👤 M. Hattori

We have designed cell-penetrating peptides that target the leucine zipper transcription factors ATF5, CEBPB and CEBPD and that promote apoptotic death of a wide range of cancer cell types, but not normal cells, in vitro and in vivo. Though such peptides have the potential for clinical application, their mechanisms of action are not fully understood. Here, we show that one such peptide, Dpep, compromises glucose uptake and glycolysis in a cell context-dependent manner (in about two-thirds of cancer lines assessed). These actions are dependent on induction of tumor suppressor TXNIP (thioredoxin-interacting protein) mRNA and protein. Knockdown studies show that TXNIP significantly contributes to apoptotic death in those cancer cells in which it is induced by Dpep. The metabolic actions of Dpep on glycolysis led us to explore combinations of Dpep with clinically approved drugs metformin and atovaquone that inhibit oxidative phosphorylation and that are in trials for cancer treatment. Dpep showed additive to synergistic activities in all lines tested. In summary, we find that Dpep induces TXNIP in a cell context-dependent manner that in turn suppresses glucose uptake and glycolysis and contributes to apoptotic death of a range of cancer cells. Show less

The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/ . Show less

Photochemical reactions of azo and triazo derivatives of mesoionic 1,3-diphenyltetrazolium heterocycles and related compounds were studied. The reaction paths were found to depend markedly on the types of substrate, substituent and reaction solvent giving diverse products. Upon irradiation of the 1,1′3,3′-tetraphenylazoditetrazolium salt 1, the addition of hydrogen and acetone to the NN bond was observed in methanol and acetone, respectively, whereas the bond was cleaved in diethyl ketone to give the 5-aminotetrazolium salt 10. The corresponding radical cation 11 also gave the reduction product in methanol. On the other hand, the 1,3-diphenyl-5-(phenylazo)tetrazolium salt 12 underwent nitrogen evolution giving the 1,3-diphenyltetrazolium salt 13via the corresponding tetrazolium radical. Triazene derivatives 14 and 17 underwent an N–N bond cleavage to give tetrazolio-5-amide 4. The mesoionic triazene compounds bearing a tosyl 18 or cyano group 19 gave products 20 and 23. Triphenylphosphinotriazene 24 liberated nitrogen to give phosphinoimide 25 and its hydrolysis product 10. Tetrazolylamide 26 lost a phenyldiazonium group from the 1,3-diphenyltetrazolium ring to give the guanidine derivative 27. Show less