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High-quality and complete reference genome assemblies are fundamental for the application of genomics to biology, disease, and biodiversity conservation. However, such assemblies are available for only a few non-microbial species1,2,3,4. To address this issue, the international Genome 10K (G10K) consortium5,6 has worked over a five-year period to evaluate and develop cost-effective methods for assembling highly accurate and nearly complete reference genomes. Here we present lessons learned from generating assemblies for 16 species that represent six major vertebrate lineages. We confirm that long-read sequencing technologies are essential for maximizing genome quality, and that unresolved complex repeats and haplotype heterozygosity are major sources of assembly error when not handled correctly. Our assemblies correct substantial errors, add missing sequence in some of the best historical reference genomes, and reveal biological discoveries. These include the identification of many false gene duplications, increases in gene sizes, chromosome rearrangements that are specific to lineages, a repeated independent chromosome breakpoint in bat genomes, and a canonical GC-rich pattern in protein-coding genes and their regulatory regions. Adopting these lessons, we have embarked on the Vertebrate Genomes Project (VGP), an international effort to generate high-quality, complete reference genomes for all of the roughly 70,000 extant vertebrate species and to help to enable a new era of discovery across the life sciences., We thank them for their permission to publish. A.R., S.K., B.P.W. and A.M.P. were supported by the Intramural Research Program of the NHGRI, NIH (1ZIAHG200398). A.R. was also supported by the Korea Health Technology R&D Project through KHIDI, funded by the Ministry of Health & Welfare, Republic of Korea (HI17C2098). S.A.M., I.B. and R.D. were supported by Wellcome Trust grant WT207492; W.C., M. Smith, Z.N., Y.S., J.C., S. Pelan, J.T., A.T., J.W. and Kerstin Howe by WT206194; L.H., F.M., Kevin Howe and P. Flicek by WT108749/Z/15/Z, WT218328/B/19/Z and the European Molecular Biology Laboratory. O.F. and E.D.J. were supported by Howard Hughes Medical Institute and Rockefeller University start-up funds for this project. J.D. and H.A.L. were supported by the Robert and Rosabel Osborne Endowment. M.U.-S. received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement (750747). F.T.-N., J. Hoffman, P. Masterson and K.C. were supported by the Intramural Research Program of the NLM, NIH. C.L., B.J.K., J. Kim and H.K. were supported by the Marine Biotechnology Program of KIMST, funded by the Ministry of Ocean and Fisheries, Republic of Korea (20180430). M.C. was supported by Sloan Research Fellowship (FG-2020-12932). S.C.V. was funded by a Max Planck Research Group award from the Max Planck Society, and a Human Frontiers Science Program (HFSP) Research grant (RGP0058/2016). T.M.L., W.E.J. and the Canada lynx genome were funded by the Maine Department of Inland Fisheries & Wildlife (F11AF01099), including when W.E.J. held a National Research Council Research Associateship Award at the Walter Reed Army Institute of Research (WRAIR). C.B. was supported by the NSF (1457541 and 1456612). D.B. was funded by The University of Queensland (HFSP - RGP0030/2015). D.I. was supported by Science Exchange Inc. (Palo Alto, CA). H.W.D. was supported by NSF grants (OPP-0132032 ICEFISH 2004 Cruise, PLR-1444167 and OPP-1955368) and the Marine Science Center at Northeastern University (416). G.J.P.N. and the thorny skate genome were funded by Lenfest Ocean Program (30884). M.P. was funded by the German Federal Ministry of Education and Research (01IS18026C). M. Malinsky was supported by an EMBO fellowship (ALTF 456-2016). The following authors’ contributions were supported by the NIH: S. Selvaraj (R44HG008118); C.V.M., S.R.F., P.V.L. (R21 DC014432/DC/NIDCD); K.D.M. (R01GM130691); H.C. (5U41HG002371-19); M.D. (U41HG007234); and B.P. (R01HG010485). D.G. was supported by the National Key Research and Development Program of China (2017YFC1201201, 2018YFC0910504 and 2017YFC0907503). F.O.A. was supported by Al-Gannas Qatari Society and The Cultural Village Foundation-Katara, Doha, State of Qatar and Monash University Malaysia. C.T. was supported by The Rockefeller University. M. Hiller was supported by the LOEWE-Centre for Translational Biodiversity Genomics (TBG) funded by the Hessen State Ministry of Higher Education, Research and the Arts (HMWK). H.C. was supported by the NHGRI (5U41HG002371-19). R.H.S.K. was funded by the Max Planck Society with computational resources at the bwUniCluster and BinAC funded by the Ministry of Science, Research and the Arts Baden-Württemberg and the Universities of the State of Baden-Württemberg, Germany (bwHPC-C5). B.V. was supported by the Biomedical Research Council of A*STAR, Singapore. T.M.-B. was funded by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (864203), MINECO/FEDER, UE (BFU2017-86471-P), Unidad de Excelencia María de Maeztu, AEI (CEX2018-000792-M), a Howard Hughes International Early Career award, Obra Social “La Caixa” and Secretaria d’Universitats i Recerca and CERCA Programme del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 880). E.C.T. was supported by the European Research Council (ERC-2012-StG311000) and an Irish Research Council Laureate Award. M.T.P.G. was supported by an ERC Consolidator Award 681396-Extinction Genomics, and a Danish National Research Foundation Center Grant (DNRF143). T.W. was supported by the NSF (1458652). J. M. Graves was supported by the Australian Research Council (CEO561477). E.W.M. was partially supported by the German Federal Ministry of Education and Research (01IS18026C). Complementary sequencing support for the Anna’s hummingbird and several genomes was provided by Pacific Biosciences, Bionano Genomics, Dovetail Genomics, Arima Genomics, Phase Genomics, 10X Genomics, NRGene, Oxford Nanopore Technologies, Illumina, and DNAnexus. All other sequencing and assembly were conducted at the Rockefeller University, Sanger Institute, and Max Planck Institute Dresden genome labs. Part of this work used the computational resources of the NIH HPC Biowulf cluster (https://hpc.nih.gov). We acknowledge funding from the Wellcome Trust (108749/Z/15/Z) and the European Molecular Biology Laboratory., With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2018-000792-M).

Type 4 sphingosine-1-phosphate receptor, which mediates the activation of the Nod-like receptor (NLR) family pyrin domain containing 3 inflammasome, emerges as a new therapeutic target for nonalcoholic steatohepatitis. We developed a selective functional antagonist for type 4 sphingosine-1-phosphate receptor (SLB736), which protected mice against nonalcoholic steatohepatitis and fibrosis, Supported by the National Research Foundation of Korea, funded by the Ministry of Education, Science, and Technology, Korea, grants 2017R1E1A1A01073206 (K.-U.L.), 2020R1A2B5B02098524 (E.H.K.), 2019R1C1C1003567 (J.-Y.Y.), and 2021R1A2C1005520 (J.W.C.), the Korea Drug Development Fund funded by the Ministry of Science and ICT, Ministry of Trade, Industry, and Energy, and Ministry of Health and Welfare grant KDDF-201406-03 (K.-U.L.), and Asan Institute for Life Sciences, Korea, grant 2020IP0016-1 (E.H.K.). This work also was supported by the Mid-Career Researcher Programs grant NRF-2019R1A2C2009905 (S.K.) of the National Research Foundation of Korea, funded by the Government of Korea (MSIP); and by grants PID2019-111669RB-I00 and PID2020-115055RB-I00 from Plan Nacional de Investigación y Desarrollo, Spain, by the Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Instituto de Salud Carlos III, Spain; by grant P50AA011999 from the Southern California Research Center for Alcoholic Liver and Pancreatic Diseases and Cirrhosis funded by National Institute of Alcohol Abuse and Alcoholism; by Agency for Administration of University and Research Grants of the Generalitat de Cataluña SGR-2017-1112 and the “ER stress-mitochondrial cholesterol axis in obesity-associated insulin resistance and comorbidities” Ayudas Fundación Banco Bilbao Vizcaya Argentaria a Equipos de Investigación Científica 2017, the Red Nacional 2018-102799-T de Enfermedades Metabólicas y Cáncer, and by project 201916/31 Contribution of mitochondrial oxysterol and bile acid metabolism to liver carcinogenesis 2019 by Fundació Marato Televisión 3. None of the funders had a role in the study design, collection, analysis, or interpretation of data.

The Human Proteome Organization’s (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems, and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well, and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20 000 human proteins encoded by the human genome., This work was funded in part by the National Institutes of Health grants R01GM087221 (EWD/RLM), R24GM127667 (EWD), U54EB020406 (EWD), R01HL133135 (RLM), U19AG02312 (RLM), U54ES017885 (GSO), U24CA210967-01 (GSO), R01LM013115 (NB) and P41GM103484 (NB); National Science Foundation grants ABI-1759980 (NB), DBI-1933311 (EWD), and IOS-1922871 (EWD); Canadian Institutes of Health Research 148408 (CMO); Korean Ministry of Health and Welfare HI13C2098 (YKP); French Ministry of Higher Education, Research and Innovation, ProFI project, ANR-10-INBS-08 (YV); also in part by the National Eye Institute (NEI), National Human Genome Research Institute (NHGRI), National Heart, Lung, and Blood Institute (NHLBI), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of General Medical Sciences (NIGMS), and National Institute of Mental Health (NIMH) of the National Institutes of Health under Award Number U24HG007822 (SO) (the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health).

Alzheimer’s Disease Neuroimaging Initiative., Alzheimer’s disease (AD) is characterized by the spread of tau pathology throughout the cerebral cortex. This spreading pattern was thought to be fairly consistent across individuals, although recent work has demonstrated substantial variability in the population with AD. Using tau-positron emission tomography scans from 1,612 individuals, we identified 4 distinct spatiotemporal trajectories of tau pathology, ranging in prevalence from 18 to 33%. We replicated previously described limbic-predominant and medial temporal lobe-sparing patterns, while also discovering posterior and lateral temporal patterns resembling atypical clinical variants of AD. These ‘subtypes’ were stable during longitudinal follow-up and were replicated in a separate sample using a different radiotracer. The subtypes presented with distinct demographic and cognitive profiles and differing longitudinal outcomes. Additionally, network diffusion models implied that pathology originates and spreads through distinct corticolimbic networks in the different subtypes. Together, our results suggest that variation in tau pathology is common and systematic, perhaps warranting a re-examination of the notion of ‘typical AD’ and a revisiting of tau pathological staging., J.W.V. acknowledges support from the government of Canada through a tri-council Vanier Canada Graduate Doctoral fellowship from the McGill Centre for Integrative Neuroscience and the Healthy Brains, Healthy Lives initiative, and from the National Institutes of Health (NIH) (no. T32MH019112). A.L.Y. is supported by a Medical Research Council Skills Development Fellowship (MR/T027800/1). N.P.O. is a UK Research and Innovation Future Leaders Fellow (no. MR/S03546X/1). N.P.O. and D.C.A. acknowledge support from the UK National Institute for Health Research University College London Hospitals Biomedical Research Centre, and D.C.A. acknowledges support from the Engineering and Physical Sciences Research Council grant no. EP/M020533/1. M.J.G. is supported by the Miguel Servet program (no. CP19/00031) and a research grant (no. PI20/00613) of the Instituto de Salud Carlos III-Fondo Europeo de Desarrollo Regional. R.L.J. acknowledges support from the NIH (no. K99AG065501). This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant no. 666992. The BioFINDER studies are supported by the Swedish Research Council (no. 2016-00906), the Knut and Alice Wallenberg Foundation (no. 2017-0383), the Marianne and Marcus Wallenberg Foundation (no. 2015.0125), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the Swedish Alzheimer’s Foundation (no. AF-939932), the Swedish Brain Foundation (no. FO2019-0326), the Swedish Parkinson Foundation (no. 1280/20), the Skåne University Hospital Foundation (no. 2020-O000028), Regionalt Forskningsstöd (no. 2020-0314) and the Swedish Federal Government under the ALF agreement (no. 2018-Projekt0279). The Tau PET study in Gangnam Severance Hospital was supported by a grant from the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (nos. NRF2018R1D1A1B07049386 and NRF2020R1F1A1076154) and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute funded by the Ministry of Health and Welfare, Republic of Korea (grant no. HI18C1159). We also thank B. L. Miller, H. J. Rosen, M. Gorno Tempini and W. Jagust for supporting the UCSF tau-PET studies, which were funded through the following sources: National Institute on Aging (NIA) no. R01 AG045611 (G.D.R.), no. P50 AG23501 (B.L.M., H.J.R., G.D.R.), no. P01 AG019724 (B.L.M., H.J.R., G.D.R.). The precursor of 18F-flortaucipir was provided by AVID Radiopharmaceuticals. The precursor of 18F-flutemetamol was sponsored by GE Healthcare. The precursor of 18F-RO948 was provided by Roche. Data collection and sharing for this project were funded by ADNI (NIH grant no. U01 AG024904) and Department of Defense ADNI (award no. W81XWH-12-2-0012). ADNI is funded by the NIA, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; Bioclinica; Biogen; Bristol Myers Squibb; CereSpir; Cogstate; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; EUROIMMUN; F. Hoffmann-La Roche and its affiliated company Genentech; Fujirebio; GE Healthcare; IXICO; Janssen Alzheimer Immunotherapy Research Development; Johnson & Johnson Pharmaceutical Research Development; Lumosity; Lundbeck; Merck; Meso Scale Diagnostics; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020–01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the “missing proteins” (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019–01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020–01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases., G.S.O. acknowledges support from National Institutes of Health grants P30ES017885-01A1 and U24CA210967; E.W.D. from National Institutes of Health grants R01GM087221, R24GM127667, U19AG023122, and from National Science Foundation grant DBI-1933311; L.L. and neXtProt from the SIB Swiss Institute of Bioinformatics; C.M.O. by Canadian Institutes of Health Research Foundation Grant 148408 and a Canada Research Chair in Protease Proteomics and Systems Biology; M.S.B. by NHMRC Project GrantAPP1010303; C.L. by the Knut and Alice Wallenberg Foundation for the Human Protein Atlas; and Y.-K.P. by grants from the Korean Ministry of Health and Welfare HI13C22098 and HI16C0257.

According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020–01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the “missing proteins” (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019–01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020–01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases. G.S.O. acknowledges support from National Institutes of Health grants P30ES017885-01A1 and U24CA210967; E.W.D. from National Institutes of Health grants R01GM087221, R24GM127667, U19AG023122, and from National Science Foundation grant DBI-1933311; L.L. and neXtProt from the SIB Swiss Institute of Bioinformatics; C.M.O. by Canadian Institutes of Health Research Foundation Grant 148408 and a Canada Research Chair in Protease Proteomics and Systems Biology; M.S.B. by NHMRC Project GrantAPP1010303; C.L. by the Knut and Alice Wallenberg Foundation for the Human Protein Atlas; and Y.-K.P. by grants from the Korean Ministry of Health and Welfare HI13C22098 and HI16C0257.

On the basis of the January 2019 updates on the curation and annotation of the human proteome by neXtProt4 (https://www.nextprot.org/) from the standardized reanalysis of mass spectrometry (MS) data sets by PeptideAtlas5 (http://www. peptideatlas.org/), which form the baseline for the 2019 papers in this special issue, Omenn et al. (10.1021/acs.jproteome.9b00434) report that highly credible findings supportProtein Evidence Level 1 (PE1) for 17 694 protein-coding genes, 89.3% of the total 19 823 predicted human proteincoding genes. In this annual metric of the human proteome paper, these numbers represent a net increase of 224 PE1 proteins due to the promotion of 213 PE2,3,4 entries to PE1 and a net decrease of only 57 missing proteins (MPs) due to 60 PE1 demotions and 116 new PE2,3,4 entries, primarily immunoglobulins. The dynamics of the year-to-year changes in neXtProt are quite complex, as explained in this article. A chromosome-bychromosome analysis showed that four chromosomes (1, 5, 17, 19) have had reductions of between 50 and 130 MPs, and another four (2, 3, 10, X) have had 43−47 fewer MPs since the baseline for the neXt-MP50 Challenge in 2016. Utilizing ProteomeXchange, PRIDE, and other data resources, PeptideAtlas documented an increase of 495 canonical proteins from 120 new human sample data sets; three data sets from the JPR 2018 special issue contributed a major proportion of these with 161 of the 495. The canonical proteins from PeptideAtlas account for 98% of the 16 600 PE1 neXtProt entries based on MS, compliant with the stringent HPP Guidelines v2.1.6 More than half of the new canonical proteins were already PE1 but were based on other kinds of non-mass-spectrometry protein evidence, This work was supported by grants from the Korean Ministry of Health and Welfare (HI13C2098 International Consortium Project and HI16C0257 to Y.-K.P.), the Canadian Institutes of Health Research, 7-year Foundation Grant [FDN148408], and a Canada Research Chair in Protease Proteomics and Systems Biology (awarded to C.M.O), the U.S. National Institutes of Health (P30ES017885-01A1 and U24CA210967 to G.S.O.), and ProteoRed and PRB3-ISCIII PT17/0019 (Spanish Institutes of Health ISCIII to F.C.).

The Human Proteome Organization’s (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems, and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well, and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20 000 human proteins encoded by the human genome. This work was funded in part by the National Institutes of Health grants R01GM087221 (EWD/RLM), R24GM127667 (EWD), U54EB020406 (EWD), R01HL133135 (RLM), U19AG02312 (RLM), U54ES017885 (GSO), U24CA210967-01 (GSO), R01LM013115 (NB) and P41GM103484 (NB); National Science Foundation grants ABI-1759980 (NB), DBI-1933311 (EWD), and IOS-1922871 (EWD); Canadian Institutes of Health Research 148408 (CMO); Korean Ministry of Health and Welfare HI13C2098 (YKP); French Ministry of Higher Education, Research and Innovation, ProFI project, ANR-10-INBS-08 (YV); also in part by the National Eye Institute (NEI), National Human Genome Research Institute (NHGRI), National Heart, Lung, and Blood Institute (NHLBI), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of General Medical Sciences (NIGMS), and National Institute of Mental Health (NIMH) of the National Institutes of Health under Award Number U24HG007822 (SO) (the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health).

The Human Proteome Project (HPP) annually reports on progress made throughout the field in credibly identifying and characterizing the complete human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2019–01–11 contains 17 694 proteins with strong protein-level evidence (PE1), compliant with HPP Guidelines for Interpretation of MS Data v2.1; these represent 89% of all 19 823 neXtProt predicted coding genes (all PE1,2,3,4 proteins), up from 17 470 one year earlier. Conversely, the number of neXtProt PE2,3,4 proteins, termed the “missing proteins” (MPs), has been reduced from 2949 to 2129 since 2016 through efforts throughout the community, including the chromosome-centric HPP. PeptideAtlas is the source of uniformly reanalyzed raw mass spectrometry data for neXtProt; PeptideAtlas added 495 canonical proteins between 2018 and 2019, especially from studies designed to detect hard-to-identify proteins. Meanwhile, the Human Protein Atlas has released version 18.1 with immunohistochemical evidence of expression of 17 000 proteins and survival plots as part of the Pathology Atlas. Many investigators apply multiplexed SRM-targeted proteomics for quantitation of organ-specific popular proteins in studies of various human diseases. The 19 teams of the Biology and Disease-driven B/D-HPP published a total of 160 publications in 2018, bringing proteomics to a broad array of biomedical research. G.S.O. acknowledges grant support from National Institutes of Health grants P30ES017885-01A1 and U24CA210967; E.W.D. from National Institutes of Health grants, R01GM087221, R24GM127667, U54EB020406, and the U19AG023122; L.L. and neXtProt from the SIB Swiss Institute of Bioinformatics; C.M.O. by a Canadian Institutes of Health Research Foundation Grant and a Canada Research Chair in Protease Proteomics and Systems Biology; M.S.B. by NHMRC Project Grant APP1010303; J.M.S. by the Knut and Alice Wallenberg Foundation for the Human Protein Atlas; and Y.-K.P. by grants from the Korean Ministry of Health and Welfare HI13C22098 and HI16C0257.

The prevalence and spectrum of germline mutations in BRCA1 and BRCA2 have been reported in single populations, with the majority of reports focused on White in Europe and North America. The Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) has assembled data on 18,435 families with BRCA1 mutations and 11,351 families with BRCA2 mutations ascertained from 69 centers in 49 countries on six continents. This study comprehensively describes the characteristics of the 1,650 unique BRCA1 and 1,731 unique BRCA2 deleterious (disease-associated) mutations identified in the CIMBA database. We observed substantial variation in mutation type and frequency by geographical region and race/ethnicity. In addition to known founder mutations, mutations of relatively high frequency were identified in specific racial/ethnic or geographic groups that may reflect founder mutations and which could be used in targeted (panel) first pass genotyping for specific populations. Knowledge of the population-specific mutational spectrum in BRCA1 and BRCA2 could inform efficient strategies for genetic testing and may justify a more broad-based oncogenetic testing in some populations.

The OCAC OncoArray genotyping project: et al., To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC., The OCAC OncoArray genotyping project was funded through grants from the US National Institutes of Health (CA1X01HG007491-01 (C.I.A.), U19-CA148112 (T.A.S.), R01-CA149429 (C.M.P.) and R01-CA058598 (M.T.G.)); Canadian Institutes of Health Research (MOP-86727 (L.E.K.)); and the Ovarian Cancer Research Fund (A.B.). Funding for the CIMBA OncoArray genotyping was provided by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the Ministère de l’Économie, de la Science et de l’Innovation du Québec through Génome Québec, the Quebec Breast Cancer Foundation for the PERSPECTIVE project, the US National Institutes of Health (CA1X01HG007491-01 (C.I.A.)), the Odense University Hospital Research Foundation (M.T.), the National R&D Program for Cancer Control, Ministry of Health and Welfare, Republic of Korea (1420190 (S.K.P.)), the Italian Association for Cancer Research (IG16933 (L.O.)) and German Cancer Aid (110837 (R.K.S.).

Free cholesterol (FC) accumulation in the liver is an important pathogenic mechanism of nonalcoholic steatohepatitis(NASH). Plasmalogens, key structural components of the cell membrane, act as endogenous antioxidants and are primarilysynthesized in the liver. However, the role of hepatic plasmalogens in metabolic liver disease is unclear. In this study, wefound that hepatic levels of docosahexaenoic acid (DHA)-containing plasmalogens, expression of glyceronephosphate O-acyltransferase (Gnpat; the rate-limiting enzyme in plasmalogen biosynthesis), and expression ofPparawere lower in micewith NASH caused by accumulation of FC in the liver. Cyclodextrin-induced depletion of FC transactivatedD-6 desatur-ase by increasingsterol regulatory element-binding protein 2expression in cultured hepatocytes. DHA, the major product ofD-6 desaturase activation, activated GNPAT, thereby explaining the association between high hepatic FC and decreasedGnpatexpression.Gnpatsmall interfering RNA treatment significantly decreased peroxisome proliferator-activated receptora(Ppara) expression in cultured hepatocytes. In addition to GNPAT, DHA activated PPARaand increased expression ofPparaand its target genes, suggesting that DHA in the DHA-containing plasmalogens contributed to activation ofPPARa. Accordingly, administration of the plasmalogen precursor, alkyl glycerol (AG), prevented hepatic steatosis andNASH through a PPARa-dependent increase in fatty acid oxidation.Gnpat1/–mice were more susceptible to hepatic lipidaccumulation and less responsive to the preventive effect of fluvastatin on NASH development, suggesting that endoge-nous plasmalogens prevent hepatic steatosis and NASH.Conclusion:Increased hepatic FC in animals with NASHdecreased plasmalogens, thereby sensitizing animals to hepatocyte injury and NASH. Our findings uncover a novel linkbetween hepatic FC and plasmalogen homeostasis through GNPAT regulation. Further study of AG or other agents thatincrease hepatic plasmalogen levels may identify novel therapeutic strategies against NASH., National Research Foundation of Korea (NRF) Ministry of Education, Science and Technology. Grant Numbers: 2006‐2005412, 2012R1A1A3012626, 2013R1A1A1009962 Korea Health technology R&D Project through the Korea Health Industry Development Institute (KHIDI) Ministry of Health & Welfare. Grant Numbers: HI16C1501, 2009‐006, 2013‐578, 2014‐006 Asan Institute for Life Sciences Plan Nacional de I+D. Grant Number: SAF‐2015‐69944‐R CIBEREHD. Grant Number: P50‐AA‐11999 Research Center for Liver and Pancreatic Diseases funded by NIAAA/NIH

Lohmann, Katja et al., Specific mutations in COL6A3 have recently been reported as the cause of isolated recessive dystonia, which is a rare movement disorder. In all patients, at least one mutation was located in Exons 41 and 42. In an attempt to replicate these findings, we assessed by direct sequencing the frequency of rare variants in Exons 41 and 42 of COL6A3 in 955 patients with isolated or combined dystonia or with another movement disorder with dystonic features. We identified nine heterozygous carriers of rare variants including five different missense mutations and an extremely rare synonymous variant. In these nine patients, we sequenced the remaining 41 coding exons of COL6A3 to test for a second mutation in the compound heterozygous state. In only one of them, a second rare variant was identified (Thr732Met + Pro3082Arg). Of note, this patient had been diagnosed with Parkinson¿s disease (with dystonic posturing) due to homozygous PINK1 mutations. The COL6A3 mutations clearly did not segregate with the disease in the four affected siblings of this family. Further, there was no indication for a disease-modifying effect of the COL6A3 mutations since disease severity or age at onset did not correlate with the number of COL6A3 mutated alleles in this family. In conjunction with the relatively high frequency of homozygous carriers of reported mutations in publically available databases, our data call a causal role for variants in COL6A3 in isolated dystonia into question., This study was supported by funding from the German Research Foundation (LO1555/8-1 to K.L.). S.Z. is supported by an intramural grant of the University of Luebeck (E36-2014). She received honoraria for lectures from St. Jude Medical. A.A.K. and P.K. are supported by the German Research Foundation (KFO247). H.-C.J. has received funding from the Dystonia Coalition (NS065701; The Dystonia Coalition is part of the NIH Rare Diseases Clinical Research Network and has support from the NIH Office of Rare Diseases Research and National Institute of Neurological Disorders and Stroke). S.J.C. is supported by a grant of the Korea Healthcare Technology R & D Project, Ministry of Health & Welfare, Republic of Korea (HI14C2206). A.M. is supported by the Possehl Foundation (Lübeck, Germany) and the Dystonia Coalition (USA). C.K. is a recipient of a career development award from the Herman and Lilly Schilling Foundation.