Consistency of aortic distensibility and pulse wave velocity estimates with respect to the Bramwell-Hill theoretical model: a cardiovascular magnetic resonance study

<p>Abstract</p> <p>Background</p> <p>Arterial stiffness is considered as an independent predictor of cardiovascular mortality, and is increasingly used in clinical practice. This study aimed at evaluating the consistency of the automated estimation of regional and local aortic stiffness indices from cardiovascular magnetic resonance (CMR) data.</p> <p>Results</p> <p>Forty-six healthy subjects underwent carotid-femoral pulse wave velocity measurements (<it>CF_PWV</it>) by applanation tonometry and CMR with steady-state free-precession and phase contrast acquisitions at the level of the aortic arch. These data were used for the automated evaluation of the aortic arch pulse wave velocity (<it>Arch_PWV</it>), and the ascending aorta distensibility (<it>AA_Distc, AA_Distb)</it>, which were estimated from ascending aorta strain (<it>AA_Strain</it>) combined with either carotid or brachial pulse pressure. The local ascending aorta pulse wave velocity <it>AA_PWVc </it>and <it>AA_PWVb </it>were estimated respectively from these carotid and brachial derived distensibility indices according to the Bramwell-Hill theoretical model, and were compared with the <it>Arch_PWV</it>. In addition, a reproducibility analysis of <it>AA_PWV </it>measurement and its comparison with the standard <it>CF_PWV </it>was performed. Characterization according to the Bramwell-Hill equation resulted in good correlations between <it>Arch_PWV </it>and both local distensibility indices <it>AA_Distc </it>(r = 0.71, p < 0.001) and <it>AA_Distb </it>(r = 0.60, p < 0.001); and between <it>Arch_PWV </it>and both theoretical local indices <it>AA_PWVc </it>(r = 0.78, p < 0.001) and <it>AA_PWVb </it>(r = 0.78, p < 0.001). Furthermore, the <it>Arch_PWV </it>was well related to <it>CF_PWV </it>(r = 0.69, p < 0.001) and its estimation was highly reproducible (inter-operator variability: 7.1%).</p> <p>Conclusions</p> <p>The present work confirmed the consistency and robustness of the regional index <it>Arch_PWV </it>and the local indices <it>AA_Distc and AA_Distb </it>according to the theoretical model, as well as to the well established measurement of <it>CF_PWV</it>, demonstrating the relevance of the regional and local CMR indices.</p

Human Disease-Drug Network Based on Genomic Expression Profiles

BACKGROUND: Drug repositioning offers the possibility of faster development times and reduced risks in drug discovery. With the rapid development of high-throughput technologies and ever-increasing accumulation of whole genome-level datasets, an increasing number of diseases and drugs can be comprehensively characterized by the changes they induce in gene expression, protein, metabolites and phenotypes. METHODOLOGY/PRINCIPAL FINDINGS: We performed a systematic, large-scale analysis of genomic expression profiles of human diseases and drugs to create a disease-drug network. A network of 170,027 significant interactions was extracted from the approximately 24.5 million comparisons between approximately 7,000 publicly available transcriptomic profiles. The network includes 645 disease-disease, 5,008 disease-drug, and 164,374 drug-drug relationships. At least 60% of the disease-disease pairs were in the same disease area as determined by the Medical Subject Headings (MeSH) disease classification tree. The remaining can drive a molecular level nosology by discovering relationships between seemingly unrelated diseases, such as a connection between bipolar disorder and hereditary spastic paraplegia, and a connection between actinic keratosis and cancer. Among the 5,008 disease-drug links, connections with negative scores suggest new indications for existing drugs, such as the use of some antimalaria drugs for Crohn's disease, and a variety of existing drugs for Huntington's disease; while the positive scoring connections can aid in drug side effect identification, such as tamoxifen's undesired carcinogenic property. From the approximately 37K drug-drug relationships, we discover relationships that aid in target and pathway deconvolution, such as 1) KCNMA1 as a potential molecular target of lobeline, and 2) both apoptotic DNA fragmentation and G2/M DNA damage checkpoint regulation as potential pathway targets of daunorubicin. CONCLUSIONS/SIGNIFICANCE: We have automatically generated thousands of disease and drug expression profiles using GEO datasets, and constructed a large scale disease-drug network for effective and efficient drug repositioning as well as drug target/pathway identification

TEX264 coordinates p97- and SPRTN-mediated resolution of topoisomerase 1-DNA adducts

Eukaryotic topoisomerase 1 (TOP1) regulates DNA topology to ensure efficient DNA replication and transcription. TOP1 is also a major driver of endogenous genome instability, particularly when its catalytic intermediate—a covalent TOP1-DNA adduct known as a TOP1 cleavage complex (TOP1cc)—is stabilised. TOP1ccs are highly cytotoxic and a failure to resolve them underlies the pathology of neurological disorders but is also exploited in cancer therapy where TOP1ccs are the target of widely used frontline anti-cancer drugs. A critical enzyme for TOP1cc resolution is the tyrosyl-DNA phosphodiesterase (TDP1), which hydrolyses the bond that links a tyrosine in the active site of TOP1 to a 3’ phosphate group on a single-stranded (ss)DNA break. However, TDP1 can only process small peptide fragments from ssDNA ends, raising the question of how the ~90 kDa TOP1 protein is processed upstream of TDP1. Here we find that TEX264 fulfils this role by forming a complex with the p97 ATPase and the SPRTN metalloprotease. We show that TEX264 recognises both unmodified and SUMO1-modifed TOP1 and initiates TOP1cc repair by recruiting p97 and SPRTN. TEX264 localises to the nuclear periphery, associates with DNA replication forks, and counteracts TOP1ccs during DNA replication. Altogether, our study elucidates the existence of a specialised repair complex required for upstream proteolysis of TOP1ccs and their subsequent resolution