific Retrotransposon Element (HS-RE) and their roles in human genome evolution. In this study, by computationally comparing the human genome to 4 primate genomes, we identified a total of 18,860 HS-REs, among which are 11,664 Alus, 4,887 L1s, 1,526 SVAs and 783 LTRs (222 full length entries), representing the largest and most comprehensive list of HS-REs generated to date. Together, these HS-REs contributed a total of 14.2Mb sequence increase from the inserted REs and Target Site Duplications (TSDs), 71.6Kb increase from transductions, and 268.2 Kb sequence deletion of from insertion-mediated deletion, leading to a net increase of ~14 Mb sequences to the human genome. Furthermore, we observed for the first time that Y chromosome might be a hot target for new retrotransposon insertions in general and particularly for LTRs. The data also allowed for the first time the survey of frequency of TE insertions inside other TEs in comparison with TE insertion into none-TE regions. In summary, our data suggest that retrotransposon elements have played a significant role in the evolution of Homo sapiens.

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 a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.