11 research outputs found
Colombian stingless bee honeys characterized by multivariate analysis of physicochemical properties
Acute effects of ethanol on action potential and intracellular Ca2+ transient in cardiac ventricular cells: a simulation study
Survey of Selective Neurotoxins
There has been an awareness of nerve poisons from ancient times. At the dawn of the twentieth century, the actions and mechanisms of these poisons were uncovered by modern physiological and biochemical experimentation. However, the era of selective neurotoxins began with the pioneering studies of R. Levi-Montalcini through her studies of the neurotrophin nerve growth factor (NGF), a protein promoting growth and development of sensory and sympathetic noradrenergic nerves. An antibody to NGF, namely, anti-NGF - developed in the 1950s in a collaboration with S. Cohen - was shown to produce an immunosympathectomy and virtual lifelong sympathetic denervation. These Nobel Laureates thus developed and characterized the first identifiable selective neurotoxin. Other selective neurotoxins were soon discovered, and the compendium of selective neurotoxins continues to grow, so that today there are numerous selective neurotoxins, with the potential to destroy or produce dysfunction of a variety of phenotypic nerves. Selective neurotoxins are of value because of their ability to selectively destroy or disable a common group of nerves possessing (1) a particular neural transporter, (2) a unique set of enzymes or vesicular transporter, (3) a specific type of receptor or (4) membranous protein, or (5) other uniqueness. The era of selective neurotoxins has developed to such an extent that the very definition of a selective neurotoxin has warped. For example, (1) N-methyl-D- aspartate receptor (NMDA-R) antagonists, considered to be neuroprotectants by virtue of their prevention of excitotoxicity from glutamate receptor agonists, actually lead to the demise of populations of neurons with NMDA receptors, when administered during ontogenetic development. The mere lack of natural excitation of this nerve population, consequent to NMDA-R block, sends a message that these nerves are redundant - and an apoptotic cascade is set in motion to eliminate these nerves. (2) The rodenticide rotenone, a global cytotoxin that acts mainly to inhibit complex I in the respiratory transport chain, is now used in low dose over a period of weeks to months to produce relatively selective destruction of substantia nigra dopaminergic nerves and promote alpha-synuclein deposition in brain to thus model Parkinson\u27s disease. Similarly, (3) glial toxins, affecting oligodendrocytes or other satellite cells, can lead to the damage or dysfunction of identifiable groups of neurons. Consequently, these toxins might also be considered as selective neurotoxins, despite the fact that the targeted cell is nonneuronal. Likewise, (4) the dopamine D2-receptor agonist quinpirole, administered daily for a week or more, leads to development of D2-receptor supersensitivity - exaggerated responses to the D2-receptor agonist, an effect persisting lifelong. Thus, neuroprotectants can become selective neurotoxins; nonspecific cytotoxins can become classified as selective neurotoxins; and receptor agonists, under defined dosing conditions, can supersensitize and thus be classified as selective neurotoxins. More examples will be uncovered as the area of selective neurotoxins expands. The description and characterization of selective neurotoxins, with unmasking of their mechanisms of action, have led to a level of understanding of neuronal activity and reactivity that could not be understood by conventional physiological observations. This chapter will be useful as an introduction to the scope of the field of selective neurotoxins and provide insight for in-depth analysis in later chapters with full descriptions of selective neurotoxins
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GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19
Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability:
Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)