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Aims Apolipoprotein C-II (ApoC-II) is thought to activate lipoprotein lipase (LPL) and is therefore a possible target for treating hypertriglyceridemia. Its relationship with cardiovascular risk has not been investigated in large-scale epidemiologic studies, particularly allowing for apolipoprotein C-III (ApoC-III), an LPL antagonist. Furthermore, the exact mechanism of ApoC-II–mediated LPL activation is unclear. Methods and results ApoC-II was measured in 3141 LURIC participants of which 590 died from cardiovascular diseases during a median (inter-quartile range) follow-up of 9.9 (8.7–10.7) years. Apolipoprotein C-II–mediated activation of the glycosylphosphatidylinositol high-density lipoprotein binding protein 1 (GPIHBP1)–LPL complex was studied using enzymatic activity assays with fluorometric lipase and very low-density lipoprotein (VLDL) substrates. The mean ApoC-II concentration was 4.5 (2.4) mg/dL. The relationship of ApoC-II quintiles with cardiovascular mortality exhibited a trend toward an inverse J-shape, with the highest risk in the first (lowest) quintile and lowest risk in the middle quintile. Compared with the first quintile, all other quintiles were associated with decreased cardiovascular mortality after multivariate adjustments including ApoC-III as a covariate (all P < 0.05). In experiments using fluorometric substrate-based lipase assays, there was a bell-shaped relationship for the effect of ApoC-II on GPIHBP1–LPL activity when exogenous ApoC-II was added. In ApoC-II-containing VLDL substrate-based lipase assays, GPIHBP1–LPL enzymatic activity was almost completely blocked by a neutralizing anti-ApoC-II antibody. Conclusion The present epidemiologic data suggest that increasing low circulating ApoC-II levels may reduce cardiovascular risk. This conclusion is supported by the observation that optimal ApoC-II concentrations are required for maximal GPIHBP1–LPL enzymatic activity.

Triglyceride (TG) metabolism is highly regulated by angiopoietin-like protein (ANGPTL) family members [Y. Q. Chen et al. , J. Lipid Res. 61 , 1203–1220 (2020)]. During feeding, ANGPTL8 forms complexes with the fibrinogen-like domain-containing protein ANGPTL4 in adipose tissue to decrease ANGPTL3/8- and ANGPTL4-mediated lipoprotein lipase (LPL)-inhibitory activity and promote TG hydrolysis and fatty acid (FA) uptake. The ANGPTL4/8 complex, however, tightly binds LPL and partially inhibits it in vitro. To try to reconcile the in vivo and in vitro data on ANGPTL4/8, we aimed to find novel binding partners of ANGPTL4/8. To that end, we performed pulldown experiments and found that ANGPTL4/8 bound both tissue plasminogen activator (tPA) and plasminogen, the precursor of the fibrinolytic enzyme plasmin. Remarkably, ANGPTL4/8 enhanced tPA activation of plasminogen to generate plasmin in a manner like that observed with fibrin, while minimal plasmin generation was observed with ANGPTL4 alone. The addition of tPA and plasminogen to LPL-bound ANGPTL4/8 caused rapid, complete ANGPTL4/8 cleavage and increased LPL activity. Restoration of LPL activity in the presence of ANGPTL4/8 was also achieved with plasmin but was blocked when catalytically inactive plasminogen (S760A) was added to tPA or when plasminogen activator inhibitor-1 was added to tPA + plasminogen, indicating that conversion of plasminogen to plasmin was essential. Together, these results suggest that LPL-bound ANGPTL4/8 mimics fibrin to recruit tPA and plasminogen to generate plasmin, which then cleaves ANGPTL4/8, enabling LPL activity to be increased. Our observations thus reveal a unique link between the ANGPTL4/8 complex and plasmin generation.

PURPOSE OF REVIEW Over the last two decades, evolving discoveries around angiopoietin-like (ANGPTL) proteins, particularly ANGPTL3, ANGPTL4, and ANGPTL8, have generated significant interest in understanding their roles in fatty acid (FA) metabolism. Until recently, exactly how this protein family regulates lipoprotein lipase (LPL) in a tissue-specific manner to control FA partitioning has remained elusive. This review summarizes the latest insights into mechanisms by which ANGPTL3/4/8 proteins regulate postprandial FA partitioning. RECENT FINDINGS Accumulating evidence suggests that ANGPTL8 is an insulin-responsive protein that regulates ANGPTL3 and ANGPTL4 by forming complexes with them to increase or decrease markedly their respective LPL-inhibitory activities. After feeding, when insulin levels are high, ANGPTL3/8 secreted by hepatocytes acts in an endocrine manner to inhibit LPL in skeletal muscle, whereas ANGPTL4/8 secreted by adipocytes acts locally to preserve adipose tissue LPL activity, thus shifting FA toward the fat for storage. Insulin also decreases hepatic secretion of the endogenous ANGPTL3/8 inhibitor, apolipoprotein A5 (ApoA5), to accentuate ANGPTL3/8-mediated LPL inhibition in skeletal muscle. SUMMARY The ANGPTL3/4/8 protein family and ApoA5 play critical roles in directing FA toward adipose tissue postprandially. Selective targeting of these proteins holds significant promise for the treatment of dyslipidemias, metabolic syndrome, and their related comorbidities.

Background & Aim: Oxyntomodulin (Oxm) is a proglucagon-derived peptide agonist of both the GLP-1 and glucagon receptors and is a key regulator of gastric acid secretion and energy expenditure. Differential processing from proglucagon hinders assay immunoassay selectivity. Method & results: Antibody engineering was used to develop a sandwich immunoassay that selectively measures endogenous Oxm. The pre- and postprandial levels of Oxm from 19 healthy individuals over the course of 2 h were measured. Postprandial increases in Oxm occurred within minutes and levels significantly correlated with those obtained using previously published mass spectrometry assays. Conclusion: This sandwich immunoassay is appropriately sensitive and selective and is also amenable to high-throughput application for the reliable determination of endogenous levels of intact Oxm from human samples.

Lay abstract Measuring immunogenicity, an immune response to a drug, is important in understanding the benefits and risks associated with a drug. Immunogenicity is measured by specific tests within a laboratory; however, these tests and laboratories may change over time. This paper proposes a method to determine if a change in test and laboratory will impact the interpretation of immunogenicity for a drug. Blood samples from clinical trial patients were combined in order to provide representative samples for the immunogenicity tests. The samples were tested at two laboratories with two tests to measure if any interpretation of immunogenicity results would change due to the different tests and laboratories. Laboratories and tests demonstrated similar and reliable results of the samples. This study has established a method which allows for the comparison of immunogenicity results when tests and/or laboratories are changed.

Triacylglycerol (TG) metabolism is tightly regulated to maintain a pool of TG within circulating lipoproteins that can be hydrolyzed in a tissue-specific manner by lipoprotein lipase (LPL) to enable the delivery of fatty acids to adipose or oxidative tissues as needed. Elevated serum TG concentrations, which result from a deficiency of LPL activity or, more commonly, an imbalance in the regulation of tissue-specific LPL activities, have been associated with an increased risk of atherosclerotic cardiovascular disease through multiple studies. Among the most critical LPL regulators are the angiopoietin-like (ANGPTL) proteins ANGPTL3, ANGPTL4, and ANGPTL8, and a number of different apolipoproteins including apolipoprotein A5 (ApoA5), apolipoprotein C2 (ApoC2), and apolipoprotein C3 (ApoC3). These ANGPTLs and apolipoproteins work together to orchestrate LPL activity and therefore play pivotal roles in TG partitioning, hydrolysis, and utilization. This review summarizes the mechanisms of action, epidemiological findings, and genetic data most relevant to these ANGPTLs and apolipoproteins. The interplay between these important regulators of TG metabolism in both fasted and fed states is highlighted with a holistic view toward understanding key concepts and interactions. Strategies for developing safe and effective therapeutics to reduce circulating TG by selectively targeting these ANGPTLs and apolipoproteins are also discussed.

Background Physicians treating COVID-19 patients increasingly believe that the hyperinflammatory acute stage of COVID-19 results in a cytokine storm. The circulating biomarkers seen across the spectrum of COVID-19 have not been characterized compared to healthy controls, but such analyses are likely to yield insights into the pursuit of interventions that adequately reduce the burden of these cytokine storms. Objective To identify and characterize the host inflammatory response to SARS-CoV-2 infection, we assessed levels of proteins related to immune responses and cardiovascular disease, in patients stratified as mild, moderate, and severe, versus matched healthy controls. Methods Blood samples from adult patients hospitalized with COVID-19 were analyzed using high-throughput and ultrasensitive proteomic platforms and compared with age- and sex-matched healthy controls to provide insights into differential regulation of 185 markers. Results Results indicate a dominant hyperinflammatory milieu in the circulation and vascular endothelial damage markers within COVID-19 patients, and strong biomarker association with patient response as measured by Ordinal scale. As patients progress, we observe statistically significant dysregulation of IFNγ, IL-1RA, IL-6, IL-10, IL-19, MCP-1, -2, -3, CXCL9, CXCL10, CXCL5, ENRAGE and PARP-1. Furthermore, in a limited series of patients who were sampled frequently confirming reliability and reproducibility of our assays, we demonstrate that intervention with baricitinib attenuates these circulating biomarkers associated with the cytokine storm. Conclusion These wide-ranging circulating biomarkers show an association with increased disease severity and may help stratify patients and selection of therapeutic options. They also provide insights into mechanisms of SARS-CoV-2 pathogenesis and the host response., Capsule Summary: This detailed analysis of plasma biomarkers in COVID-19 patients provides definitive understanding of the cytokine storm and changes in vascular endothelial markers that may inform us of the changes in the Ordinal scale for COVID-19.

We previously demonstrated that angiopoietin-like 8 (ANGPTL8) forms a localized complex with ANGPTL4 to reduce its lipoprotein lipase (LPL)-inhibitory activity and enable increased postprandial uptake of fatty acids (FA) into adipose tissue. Because prolonged cold exposure may increase adipose tissue FA uptake and decrease circulating triglycerides (TG) by reducing ANGPTL4 expression and inducing ANGPTL8 expression (and thus ANGPTL4/8 expression), we investigated the effect of temperature on ANGPTL4 and ANGPTL4/8 LPL-inhibitory activities in vitro. As the ANGPTL4(E40K) mutation results in decreased TG, we also characterized ANGPTL4(E40K) and ANGPTL4(E40K)/8 complex LPL-inhibitory activities. Interestingly, while ANGPTL3, ANGPTL3/8, and ANGPTL4 showed similar LPL inhibition at 37 °C and 22 °C, the already reduced LPL-inhibitory activity of ANGPTL4/8 at 37 °C was even more decreased at 22 °C. At 37 °C, ANGPTL4(E40K) manifested decreased LPL-inhibitory activity compared to ANGPTL4/8, while ANGPTL4(E40K)/8 had even further reduced potency. Remarkably, ANGPTL4/8, ANGPTL4(E40K), and ANGPTL4(E40K)/8 were each actually capable of stimulating LPL activity at 22 °C. Together, these results indicate that ANGPTL4/8 stimulation of LPL activity at low temperatures may represent an additional mechanism for further increasing adipose tissue FA uptake during cold exposure, beyond that already occurring due to decreased ANGPTL4 expression and increased ANGPTL8 expression. In addition, because ANGPTL4(E40K) has decreased LPL-inhibitory activity compared to ANGPTL4/8, our findings also suggest why ANGPTL4(E40K) carriers have decreased circulating TG levels.

Over the developmental lifetime of a therapeutic protein, the immunogenicity assay validation history can become substantial, frustrating review of clinical immunogenicity within the biologics license application. In our experience, this can lead to questions by regulators, resulting in numerous information requests during the review process. To address this, we propose a new document, the method history report (MHR), which can comprehensively present the history of the immunogenicity assay for regulators, including assay development and validation. The flexibility of the MHR allows for adaptation to the specific needs of each therapeutic program, while maintaining a consistent template. Here, we detail the rationale, general outline and template for the MHR and recommend others consider adopting it for their biologics license application-related activities.

Angiopoietin-like protein (ANGPTL)8 has been implicated in metabolic syndrome and reported to regulate adipose FA uptake through unknown mechanisms. Here, we studied how complex formation of ANGPTL8 with ANGPTL3 or ANGPTL4 varies with feeding to regulate LPL. In human serum, ANGPTL3/8 and ANGPTL4/8 complexes both increased postprandially, correlated negatively with HDL, and correlated positively with all other metabolic syndrome markers. ANGPTL3/8 also correlated positively with LDL-C and blocked LPL-facilitated hepatocyte VLDL-C uptake. LPL-inhibitory activity of ANGPTL3/8 was >100-fold more potent than that of ANGPTL3, and LPL-inhibitory activity of ANGPTL4/8 was >100-fold less potent than that of ANGPTL4. Quantitative analyses of inhibitory activities and competition experiments among the complexes suggested a model in which localized ANGPTL4/8 blocks the LPL-inhibitory activity of both circulating ANGPTL3/8 and localized ANGPTL4, allowing lipid sequestration into fat rather than muscle during the fed state. Supporting this model, insulin increased ANGPTL3/8 secretion from hepatocytes and ANGPTL4/8 secretion from adipocytes. These results suggest that low ANGPTL8 levels during fasting enable ANGPTL4-mediated LPL inhibition in fat tissue to minimize adipose FA uptake. During feeding, increased ANGPTL8 increases ANGPTL3 inhibition of LPL in muscle via circulating ANGPTL3/8, while decreasing ANGPTL4 inhibition of LPL in adipose tissue through localized ANGPTL4/8, thereby increasing FA uptake into adipose tissue. Excessive caloric intake may shift this system toward the latter conditions, possibly predisposing to metabolic syndrome.

Frontiers in cell and developmental biology 10, 1-15 (2022). doi:10.3389/fcell.2022.947430, Published by Frontiers Media, Lausanne

Adeno-associated virus (AAV) based gene therapies are gaining significant momentum as a novel therapeutic modality. However, a yet unsolved concern for using AAV as a vector is the high potential to elicit humoral and cellular responses, which are often exacerbated by pre-existing immunity due to exposure to wild type AAV. Therefore, characterization of pre-existing and treatment emergent anti-AAV antibodies is of great importance to the development of AAV based gene therapies. In this project, a sensitive and drug tolerant total antibody (TAb) assay was developed using recombinant AAV9-GFP (green fluorescent protein) as a surrogate AAV9. The assay format was affinity capture and elution (ACE) with ruthenium labeled AAV9-GFP as detection. Upon evaluation, three commercial anti-AAV9 monoclonal antibodies (clones HI17, HI35, and HL2374) were chosen and mixed at equal concentrations as positive control material. The assay sensitivity was estimated to be 11.2 ng/mL. Drug tolerance was estimated to be 5.4 × 10E10 DRP/mL AAV9-GFP at 100 ng/mL anti-AAV9 antibodies and to be at least 1 × 10E11 DRP/mL at 500 ng/mL and 250 ng/mL anti-AAV9 antibodies. The assay showed desirable specificity and precision. Using this TAb assay, significant pre-existing antibodies were detected from normal human sera.

Triglycerides (TG) are required for fatty acid transport and storage and are essential for human health. Angiopoietin-like-protein 8 (ANGPTL8) has previously been shown to form a complex with ANGPTL3 that increases circulating TG by potently inhibiting LPL. We also recently showed that the TG-lowering apolipoprotein A5 (ApoA5) decreases TG levels by suppressing ANGPTL3/8-mediated LPL inhibition. To understand how LPL binds ANGPTL3/8 and ApoA5 blocks this interaction, we used hydrogen-deuterium exchange mass-spectrometry and molecular modeling to map binding sites of LPL and ApoA5 on ANGPTL3/8. Remarkably, we found that LPL and ApoA5 both bound a unique ANGPTL3/8 epitope consisting of N-terminal regions of ANGPTL3 and ANGPTL8 that are unmasked upon formation of the ANGPTL3/8 complex. We further used ANGPTL3/8 as an immunogen to develop an antibody targeting this same epitope. After refocusing on antibodies that bound ANGPTL3/8, as opposed to ANGPTL3 or ANGPTL8 alone, we utilized bio-layer interferometry to select an antibody exhibiting high-affinity binding to the desired epitope. We revealed an ANGPTL3/8 leucine zipper-like motif within the anti-ANGPTL3/8 epitope, the LPL-inhibitory region, and the ApoA5-interacting region, suggesting the mechanism by which ApoA5 lowers TG is via competition with LPL for the same ANGPTL3/8-binding site. Supporting this hypothesis, we demonstrate that the anti-ANGPTL3/8 antibody potently blocked ANGPTL3/8-mediated LPL inhibition in vitro and dramatically lowered TG levels in vivo. Together, these data show that an anti-ANGPTL3/8 antibody targeting the same leucine zipper-containing epitope recognized by LPL and ApoA5 markedly decreases TG by suppressing ANGPTL3/8-mediated LPL inhibition.

We previously demonstrated that angiopoietin-like protein 8 (ANGPTL8) forms ANGPTL3/8 and ANGPTL4/8 complexes that increase with feeding to direct fatty acids (FA) toward adipose tissue through differential modulation of lipoprotein lipase (LPL) activity. Each complex correlated inversely with high density lipoprotein cholesterol (HDL) in control subjects. We thus investigated ANGPTL3/8 and ANGPTL4/8 levels in type 2 diabetes patients, who can present with decreased HDL. While ANGPTL3/8 levels in type 2 diabetes patients were similar to those previously observed in normal controls, ANGPTL4/8 levels were roughly twice as high as those in control subjects. Concentrations of ANGPTL3/8 and ANGPTL4/8 in type 2 diabetes patients were inversely correlated with HDL, with the correlation being significant for ANGPTL4/8. We therefore measured the ability of the various ANGPTL proteins and complexes to inhibit endothelial lipase (EL), the enzyme which hydrolyzes phospholipids (PL) in HDL. While confirming ANGPTL3 as an EL inhibitor, we found that ANGPTL4 was a more potent EL inhibitor than ANGPTL3. Interestingly, we observed that while ANGPTL3/8 had increased EL-inhibitory activity compared to ANGPTL3 alone, ANGPTL4/8 exhibited decreased potency in inhibiting EL compared to ANGPTL4 alone. Together, these results show for the first time that ANGPTL4 is a more potent EL inhibitor than ANGPTL3 and suggest a possible reason for why ANGPTL4/8 levels are correlated inversely with HDL., Highlights • ANGPTL4/8 levels are increased in patients with type 2 diabetes. • ANGPTL4/8 levels are inversely correlated with HDL in type 2 diabetes patients. • ANGPTL4 is an inhibitor of endothelial lipase (EL). • ANGPTL4 inhibits EL more potently than ANGPTL3 inhibits EL. • ANGPTL4/8 inhibits EL less potently than ANGPTL4 inhibits EL., Endothelial lipase; HDL; triglycerides; angiopoietin-like protein; phospholipids; postprandial state

Despite recent advances in the development of tools to predict immunogenicity risk of biotherapeutic molecules, the ability of a protein to elicit the formation of anti-drug antibodies (ADA) remains one of the most common causes for termination of clinical development programs. In this study, we use ADA assays to detect and measure pre-existing reactivity or the ability of a molecule to produce an ADA-like response in serum from treatment-naïve, healthy donors. We report herein that the magnitude of pre-existing reactivity evaluated pre-clinically and expressed as the 90th percentile of Tier 2 inhibition correlates with the subsequent rate of ADA emergence in the clinic. Furthermore, a multi-domain biotherapeutic (IgG-scFv bispecific antibody) showed the highest pre-existing reactivity and incidence of treatment-emergent ADA (TE-ADA) (57% and 93%, respectively). Using the components of the multidomain molecule in the Tier 2 step of the ADA assay, we were able to identify the scFv as the target of the serum pre-existing reactivity. Most importantly, the domain specificity of pre-existing ADA was the same as that of the TE-ADA from patients treated with the molecule. Based on these data, we propose the evaluation of the magnitude and of the domain specificity of pre-existing reactivity as a powerful tool to understand the immunogenic potential of novel biotherapeutics.

Precise elucidation of the antigen sequences for T cell immunosurveillance greatly enhances our ability to understand and modulate humoral responses to viral infection or active immunization. Mass spectrometry is used to identify 526 unique sequences from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein extracellular domain in a complex with human leukocyte antigen class II molecules on antigen-presenting cells from a panel of healthy donors selected to represent a majority of allele usage from this highly polymorphic molecule. The identified sequences span the entire spike protein, and several sequences are isolated from a majority of the sampled donors, indicating promiscuous binding. Importantly, many peptides derived from the receptor binding domain used for cell entry are identified. This work represents a precise and comprehensive immunopeptidomic investigation with the SARS-CoV-2 spike glycoprotein and allows detailed analysis of features that may aid vaccine development to end the current coronavirus disease 2019 (COVID-19) pandemic., Graphical Abstract, Highlights • Dendritic cells display peptides spanning the entire SARS-CoV-2 spike protein • HLA-II peptides from 11 regions are presented by a majority of the analyzed donors • One region with promiscuous HLA-II presentation is conserved with SARS-CoV • The correlation of presented to predicted peptides is low, Knierman et al. pulse dendritic cells derived from healthy human donors with the SARS-CoV-2 spike glycoprotein to determine the precise sequences presented for T cell surveillance. Regions with promiscuous presentation are identified, with poor correlation to predicted epitopes. One region with promiscuous presentation is conserved with the SARS-CoV spike glycoprotein sequence.

Upstream open reading frames (uORFs) are tissue-specific cis-regulators of protein translation. Isolated reports have shown that variants that create or disrupt uORFs can cause disease. Here, in a systematic genome-wide study using 15,708 whole genome sequences, we show that variants that create new upstream start codons, and variants disrupting stop sites of existing uORFs, are under strong negative selection. This selection signal is significantly stronger for variants arising upstream of genes intolerant to loss-of-function variants. Furthermore, variants creating uORFs that overlap the coding sequence show signals of selection equivalent to coding missense variants. Finally, we identify specific genes where modification of uORFs likely represents an important disease mechanism, and report a novel uORF frameshift variant upstream of NF2 in neurofibromatosis. Our results highlight uORF-perturbing variants as an under-recognised functional class that contribute to penetrant human disease, and demonstrate the power of large-scale population sequencing data in studying non-coding variant classes. Genome Aggregation Database Consortium Carlos A. Aguilar Salinas21, Tariq Ahmad22, Christine M. Albert23,24, Diego Ardissino25, Gil Atzmon26,27,John Barnard28, Laurent Beaugerie29, Emelia J. Benjamin30,31,32, Michael Boehnke33, Lori L. Bonnycastle34,Erwin P. Bottinger35, Donald W. Bowden36,37,38, Matthew J. Bown39,40, John C. Chambers41,42,43,Juliana C. Chan44, Daniel Chasman23,45, Judy Cho35, Mina K. Chung46, Bruce Cohen45,47, Adolfo Correa48,Dana Dabelea49, Mark J. Daly3,4,18, Dawood Darbar50, Ravindranath Duggirala51, Josée Dupuis52,53,Patrick T. Ellinor3,54, Roberto Elosua55,56,57, Jeanette Erdmann58,59,60, Tõnu Esko3,61, Martti Färkkilä62,Jose Florez63, Andre Franke64, Gad Getz45,65,66, Benjamin Glaser67, Stephen J. Glatt68, David Goldstein69,70,Clicerio Gonzalez71, Leif Groop72,73, Christopher Haiman74, Craig Hanis75, Matthew Harms76,77,Mikko Hiltunen78, Matti M. Holi79, Christina M. Hultman80,81, Mikko Kallela82, Jaakko Kaprio73,83,Sekar Kathiresan45,84,85, Bong-Jo Kim86, Young Jin Kim86, George Kirov87, Jaspal Kooner42,43,88,Seppo Koskinen89, Harlan M. Krumholz90, Subra Kugathasan91, Soo Heon Kwak92, Markku Laakso93,94,Terho Lehtimäki95, Ruth J.F. Loos35,96, Steven A. Lubitz3,56, Ronald C.W. Ma44,97,98, Jaume Marrugat58,99,Kari M. Mattila95, Steven McCarroll51,100, Mark I. McCarthy101,102,103, Dermot McGovern104,Ruth McPherson105, James B. Meigs45,106,107, Olle Melander108, Andres Metspalu61, Benjamin M. Neale3,4,Peter M. Nilsson109, Michael C. O’Donovan87, Dost Ongur45,47, Lorena Orozco110, Michael J. Owen87,Colin N.A. Palmer111, Aarno Palotie4,51,73, Kyong Soo Park92,112, Carlos Pato113, Ann E. Pulver114,Nazneen Rahman115, Anne M. Remes116, John D. Rioux117,118, Samuli Ripatti73,83,119, Dan M. Roden120,121,Danish Saleheen122,123,124, Veikko Salomaa125, Nilesh J. Samani39,40, Jeremiah Scharf3,18,84,Heribert Schunkert126,127, Moore B. Shoemaker128, Pamela Sklar129,130,131, Hilkka Soininen132, Harry Sokol29,Tim Spector133, Patrick F. Sullivan80,134, Jaana Suvisaari125, E. Shyong Tai135,136,137, Yik Ying Teo136,138,139,Tuomi Tiinamaija73,140,141, Ming Tsuang142,143, Dan Turner144, Teresa Tusie-Luna145,146, Erkki Vartiainen83,Hugh Watkins147, Rinse K. Weersma148, Maija Wessman73,140, James G. Wilson149, Ramnik J. Xavier150,151 21Unidad de Investigacion de Enfermedades Metabolicas, Instituto Nacional de Ciencias Medicas y Nutricion, Mexico City, Mexico.22PeninsulaCollege of Medicine and Dentistry, Exeter EX2 4TH, UK.23Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA 02115,USA.24Division of Cardiovascular Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA.25Departmentof Cardiology University Hospital, 43100 Parma, Italy.26Department of Biology, Faculty of Natural Sciences, University of Haifa, 3498838 Haifa,Israel.27Departments of Medicine and Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.28Department of Quantitative HealthSciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44122, USA.29APHP, Gastroenterology Department, Saint Antoine Hospital,Sorbonne Université, Paris, France.30NHLBI and Boston University’s Framingham Heart Study, Framingham, MA 01701, USA.31Department ofMedicine, Boston University School of Medicine, Boston, MA 02118, USA.32Department of Epidemiology, Boston University School of Public Health,Boston, MA 02118, USA.33Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.34National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.35The Charles Bronfman Institute forPersonalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.36Department of Biochemistry, Wake Forest School ofMedicine, Winston-Salem, NC 27101, USA.37Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine,Winston-Salem, NC 27101, USA.38Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA.39Departmentof Cardiovascular Sciences, University of Leicester, Leicester LE1 7RH, UK.40NIHR Leicester Biomedical Research Centre, Glenfield Hospital,Leicester LE3 9QP, UK.41Department of Epidemiology and Biostatistics, Imperial College London, London SW7 2BX, UK.42Department of Cardiology, Ealing Hospital NHS Trust, Southall UB1 3HW, UK.43Imperial College Healthcare NHS Trust, Imperial College London, London SW72BX, UK.44Department of Medicine and Therapeutics, The Chinese University of Hong Kong, 999077 Hong Kong, China.45Department ofMedicine, Harvard Medical School, Boston, MA 02115, USA.46Departments of Cardiovascular Medicine, Cellular and Molecular Medicine,Molecular Cardiology, and Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH 44195, USA.47McLean Hospital, Belmont, MA 02478,USA.48Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA.49Department of Epidemiology, ColoradoSchool of Public Health, Aurora, Colorado 80246, USA.50Department of Medicine and Pharmacology, University of Illinois at Chicago, Chicago, IL60607, USA.51Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX 78227, USA.52Department of Biostatistics, BostonUniversity School of Public Health, Boston, MA 02118, USA.53National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham,MA 01702, USA.54Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA.55Cardiovascular Epidemiology and Genetics, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Catalonia, Spain.56CIBER CV,Barcelona, Catalonia, Spain.57Departament of Medicine, Medical School, University of Vic-Central University of Catalonia, Catalonia 08500, Spain.58Institute for Cardiogenetics, University of Lübeck, Lübeck 23562, Germany.59DZHK (German Research Centre for Cardiovascular Research)partner site Hamburg/Lübeck/Kiel, 23562 Lübeck, Germany.60University Heart Center Lübeck, 23562 Lübeck, Germany.61Estonian GenomeCenter, Institute of Genomics, University of Tartu, Tartu 50090, Estonia.62Clinic of Gastroenterology, Helsinki University and Helsinki UniversityHospital, FL 00014 Helsinki, Finland.63Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital; Programs in Metabolismand Medical and Population Genetics, Broad Institute; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.64Institute ofClinical Molecular Biology (IKMB), Christian-Albrechts-University of Kiel, Kiel 24118, Germany.65Bioinformatics Program, Department of Pathology,MGH Cancer Center, Boston 02114, USA.66Cancer Genome Computational Analysis, Broad Institute, Cambridge 02142, USA.67Endocrinology andMetabolism Department, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.68Department of Psychiatry and Behavioral Sciences,SUNY Upstate Medical University, Syracuse, NY 13421, USA.69Institute for Genomic Medicine, Columbia University Medical Center, HammerHealth Sciences, 1408, 701 West 168th Street, New York, NY 10032, USA.70Department of Genetics & Development, Columbia University MedicalCenter, Hammer Health Sciences, 1602, 701 West 168th Street, New York, NY 10032, USA.71Centro de Investigacion en Salud Poblacional InstitutoNacional de Salud Publica MEXICO, Cuernavace 62100, Mexico.72Lund University, SE-221 00 Lund, Sweden.73Institute for Molecular MedicineFinland (FIMM), HiLIFE, University of Helsinki, FL 00014 Helsinki, Finland.74Lund University Diabetes Centre, SE-221 00 Lund, Sweden.75HumanGenetics Center, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.76Department of Neurology, ColumbiaUniversity, New York 10032, USA.77Institute of Genomic Medicine, Columbia University, New York 10032, USA.78Institute of Biomedicine,University of Eastern Finland, Kuopio FI-80101, Finland.79Department of Psychiatry, PL 320, Helsinki University Central Hospital, Lapinlahdentie, 00180, Helsinki, Finland.80Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 77, Sweden.81Icahn School ofMedicine at Mount Sinai, New York, NY 10029, USA.82Department of Neurology, Helsinki University Central Hospital, Fl 00290 Helsinki, Finland.83Department of Public Health, Faculty of Medicine, University of Helsinki, FL 00014 Helsinki, Finland.84Center for Genomic Medicine,Massachusetts General Hospital, Boston, MA 02114, USA.85Cardiovascular Disease Initiative and Program in Medical and Population Genetics,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.86Center for Genome Science, Korea National Institute of Health,Chungcheongbuk-do, Republic of Korea.87MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, HadynEllis Building, Maindy Road, Cardiff CF24 4HQ, UK.88National Heart and Lung Institute, Cardiovascular Sciences, Imperial College London,Hammersmith Campus, London SW7 2BX, UK.89Department of Health, THL-National Institute for Health and Welfare, 00271 Helsinki, Finland.90Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, USA.91Division ofPediatric Gastroenterology, Emory University School of Medicine, Atlanta, GA 30322, USA.92Department of Internal Medicine, Seoul NationalUniversity Hospital, Seoul, Republic of Korea.93Institute of Clinical Medicine, The University of Eastern Finland, Kuopio, Finland.94Kuopio UniversityHospital, Kuopio 70210, Finland.95Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere,Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland.96The Mindich Child Health and DevelopmentInstitute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.97Li Ka Shing Institute of Health Sciences, The Chinese University ofHong Kong, Hong Kong, China.98Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China.99Cardiovascular Research REGICOR Group, Hospital del Mar Medical Research Institute (IMIM), Barcelona 08003 Catalonia, Spain.100Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.101Oxford Centre for Diabetes, Endocrinology and Metabolism,University of Oxford, Churchill Hospital, Old Road, Headington, Oxford OX3 7LJ, UK.102Wellcome Centre for Human Genetics, University of Oxford,Roosevelt Drive, Oxford OX3 7BN, UK.103Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, JohnRadcliffe Hospital, Oxford OX3 9DU, UK.104F Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-SinaiMedical Center, Los Angeles, CA 90048, USA.105Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, ON K1Y 4W7,Canada.106Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.107Program in Population and MedicalGenetics, Broad Institute, Cambridge, MA 02142, USA.108Department of Clinical Sciences, University Hospital Malmo Clinical Research Center,Lund University, SE-221 00 Malmo, Sweden.109Department of Clinical Sciences, Lund University, Skane University Hospital, SE-221 00 Malmo,Sweden.110Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City 14610, Mexico.111Medical Research Institute, Ninewells Hospitaland Medical School, University of Dundee, Dundee DD1 4HN, UK.112Department of Molecular Medicine and Biopharmaceutical Sciences, GraduateSchool of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.113Department of Psychiatry, Keck School ofMedicine at the University of Southern California, Los Angeles, CA 90033, USA.114Department of Psychiatry and Behavioral Sciences, JohnsHopkins University School of Medicine, Baltimore, MD 20105, USA.115Division of Genetics and Epidemiology, Institute of Cancer Research, LondonSM2 5NG, UK.116Medical Research Center, Oulu University Hospital, Oulu, Finland and Research Unit of Clinical Neuroscience, Neurology,University of Oulu, FI-90014 Oulu, Finland.117Research Center, Montreal Heart Institute, Montreal, QC H1T 1C8, Canada.118Department ofMedicine, Faculty of Medicine, Université de Montréal, Montréal H3T 1J4 Québec, Canada.119Broad Institute of MIT and Harvard, Cambridge, MA02142, USA.120Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37212, USA.121Department of Medicine,Vanderbilt University Medical Center, Nashville, TN 37212, USA.122Department of Biostatistics and Epidemiology, Perelman School of Medicine atthe University of Pennsylvania, Philadelphia, PA 19104, USA.123Department of Medicine, Perelman School of Medicine at the University ofPennsylvania, Philadelphia, PA 19104, USA.124Center for Non-Communicable Diseases, Karachi 75300, Pakistan.125National Institute for Healthand Welfare, Helsinki FI 00271, Finland.126Deutsches Herzzentrum München, München 80636, Germany.127Technische Universität München,München 80333, Germany.128Division of Cardiovascular Medicine, Nashville VA Medical Center and Vanderbilt University, School of Medicine,Nashville, TN 37232-8802, USA.129Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.130Departmentof Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.131Institute for Genomics and MultiscaleBiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.132Institute of Clinical Medicine, neurology, University of EasternFinland, Kuopio, Finland.133Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK.134Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA.135Saw Swee Hock School of Public Health, NationalUniversity of Singapore, National University Health System, Singapore 117549, Singapore.136Department of Medicine, Yong Loo Lin School ofMedicine, National University of Singapore, Singapore 117597, Singapore.137Duke-NUS Graduate Medical School, Singapore 169857, Singapore.138Life Sciences Institute, National University of Singapore, Singapore 117456, Singapore.139Department of Statistics and Applied Probability,National University of Singapore, Singapore 119077, Singapore.140Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.141HUCH Abdominal Center, Helsinki University Hospital, Helsinki 00260, Finland.142Center for Behavioral Genomics, Department of Psychiatry,University of California, San Diego, CA 94720, USA.143Institute of Genomic Medicine, University of California, San Diego, CA 94720, USA.144JulietKeidan Institute of Pediatric Gastroenterology, Shaare Zedek Medical Center, The Hebrew University of Jerusalem, Jerusalem, Israel.145Instituto deInvestigaciones Biomédicas UNAM, Mexico City 04510, Mexico.146Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán MexicoCity, Mexico City 14080, Mexico.147Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK.148Department ofGastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen 9713 GZ, The Netherlands.149Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA.150Program in Infectious Diseaseand Microbiome, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.151Center for Computational and Integrative Biology,Massachusetts General Hospital, Boston, MA 02114, U

The precise elucidation of the antigen sequences for T-cell immunosurveillance greatly enhances our ability to both understand and modulate humoral responses to viral infection or active immunization. Mass spectrometry was used to identify 529 unique sequences from SARS-CoV-2 spike glycoprotein extracellular domain in a complex with human leukocyte antigen class II molecules on antigen presenting cells from a panel of healthy donors selected to represent a majority of allele usage from this highly polymorphic molecule. The identified sequences span the entire spike protein and several sequences were isolated from a majority of the donors sampled indicating promiscuous binding. Importantly, many peptides derived from the receptor binding domain used for cell entry were identified. This work represents the most precise and comprehensive immunopeptidomic investigation with SARS-CoV-2 spike glycoprotein and allows detailed analysis of features which may aid vaccine development to end the current COVID-19 pandemic. Funding: This work was supported by Eli Lilly and Company. Conflict of Interest: The authors declare no competing interests. Ethical Approval: Frozen PBMCs were obtained from 9 informed consent healthy donors (Discovery Life Sciences) according to local ethical practice.

Medikationsfehler sind ein haufiges und ernstes Sicherheitsproblem. Chlorhexidin (CHX) ist ein taglich eingesetztes Desinfektionsmittel. Die akzidentelle intravasale Injektion von CHX ist selten beschrieben. Schwerwiegende Komplikationen wie akutes Atemnotsyndrom sind moglich. Wir berichten uber die zentralvenose Injektion von 0,1 %iger CHX-Mundspullosung, deren potenzielle Komplikationen und mogliche Therapiemasnahmen. Im Vergleich zu einem anderen Fallbericht traten bei unserem Patienten keine Schaden auf. Die sofortige Hamofiltration und Reinigung der i.v.-Leitung haben moglicherweise zum guten Outcome beigetragen.

Traditional production of fermented dairy products in Montenegro is carried out without adding defined starter cultures. This way of production involves lactic acid bacteria (LAB) that are normally present in the raw milk and production environment. This autochthonous ("wild") fermentation microbiota represents a reservoir of unknown strains. In order to study the LAB diversity, 25 indigenous dairy products in Montenegro have been tested. Isolation was performed on microbial media M17 and MRS agar with or without supplementations under aerobic and anaerobic conditions at temperatures of 30°C, 37 °C and 44°C. Identification of these isolates at species level was done by species-specific PCR and gene regions sequencing of representatives of each RAPD-cluster. RAPD-PCR was used to characterize the isolates at strain level. Nine Lactobacillus species, five Leuconostoc species, four Enterococcus species as well as strains of the species Lactococcus lactis, Pediococcus pentosaceus and Streptococcus thermophilus were detected. It can be concluded that a rich lactic acid bacteria diversity existed in the analyzed Montenegrin dairy products. Further examination of the isolates could lead to the development of autochthonous starter cultures that would contribute to a product that is characteristic for the geographical area and to which the local population is accustomed.

Importance Psoriasis relapse may involve compensatory T-cell activation pathways in the presence of CD28-CD80/CD86 blockade with abatacept. Objective To determine whether costimulatory signaling blockade with abatacept prevents psoriasis relapse after ustekinumab withdrawal. Design, Setting, and Participants Psoriasis Treatment with Abatacept and Ustekinumab: a Study of Efficacy (PAUSE), a parallel-design, double-blind, placebo-controlled randomized clinical trial, was conducted at 10 sites in the US and Canada. Participant enrollment opened on March 19, 2014, and concluded on April 11, 2016. Participants were adults with moderate to severe plaque psoriasis and received ustekinumab in a lead-in phase. Those who responded to ustekinumab at week 12 were randomized 1:1 to either the continued with ustekinumab group (ustekinumab group) or the switched to abatacept group (abatacept group). Treatment was discontinued at week 39, and participants were followed up for psoriasis relapse until week 88. Statistical analyses were performed in the intention-to-treat (ITT) and safety samples from May 3, 2018, to July 6, 2021. Interventions Participants received subcutaneous ustekinumab at weeks 0 and 4 (45 mg per dose for those ≤100 kg; 90 mg per dose for those >100 kg). Participants randomized to the abatacept group at week 12 received subcutaneous abatacept, 125 mg weekly, from weeks 12 to 39 and ustekinumab placebo at weeks 16 and 28. Participants randomized to the ustekinumab group received ustekinumab at weeks 16 and 28 and abatacept placebo weekly from weeks 12 to 39. Main Outcomes and Measures The primary end point was the proportion of participants with psoriasis relapse (loss of ≥50% of the initial Psoriasis Area and Severity Index improvement) between weeks 12 and 88. Secondary end points included time to psoriasis relapse, proportion of participants with psoriasis relapse between weeks 12 and 40, and adverse events. The psoriasis transcriptome and serum cytokines were evaluated. Results A total of 108 participants (mean [SD] age, 46.1 [12.1] years; 73 [67.6%] men) were treated with open-label ustekinumab; 91 were randomized to blinded treatment. Similar proportions of participants in the abatacept group and the ustekinumab group relapsed between weeks 12 and 88 (41 of 45 [91.1%] vs 40 of 46 [87.0%];P = .41). Median time to relapse from the last dose of ustekinumab was similar between groups as well: 36 weeks (95% CI, 36-48 weeks) in the abatacept group vs 32 weeks (95% CI, 28-40 weeks) in the ustekinumab group. Similar numbers and rates of adverse events occurred. Abatacept did not maintain suppression of the pathogenic IL-23-mediated psoriasis molecular signature in lesions after ustekinumab withdrawal, and serum IL-19 levels increased. Conclusions and Relevance This parallel-design, double-blind randomized clinical trial found that abatacept did not prevent psoriasis relapse that occurred after ustekinumab withdrawal because it did not completely block the pathogenic psoriasis molecular pathways that led to relapse. Trial Registration ClinicalTrials.gov Identifier:NCT01999868

Summary Background & aims In vitro studies suggest that vitamin D may reduce hepcidin expression and pro-inflammatory cytokine release from monocytes. However, data assessing the vitamin D-mediated effects on iron recycling in healthy individuals are lacking. We aimed to examine the effect of high-dose vitamin D3 on plasma hepcidin, inflammatory cytokine, and ferritin concentrations in healthy adults. Methods This was a pilot, double-blind, placebo-controlled trial in healthy adults (N = 28) randomized to receive a one-time oral dose of 250,000 IU of vitamin D3 or placebo. Between- and within-group differences in plasma hepcidin, pro-inflammatory cytokine [interleukin (IL)-1β, IL-6, IL-8, monocyte chemoattractant protein-1 (MCP-1)], and ferritin concentrations at baseline and 1 week were determined using two-sample and paired t-tests, respectively. Results At baseline, plasma 25-hydroxyvitamin D [25(OH)D], hepcidin, pro-inflammatory cytokine, and ferritin concentrations did not differ between the two groups, and greater than 70% of subjects in both groups were vitamin D deficient (25(OH)D Conclusions High-dose vitamin D3 significantly reduced plasma hepcidin concentrations in healthy adults 1 week post-dosing, without a change in plasma pro-inflammatory cytokine or ferritin concentrations. These data suggest that vitamin D may have a role in regulating iron recycling by acting independently of changes in pro-inflammatory markers.

During the Engineering Validation and Engineering Design Activities (EVEDA) phase (2007-2014) of the International Fusion Materials Irradiation Facility (IFMIF), an advanced engineering design of the High Flux Test Module (HFTM) has been developed with the objective to facilitate the controlled irradiation of steel samples in the high flux area directly behind the IFMIF neutron source. The development process addressed included manufacturing techniques, CAD, neutronic, thermal-hydraulic and mechanical analyses complemented by a series of validation activities. Validation included manufacturing of 1:1 parts and mockups, test of prototypes in the FLEX and HELOKA-LP helium loops of KIT for verification of the thermal and mechanical properties, and irradiation of specimen filled capsule prototypes in the BR2 test reactor. The prototyping activities were backed by several R&D studies addressing focused issues like handling of liquid NaK (as filling medium) and insertion of Small Specimen Test Technique (SSTT) specimens into the irradiation capsules. This paper provides an up-todate design description of the HFTM irradiation device, and reports on the achieved performance criteria related to the requirements. Results of the validation activities are accounted for and the most important issues for further development are identified.