Ternary copper(II)-polypyridyl enantiomers: aldol-type condensation, characterization, DNA-binding recognition, BSA-binding and anticancer property

Chiral enantiomers [Cu(phen)(l-threo)(H2O)]NO31 and [Cu(phen)(d-threo)(H2O)]NO32 (threo = threoninate) underwent aldol-type condensation with formaldehyde, with retention of chirality, to yield their respective enantiomeric ternary copper(ii) complexes, viz.l- and d-[Cu(phen)(5MeOCA)(H2O)]NO3·xH 2O (3 and 4; phen = 1,10-phenanthroline; 5MeOCA = 5-methyloxazolidine-4-carboxylate; x = 0-3) respectively. These chiral complexes were characterized by FTIR, elemental analysis, circular dichroism, UV-Visible spectroscopy, fluorescence spectroscopy (FL), molar conductivity measurement, ESI-MS and X-ray crystallography. Analysis of restriction enzyme inhibition by these four complexes revealed modulation of DNA binding selectivity by the type of ligand, ligand modification and chirality. Their interaction with bovine serum albumin was investigated by FL and electronic spectroscopy. With the aid of the crystal structure of BSA, spectroscopic evidence suggested their binding at the cavity containing Trp134 with numerous Tyr residues in subdomain IA. The products were more antiproliferative than cisplatin against cancer cell lines HK-1, MCF-7, HCT116, HSC-2 and C666-1 except HL-60, and were selective towards nasopharyngeal cancer HK-1 cells over normal NP69 cells of the same organ type

A global reference for human genetic variation

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.We thank the many people who were generous with contributing their samples to the project: the African Caribbean in Barbados; Bengali in Bangladesh; British in England and Scotland; Chinese Dai in Xishuangbanna, China; Colombians in Medellin, Colombia; Esan in Nigeria; Finnish in Finland; Gambian in Western Division – Mandinka; Gujarati Indians in Houston, Texas, USA; Han Chinese in Beijing, China; Iberian populations in Spain; Indian Telugu in the UK; Japanese in Tokyo, Japan; Kinh in Ho Chi Minh City, Vietnam; Luhya in Webuye, Kenya; Mende in Sierra Leone; people with African ancestry in the southwest USA; people with Mexican ancestry in Los Angeles, California, USA; Peruvians in Lima, Peru; Puerto Ricans in Puerto Rico; Punjabi in Lahore, Pakistan; southern Han Chinese; Sri Lankan Tamil in the UK; Toscani in Italia; Utah residents (CEPH) with northern and western European ancestry; and Yoruba in Ibadan, Nigeria. Many thanks to the people who contributed to this project: P. Maul, T. Maul, and C. Foster; Z. Chong, X. Fan, W. Zhou, and T. Chen; N. Sengamalay, S. Ott, L. Sadzewicz, J. Liu, and L. Tallon; L. Merson; O. Folarin, D. Asogun, O. Ikpwonmosa, E. Philomena, G. Akpede, S. Okhobgenin, and O. Omoniwa; the staff of the Institute of Lassa Fever Research and Control (ILFRC), Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria; A. Schlattl and T. Zichner; S. Lewis, E. Appelbaum, and L. Fulton; A. Yurovsky and I. Padioleau; N. Kaelin and F. Laplace; E. Drury and H. Arbery; A. Naranjo, M. Victoria Parra, and C. Duque; S. Däkel, B. Lenz, and S. Schrinner; S. Bumpstead; and C. Fletcher-Hoppe. Funding for this work was from the Wellcome Trust Core Award 090532/Z/09/Z and Senior Investigator Award 095552/Z/11/Z (P.D.), and grants WT098051 (R.D.), WT095908 and WT109497 (P.F.), WT086084/Z/08/Z and WT100956/Z/13/Z (G.M.), WT097307 (W.K.), WT0855322/Z/08/Z (R.L.), WT090770/Z/09/Z (D.K.), the Wellcome Trust Major Overseas program in Vietnam grant 089276/Z.09/Z (S.D.), the Medical Research Council UK grant G0801823 (J.L.M.), the UK Biotechnology and Biological Sciences Research Council grants BB/I02593X/1 (G.M.) and BB/I021213/1 (A.R.L.), the British Heart Foundation (C.A.A.), the Monument Trust (J.H.), the European Molecular Biology Laboratory (P.F.), the European Research Council grant 617306 (J.L.M.), the Chinese 863 Program 2012AA02A201, the National Basic Research program of China 973 program no. 2011CB809201, 2011CB809202 and 2011CB809203, Natural Science Foundation of China 31161130357, the Shenzhen Municipal Government of China grant ZYC201105170397A (J.W.), the Canadian Institutes of Health Research Operating grant 136855 and Canada Research Chair (S.G.), Banting Postdoctoral Fellowship from the Canadian Institutes of Health Research (M.K.D.), a Le Fonds de Recherche duQuébec-Santé (FRQS) research fellowship (A.H.), Genome Quebec (P.A.), the Ontario Ministry of Research and Innovation – Ontario Institute for Cancer Research Investigator Award (P.A., J.S.), the Quebec Ministry of Economic Development, Innovation, and Exports grant PSR-SIIRI-195 (P.A.), the German Federal Ministry of Education and Research (BMBF) grants 0315428A and 01GS08201 (R.H.), the Max Planck Society (H.L., G.M., R.S.), BMBF-EPITREAT grant 0316190A (R.H., M.L.), the German Research Foundation (Deutsche Forschungsgemeinschaft) Emmy Noether Grant KO4037/1-1 (J.O.K.), the Beatriu de Pinos Program grants 2006 BP-A 10144 and 2009 BP-B 00274 (M.V.), the Spanish National Institute for Health Research grant PRB2 IPT13/0001-ISCIII-SGEFI/FEDER (A.O.), Ewha Womans University (C.L.), the Japan Society for the Promotion of Science Fellowship number PE13075 (N.P.), the Louis Jeantet Foundation (E.T.D.), the Marie Curie Actions Career Integration grant 303772 (C.A.), the Swiss National Science Foundation 31003A_130342 and NCCR “Frontiers in Genetics” (E.T.D.), the University of Geneva (E.T.D., T.L., G.M.), the US National Institutes of Health National Center for Biotechnology Information (S.S.) and grants U54HG3067 (E.S.L.), U54HG3273 and U01HG5211 (R.A.G.), U54HG3079 (R.K.W., E.R.M.), R01HG2898 (S.E.D.), R01HG2385 (E.E.E.), RC2HG5552 and U01HG6513 (G.T.M., G.R.A.), U01HG5214 (A.C.), U01HG5715 (C.D.B.), U01HG5718 (M.G.), U01HG5728 (Y.X.F.), U41HG7635 (R.K.W., E.E.E., P.H.S.), U41HG7497 (C.L., M.A.B., K.C., L.D., E.E.E., M.G., J.O.K., G.T.M., S.A.M., R.E.M., J.L.S., K.Y.), R01HG4960 and R01HG5701 (B.L.B.), R01HG5214 (G.A.), R01HG6855 (S.M.), R01HG7068 (R.E.M.), R01HG7644 (R.D.H.), DP2OD6514 (P.S.), DP5OD9154 (J.K.), R01CA166661 (S.E.D.), R01CA172652 (K.C.), P01GM99568 (S.R.B.), R01GM59290 (L.B.J., M.A.B.), R01GM104390 (L.B.J., M.Y.Y.), T32GM7790 (C.D.B., A.R.M.), P01GM99568 (S.R.B.), R01HL87699 and R01HL104608 (K.C.B.), T32HL94284 (J.L.R.F.), and contracts HHSN268201100040C (A.M.R.) and HHSN272201000025C (P.S.), Harvard Medical School Eleanor and Miles Shore Fellowship (K.L.), Lundbeck Foundation Grant R170-2014-1039 (K.L.), NIJ Grant 2014-DN-BX-K089 (Y.E.), the Mary Beryl Patch Turnbull Scholar Program (K.C.B.), NSF Graduate Research Fellowship DGE-1147470 (G.D.P.), the Simons Foundation SFARI award SF51 (M.W.), and a Sloan Foundation Fellowship (R.D.H.). E.E.E. is an investigator of the Howard Hughes Medical Institute

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

The effect of lersivirine, a next-generation NNRTI, on the pharmacokinetics of midazolam and oral contraceptives in healthy subjects

Purpose: Lersivirine is a next-generation non-nucleoside reverse transcriptase inhibitor (NNRTI) with a unique resistance profile that exhibits potent antiretroviral activity against wild-type human immunodeficiency virus and clinically relevant NNRTI-resistant strains. Results from in vitro and in vivo investigations suggest that lersivirine is a cytochrome P450 (CYP3A4) inducer that is metabolized by CYP3A4 and uridine diphosphate glucuronosyltransferase (UGT) 2B7. In order to formally assess the effects of lersivirine on CYP3A4 metabolism and/or glucuronidation, we performed studies aimed at investigating the effects of lersivirine co-administration on the pharmacokinetics (PK) of midazolam, ethinylestradiol and levonorgestrel. Methods: Two drug-drug interaction studies were performed. Healthy subjects were co-administered (1) single dose midazolam, a prototypical CYP3A4 substrate, followed by 14 days of lersivirine twice daily with single dose midazolam on the final day of lersivirine dosing or (2) 10 days of once-daily (QD) lersivirine and QD oral contraceptives (OCs; ethinylestradiol and levonorgestrel), substrates for CYP3A4, UGT2B7, and/or P-glycoprotein. The effects of co-administration on the PK parameters of midazolam and OCs were assessed. Results: At clinically relevant lersivirine doses (500-1,000 mg total daily dose), the mean plasma exposure of midazolam was reduced in a dose-dependent manner by 20-36 %. Co-administration of lersivirine 1,000 mg QD with OCs had minor PK effects, increasing ethinylestradiol exposure by 10 % and reducing levonorgestrel exposure by 13 %. Conclusions: These data further support previous observations that lersivirine is a weak CYP3A4 inducer, a weak inhibitor of glucuronidation, and a P-glycoprotein inhibitor. In both studies, lersivirine appeared to have a good safety and tolerability profile. © 2012 Springer-Verlag.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

The effect of lersivirine, a next-generation NNRTI, on the pharmacokinetics of midazolam and oral contraceptives in healthy subjects

Purpose: Lersivirine is a next-generation non-nucleoside reverse transcriptase inhibitor (NNRTI) with a unique resistance profile that exhibits potent antiretroviral activity against wild-type human immunodeficiency virus and clinically relevant NNRTI-resistant strains. Results from in vitro and in vivo investigations suggest that lersivirine is a cytochrome P450 (CYP3A4) inducer that is metabolized by CYP3A4 and uridine diphosphate glucuronosyltransferase (UGT) 2B7. In order to formally assess the effects of lersivirine on CYP3A4 metabolism and/or glucuronidation, we performed studies aimed at investigating the effects of lersivirine co-administration on the pharmacokinetics (PK) of midazolam, ethinylestradiol and levonorgestrel. Methods: Two drug-drug interaction studies were performed. Healthy subjects were co-administered (1) single dose midazolam, a prototypical CYP3A4 substrate, followed by 14 days of lersivirine twice daily with single dose midazolam on the final day of lersivirine dosing or (2) 10 days of once-daily (QD) lersivirine and QD oral contraceptives (OCs; ethinylestradiol and levonorgestrel), substrates for CYP3A4, UGT2B7, and/or P-glycoprotein. The effects of co-administration on the PK parameters of midazolam and OCs were assessed. Results: At clinically relevant lersivirine doses (500-1,000 mg total daily dose), the mean plasma exposure of midazolam was reduced in a dose-dependent manner by 20-36 %. Co-administration of lersivirine 1,000 mg QD with OCs had minor PK effects, increasing ethinylestradiol exposure by 10 % and reducing levonorgestrel exposure by 13 %. Conclusions: These data further support previous observations that lersivirine is a weak CYP3A4 inducer, a weak inhibitor of glucuronidation, and a P-glycoprotein inhibitor. In both studies, lersivirine appeared to have a good safety and tolerability profile. © 2012 Springer-Verlag.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Dynamic phenotypic heterogeneity and the evolution of multiple RNA subtypes in hepatocellular carcinoma: the PLANET study

Intra-tumor heterogeneity (ITH) is a key challenge in cancer treatment, but previous studies have focused mainly on the genomic alterations without exploring phenotypic (transcriptomic and immune) heterogeneity. Using one of the largest prospective surgical cohorts for hepatocellular carcinoma (HCC) with multi-region sampling, we sequenced whole genomes and paired transcriptomes from 67 HCC patients (331 samples). We found that while genomic ITH was rather constant across stages, phenotypic ITH had a very different trajectory and quickly diversified in stage II patients. Most strikingly, 30% of patients were found to contain more than one transcriptomic subtype within a single tumor. Such phenotypic ITH was found to be much more informative in predicting patient survival than genomic ITH and explains the poor efficacy of single-target systemic therapies in HCC. Taken together, we not only revealed an unprecedentedly dynamic landscape of phenotypic heterogeneity in HCC, but also highlighted the importance of studying phenotypic evolution across cancer types.National Medical Research Council (NMRC)National Research Foundation (NRF)Published versionThis work is supported in part by the Singapore National Medical Research Council grants (TCR/015-NCC/2016, CIRG18may-0057l, NMRC/CSA-SI/0018/2017, and NMRC/ OFIRG/0064/2017) and the National Research Foundation, Singapore (NRF-NRFF2015-04). W.Z. is supported in part by the National Key R&D Program of China (2018YFC1406902 and 2018YFC0910400), the National Natural Science Foundation of China (31970566), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDPB17). H.Y. is supported by the National Natural Science Foundation of China (32000407)

Multi-region sampling with paired sample sequencing analyses reveals sub-groups of patients with novel patient-specific dysregulation in Hepatocellular Carcinoma

Abstract Background Conventional differential expression (DE) testing compares the grouped mean value of tumour samples to the grouped mean value of the normal samples, and may miss out dysregulated genes in small subgroup of patients. This is especially so for highly heterogeneous cancer like Hepatocellular Carcinoma (HCC). Methods Using multi-region sampled RNA-seq data of 90 patients, we performed patient-specific differential expression testing, together with the patients’ matched adjacent normal samples. Results Comparing the results from conventional DE analysis and patient-specific DE analyses, we show that the conventional DE analysis omits some genes due to high inter-individual variability present in both tumour and normal tissues. Dysregulated genes shared in small subgroup of patients were useful in stratifying patients, and presented differential prognosis. We also showed that the target genes of some of the current targeted agents used in HCC exhibited highly individualistic dysregulation pattern, which may explain the poor response rate. Discussion/conclusion Our results highlight the importance of identifying patient-specific DE genes, with its potential to provide clinically valuable insights into patient subgroups for applications in precision medicine