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Context Aromatase deficiency causes obesity and insulin resistance in aromatase knockout mice and humans with rare mutations of the aromatase gene (CYP19). Aromatase inhibitors are a commonly prescribed therapy for postmenopausal breast cancer. Objective We hypothesized that aromatase inhibitors induce obesity and insulin resistance when used in treatment of breast cancer. Design Case-control study. Setting University teaching hospital. Participants Patients with postmenopausal breast cancer (n = 20) treated with aromatase inhibitors and 20 age-matched control subjects. Main outcome measures The primary outcome measure was insulin sensitivity index – Matsuda, derived from a 75-g oral glucose tolerance test. Body composition was assessed by dual energy x-ray absorptiometry and biopsy specimens of subcutaneous adipose tissue obtained for assessment of mRNA transcript levels. Data are reported as mean ± SEM (patients receiving inhibitors vs control group, respectively). Results Aromatase inhibitor therapy was associated with significantly lower insulin sensitivity (5.15 ± 0.45 vs 6.80 ± 0.64; P = 0.041), higher peak insulin concentration after oral glucose tolerance test (693.4 ± 78.6 vs 527.6 ± 85.5 pmol/L; P = 0.035), greater percentage of body fat (38.4% ± 1.0% vs 34.6% ± 1.3%; P = 0.026), and higher plasma leptin concentration (23.5 ± 2.8 vs 15.5 ± 2.3 ng/mL; P = 0.035). Conclusion Women who received aromatase inhibitors for postmenopausal breast cancer had greater percentage body fat and insulin resistance compared with control subjects with no history of breast cancer., We demonstrate increased insulin resistance, adiposity, and plasma leptin in patients with breast cancer treated with aromatase inhibitors, compared with control subjects matched for age and body mass index.

Genome-wide association studies (GWAS) have transformed our understanding of susceptibility to multiple myeloma (MM), but much of the heritability remains unexplained. We report a new GWAS, a meta-analysis with previous GWAS and a replication series, totalling 9974 MM cases and 247,556 controls of European ancestry. Collectively, these data provide evidence for six new MM risk loci, bringing the total number to 23. Integration of information from gene expression, epigenetic profiling and in situ Hi-C data for the 23 risk loci implicate disruption of developmental transcriptional regulators as a basis of MM susceptibility, compatible with altered B-cell differentiation as a key mechanism. Dysregulation of autophagy/apoptosis and cell cycle signalling feature as recurrently perturbed pathways. Our findings provide further insight into the biological basis of MM.

PARP inhibitors can induce synthetic lethality in tumors characterized by homologous recombination deficiency (HRD), which can be detected by evaluating genome-wide loss of heterozygosity (LOH). Multiple myeloma (MM) is a genetically unstable tumor and we hypothesized that HRD-related LOH (HRD-LOH) could be detected in patient samples, supporting a potential role for PARP inhibition in MM. Using results from targeted next-generation sequencing studies (FoundationOne® Heme), we analyzed HRD-LOH in patients at all disease stages (MGUS (n = 7), smoldering MM (SMM, n = 30), newly diagnosed MM (NDMM, n = 71), treated MM (TRMM, n = 64), and relapsed MM (RLMM, n = 234)) using an algorithm to identify HRD-LOH segments. We demonstrated HRD-LOH in MM samples, increasing as disease progresses. The extent of genomic HRD-LOH correlated with high-risk disease markers. Outcome of RLMM patients, the biggest clinical group, was analyzed and patients with HRD-LOH above the third quartile (≥5% HRD-LOH) had significantly worse progression-free and overall survival than those with lower levels (p

Background and Purpose Reducing glucocorticoid exposure in the brain via intracellular inhibition of the cortisol‐regenerating enzyme 11β‐hydroxysteroid dehydrogenase type 1 (11β‐HSD1) has emerged as a therapeutic strategy to treat cognitive impairment in early Alzheimer's disease (AD). We sought to discover novel, brain‐penetrant 11β‐HSD1 inhibitors as potential medicines for the treatment of AD. Experimental Approach Medicinal chemistry optimization of a series of amido‐thiophene analogues was performed to identify potent and selective 11β‐HSD1 inhibitors with optimized oral pharmacokinetics able to access the brain. Single and multiple ascending dose studies were conducted in healthy human subjects to determine the safety, pharmacokinetic and pharmacodynamic characteristics of the candidate compound. Results UE2343 was identified as a potent, orally bioavailable, brain‐penetrant 11β‐HSD1 inhibitor and selected for clinical studies. No major safety issues occurred in human subjects. Plasma adrenocorticotropic hormone was elevated (a marker of systemic enzyme inhibition) at doses of 10 mg and above, but plasma cortisol levels were unchanged. Following multiple doses of UE2343, plasma levels were approximately dose proportional and the terminal t 1/2 ranged from 10 to 14 h. The urinary tetrahydrocortisols/tetrahydrocortisone ratio was reduced at doses of 10 mg and above, indicating maximal 11β‐HSD1 inhibition in the liver. Concentrations of UE2343 in the CSF were 33% of free plasma levels, and the peak concentration in CSF was ninefold greater than the UE2343 IC50. Conclusions and Implications UE2343 is safe, well tolerated and reaches the brain at concentrations predicted to inhibit 11β‐HSD1. UE2343 is therefore a suitable candidate to test the hypothesis that 11β‐HSD1 inhibition in brain improves memory in patients with AD.

Background While genome-wide association studies (GWAS) of multiple myeloma (MM) have identified variants at 23 regions influencing risk, the genes underlying these associations are largely unknown. To identify candidate causal genes at these regions and search for novel risk regions, we performed a multi-tissue transcriptome-wide association study (TWAS). Results GWAS data on 7319 MM cases and 234,385 controls was integrated with Genotype-Tissue Expression Project (GTEx) data assayed in 48 tissues (sample sizes, N = 80–491), including lymphocyte cell lines and whole blood, to predict gene expression. We identified 108 genes at 13 independent regions associated with MM risk, all of which were in 1 Mb of known MM GWAS risk variants. Of these, 94 genes, located in eight regions, had not previously been considered as a candidate gene for that locus. Conclusions Our findings highlight the value of leveraging expression data from multiple tissues to identify candidate genes responsible for GWAS associations which provide insight into MM tumorigenesis. Among the genes identified, a number have plausible roles in MM biology, notably APOBEC3C, APOBEC3H, APOBEC3D, APOBEC3F, APOBEC3G, or have been previously implicated in other malignancies. The genes identified in this TWAS can be explored for follow-up and validation to further understand their role in MM biology. Electronic supplementary material The online version of this article (10.1186/s40246-019-0231-5) contains supplementary material, which is available to authorized users.

The clustering of different types of B-cell malignancies in families raises the possibility of shared aetiology. To examine this, we performed cross-trait linkage disequilibrium (LD)-score regression of multiple myeloma (MM) and chronic lymphocytic leukaemia (CLL) genome-wide association study (GWAS) data sets, totalling 11,734 cases and 29,468 controls. A significant genetic correlation between these two B-cell malignancies was shown (R g = 0.4, P = 0.0046). Furthermore, four of the 45 known CLL risk loci were shown to associate with MM risk and five of the 23 known MM risk loci associate with CLL risk. By integrating eQTL, Hi-C and ChIP-seq data, we show that these pleiotropic risk loci are enriched for B-cell regulatory elements and implicate B-cell developmental genes. These data identify shared biological pathways influencing the development of CLL and, MM and further our understanding of the aetiological basis of these B-cell malignancies.

Chronic exposure to elevated levels of glucocorticoids has been linked to age-related cognitive decline and may play a role in Alzheimer's disease. In the brain, 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) amplifies intracellular glucocorticoid levels. We show that short-term treatment of aged, cognitively impaired C57BL/6 mice with the potent and selective 11β-HSD1 inhibitor UE2316 improves memory, including after intracerebroventricular drug administration to the central nervous system alone. In the Tg2576 mouse model of Alzheimer's disease, UE2316 treatment of mice aged 14 months for 4 weeks also decreased the number of β-amyloid (Aβ) plaques in the cerebral cortex, associated with a selective increase in local insulin-degrading enzyme (involved in Aβ breakdown and known to be glucocorticoid regulated). Chronic treatment of young Tg2576 mice with UE2316 for up to 13 months prevented cognitive decline but did not prevent Aβ plaque formation. We conclude that reducing glucocorticoid regeneration in the brain improves cognition independently of reduced Aβ plaque pathology and that 11β-HSD1 inhibitors have potential as cognitive enhancers in age-associated memory impairment and Alzheimer's dementia.

The genetic basis underlying the inherited risk of developing multiple myeloma (MM) is largely unknown. To examine the impact of rare protein altering variants on the risk of developing MM we analyzed high-coverage exome sequencing data on 513 MM cases and 1,569 healthy controls, performing both single variant and gene burden tests. We did not identify any recurrent coding low-frequency alleles (1-5%) with moderate effect that were statistically associated with MM. In a gene burden analysis we did however identify a promising relationship between variation in the marrow kinetochore microtubule stromal gene KIF18A, which plays a role in control mitotic chromosome positioning dynamics, and risk of MM (P =3.6x10-6). Further analysis showed KIF18A displays a distinct pattern of expression across molecular subgroups of MM as well as being associated with patient survival. Our results inform future study design and provide a resource for contextualizing the impact of candidate MM susceptibility genes.

Hair cortisol concentration (HCC) is a promising measure of long-term hypothalamus-pituitary-adrenal (HPA) axis activity. Previous research has suggested an association between HCC and psychological variables, and initial studies of inter-individual variance in HCC have implicated genetic factors. However, whether HCC and psychological variables share genetic risk factors remains unclear. The aims of the present twin study were to: (i) assess the heritability of HCC; (ii) estimate the phenotypic and genetic correlation between HPA axis activity and the psychological variables perceived stress, depressive symptoms, and neuroticism; using formal genetic twin models and molecular genetic methods, i.e. polygenic risk scores (PRS). HCC was measured in 671 adolescents and young adults. These included 115 monozygotic and 183 dizygotic twin-pairs. For 432 subjects PRS scores for plasma cortisol, major depression, and neuroticism were calculated using data from large genome wide association studies. The twin model revealed a heritability for HCC of 72%. No significant phenotypic or genetic correlation was found between HCC and the three psychological variables of interest. PRS did not explain variance in HCC. The present data suggest that HCC is highly heritable. However, the data do not support a strong biological link between HCC and any of the investigated psychological variables. Consortia CORtisolNETwork (CORNET) Consortium Jennifer L. Bolton21 Caroline Hayward23 Nese Direk24,25 Anna Anderson21 Jennifer Huffman23 James F. Wilson26 Harry Campbell26 Igor Rudan26 Alan Wright23 Nicholas Hastie23 Sarah H. Wild26 Fleur P. Velders24 Albert Hofman24 Andre G. Uitterlinden24,27 Jari Lahti28 Katri Räikkönen28 Eero Kajantie29 Elisabeth Widen30 Aarno Palotie30,31 Johan G. Eriksson29,32,33,34,35 Marika Kaakinen36 Marjo-Riitta Järvelin36,37,38,39 Nicholas J. Timpson40 George Davey Smith40 Susan M. Ring41 David M. Evans40 Beate St Pourcain41 Toshiko Tanaka42 Yuri Milaneschi42,43 Stefania Bandinelli44 Luigi Ferrucci42 Pim van der Harst45,46,47 Judith GM Rosmalen48 Stephen JL Bakker49 Niek Verweij45 Robin PF Dullaart49 Anubha Mahajan50 Cecilia M. Lindgren50 Andrew Morris50 Lars Lind51 Erik Ingelsson51 Laura N. Anderson52 Craig E. Pennell53 Stephen J. Lye52 Stephen G. Matthews54 Joel Eriksson55 Dan Mellstrom55 Claes Ohlsson55 Jackie F. Price26 Mark WJ Strachan21 Rebecca M. Reynolds21 Henning Tiemeier24,56,57 Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (PGC) Stephan Ripke58,59,60 Manuel Mattheisen61,62,63 Abdel Abdellaoui64 Mark J. Adams65 Esben Agerbo66,8,67 Tracy M. Air68 Till FM Andlauer69,70 Silviu-Alin Bacanu71 Marie Bækvad-Hansen67,72 Aartjan TF Beekman43 David A. Bennett73 Klaus Berger74 Tim B. Bigdeli71,11 Jonas Bybjerg-Grauholm67,72 Enda M. Byrne5 Na Cai75 Enrique Castelao76 Toni-Kim Clarke65 Jonathan RI Coleman77 Converge Consortium78 Nick Craddock79 Udo Dannlowski80,81 Gareth Davies82 Gail Davies83 Eco. J. C. de Geus64,84 Philip De Jager85 Ian J. Deary83 Franziska Degenhardt12,13 Erin C. Dunn86,87,88 Erik A. Ehli82 Thalia C. Eley77 Valentina Escott-Price89 Tõnu Esko58,90,91,92 Hilary K. Finucane93,94 Michael Gill95 Scott D. Gordon96 Jakob Grove61,62,67,97 Lynsey S. Hall65,98 Thomas F. Hansen99,100 Christine Søholm Hansen67,72 Thomas F. Hansen101 Andrew C. Heath102 Anjali K. Henders5 Stefan Herms12,13,15 Per Hoffmann12,13,15 Georg Homuth103 Carsten Horn104 Jouke- Jan Hottenga64 David Hougaard67,72 Hailiang Huang59,86,105 Marcus Ising106 Rick Jansen43 Eric Jorgenson107 Stefan Kloiber108,109 James A Knowles110 Warren W. Kretzschmar50 Jesper Krogh111 Zoltán Kutalik112,113 Maren Lang3 Glyn Lewis114 Yihan Li50 Donald J. MacIntyre115,116 Pamela AF Madden102 Jonathan Marchine117 Hamdi Mbarek118,64 Peter McGuffin77 Divya Mehta119 Andres Metspalu92,120 Christel M. Middeldorp64 Evelin Mihailov92,121 Lili Milani92 Grant W. Montgomery122 Sara Mostafavi123,124 Niamh Mullins77 Matthias Nauck125,126 Bernard Ng124 Merete Nordentoft67,127 Dale R. Nyholt128 Michael C. O’Donovan129 Paul F. O’Reilly77 Hogni Oskarsson130 Michael J. Owen129 Sara A. Paciga131 Carsten Bøcker Pedersen66,67,8 Marianne Giørtz Pedersen66,67,8 Nancy L. Pedersen132 Michele L. Pergadia133 Roseann E. Peterson11,71 Erik Pettersson134 Wouter J. Peyrot43 David J. Porteous135 Danielle Posthuma136,137 James B. Potash138 Jorge A. Quiroz139 John P. Rice102 Brien P. Riley71 Margarita Rivera77,140 Douglas M. Ruderfer141 Saira Saeed Mirza24 Robert Schoevers142 Ling Shen107 Jianxin Shi143 Engilbert Sigurdsson144 Grant CB Sinnamon145 Johannes H. Smit43 Daniel J. Smith146 Jordan W. Smoller86 Hreinn Stephansson147 Stacy Steinberg147 Jana Strohmaier3 Katherine E. Tansey148 Alexander Teumer149 Wesley Thompson100,135,150,151,152 Pippa A. Thomson135 Thorgeir E. Thorgeirsson147 Jens Treutlein3 Maciej Trzaskowski153 Daniel Umbricht154 Sandra van der Auwera155 Gerard van Grootheest43 Albert M. van Hemert156 Alexander Viktorin132 Henry Völzke149 Yunpeng Wang67,100,151 Bradley T. Webb157 Myrna M. Weissman158,159 Jürgen Wellmann74 Gonneke Willemsen64 Hualin S. Xi160 Bernhard T. Baune68 Douglas H. R. Blackwood65 Dorret I. Boomsma64 Anders D. Børglum61,62,67 Henriette N. Buttenschøn62,67,161 Sven Cichon12,162,163,164 Enrico Domenici165 Jonathan Flint50,166 Hans J. Grabe155 Steven P. Hamilton167 Kenneth S. Kendler71 Qingqin S. Li168 Susanne Lucae106 Patrik K. Magnusson132 Andrew M. McIntosh65,83 Ole Mors67,169 Preben Bo Mortensen62,8,67 Bertram Müller-Myhsok69,70,170 Brenda WJH Penninx43 Roy H. Perlis87,171 Martin Preisig76 Catherine Schaefer107 Jordan W. Smoller87,88 Kari Stephansson147 Henning Tiemeier24,56,57 Rudolf Uher172 Thomas Werge100,150,173 Ashley R. Winslow174,175 Gerome Breen77,176 Douglas F. Levinson177 Cathryn M. Lewis77,178 Naomi R. Wray5,153 Patrick F. Sullivan134,179,180 23MRC Human Genetics Unit, Institute for Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK. 24Department of Epidemiology, Erasmus Medical Centre, Rotterdam, Netherlands. 25Psychiatry, Dokuz Eylul University School Of Medicine, Izmir, TR, Turkey. 26Centre for Population Health Sciences, Institute for Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH8 9AG, UK. 27Internal Medicine, Erasmus MC, Rotterdam, NL, Netherlands. 28Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland. 29National Institute for Health and Welfare, Helsinki, Finland. 30Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. 31Department of Medical Genetics, University of Helsinki and University Central Hospital, Helsinki, Finland. 32Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland. 33Helsinki University Central Hospital, Unit of General Practice, Helsinki, Finland. 34Folkhalsan Research Centre, Helsinki, Finland. 35Vasa Central Hospital, Vasa, Finland. 36Institute of Health Sciences and Biocenter Oulu, University of Oulu, Oulu, Finland. 37Department of Children and Yond People and Families, National Institute for Health and elfare, Oulu, Finland. 38Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, Imperial College London, London, UK. 39Unit of Primary Care, Oulu University Hospital, Oulu, Finland. 40MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, UK. 41School of Social and Community Medicine, University of Bristol, Bristol, UK. 42Longitudinal Studies Section, Clinical Research Branch, National Institute on Aging, Baltimore, MD, USA. 43Department of Psychiatry, VU University Medical Center/GGZ inGeest, Amsterdam, Netherlands. 44Geriatric Unit, ASF, Florence, Italy. 45University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, Netherlands. 46University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, Netherlands. 47Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, Netherlands. 48University of Groningen, University Medical Center Groningen, Interdisciplinary Center for Psychiatric Epidemiology, Groningen, Netherlands. 49University of Groningen, University Medical Center Groningen, Department of Internal Medicine, Groningen, Netherlands. 50Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. 51Department of Medical Sciences, Uppsala University, Uppsala, Sweden. 52Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada. 53School of Women’s and Infant’s Health, The University of Western Australia, Crawley, Australia. 54Department of Physiology, University of Toronto, Toronto, Ontario, Canada. 55Center for Bone and Arthritis Research, Institute of Medicin, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 56Child and Adolescent Psychiatry, Erasmus MC, Rotterdam, Netherlands. 57Psychiatry, Erasmus MC, Rotterdam, Netherlands. 58Medical and Population Genetics, Broad Institute, Cambridge, USA. 59Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, USA. 60Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte, Berlin, Germany. 61Department of Biomedicine, Aarhus University, Aarhus, Denmark. 62iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark. 63iSPYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark. 64Dept of Biological Psychology, VU University Amsterdam, Amsterdam, Netherlands. 65Division of Psychiatry, University of Edinburgh, Edinburgh, UK. 66Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark. 67iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark. 68Discipline of Psychiatry, University of Adelaide, Adelaide, Australia. 69Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany. 70Munich Cluster for Systems Neurology (SyNergy), Munich, Germany. 71Department of Psychiatry, Virginia Commonwealth University, Richmond, USA. 72Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark. 73Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, USA. 74Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, UK. 75Human Genetics, Wellcome Trust Sanger Institute, Cambridge, UK. 76Department of Psychiatry, University Hospital of Lausanne, Prilly, Switzerland. 77MRC Social Genetic and Developmental Psychiatry Centre, King’s College London, London, UK. 78University of Oxford, Oxford, UK. 79Psychological Medicine, Cardiff University, Cardiff, UK. 80Department of Psychiatry, University of Marburg, Marburg, Germany. 81Department of Psychiatry, University of Münster, Münster, Germany. 82Avera Institute for Human Genetics, Sioux Falls, USA. 83Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK. 84EMGO+ Institute, VU University Medical Center, Amsterdam, Netherlands. 85Neurology, Brigham and Women’s Hospital, Boston, USA. 86Stanley Center for Psychiatric Research, Broad Institute, Cambridge, USA. 87Department of Psychiatry, Massachusetts General Hospital, Boston, USA. 88Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, USA. 89Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK. 90Division of Endocrinology, Children’s Hospital Boston, Boston, USA. 91Department of Genetics, Harvard Medical School, Boston, USA. 92Estonian Genome Center, University of Tartu, Tartu, Estonia. 93Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA. 94Department of Mathematics, Massachusetts Institute of Technology, Cambridge, USA. 95Department of Psychiatry, Trinity College Dublin, Dublin, Ireland. 96Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Australia. 97Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark. 98Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK. 99Danish Headache Centre, Department of Neurology, Rigshospitalet Glostrup, Glostrup, Denmark. 100Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Capital Region of Denmark, Roskilde, Denmark. 101iPSYCH, The Lundbeck Foundation Initiative for Psychiatric Research, Copenhagen, Denmark. 102Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, USA. 103Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany. 104Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland. 105Department of Medicine, Harvard Medical School, Boston, USA. 106Max Planck Institute of Psychiatry, Munich, Germany. 107Division of Research, Kaiser Permanente Northern California, Oakland, USA. 108Centre for Addiction and Mental Health, Toronto, Canada. 109Department of Psychiatry, University of Toronto, Toronto, Canada. 110Psychiatry & The Behavioral Sciences, University of Southern California, Los Angeles, USA. 111Department of Endocrinology at Herlev University Hospital, University of Copenhagen, Copenhagen, Denmark. 112Swiss Institute of Bioinformatics, Lausanne, Switzerland. 113Institute of Social and Preventive Medicine (IUMSP), Lausanne University Hospital, Lausanne, Switzerland. 114Division of Psychiatry, University College London, London, UK. 115Mental Health NHS 24, Glasgow, UK. 116Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK. 117Statistics, University of Oxford, Oxford, UK. 118EMGO+ Institute for Health and Care Research, Amsterdam, Netherlands. 119School of Psychology and Counseling, Queensland University of Technology, Brisbane, Australia. 120Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia. 121Estonian Biocentre, Tartu, Estonia. 122Institute for Molecular Biology, University of Queensland, Brisbane, Australia. 123Medical Genetics, University of British Columbia, Vancouver, Canada. 124Statistics, University of British Columbia, Vancouver, Canada. 125DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Matthias Nauck, Greifswald, Germany. 126Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany. 127Mental Health Centre Copenhagen, Copenhagen Universtity Hospital, Copenhagen, Denmark. 128Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia. 129MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK. 130Humus, Reykjavik, Iceland. 131Human Genetics and Computational Biomedicine, Pfizer Global Research and Development, Groton, USA. 132Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 133Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, USA. 134Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 135Medical Genetics Section, CGEM, IGMM, University of Edinburgh, Edinburgh, UK. 136Complex Trait Genetics, VU University Amsterdam, Amsterdam, Netherlands. 137Clinical Genetics, VU University Medical Center, Amsterdam, Netherlands. 138Psychiatry, University of Iowa, Iowa City, USA. 139Solid GT, Boston, USA. 140Department of Biochemistry and Molecular Biology II, Institute of Neurosciences, Center for Biomedical Research, University of Granada, Granada, Spain. 141Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA. 142Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. 143Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, USA. 144Faculty of Medicine, Department of Psychiatry, School of Health Sciences, University of Iceland, Reykjavik, Iceland. 145School of Medicine and Dentistry, James Cook University, Townsville, Australia. 146Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK. 147deCODE Genetics/Amgen, Reykjavik, Iceland. 148College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK. 149Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany. 150iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark. 151KG Jebsen Centre for Psychosis Research, Norway Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway. 152Department of Psychiatry, University of California, San Diego, San Diego, USA. 153Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia. 154Roche Pharmaceutical Research and Early Development, Neuroscience, Ophthalmology and Rare Diseases Discovery & Translational Medicine Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland. 155Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany. 156Department of Psychiatry, Leiden University Medical Center, Leiden, Netherlands. 157Virginia Institute of Psychiatric & Behavioral Genetics, Virginia Commonwealth University, Richmond, USA. 158Psychiatry, Columbia University College of Physicians and Surgeons, New York, USA. 159Division of Epidemiology, New York State Psychiatric Institute, New York, USA. 160Computational Sciences Center of Emphasis, Pfizer Global Research and Development, Cambridge, USA. 161Department of Clinical Medicine, Translational Neuropsychiatry Unit, Aarhus University, Aarhus, Denmark. 162Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany. 163Department of Biomedicine, University of Basel, Basel, CH, Switzerland. 164Division of Medical Genetics, University of Basel, Basel, CH, Switzerland. 165Centre for Integrative Biology, Università degli Studi di Trento, Trento, Italy. 166Psychiatry, University of California Los Angeles, Los Angeles, USA. 167Psychiatry, Kaiser Permanente Northern California, San Francisco, USA. 168Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, USA. 169Psychosis Research Unit, Aarhus University Hospital, Risskov, Aarhus, Denmark. 170Institute of Translational Medicine, University of Liverpool, Liverpool, UK. 171Psychiatry, Harvard Medical School, Boston, USA. 172Psychiatry, Dalhousie University, Halifax, Canada. 173Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. 174Human Genetics and Computational Biomedicine, Pfizer Global Research and Development, Cambridge, USA. 175Orphan Disease Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA. 176NIHR BRC for Mental Health, King’s College London, London, UK. 177Psychiatry & Behavioral Sciences, Stanford University, Stanford, USA. 178Department of Medical & Molecular Genetics, King’s College London, London, UK. 179Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, USA. 180Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, USA.

Many bacterial and viral pathogens (or their toxins), including Pseudomonas aeruginosa exotoxin A, require processing by host pro-protein convertases such as furin to cause dis- ease. We report the development of a novel irreversible inhibitor of furin (QUB-F1) consist- ing of a diphenyl phosphonate electrophilic warhead coupled with a substrate-like peptide (RVKR), that also includes a biotin tag, to facilitate activity-based profiling/visualisation. QUB-F1 displays greater selectivity for furin, in comparison to a widely used exemplar com- pound (furin I) which has a chloromethylketone warhead coupled to RVKR, when tested against the serine trypsin-like proteases (trypsin, prostasin and matriptase), factor Xa and the cysteine protease cathepsin B. We demonstrate QUB-F1 does not prevent P. aerugi- nosa exotoxin A-induced airway epithelial cell toxicity; in contrast to furin I, despite inhibiting cell surface furin-like activity to a similar degree. This finding indicates additional proteases, which are sensitive to the more broad-spectrum furin I compound, may be involved in this process.

Benign prostatic hyperplasia and prostate cancer can be treated with the 5α-reductase inhibitors, finasteride and dutasteride, when pharmacodynamic biomarkers are useful in assessing response. A novel method was developed to measure the substrates and products of 5α-reductases (testosterone, 5α-dihydrotestosterone (DHT), androstenedione) and finasteride and dutasteride simultaneously by liquid chromatography tandem mass spectrometry, using an ABSciex QTRAP® 5500, with a Waters Acquity™ UPLC. Analytes were extracted from serum (500 µL) via solid-phase extraction (Oasis® HLB), with 13C3-labelled androgens and d9-finasteride included as internal standards. Analytes were separated on a Kinetex C18 column (150×3 mm, 2.6 µm), using a gradient run of 19 min. Temporal resolution of analytes from naturally occurring isomers and mass +2 isotopomers was ensured. Protonated molecular ions were detected in atmospheric pressure chemical ionisation mode and source conditions optimised for DHT, the least abundant analyte. Multiple reaction monitoring was performed as follows: testosterone (m/z 289→97), DHT (m/z 291→255), androstenedione (m/z 287→97), dutasteride (m/z 529→461), finasteride (m/z 373→317). Validation parameters (intra- and inter-assay precision and accuracy, linearity, limits of quantitation) were within acceptable ranges and biological extracts were stable for 28 days. Finally the method was employed in men treated with finasteride or dutasteride; levels of DHT were lowered by both drugs and furthermore the substrate concentrations increased., Graphical abstract, Highlights • Simultaneous analysis of 5α-reductase inhibitors with enzyme substrates and products. • Combined pharmacokinetic/pharmacodynamic analysis. • Rapid, sensitive and robust analysis of androgens, dutasteride and finasteride.

Constitutive NF-B-K signaling promotes survival in multiple myeloma (MM) and other cancers; however, current NF-B-K-targeting strategies lack cancer cell specificity. Here, we identify the interaction between the NF-B-K-regulated antiapoptotic factor GADD45 beta and the JNK kinase MKK7 as a therapeutic target in MM. Using a drug-discovery strategy, we developed DTP3, a D-tripeptide, which disrupts the GADD45 beta/ MKK7 complex, kills MM cells effectively, and, importantly, lacks toxicity to normal cells. DTP3 has similar anticancer potency to the clinical standard, bortezomib, but more than 100-fold higher cancer cell specificity in vitro. Notably, DTP3 ablates rnyeloma xenografts in mice with no apparent side effects at the effective doses. Hence, cancer-selective targeting of the NF-KB pathway is possible and, at least for myeloma patients, promises a profound benefit.

Regeneration of active glucocorticoids within liver and adipose tissue by the enzyme 11 beta-hydroxysteroid dehydrogenase type 1 (11 beta-HSD1) may be of pathophysiological importance in obesity and metabolic syndrome and is a therapeutic target in type 2 diabetes. Polymorphisms in HSD11B1, the gene encoding 11 beta-HSD1, have been associated with metabolic phenotype in humans, including type 2 diabetes and hypertension. Here, we have tested the functional consequences of two single nucleotide polymorphisms located in contexts that potentially affect tissue levels of 11 beta-HSD1. We report no effect of allelic variation at rs846910, a polymorphism within the 5'-flanking region of the gene on HSD11B1 promoter activity in vitro. However, compared with the common G allele, the A allele of rs13306421, a polymorphism located two nucleotides 5' to the translation initiation site, gave higher 11 beta-HSD1 expression and activity in vitro and was translated at higher levels in in vitro translation reactions, possibly associated with a lower frequency of "leaky scanning." These data suggest that this polymorphism may have direct functional consequences on levels of 11 beta-HSD1 enzyme activity in vivo. However, the rs13306421 A sequence variant originally reported in other ethnic groups may be of low prevalence because it was not detected in a population of 600 European Caucasian women.

To examine the possibility that low birth weight is a feature of the inherited predisposition to high blood pressure.Cross sectional study.Primary care medical centre in Edinburgh.One offspring of 452 families (231 men and 221 women aged 16-26 years) in whom blood pressure, weight, and height were measured in 1986 and whose parents had blood pressure measured in 1979. Birth weights were obtained from case records (270 offspring) or by questionnaires sent to the mothers (182 offspring).Birth weight and adult systolic blood pressure in offspring in relation to parental blood pressure.If parental blood pressures were not considered, a 1 kg decrease in birth weight was associated with a 2.24 mm Hg increase in systolic blood pressure of offspring (P = 0.06) after correction for current weight and sex. However, parental blood pressures correlated positively with blood pressure of offspring, and higher maternal blood pressure was associated with lower birth weight (-3.03 g/mm Hg, P0.01). After correction for parental blood pressures, a 1 kg decrease in birth weight was associated with only a 1.71 mm Hg increase in the systolic blood pressure of the offspring (P = 0.15).Low birth weight is a feature of the inherited predisposition to hypertension, perhaps because it is associated with higher maternal blood pressure during pregnancy. Parental blood pressure may be an important confounding factor in the relation between low birth weight and subsequent hypertension.

Aims Disrupted intermediary metabolism may contribute to the adverse pregnancy outcomes in women with very severe obesity. Our aim was to study metabolism in such pregnancies. Methods We recruited a longitudinal cohort of very severely obese (n = 190) and lean (n = 118) glucose-tolerant women for anthropometric and metabolic measurements at early, mid and late gestation and postpartum. In case–control studies of very severely obese and lean women we measured glucose and glycerol turnover during low- and high-dose hyperinsulinaemic–euglycaemic clamps (HEC) at early and late pregnancy and in non-pregnant women (each n = 6–9) and body fat distribution by MRI in late pregnancy (n = 10/group). Results Although greater glucose, insulin, NEFA and insulin resistance (HOMA-IR), and greater weight and % fat mass (FM) was observed in very severely obese vs lean participants, the degree of worsening was attenuated in the very severely obese individuals with advancing gestation, with no difference in triacylglycerol (TG) concentrations between very severely obese and lean women at term. Enhanced glycerol production was observed in early pregnancy only in very severely obese individuals, with similar intrahepatic FM in very severely obese vs lean women by late gestation. Offspring from obese mothers were heavier (p = 0.04). Conclusions/interpretation Pregnancies complicated by obesity demonstrate attenuation in weight gain and insulin resistance compared with pregnancies in lean women. Increased glycerol production is confined to obese women in early pregnancy and obese and lean individuals have similar intrahepatic FM by term. When targeting maternal metabolism to treat adverse pregnancy outcomes, therapeutic intervention may be most effective applied early in pregnancy. Electronic supplementary material The online version of this article (doi:10.1007/s00125-015-3708-3) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

Summary The discovery of brown adipose tissue (BAT) in adult humans presents a new therapeutic target for metabolic disease; however, little is known about the regulation of human BAT. Chronic glucocorticoid excess causes obesity in humans, and glucocorticoids suppress BAT activation in rodents. We tested whether glucocorticoids regulate BAT activity in humans. In vivo, the glucocorticoid prednisolone acutely increased 18fluorodeoxyglucose uptake by BAT (measured using PET/CT) in lean healthy men during mild cold exposure (16°C–17°C). In addition, prednisolone increased supraclavicular skin temperature (measured using infrared thermography) and energy expenditure during cold, but not warm, exposure in lean subjects. In vitro, glucocorticoids increased isoprenaline-stimulated respiration and UCP-1 in human primary brown adipocytes, but substantially decreased isoprenaline-stimulated respiration and UCP-1 in primary murine brown and beige adipocytes. The highly species-specific regulation of BAT function by glucocorticoids may have important implications for the translation of novel treatments to activate BAT to improve metabolic health., Graphical Abstract, Highlights • Glucocorticoids acutely increase but chronically suppress BAT activity in humans • Glucocorticoids increase UCP-1 and respiration in human brown adipocytes • Glucocorticoids decrease UCP-1 and respiration in murine brown and beige adipocytes • Species-specific differences exist in the regulation of BAT activation, The regulation of brown adipose tissue (BAT) in humans is not well understood. Ramage et al. show that glucocorticoids acutely increase BAT in vivo and in vitro in humans through increasing UCP-1. However, glucocorticoids decrease UCP-1 in murine beige/ brown adipocytes, identifying species-specific differences in the regulation of BAT function.

Estrogens circulate at concentrations less than 20 pg/mL in men and postmenopausal women, presenting analytical challenges. Quantitation by immunoassay is unreliable at these low concentrations. Liquid chromatography tandem mass spectrometry (LC–MS/MS) offers greater specificity and sometimes greater sensitivity, but ionization of estrogens is inefficient. Introduction of charged moieties may enhance ionization, but many such derivatives of estrogens generate non-specific product ions originating from the “reagent” group. Therefore an approach generating derivatives with product ions specific to individual estrogens was sought. Estrogens were extracted from human plasma and serum using solid phase extraction and derivatized using 2-fluoro-1-methylpyridinium-p-toluenesulfonate (FMP-TS). Electrospray in positive mode with multiple reaction monitoring using a QTrap 5500 mass spectrometer was used to quantify “FMP” derivatives of estrogens, following LC separation. Transitions for the FMP derivatives of estrone (E1) and estradiol (E2) were compound specific (m/z 362→238 and m/z 364→128, respectively). The limits of detection and quantitation were 0.2 pg on-column and the method was linear from 1–400 pg/sample. Measures of intra- and inter-assay variability, precision and accuracy were acceptable (, Graphical abstract fx1, Highlights • Quantitative analysis of low amounts of estrone and estradiol in plasma and serum. • Quantitation across physiological range in men and pre- and post-menopausal women. • Methylpyridinium ether derivatives improve analytical specificity and sensitivity.

CONTEXT: GDF15 is a stress-induced hormone acting in the hindbrain that activates neural circuitry involved in establishing aversive responses and reducing food intake and body weight in animal models. Anorexia, weight loss, nausea and vomiting are common manifestations of glucocorticoid deficiency, and we hypothesized that glucocorticoid deficiency may be associated with elevated levels of GDF15. OBJECTIVE: To determine the impact of primary adrenal insufficiency (PAI) and glucocorticoid replacement on circulating GDF15 levels. METHODS AND RESULTS: We measured circulating concentrations of GDF15 in a cohort of healthy volunteers and Addison's disease patients following steroid withdrawal. Significantly higher GDF15 (mean ± standard deviation [SD]) was observed in the Addison's cohort, 739.1 ± 225.8 pg/mL compared to healthy controls, 497.9 ± 167.7 pg/mL (P = 0.01). The effect of hydrocortisone replacement on GDF15 was assessed in 3 independent PAI cohorts with classical congenital adrenal hyperplasia or Addison's disease; intravenous hydrocortisone replacement reduced GDF15 in all groups. We examined the response of GDF15 to increasing doses of glucocorticoid replacement in healthy volunteers with pharmacologically mediated cortisol deficiency. A dose-dependent difference in GDF15 (mean ± SD) was observed between the groups with values of 491.0 ± 157.7 pg/mL, 427.0 ± 152.1 pg/mL and 360 ± 143.1 pg/mL, in the low, medium and high glucocorticoid replacement groups, respectively, P < .0001. CONCLUSIONS: GDF15 is increased in states of glucocorticoid deficiency and restored by glucocorticoid replacement. Given the site of action of GDF15 in the hindbrain and its effects on appetite, further study is required to determine the effect of GDF15 in mediating the anorexia and nausea that is a common feature of glucocorticoid deficiency.

Multiple myeloma (MM) is consistently preceded by precursor conditions recognized clinically as monoclonal gammopathy of undetermined significance (MGUS) or smoldering myeloma (SMM). We interrogate the whole genome sequence (WGS) profile of 18 MGUS and compare them with those from 14 SMMs and 80 MMs. We show that cases with a non-progressing, clinically stable myeloma precursor condition (n = 15) are characterized by later initiation in the patient’s life and by the absence of myeloma defining genomic events including: chromothripsis, templated insertions, mutations in driver genes, aneuploidy, and canonical APOBEC mutational activity. This data provides evidence that WGS can be used to recognize two biologically and clinically distinct myeloma precursor entities that are either progressive or stable., The factors that are associated with myeloma precursor condition progression are not well understood. Here the authors find that monoclonal gammopathies of undetermined significance and smoldering myelomas that did not progress to multiple myelomas have a distinct genomic profile and emerge later in the patient’s life.

Despite the recent progress in treatment of multiple myeloma (MM), it is still an incurable malignant disease, and we are therefore in need of new risk stratification tools that can help us to understand the disease and optimize therapy. Here we propose a new subtyping of myeloma plasma cells (PCs) from diagnostic samples, assigned by normal B-cell subset associated gene signatures (BAGS). For this purpose, we combined fluorescence-activated cell sorting and gene expression profiles from normal bone marrow (BM) Pre-BI, Pre-BII, immature, naïve, memory, and PC subsets to generate BAGS for assignment of normal BM subtypes in diagnostic samples. The impact of the subtypes was analyzed in 8 available data sets from 1772 patients’ myeloma PC samples. The resulting tumor assignments in available clinical data sets exhibited similar BAGS subtype frequencies in 4 cohorts from de novo MM patients across 1296 individual cases. The BAGS subtypes were significantly associated with progression-free and overall survival in a meta-analysis of 916 patients from 3 prospective clinical trials. The major impact was observed within the Pre-BII and memory subtypes, which had a significantly inferior prognosis compared with other subtypes. A multiple Cox proportional hazard analysis documented that BAGS subtypes added significant, independent prognostic information to the translocations and cyclin D classification. BAGS subtype analysis of patient cases identified transcriptional differences, including a number of differentially spliced genes. We identified subtype differences in myeloma at diagnosis, with prognostic impact and predictive potential, supporting an acquired B-cell trait and phenotypic plasticity as a pathogenetic hallmark of MM.

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of twelve anthropometric traits associated with height, body mass and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. Seventy-one percent of signals reside within genes and fine-mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits, and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically-relevant discoveries across the frequency spectrum.

Aquaculture is the fastest growing food sector and continues to expand alongside terrestrial crop and livestock production. Using portfolio theory as a conceptual framework, we explore how current interconnections between the aquaculture, crop, livestock, and fisheries sectors act as an impediment to, or an opportunity for, enhanced resilience in the global food system given increased resource scarcity and climate change. Aquaculture can potentially enhance resilience through improved resource use efficiencies and increased diversification of farmed species, locales of production, and feeding strategies. However, aquaculture’s reliance on terrestrial crops and wild fish for feeds, its dependence on freshwater and land for culture sites, and its broad array of environmental impacts diminishes its ability to add resilience. Feeds for livestock and farmed fish that are fed rely largely on the same crops, although the fraction destined for aquaculture is presently small (∼4%). As demand for high-value fed aquaculture products grows, competition for these crops will also rise, as will the demand for wild fish as feed inputs. Many of these crops and forage fish are also consumed directly by humans and provide essential nutrition for low-income households. Their rising use in aquafeeds has the potential to increase price levels and volatility, worsening food insecurity among the most vulnerable populations. Although the diversification of global food production systems that includes aquaculture offers promise for enhanced resilience, such promise will not be realized if government policies fail to provide adequate incentives for resource efficiency, equity, and environmental protection.

ContextAbnormal cortisol metabolism in polycystic ovary syndrome (PCOS) has been invoked as a cause of secondary activation of the hypothalamic–pituitary–adrenal axis and hence androgen excess. However, this is based on urinary excretion of cortisol metabolites, which cannot detect tissue-specific changes in metabolism and may be confounded by obesity.ObjectiveTo assess cortisol clearance and whole-body and tissue-specific activities of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1 (HSD11B1)) in PCOS.DesignCase–control study.SettingMedical center.PatientsA total of 20 overweight–obese unmedicated Caucasian women with PCOS, aged 18–45 years, and 20 Caucasian controls matched for age, BMI, body fat distribution, andHSD11B1genotypes (rs846910 and rs12086634).Main outcome measuresCortisol metabolites were measured in 24 h urine. During steady-state 9,11,12,12-[2H]4-cortisol infusion, cortisol clearance was calculated and whole-body HSD11B1 activity was assessed as the rate of appearance of 9,12,12-2H3-cortisol (d3-cortisol). Hepatic HSD11B1 activity was quantified as the generation of plasma cortisol following an oral dose of cortisone. Subcutaneous adipose HSD11B1 activity andHSD11B1mRNA were measured,ex vivo, in biopsies.ResultsUrinary cortisol metabolite excretion, deuterated cortisol clearance, and the rate of appearance of d3-cortisol did not differ between patients with PCOS and controls. However, hepatic HSD11B1 conversion of oral cortisone to cortisol was impaired (PHSD11B1mRNA levels and activity were increased (PConclusionsTissue-specific dysregulation of HSD11B1 is a feature of PCOS, over and above obesity, whereas increased clearance of cortisol may result from obesity rather than PCOS.

Climate change, biodiversity loss, antibiotic resistance, and other global challenges pose major collective action problems: A group benefits from a certain action, but no individual has sufficient incentive to act alone. Formal institutions, e.g., laws and treaties, have helped address issues like ozone depletion, lead pollution, and acid rain. However, formal institutions are not always able to enforce collectively desirable outcomes. In such cases, informal institutions, such as social norms, can be important. If conditions are right, policy can support social norm changes, helping address even global problems. To judge when this is realistic, and what role policy can play, we discuss three crucial questions: Is a tipping point likely to exist, such that vicious cycles of socially damaging behavior can potentially be turned into virtuous ones? Can policy create tipping points where none exist? Can policy push the system past the tipping point?