Role of p73 in Alzheimer disease: lack of association in mouse models or in human cohorts.

BACKGROUND: P73 belongs to the p53 family of cell survival regulators with the corresponding locus Trp73 producing the N-terminally distinct isoforms, TAp73 and DeltaNp73. Recently, two studies have implicated the murine Trp73 in the modulation in phospho-tau accumulation in aged wild type mice and in young mice modeling Alzheimer's disease (AD) suggesting that Trp73, particularly the DeltaNp73 isoform, links the accumulation of amyloid peptides to the creation of neurofibrillary tangles (NFTs). Here, we reevaluated tau pathologies in the same TgCRND8 mouse model as the previous studies. RESULTS: Despite the use of the same animal models, our in vivo studies failed to demonstrate biochemical or histological evidence for misprocessing of tau in young compound Trp73+/- + TgCRND8 mice or in aged Trp73+/- mice analyzed at the ages reported previously, or older. Secondly, we analyzed an additional mouse model where the DeltaNp73 was specifically deleted and confirmed a lack of impact of the DeltaNp73 allele, either in heterozygous or homozygous form, upon tau pathology in aged mice. Lastly, we also examined human TP73 for single nucleotide polymorphisms (SNPs) and/or copy number variants in a meta-analysis of 10 AD genome-wide association datasets. No SNPs reached significance after correction for multiple testing and no duplications/deletions in TP73 were found in 549 cases of AD and 544 non-demented controls. CONCLUSION: Our results fail to support P73 as a contributor to AD pathogenesis.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

15th Annual Environmental Law Institute

Materials from the 15th Annual Environmental Law Institute held by UK/CLE in March 1999

Association of Long Runs of Homozygosity With Alzheimer Disease Among African American Individuals

IMPORTANCE: Mutations in known causal Alzheimer disease (AD) genes account for only 1% to 3% of patients and almost all are dominantly inherited. Recessive inheritance of complex phenotypes can be linked to long (>1-megabase [Mb]) runs of homozygosity (ROHs) detectable by single-nucleotide polymorphism (SNP) arrays. OBJECTIVE: To evaluate the association between ROHs and AD in an African American population known to have a risk for AD up to 3 times higher than white individuals. DESIGN, SETTING, AND PARTICIPANTS: Case-control study of a large African American data set previously genotyped on different genome-wide SNP arrays conducted from December 2013 to January 2015. Global and locus-based ROH measurements were analyzed using raw or imputed genotype data. We studied the raw genotypes from 2 case-control subsets grouped based on SNP array: Alzheimer's Disease Genetics Consortium data set (871 cases and 1620 control individuals) and Chicago Health and Aging Project-Indianapolis Ibadan Dementia Study data set (279 cases and 1367 control individuals). We then examined the entire data set using imputed genotypes from 1917 cases and 3858 control individuals. MAIN OUTCOMES AND MEASURES: The ROHs larger than 1 Mb, 2 Mb, or 3 Mb were investigated separately for global burden evaluation, consensus regions, and gene-based analyses. RESULTS: The African American cohort had a low degree of inbreeding (F ~ 0.006). In the Alzheimer's Disease Genetics Consortium data set, we detected a significantly higher proportion of cases with ROHs greater than 2 Mb (P = .004) or greater than 3 Mb (P = .02), as well as a significant 114-kilobase consensus region on chr4q31.3 (empirical P value 2 = .04; ROHs >2 Mb). In the Chicago Health and Aging Project-Indianapolis Ibadan Dementia Study data set, we identified a significant 202-kilobase consensus region on Chr15q24.1 (empirical P value 2 = .02; ROHs >1 Mb) and a cluster of 13 significant genes on Chr3p21.31 (empirical P value 2 = .03; ROHs >3 Mb). A total of 43 of 49 nominally significant genes common for both data sets also mapped to Chr3p21.31. Analyses of imputed SNP data from the entire data set confirmed the association of AD with global ROH measurements (12.38 ROHs >1 Mb in cases vs 12.11 in controls; 2.986 Mb average size of ROHs >2 Mb in cases vs 2.889 Mb in controls; and 22% of cases with ROHs >3 Mb vs 19% of controls) and a gene-cluster on Chr3p21.31 (empirical P value 2 = .006-.04; ROHs >3 Mb). Also, we detected a significant association between AD and CLDN17 (empirical P value 2 = .01; ROHs >1 Mb), encoding a protein from the Claudin family, members of which were previously suggested as AD biomarkers. CONCLUSIONS AND RELEVANCE: To our knowledge, we discovered the first evidence of increased burden of ROHs among patients with AD from an outbred African American population, which could reflect either the cumulative effect of multiple ROHs to AD or the contribution of specific loci harboring recessive mutations and risk haplotypes in a subset of patients. Sequencing is required to uncover AD variants in these individuals

Crop Updates 2002 - Weeds

This session covers fifty eight papers from different authors: 1. INTRODUCTION Vanessa Stewart, DEPARTMENT OF AGRICULTURE INTEGRATED WEED MANAGEMENT IWM system studies / demonstration sites 2. Major outcomes from IWM demonstration sites, Alexandra Douglas Department of Agriculture 3. Integrated weed management: Katanning, Alexandra Douglas Department of Agriculture 4. Integrated weed management: Merredin, Vanessa Stewart Department of Agriculture 5. Long term resistance site: Get ryegrass numbers low and keep them low! Peter Newman and Glen Adams Department of Agriculture 6. Using pastures to manage ryegrass populations, Andrew Blake and Natalie Lauritsen Department of Agriculture Weed biology and competition 7. Understanding the weed seed bank life if important agricultural weeds, Sally Peltzer and Paul Matson Department of Agriculture 8. Consequence of radish competition on lupin nutrients in wheat-lupin rotation, Abul Hashem and Nerys Wilkins Department of Agriculture 9. Consequence of ryegrass competition on lupin nutrients in a wheat-lupin rotation, Abul Hashem and Nerys Wilkins Department of Agriculture 10. Brome grass too competitive for early sown wheat in a dry year at Mullewa, Peter Newman and Glenn Adam Department of Agriculture Crop establishment and weed management 11. Seeding rate, row spacing and herbicides for weed control, David Minkey Department of Agriculture 12. Effect of different seeding methods on wheat and ryegrass, Abul Hashem, Glen Riethmuller and Nerys Wilkins Department of Agriculture 13. Role of tillage implements and trifluralin on the effectiveness of the autumn tickle for stimulating annual ryegrass emergence, Tim Cusack1, Kathryn Steadman1 and Abul Hashem2,1Western Australia Herbicide Resistance Initiative, UWA; 2Department of Agriculture, 14. Timing of autumn tickle in important for non-wetting soils, Pippa Michael1, Peter Newman2 and Kathryn Steadman 2, 1Western Australia Herbicide Resistance Initiative, UWA, 2Department of Agriculture 15. Early investigation into weed seed burial by mouldboard plough, Sally Peltzer and Alex Douglas Department of Agriculture 16. Rolling post-emergent lupins to improve weed emergence and control on loamy sand, Paul Blackwell, Department of Agriculture and Dave Brindal, Strawberry via Mingenew IWM tools 17. Crop topping in 2001: How did we do? Peter Newman and Glenn Adam Department of Agriculture 18. Wickwipers work! Peter Newman and Glenn Adam Department of Agriculture 19. Wild radish and ryegrass seed collection at harvest: Chaff carts and other devices, Michael Walsh Western Australia Herbicide Resistance Initiative, UWA and Wayne Parker Department of Agriculture 20. Improving weed control in grazed pastures using legumes with low palatability, Clinton Revell, Giles Glasson Department of Agriculture, and Dean Thomas Faculty of Agriculture, University of Western Australia Adoption and modelling 21. Grower weed survey, Peter Newman and Glenn Adam Department of Agriculture 22. Agronomist survey, Peter Newman and Glenn Adam Department of Agriculture 23. Ryegrass RIM model stands the test of IWM field trial data, Alister Draper Western Australia Herbicide Resistance Initiative, UWA and Bill Roy, Western Australia Herbicide Resistance Initiative, UWA Agricultural Consulting and Research Services 24. Multi-species RIM: An update, Marta Monjardin1,2, David Pannell2 and Stephen Powles 1, 1Western Australia Herbicide Resistance Initiative, UWA, 2 ARE, University of Western Australia 25. RIM survey feedback, Robert Barrett-Lennard and Alister Draper Western Australia Herbicide Resistance Initiative, UWA 26. Effect of historic input and product prices on choice of ryegrass management strategies, Alister Draper1 and Martin Bent2, 1Western Australia Herbicide Resistance Initiative, UWA, 2Muresk Institute of Agriculture 27. Living with ryegrass – trading off weed control and economic performance, Martin Bent1 and Alister Draper2 , 1Muresk Institute of Agriculture, Curtin University, 2Western Australia Herbicide Resistance Initiative, UWA HERBICIDE RESISTANCE 28. Glyphosate resistance in WA and Australia: Where are we at? Paul Neve1, Art Diggle2, Patrick Smith3, Mechelle Owen1, Abul Hashem2, Christopher Preston4and Stephen Powles1,1Western Australian Herbicide Resistance Initiative, University of Western Australia, 2Department of Agriculture, 3CSIRO Sustainable Ecosystems, 4CRC for Australian Weed Management and Department of Applied and Molecular Ecology, Waite Campus, University of Adelaide 29. We need you weeds: A survey of knockdown resistance in the WA wheatbelt, Paul Neve1, Mechelle Owen1, Abul Hashem2 and Stephen Powles1 1Western Australian Herbicide Resistance Initiative, University of Western Australia, 2Department of Agriculture 30. A test for resistance testing, Mechelle Owen, Tracey Gillam, Rick Llewellyn and Steve Powles,Western Australia Herbicide Resistance Initiative, University of Western Australia 31. In field testing for herbicide resistance, a purpose built multi-treatment spray boom with results from 2001, Richard Quinlan, Elders Ltd 32. Advantages and limitations of a purpose built multi-treatment spray boom, Richard Quinlan, Elders Ltd 33. Group F resistant wild radish: What’s new? Aik Cheam, Siew Lee Department of Agriculture, and Mike Clarke Aventis Crop Science 34. Cross resistance of Brodal® resistant wild radish to Sniper®, Aik Cheam and Siew Lee, Department of Agriculture 35. Managing a biotype of wild radish with Group F and Group C resistance, Aik Cheam, Siew Lee, David Nicholson, Peter Newman Department of Agriculture and Mike Clarke, Aventis Crop Science HERBICIDE TOLERANCE 36. Herbicide tolerance of new wheat varieties, Harmohinder S. Dhammu, Terry Piper and David Nicholson, Agriculture Western Australia 37. Response of barley varieties to herbicides, Harmohinder S. Dhammu, Terry Piper, Department of Agriculture 38. Tolerance of barley to phenoxy herbicides, Harmohinder S. Dhammu, Terry Piper, Department of Agriculture and Chad Sayer, Nufarm Australia Limited 39. Response of Durum wheats to herbicides, Harmohinder S. Dhammu, Terry Piper, Department of Agriculture 40. Response of new field pea varieties to herbicides, Harmohinder S. Dhammu, Terry Piper and David Nicholson, Department of Agriculture 41. Herbicide tolerance of Desi chickpeas on marginal soil, Harmohinder S. Dhammu, Terry Piper and David Nicholson, Department of Agriculture 42. Herbicide tolerance of newer lupin varieties, Terry Piper, Harmohinder Dhammu and David Nicholson, Department of Agriculture 43. Herbicide tolerance of some annual pasture legumes, Clinton Revell and Ian Rose, Department of Agriculture 44. Herbicide tolerance of pasture legumes, Andrew Blake, Department of Agriculture HERBICIDES – NEW PRODUCTS/PRODUCT USES; USE 45. Knockdown herbicides do not reliably kill small grass weeds, Peter Newman and Glenn Adam, Department of Agriculture 46. ‘Hair Cutting’ wheat with Spray.Seed®: Does it work? Peter Newman and Glenn Adam, Department of Agriculture 47. ‘Haircutting’: Does the number one cut work? Robert Barrett-Lennard1 and Jerome Critch2,1WA Herbicide Resistance Initiative, University of WA, 2Student, University of WA 48. Hammer EC (Carfentrazone-ethyl): A mixing partner for glyphosate to enhance the control of difficult broadleaf weeds, Gordon R. Cumming, Crop Care Australasia 49. Marshmallow control in reduced tillage systems, Sam Taylor, Wesfarmers Landmark 50. Herbicide options for summer germinating marshmallow, Vanessa Stewart, Department of Agriculture 51. Dual Gold® safe in a dry year at Coorow, Peter Newman and Glenn Adam, Department of Agriculture 52. The effect of glyphosate, paraquat and diquat as a crop topping application on the germination of barley, John Moore and Roslyn Jettner, Department of Agriculture 53. Herbicide options for melon control, Vanessa Stewart, Department of Agriculture 54. Herbicide options for the control of Chloris truncate (windmill grass) Vanessa Stewart, Department of Agriculture 55. Allelopathic effects of crop, pasture and weed residues on subsequent crop and pasture establishment, Stuart Bee1, Lionel Martin1, Keith Devenish2 and Terry Piper2, 1Muresk Institute of Agriculture, Curtin University of Technology, Northam, Western Australia, 2Centre for Cropping Systems, Department of Agriculture WEED ISSUES 56. Role of Roundup ReadyÒ canola in the farming system, Art Diggle1, Patrick Smith2, Paul Neve3, Felicity Flugge4, Amir Abadi5 and Stephen Powles3, 1Department of Agriculture; 2CSIRO, Sustainable Ecosystems; 3Western Australian Herbicide Resistance Initiative; 4Centre for Legumes in Mediterranean Agriculture; 5Touchstone Consulting 57. ’Weeds for Feed’ and livestock enterprise structures: A feasibility study and farmer survey in the north-easern wheatbelt, Duncan Peter and Stuart McAlpine, Department of Agriculture and Liebe Group, Buntine 58. e-weed, Vanessa Stewart, Agriculture Western Australi

Crop Updates 2008 - Farming Systems

This session covers thirty nine papers from different authors: PLENARY 1. Developments in grain end use, Dr John de Majnik, New Grain Products, GRDC, Mr Paul Meibusch, New Farm Products and Services, GRDC, Mr Vince Logan, New Products Executive Manager, GRDC PRESENTATIONS 2. Global warming potential of wheat production in Western Australia: A life cycle assessment, Louise Barton1, Wahid Biswas2 and Daniel Carter3, 1School of Earth & Geographical Sciences, The University of Western Australia, 2Centre of Excellence in Cleaner Production, Division of Science and Engineering, Curtin University of Technology, 3Department of Agriculture and Food 3. How much fuel does your farm use for different farm operations? Nicolyn Short1, Jodie Bowling1, Glen Riethmuller1, James Fisher2 and Moin Salam1, 1Department of Agriculture and Food, 2Muresk Institute, Curtin University of Technology 4. Poor soil water storage and soil constraints are common in WA cropping soils, Stephen Davies, Jim Dixon, Dennis Van Gool and Alison Slade, Department of Agriculture and Food, Bob Gilkes, School of Earth and Geographical Sciences, University of Western Australia 5. Developing potential adaptations to climate change for low rainfall farming system using economic analysis tool. STEP, Megan Abrahams, Caroline Peek, Dennis Van Gool, Daniel Gardiner and Kari-Lee Falconer, Department of Agriculture and Food 6. What soil limitations affect the profitability of claying on non-wetting sandplain soils? David Hall1, Jeremy Lemon1, Harvey Jones1, Yvette Oliver2 and Tania Butler1, 1Department of Agriculture and Food, 2CSIRO Div Sustainable Ecology, Perth 7. Farming systems adapting to a variable climate; Two case studies, Kari-Lee Falconer, Department of Agriculture and Food 8. Importance of accounting for variation in crop yield potential when making fertiliser decisions, Michael Robertson and Yvette Oliver, CSIRO Sustainable Ecosystems, Floreat 9. Soil acidity is a widespread problem across the Avon River Basin, Stephen Carr1, Chris Gazey2, David York1 and Joel Andrew1, 1Precision SoilTech, 2Department of Agriculture and Food 10. The use of soil testing kits and ion-selective electrodes for the analysis of plant available nutrients in Western Australian soils, Michael Simeoni and Bob Gilkes School of Earth and Geographical Sciences, University of Western Australia 11. Redlegged earth mite resistance and integrated strategies for their control in Western Australia, Mangano G. Peter and Micic Svetlana, Department of Agriculture and Food 12. The economics of treating soil pH (liming), Chris Gazey, Steve Davies, Dave Gartner and Adam Clune, Department of Agriculture and Food, 13. Health benefits – A future differentiator for high value grains, Matthew Morell, Theme Leader, CSIRO Food Futures Flagship 14. Carbon in Sustralian cropping soils – We need to be realistic, Alan Umbers (M Rur Sc), GRDC/DAFF Sustainable Industries Initiative Project 15. AGWEST® Bartolo bladder clover (Trifolium spumosum) − a low cost annual pasture legume for the wheat/sheep zone, Angelo Loi, Brad Nutt and Clinton Revell, Department of Agriculture and Food 16. Maximising the value of point based soil sampling: Monitering trends in soil pH through time, Joel Andrew1, David York1, Stephen Carr1 and Chris Gazey2, 1Precision SoilTech, 2Department of Agriculture and Food 17. Improved crop root growth and productivity with deep ripping and deep placed lime, Stephen Davies1, Geoff Kew2*, Chris Gazey1, David Gartner1 and Adam Clune1, 1Department of Agriculture and Food, 2School of Earth and Geographical Sciences University of Western Australia, *Presenting author 18. The role of pastures in hosting Root Lesion Nematode (RLN, Pratylenchus neglectus), Vivien Vanstone, Ali Bhatti and Ming Pei You, Department of Agriculture and Food 19. To rip or not to rip. When does it pay? Imma Farre, Bill Bowden and Stephen Davies, Department of Agriculture and Food 20. Can yield be predicted from remotely sensed data, Henry Smolinski, Jane Speijers and John Bruce, Department of Agriculture and Food 21. Rotations for profit, David McCarthy and Gary Lang, Facey Group, Wickepin, WA 22. Rewriting rules for the new cropping economics, David Rees, Consultant, Albany 23. Reducing business risk in Binnu! – A case study, Rob Grima, Department of Agriculture and Food 24. Does improved ewe management offer grain farmers much extra profit? John Young, Farming Systems Analysis Service, Ross Kingwell, Department of Agriculture and Food, and UWA, Chris Oldham, Department of Agriculture and Food RESEARCH HIGHLIGHTS 25. Crop establishment and productivity with improved root zone drainage, Dr Derk Bakker, Research Officer, Department of Agriculture and Food 26. Will wheat production in Western Australia be more risky in the future? Imma Farre and Ian Foster, Department of Agriculture and Food PAPERS 27. Building farmers’ adaptive capacity to manage seasonal variability and climate change, David Beard, Department of Agriculture and Food 28. Precision placement increases crop phosphorus uptake under variable rainfall: Simulation studies, Wen Chen1 2, Richard Bell1, Bill Bowden2, Ross Brennan2, Art Diggle2 and Reg Lunt2, 1School of Environmental Science, Murdoch University, 2Department of Agriculture and Food 29. What is the role of grain legumes on red soil farms? Rob Grima, Department of Agriculture and Food 30. Fertiliser placement influences plant growth and seed yield of grain crops at different locations of WA, Qifu Ma1, Zed Rengel1, Bill Bowden2, Ross Brennan2, Reg Lunt2 and Tim Hilder2, 1Soil Science & Plant Nutrition, University of Western Australia, 2Department of Agriculture and Food 31. A review of pest and disease occurrences for 2007, Peter Mangano and Dusty Severtson, Department of Agriculture and Food 32. Effect of stocking rates on grain yield and quality of wheat in Western Australia in 2007, Shahajahan Miyan, Sam Clune, Barb Sage and Tenielle Martin, Department of Agriculture and Food 33. Storing grain is not ‘set and forget’ management, Chris Newman, Department of Agriculture and Food 34. Improving understanding of soil plant available water capacity (PAWC): The WA soil water database (APSoil), Yvette Oliver, Neal Dalgliesh and Michael Robertson, CSIRO Sustainable Ecosystems 35. The impact of management decisions in drought on a low rainfall northern wheatbelt farm, Caroline Peek and Andrew Blake, Department of Agriculture and Food 37. Cullen – A native pasture legume shows promise for the low-medium rainfall cropping zone, Megan Ryan, Richard Bennett, Tim Colmer, Daniel Real, Jiayin Pang, Lori Kroiss, Dion Nicol and Tammy Edmonds-Tibbett, School of Plant Biology, The University of Western Australia and Future Farm Industries CRC 38. Climate risk management tools – useful, or just another gadget? Lisa Sherriff, Kari-Lee Falconer, Daniel Gardiner and Ron McTaggart Department of Agriculture and Food 39. Benefits of crop rotation for management of Root Lesion Nematode (RLN, Pratylenchus neglectus), Vivien Vanstone, Sean Kelly and Helen Hunter, Department of Agriculture and Foo

Common genetic variants in the CLDN2 and PRSS1-PRSS2 loci alter risk for alcohol-related and sporadic pancreatitis

Pancreatitis is a complex, progressively destructive inflammatory disorder. Alcohol was long thought to be the primary causative agent, but genetic contributions have been of interest since the discovery that rare PRSS1, CFTR, and SPINK1 variants were associated with pancreatitis risk. We now report two significant genome-wide associations identified and replicated at PRSS1-PRSS2 (1×10-12) and x-linked CLDN2 (p < 1×10-21) through a two-stage genome-wide study (Stage 1, 676 cases and 4507 controls; Stage 2, 910 cases and 4170 controls). The PRSS1 variant affects susceptibility by altering expression of the primary trypsinogen gene. The CLDN2 risk allele is associated with atypical localization of claudin-2 in pancreatic acinar cells. The homozygous (or hemizygous male) CLDN2 genotype confers the greatest risk, and its alleles interact with alcohol consumption to amplify risk. These results could partially explain the high frequency of alcohol-related pancreatitis in men – male hemizygous frequency is 0.26, female homozygote is 0.07

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University

Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease

We identified rare coding variants associated with Alzheimer’s disease (AD) in a 3-stage case-control study of 85,133 subjects. In stage 1, 34,174 samples were genotyped using a whole-exome microarray. In stage 2, we tested associated variants (P<1×10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, an additional 14,997 samples were used to test the most significant stage 2 associations (P<5×10-8) using imputed genotypes. We observed 3 novel genome-wide significant (GWS) AD associated non-synonymous variants; a protective variant in PLCG2 (rs72824905/p.P522R, P=5.38×10-10, OR=0.68, MAFcases=0.0059, MAFcontrols=0.0093), a risk variant in ABI3 (rs616338/p.S209F, P=4.56×10-10, OR=1.43, MAFcases=0.011, MAFcontrols=0.008), and a novel GWS variant in TREM2 (rs143332484/p.R62H, P=1.55×10-14, OR=1.67, MAFcases=0.0143, MAFcontrols=0.0089), a known AD susceptibility gene. These protein-coding changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified AD risk genes. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to AD development

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570