Based on this evidence, a new scenario for the origin and distribution of this important fruit crop has been traced. The evaluation of olive trees growing in the eastern part of the Levant highlighted a new perspective on the spread and distribution of olive, suggesting two routes of olive differentiation, one westward, spreading along the Mediterranean basin, and another moving towards the east and reaching the Iranian plateau before its domestication.

BackgroundOlive trees (Olea europaea subsp. europaea var. europaea) naturally grow in areas spanning the Mediterranean basin and towards the East, including the Middle East. In the Iranian plateau, the presence of olives has been documented since very ancient times, though the early history of the crop in this area is shrouded in uncertainty.MethodsThe varieties presently cultivated in Iran and trees of an unknown cultivation status, surviving under extreme climate and soil conditions, were sampled from different provinces and compared with a set of Mediterranean cultivars. All samples were analyzed using SSR and chloroplast markers to establish the relationships between Iranian olives and Mediterranean varieties, to shed light on the origins of Iranian olives and to verify their contribution to the development of the current global olive variation.ResultsIranian cultivars and ecotypes, when analyzed using SSR markers, clustered separately from Mediterranean cultivars and showed a high number of private alleles, on the contrary, they shared the same single chlorotype with the most widespread varieties cultivated in the Mediterranean.ConclusionWe hypothesized that Iranian and Mediterranean olive trees may have had a common origin from a unique center in the Near East region, possibly including the western Iranian area. The present pattern of variation may have derived from different environmental conditions, distinct levels and selection criteria, and divergent breeding opportunities found by Mediterranean and Iranian olives.These unexpected findings emphasize the importance of studying the Iranian olive germplasm as a promising but endangered source of variation.

Human Immunodeficiency Virus-1 (HIV-1) is the causative agent of Acquired Immunodeficiency Syndrome (AIDS), infecting nearly 37 million people worldwide. Currently, there is no definitive cure, mainly due to HIV-1′s ability to enact latency. Our previous work has shown that exosomes, a small extracellular vesicle, from uninfected cells can activate HIV-1 in latent cells, leading to increased mostly short and some long HIV-1 RNA transcripts. This is consistent with the notion that none of the FDA-approved antiretroviral drugs used today in the clinic are transcription inhibitors. Furthermore, these HIV-1 transcripts can be packaged into exosomes and released from the infected cell. Here, we examined the differences in protein and nucleic acid content between exosomes from uninfected and HIV-1-infected cells. We found increased cyclin-dependent kinases, among other kinases, in exosomes from infected T-cells while other kinases were present in exosomes from infected monocytes. Additionally, we found a series of short antisense HIV-1 RNA from the 3′ LTR that appears heavily mutated in exosomes from HIV-1-infected cells along with the presence of cellular noncoding RNAs and cellular miRNAs. Both physical and functional validations were performed on some of the key findings. Collectively, our data indicate distinct differences in protein and RNA content between exosomes from uninfected and HIV-1-infected cells, which can lead to different functional outcomes in recipient cells.

HIV-1 Tat protein is released from HIV-1-infected cells and can enter non-permissive cells including neurons. Tat disrupts neuronal homeostasis and may contribute to the neuropathogenesis in people living with HIV (PLWH). The use of cocaine by PLWH exacerbates neuronal dysfunction. Here, we examined the mechanisms by which Tat and cocaine facilitate alterations in neuronal homeostatic processes. Bioinformatic interrogation of the results from RNA deep sequencing of rat hippocampal neurons exposed to Tat alone indicated the dysregulation of several genes involved in lipid and cholesterol metabolism. Following exposure to Tat and cocaine, the activation of cholesterol biosynthesis genes led to increased levels of free cholesterol and cholesteryl esters in rat neurons. Results from lipid metabolism arrays validated upregulation of several processes implicated in the biogenesis of β-amyloid and Alzheimer’s disease (AD), including sterol o-acyltransferase 1/acetyl-coenzyme A acyltransferase 1 (SOAT1/ACAT1), sortilin-related receptor L1 (SORL1) and low-density lipoprotein receptor-related protein 12 (LRP12). Further studies in Tat-treated primary neuronal cultures and brain tissues from HIV-1 transgenic mice as well as SIV-infected macaques confirmed elevated levels of SOAT1/ACAT 1 proteins. Our results offer novel insights into the molecular events involved in HIV and cocaine-mediated neuronal dysfunction that may also contribute to neuropathogenic events associated with the development of AD.

Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two ‘4-targeted’ and ‘16-targeted’ experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations.