Dietary and plasma blood α-linolenic acid as modulators of fat oxidation and predictors of aerobic performance

Background Among n-3 polyunsaturated fatty acids (PUFAs), the most important is α-linolenic acid (ALA). The biological activity of ALA is not equivalent to that of the long-chain n-3 PUFAs, and it has pleiotropic effects, such as functioning as an energy substrate during long-term training when carbohydrate reserves are depleted. The purpose of this investigation was to study the link between the essential dietary and plasma ALA and aerobic performance, which is estimated via maximal fat oxidation (MFO), among skiers. Methods Twenty-four highly trained male athletes from the Russian cross-country skiing team participated in the study. ALA intake was determined by an original program used to assess the actual amount and frequency of fat consumption. The plasma level of ALA was determined using gas-liquid chromatography. The skiers’ aerobic performance was estimated via MFO and determined by indirect calorimetry using the system “Oxycon Pro”. Results The consumption of ALA in the diet in half of the skiers was below the recommended level at 0.5 ± 0.2 g/day. The deficiency of plasma ALA levels was on average 0.2 ± 0.1 Mol% for almost all participants. The consumption of ALA in the diet and its level in plasma were associated with MFO (rs = 0.507, p = 0.011; rs = 0.460, p = 0.023). Levels of ALA in plasma (p = 0.0523) and the consumption of ALA in the diet (p = 0.0039) were associated with high aerobic performance. Conclusions ALA in the diet of the athletes may be used as nutritional support to increase MFO and aerobic performance

Gene Transcription as a Therapeutic Target in Leukemia

Blood malignancies often arise from undifferentiated hematopoietic stem cells or partially differentiated stem-like cells. A tight balance of multipotency and differentiation, cell division, and quiescence underlying normal hematopoiesis requires a special program governed by the transcriptional machinery. Acquisition of drug resistance by tumor cells also involves reprogramming of their transcriptional landscape. Limiting tumor cell plasticity by disabling reprogramming of the gene transcription is a promising strategy for improvement of treatment outcomes. Herein, we review the molecular mechanisms of action of transcription-targeted drugs in hematological malignancies (largely in leukemia) with particular respect to the results of clinical trials

Limitations of Tamoxifen Application for In Vivo Genome Editing Using Cre/ERT2 System

Inducible Cre-dependent systems are frequently used to produce both conditional knockouts and transgenic mice with regulated expression of the gene of interest. Induction can be achieved by doxycycline-dependent transcription of the wild type gene or OH-tamoxifen-dependent nuclear translocation of the chimeric Cre/ERT2 protein. However, both of these activation strategies have some limitations. We analyzed the efficiency of knockout in different tissues and found out that it correlates with the concentration of the hydroxytamoxifen and endoxifen—the active metabolites of tamoxifen—measured by LC-MS in these tissues. We also describe two cases of Cdk8floxed/floxed/Rosa-Cre-ERT2 mice tamoxifen-induced knockout limitations. In the first case, the standard scheme of tamoxifen administration does not lead to complete knockout formation in the brain or in the uterus. Tamoxifen metabolite measurements in multiple tissues were performed and it has been shown that low recombinase activity in the brain is due to the low levels of tamoxifen active metabolites. Increase of tamoxifen dosage (1.5 fold) and duration of activation (from 5 to 7 days) allowed us to significantly improve the knockout rate in the brain, but not in the uterus. In the second case, knockout induction during embryonic development was impossible due to the negative effect of tamoxifen on gestation. Although DNA editing in the embryos was achieved in some cases, the treatment led to different complications of the pregnancy in wild-type female mice. We propose to use doxycycline-induced Cre systems in such models

QSAR Modeling and Prediction of Drug–Drug Interactions

Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100 000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug–drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27 966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72–79% for the external test sets with a coverage of 81.36–100% when a conservative threshold for the model’s applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database