👤 Lalehan Akyüz

Propylene glycol (PG) is incorporated into ruminant diets to boost glucogenic energy availability, yet its precise effects on adipose tissue development remain incompletely defined. The study was designed as a 3 × 3 factorial experiment with two independent variables: dose of PG and duration of fattening. Three groups were formed, including a dose group of PG 1.5 mL/kg live weight (PG1.5), a dose group of PG 3 mL/kg live weight (PG3), and a group without PG (PG0). Gluteal adipose tissues were collected from animals slaughtered on days 60, 90, and 120. mRNA, protein, and fatty acid profiles were analyzed. Protein-protein interaction and gene set enrichment analysis were also performed. On day 60, FABP4 was approximately 3-fold higher at both mRNA and protein levels in PG3 compared to PG0, nearly 2-fold higher at the protein level in PG1.5, and SREBP-1c protein levels were reduced in PG1.5 compared to PG0. On day 120, FABP4, PPARγ, C/EBPα exhibited an increasing trend at both mRNA and protein levels in PG groups, whereas SREBP-1c was decreased in PG3. Fatty acid profiling revealed C16:0, C18:0, and C18:1 comprised over 70% of total lipids. PG supplementation shifted the profile toward unsaturated species, reducing saturated fatty acid proportions and enhancing nutritional indices, particularly in PG1.5. Findings at the bioinformatics levels demonstrate PG exerts clear dose- and time-dependent modulation of adipogenic transcription factors, fatty acid composition, and molecular interaction networks in lamb adipose tissue. Early PG3 feeding elevates FABP4 and suppresses SREBP-1c, whereas prolonged supplementation enhances PPARγ and C/EBPα and drives a favorable shift in lipid profiles. Network and pathway analyses reveal coordinated regulation via NR1H3/RXR and PPAR axes, suggesting PG not only optimizes energy partitioning but also supports cellular homeostasis. These results could contribute to the development of potential strategies aimed at supporting adipose tissue quality and metabolic health in sheep. Show less

The electron conformational and genetic algorithm methods (EC-GA) were integrated for the identification of the pharmacophore group and predicting the anti HIV-1 activity of tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepinone (TIBO) derivatives. To reveal the pharmacophore group, each conformation of all compounds was arranged by electron conformational matrices of congruity. Multiple comparisons of these matrices, within given tolerances for high active and low active TIBO derivatives, allow the identification of the pharmacophore group that refers to the electron conformational submatrix of activity. The effects of conformations, internal and external validation were investigated by four different models based on an ensemble of conformers and a single conformer, both with and without a test set. Model 1 using an ensemble of conformers for the training (39 compounds) and test sets (13 compounds), obtained by the optimum seven parameters, gave satisfactory results (R²(training) = 0.878, R²(test)= 0.910, q² = 0.840, q²(ext1) = 0.926 and q²(ext2) = 0.900). Show less

In this work, the EC-GA method, a hybrid 4D-QSAR approach that combines the electron conformational (EC) and genetic algorithm optimization (GA) methods, was applied in order to explain pharmacophore (Pha) and predict anti-HIV-1 activity by studying 115 compounds in the class of 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio) thymine (HEPT) derivatives as non-nucleoside reverse transcriptase inhibitors (NNRTIs). The series of NNRTIs were partitioned into four training and test sets from which corresponding quantitative structure-activity relationship (QSAR) models were constructed. Analysis of the four QSAR models suggests that the three models generated from the training and test sets used in previous works yielded comparable results with those of previous studies. Model 4, the data set of which was partitioned randomly into two training and test sets with 11 descriptors, including electronical and geometrical parameters, showed good statistics both in the regression (r2(training) )= 0.867, r2test = 0.923) and cross-validation (q (2) = 0.811, q2(ext1) = 0.909, q2(ext2) = 0.909) for the training set of 80 compounds and the test set of 27 compounds. The prediction of the anti-HIV-1 activity of HEPT compounds by means of the EC-GA method allowed for a quantitatively consistent QSAR model. In addition, eight novel compounds never tested experimentally have been designed theoretically using model 4. Show less