PHILOSOPHICAL CONSIDERATIONS ON BRAIN DEATH AND THE CONCEPT OF THE ORGANISM AS A WHOLE

Since intensive care medicine enables us to maintain blood circulation and respiration artificially for some time, the usual criteria for death, such as cardiac arrest and cessation of respiration, are not applicable in all cases. Thus, the irreversible breakdown of the brain functions have come to be accepted as the most prominent factor for the occurrence of death. This criterion is linked primarily to the disintegration of the organism as a whole. Yet the controversy surrounding the moment when a man can be declared dead has not yet been resolved. The decisive weak point in this controversial discussion seems to be that the notion of the "organism as a whole" is inadequately defined. The aim of this work is to fill this void. We developed four general criteria of life: integration, coordination, dynamics, and immanency. Moreover, four additional characteristics are necessary for a living being (organism as a whole): completion, indivisibility, autofinality, and identity. If one of these four characteristics is missing we can only speak of derivative life but not of a living being. In a brain dead body one finds a number of signs of life. These signs of life, however, are not signs of an organism as a whole but signs of a physiological combination of organs whose parts — directed from the outside - are dependent on each other. The brain dead body lacks the four criteria of a living being. Thus it is no longer a living person but purely derivated biological life

PHILOSOPHICAL CONSIDERATIONS ON BRAIN DEATH AND THE CONCEPT OF THE ORGANISM AS A WHOLE

Since intensive care medicine enables us to maintain blood circulation and respiration artificially for some time, the usual criteria for death, such as cardiac arrest and cessation of respiration, are not applicable in all cases. Thus, the irreversible breakdown of the brain functions have come to be accepted as the most prominent factor for the occurrence of death. This criterion is linked primarily to the disintegration of the organism as a whole. Yet the controversy surrounding the moment when a man can be declared dead has not yet been resolved. The decisive weak point in this controversial discussion seems to be that the notion of the "organism as a whole" is inadequately defined. The aim of this work is to fill this void. We developed four general criteria of life: integration, coordination, dynamics, and immanency. Moreover, four additional characteristics are necessary for a living being (organism as a whole): completion, indivisibility, autofinality, and identity. If one of these four characteristics is missing we can only speak of derivative life but not of a living being. In a brain dead body one finds a number of signs of life. These signs of life, however, are not signs of an organism as a whole but signs of a physiological combination of organs whose parts — directed from the outside - are dependent on each other. The brain dead body lacks the four criteria of a living being. Thus it is no longer a living person but purely derivated biological life

Cross-platform genetic discovery of small molecule products of metabolism and application to clinical outcomes

Circulating levels of small molecules or metabolites are highly heritable, but the impact of genetic differences in metabolism on human health is not well understood. In this cross-platform, genome-wide meta-analysis of 174 metabolite levels across six cohorts including up to 86,507 participants (70% unpublished data), we identify 499 (362 novel) genome-wide significant associations (p<4.9×10 -10 ) at 144 (94 novel) genomic regions. We show that inheritance of blood metabolite levels in the general population is characterized by pleiotropy, allelic heterogeneity, rare and common variants with large effects, non-linear associations, and enrichment for nonsynonymous variation in transporter and enzyme encoding genes. The majority of identified genes are known to be involved in biochemical processes regulating metabolite levels and to cause monogenic inborn errors of metabolism linked to specific metabolites, such as ASNS (rs17345286, MAF=0.27) and asparagine levels. We illustrate the influence of metabolite-associated variants on human health including a shared signal at GLP2R (p.Asp470Asn) associated with higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes risk, and demonstrate beta-arrestin signalling as the underlying mechanism in cellular models. We link genetically-higher serine levels to a 95% reduction in the likelihood of developing macular telangiectasia type 2 [odds ratio (95% confidence interval) per standard deviation higher levels 0.05 (0.03-0.08; p=9.5×10 -30 )]. We further demonstrate the predictive value of genetic variants identified for serine or glycine levels for this rare and difficult to diagnose degenerative retinal disease [area under the receiver operating characteristic curve: 0.73 (95% confidence interval: 0.70-0.75)], for which low serine availability, through generation of deoxysphingolipids, has recently been shown to be causally relevant. These results show that integration of human genomic variation with circulating small molecule data obtained across different measurement platforms enables efficient discovery of genetic regulators of human metabolism and translation into clinical insights.M.P. was supported by a fellowship from the German Research Foundation (DFG PI 1446/2-1). C.O. was founded by an early career fellowship at Homerton College, University of Cambridge. L. B. L. W. acknowledges funding by the Wellcome Trust (WT083442AIA). J.G. was supported by grants from the Medical Research Council (MC_UP_A090_1006, MC_PC_13030, MR/P011705/1 and MR/P01836X/1). Work in the Reimann/Gribble laboratories was supported by the Wellcome Trust (106262/Z/14/Z and 106263/Z/14/Z), UK Medical Research Council (MRC_MC_UU_12012/3) and PhD funding for EKB from MedImmune/AstraZeneca. Praveen Surendran is supported by a Rutherford Fund Fellowship from the Medical Research Council grant MR/S003746/1. A. W. is supported by a BHF-Turing Cardiovascular Data Science Award and by the EC-Innovative Medicines Initiative (BigData@Heart). J.D. is funded by the National Institute for Health Research [Senior Investigator Award] [*]. The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 and MC-UU_12015/1) and Cancer Research UK (C864/A14136). The genetics work in the EPIC-Norfolk study was funded by the Medical Research Council (MC_PC_13048). Metabolite measurements in the EPIC-Norfolk study were supported by the MRC Cambridge Initiative in Metabolic Science (MR/L00002/1) and the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372. We are grateful to all the participants who have been part of the project and to the many members of the study teams at the University of Cambridge who have enabled this research. The Fenland Study is supported by the UK Medical Research Council (MC_UU_12015/1 and MC_PC_13046). Participants in the INTERVAL randomised controlled trial were recruited with the active collaboration of NHS Blood and Transplant England (www.nhsbt.nhs.uk), which has supported field work and other elements of the trial. DNA extraction and genotyping was co-funded by the National Institute for Health Research (NIHR), the NIHR BioResource (http://bioresource.nihr.ac.uk) and the NIHR [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] [*]. Nightingale Health NMR assays were funded by the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). Metabolon Metabolomics assays were funded by the NIHR 26 BioResource and the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] [*]. The academic coordinating centre for INTERVAL was supported by core funding from: NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024), UK Medical Research Council (MR/L003120/1), British Heart Foundation (SP/09/002; RG/13/13/30194; RG/18/13/33946) and the NIHR [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] [*].The academic coordinating centre would like to thank blood donor centre staff and blood donors for participating in the INTERVAL trial. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. *The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. UK Biobank: This research has been conducted using the UK Biobank resource under Application Number 44448

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation

Reduced responsiveness of the reward system is associated with tolerance to cannabis impairment in chronic users

Cannabis is the most commonly used illicit drug in the world. However, because of a changing legal landscape and rising interest in therapeutic utility, there is an increasing trend in (long‐term) use and possibly cannabis impairment. Importantly, a growing body of evidence suggests that regular cannabis users develop tolerance to the impairing, as well as the rewarding, effects of the drug. However, the neuroadaptations that may underlie cannabis tolerance remain unclear. Therefore, this double‐blind, randomized, placebo‐controlled, cross‐over study assessed the acute influence of cannabis on the brain and behavioral outcomes in two distinct cannabis user groups. Twelve occasional and 12 chronic cannabis users received acute doses of cannabis (300‐μg/kg delta‐9‐tetrahydrocannabinol) and placebo and underwent ultrahigh field functional magnetic resonance imaging and magnetic resonance spectroscopy. In occasional users, cannabis induced significant neurometabolic alterations in reward circuitry, namely, decrements in functional connectivity and increments in striatal glutamate concentrations, which were associated with increases in subjective high and decreases in performance on a sustained attention task. Such changes were absent in chronic users. The finding that cannabis altered circuitry and distorted behavior in occasional, but not chronic users, suggests reduced responsiveness of the reward circuitry to cannabis intoxication in chronic users. Taken together, the results suggest a pharmacodynamic mechanism for the development of tolerance to cannabis impairment, of which is important to understand in the context of the long‐term therapeutic use of cannabis‐based medications, as well as in the context of public health and safety of cannabis use when performing day‐to‐day operations

Experimental & Molecular Medicine / IRF1 is critical for the TNF-driven interferon response in rheumatoid fibroblast-like synoviocytes : JAKinibs suppress the interferon response in RA-FLSs

Rheumatoid arthritis (RA) is an autoimmune disease characterized by persistent synovial inflammation. The major drivers of synovial inflammation are cytokines and chemokines. Among these molecules, TNF activates fibroblast-like synoviocytes (FLSs), which leads to the production of inflammatory mediators. Here, we show that TNF regulates the expression of the transcription factor interferon regulatory factor 1 (IRF1) in human FLSs as well as in a TNF transgenic arthritis mouse model. Transcriptomic analyses of IRF1-deficient, TNF-stimulated FLSs define the interferon (IFN) pathway as a major target of IRF1. IRF1 expression is associated with the expression of IFN, which leads to the activation of the JAK-STAT pathway. Blocking the JAK-STAT pathway with the Janus kinase inhibitor (JAKinib) baricitinib or tofacitinib reduces the expression of IFN-regulated genes (IRGs) in TNF-activated FLSs. Therefore, we conclude that TNF induces a distinct inflammatory cascade, in which IRGs are key elements, in FLSs. The IFN-signature might be a promising biomarker for the efficient and personalized use of new treatment strategies for RA, such as JAKinibs.(VLID)491927

M4 Safety and tolerability of BN82451B in huntington’s disease

Background BN82451B is a small, orally active molecule with good CNS penetration. Preclinical studies in tgHD R6/2 mice suggested improved motor function and prolonged survival. In addition antidyskinetic activity was observed in other models. The proposed mechanisms of action (MOA) are (1) antiexcytotoxic due to a sodium channel blocking potential, (2) antioxidant, (3) anti-inflammatory due to a cyclooxygenase (COX) inhibitory potential and (4) mitochondrial protective. Aims The primary objective of this phase 2a study (NCT02231580) is to investigate the safety and tolerability of BN82451B bid versus placebo for 28 days in male HD subjects. Secondary objectives include assessment of pharmacokinetics and of pharmacodynamics via the effects on quantitative motor (Q-Motor) measures. UHDRS subscales are implemented as exploratory measures. Methods Subjects: We intend to recruit 30 male HD subjects. 24 receive BN82451B and 6 placebo. The study is conducted in an inpatient setting at a single phase I unit in Germany. Design A sequential design was chosen to enable dose escalation starting with 40 mg bid with a potential maximum dose of 80 mg bid. Three subsequent cohorts of 10 patients each are randomised with different starting doses. Subjects in group one are treated with 40 mg bid for 14 days and may be increased to 60 mg bid the subsequent 14 days. In group 2, subjects may first receive 60 mg bid with possible increase to 80 mg bid. Group 3 subjects may receive 80 mg bid for 28 days. Dose increases in the consecutive groups are subject to approval by a Data Review Committee (DRC). The decision to increase the dose in individual patient will be based on the investigator’s judgement. Results Results of the study are expected for Q4/2016. Conclusions Recruitment in this trial is difficult as in-patient periods of nearly one month are logistically challenging. Safety data will be available soon and pharmacodynamics readouts such as Q-motor measures may help to guide decisions on the further path of development of BN82451B

A cross-platform approach identifies genetic regulators of human metabolism and health.

In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10-10) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at GLP2R (p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights