Abstract In eukaryotes, three RNA polymerases (RNAPs) play essential roles in the synthesis of various types of RNA: namely, RNAPI for rRNA; RNAPII for mRNA and most snRNAs; and RNAPIII for tRNA and Show more
Abstract In eukaryotes, three RNA polymerases (RNAPs) play essential roles in the synthesis of various types of RNA: namely, RNAPI for rRNA; RNAPII for mRNA and most snRNAs; and RNAPIII for tRNA and other small RNAs. All three RNAPs possess a short flexible tail derived from their common subunit RPB6. However, the function of this shared N-terminal tail (NTT) is not clear. Here we show that NTT interacts with the PH domain (PH-D) of the p62 subunit of the general transcription/repair factor TFIIH, and present the structures of RPB6 unbound and bound to PH-D by nuclear magnetic resonance (NMR). Using available cryo-EM structures, we modelled the activated elongation complex of RNAPII bound to TFIIH. We also provide evidence that the recruitment of TFIIH to transcription sites through the p62âRPB6 interaction is a common mechanism for transcription-coupled nucleotide excision repair (TC-NER) of RNAPI- and RNAPII-transcribed genes. Moreover, point mutations in the RPB6 NTT cause a significant reduction in transcription of RNAPI-, RNAPII-Â and RNAPIII-transcribed genes. These and other results show that the p62âRPB6 interaction plays multiple roles in transcription, TC-NER, and cell proliferation, suggesting that TFIIH is engaged in all RNAP systems. 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 Show more
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
Abstract TFIIH is a 10âsubunit complex that regulates RNA polymerase II (pol II) transcription but also serves other important biological roles. Although much remains unknown about TFIIH function in Show more
Abstract TFIIH is a 10âsubunit complex that regulates RNA polymerase II (pol II) transcription but also serves other important biological roles. Although much remains unknown about TFIIH function in eukaryotic cells, much progress has been made even in just the past few years, due in part to technological advances (e.g. cryoEM and single molecule methods) and the development of chemical inhibitors of TFIIH enzymes. This review focuses on the major cellular roles for TFIIH, with an emphasis on TFIIH function as a regulator of pol II transcription. We describe the structure of TFIIH and its roles in pol II initiation, promoterâproximal pausing, elongation, and termination. We also discuss cellular roles for TFIIH beyond transcription (e.g. DNA repair, cell cycle regulation) and summarize small molecule inhibitors of TFIIH and diseases associated with defects in TFIIH structure and function. Show less
Highly ordered interactions between immune and metabolic responses are evolutionarily conserved and paramount for tissue and organismal health. Disruption of these interactions underlies the emergence Show more
Highly ordered interactions between immune and metabolic responses are evolutionarily conserved and paramount for tissue and organismal health. Disruption of these interactions underlies the emergence of many pathologies, particularly chronic non-communicable diseases such as obesity and diabetes. Here, we examine decades of research identifying the complex immunometabolic signaling networks and the cellular and molecular events that occur in the setting of altered nutrient and energy exposures and offer a historical perspective. Furthermore, we describe recent advances such as the discovery that a broad complement of immune cells play a role in immunometabolism and the emerging evidence that nutrients and metabolites modulate inflammatory pathways. Lastly, we discuss how this work may eventually lead to tangible therapeutic advancements to promote health. Show less