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11β-Hydroxysteroid dehydrogenase 1 (11βHSD1) is a drug target to attenuate adverse effects of chronic glucocorticoid excess. It catalyses intracellular regeneration of active glucocorticoids in tissues including brain, liver and adipose tissue (coupled to hexose-6-phosphate dehydrogenase, H6PDH). 11βHSD1 activity in individual tissues is thought to contribute significantly to glucocorticoid levels at those sites, but its local contribution versus glucocorticoid delivery via the circulation is unknown. Here, we hypothesised that hepatic 11βHSD1 would contribute significantly to the circulating pool. This was studied in mice with Cre-mediated disruption of Hsd11b1 in liver (Alac-Cre) versus adipose tissue (aP2-Cre) or whole-body disruption of H6pdh. Regeneration of [9,12,12-2 46 H3]-cortisol (d3F) from [9,12,12-2 47 H3]-cortisone (d3E), measuring 11βHSD1 reductase activity was assessed at steady state following infusion of [9,11,12,12-2 48 H4]-cortisol (d4F) in male mice. Concentrations of steroids in plasma and amounts in liver, adipose tissue and brain were measured using mass spectrometry interfaced with matrix assisted laser desorption ionisation or liquid chromatography. Amounts of d3F were higher in liver, compared with brain and adipose tissue. Rates of appearance of d3F were ~6-fold slower in H6pdh-/- 52 mice, showing the importance of whole-body 11βHSD1 reductase activity. Disruption of liver 11βHSD1 reduced amounts of d3F in liver (by ~36%), without changes elsewhere. In contrast disruption of 11βHSD1 in adipose tissue reduced rates of appearance of circulating d3F (by ~67%) and also reduced regeneration of d3F in liver and brain (both by ~30%). Thus, the contribution of hepatic 11βHSD1 to circulating glucocorticoid levels and amounts in other tissues is less than that of adipose tissue.

Additional file 1. Supplementary methods, Supplementary figures (Fig. S1-S9), and Supplementary tables (Table S1-S25).

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.

Additional file 2: Supplementary Table 2. Genes that represent PHF19 transcriptional signature in multiple myeloma.

Additional file 1. Supplementary Materials.

Supplemental Material, sj-pdf-1-ear-10.1177_01455613211022098 for A Sweet Voice: Acute Febrile Neutrophilic Dermatosis of the Larynx by Brian A. Walker, Lindsey B. Stull and J. Peyton Hines in Ear, Nose & Throat Journal

Chromosomal translocations are important drivers of haematological malignancies whereby proto-oncogenes are activated by juxtaposition with super-enhancers, often called super-enhancer hijacking . We analysed the epigenomic consequences of rearrangements between the enhancers of the immunoglobulin heavy chain locus (IGH) and proto-oncogene CCND1 that are common in B-cell malignancies. By integrating BLUEPRINT epigenomic data with DNA breakpoint detection, we characterised the normal chromatin landscape of the human IGH locus and its dynamics after pathological genomic rearrangement. We detected an H3K4me3 broad domain (BD) within the IGH locus of healthy B-cells that was absent in samples with IGH-CCND1 translocations. The appearance of H3K4me3-BD over CCND1 in the latter was associated with overexpression and extensive chromatin accessibility of this locus. We observed similar cancer-specific H3K4me3-BDs associated with super-enhancer hijacking of other common oncogenes in B-cell ( MAF , MYC and FGFR3 ) and in T-cell malignancies ( LMO2 , TLX3 and TAL1 ). Our analysis suggests that H3K4me3-BDs are created by super-enhancers and supports the new concept of epigenomic translocation , where the relocation of H3K4me3-BDs accompanies the translocation of super-enhancers.

CONTEXT: Inter-individual differences in cortisol production and metabolism emerge with age, and may be explained by genetic factors.OBJECTIVE: To estimate the relative contributions of genetic and environmental factors to inter-individual differences in cortisol production and metabolism throughout adolescence.DESIGN: Prospective follow-up study of twins.SETTING: Nationwide register.PARTICIPANTS: 218 mono- and dizygotic twins (N=109 pairs) born between 1995-1996, recruited from the Netherlands Twin Register. Cortisol metabolites were determined in 213, 169 and 160 urine samples at the ages of 9, 12 and 17y, respectively.MAIN OUTCOME MEASURES: The total contribution of genetic factors (broad-sense heritability), and shared and unshared environmental influences to inter-individual differences in cortisol production, and activities of 5α-reductase, 5β-reductase, 11β-hydroxysteroid dehydrogenases and cytochrome P450 3A4.RESULTS: For cortisol production rate at the ages of 9, 12 and 17y, broad-sense heritability was estimated as: 42%, 30% and 0%, respectively and the remainder of the variance was explained by unshared environmental factors. For cortisol metabolism indices, the following heritability was observed: for the A-ring reductases (5α-and 5β-reductases), broad-sense heritability increased with age (to>50%), while for the other indices (renal 11β-HSD2, global 11β-HSD and CYP3A4), the contribution of genetic factors was highest (68%, 18% and 67%, respectively) at age 12y.CONCLUSIONS: The contribution of genetic factors to inter-individual differences in cortisol production decreased between 12 and 17y, indicative of a predominant role of individual circumstances. For cortisol metabolism, distinct patterns of genetic and environmental influences were observed, with heritability that either increased with age or peaked at age 12y.

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Additional file 1. Supplementary Information-1.

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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.

Approaches to understanding resilience from psychology and sociology emphasise individuals' agency but obscure systemic factors. Approaches to understanding resilience stemming from ecology emphasise system dynamics such as feedbacks but obscure individuals. Approaches from both psychology and ecology examine the actions or attractors available in the present, but neglect how actions taken now can affect the configuration of the social-ecological system in the future. Here, we propose an extension to resilience theory, which we label 'pathway diversity', that links existing individual, systems and temporal theories of resilience. In our theory of pathway diversity, resilience is greater if more actions are currently available and can be maintained or enhanced into the future. Using a toy model of an agricultural social-ecological system, we show how pathway diversity could deliver a context-sensitive method of assessing resilience and guiding planning. Using a toy state-and-transition model of a poverty trap, we show how pathway diversity is generally consistent with existing definitions of resilience and can illuminate long-standing questions about normative and descriptive resilience. Our results show that pathway diversity advances both theoretical understanding and practical tools for building resilience., 1 box, 1 table, 4 figures

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

Laser vision correction is a safe and effective method of reducing spectacle dependence. Photorefractive Keratectomy (PRK), Laser In Situ Keratomileusis (LASIK), and Small-Incision Lenticule Extraction (SMILE) can accurately correct myopia, hyperopia, and astigmatism. Although these procedures are nearing optimization in terms of their ability to produce a desired refractive target, the long term cellular responses of the cornea to these procedures can cause patients to regress from the their ideal postoperative refraction. In many cases, refractive regression requires follow up enhancement surgeries, presenting additional risks to patients. Although some risk factors underlying refractive regression have been identified, the exact mechanisms have not been elucidated. It is clear that cellular proliferation events are important mediators of optical regression. This review focused specifically on cellular changes to the corneal epithelium and stroma, which may influence postoperative visual regression following LASIK, PRK, and SMILE procedures.

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.

Electroconductive hydrogels (ECHs) are highly hydrated 3D networks generated through the incorporation of conductive polymers, nanoparticles, and other conductive materials into polymeric hydrogels. ECHs combine several advantageous properties of inherently conductive materials with the highly tunable physical and biochemical properties of hydrogels. Recently, the development of biocompatible ECHs has been investigated for various biomedical applications, such as tissue engineering, drug delivery, biosensors, flexible electronics, and other implantable medical devices. Several methods for the synthesis of ECHs have been reported, which include the incorporation of electrically conductive materials such as gold and silver nanoparticles, graphene, and carbon nanotubes, as well as various conductive polymers (CPs), such as polyaniline, polypyrrole, and poly(3,4-ethylenedioxyythiophene) into hydrogel networks. Theses electroconductive composite hydrogels can be used as scaffolds with high swellability, tunable mechanical properties, and the capability to support cell growth both in vitro and in vivo. Furthermore, recent advancements in microfabrication techniques such as three dimensional (3D) bioprinting, micropatterning, and electrospinning have led to the development of ECHs with biomimetic microarchitectures that reproduce the characteristics of the native extracellular matrix (ECM). In addition, smart ECHs with controlled structures and healing properties have also been engineered into devices with prolonged half-lives and increased durability. The combination of sophisticated synthesis chemistries and modern microfabrication techniques have led to engineer smart ECHs with advanced architectures, geometries, and functionalities that are being increasingly used in drug delivery systems, biosensors, tissue engineering, and soft electronics. In this review, we will summarize different strategies to synthesize conductive biomaterials. We will also discuss the advanced microfabrication techniques used to fabricate ECHs with complex 3D architectures, as well as various biomedical applications of microfabricated ECHs.

Objective: To investigate the incidence of new onset type 2 diabetes mellitus in men receiving steroid 5α-reductase inhibitors (dutasteride or finasteride) for long term treatment of benign prostatic hyperplasia.Design: Population based cohort study.Setting: UK Clinical Practice Research Datalink (CPRD; 2003-14) and Taiwanese National Health Insurance Research Database (NHIRD; 2002-12).Participants: Men in the CPRD who received dutasteride (n=8231), finasteride (n=30 774), or tamsulosin (n=16 270) were evaluated. Propensity score matching (2:1; dutasteride to finasteride or tamsulosin) produced cohorts of 2090, 3445, and 4018, respectively. In the NHIRD, initial numbers were 1251 (dutasteride), 4194 (finasteride), and 86 263 (tamsulosin), reducing to 1251, 2445, and 2502, respectively, after propensity score matching.Main outcomes measure: Incident type 2 diabetes using a Cox proportional hazard model.Results: In the CPRD, 2081 new onset type 2 diabetes events (368 dutasteride, 1207 finasteride, and 506 tamsulosin) were recorded during a mean follow-up time of 5.2 years (SD 3.1 years). The event rate per 10 000 person years was 76.2 (95% confidence interval 68.4 to 84.0) for dutasteride, 76.6 (72.3 to 80.9) for finasteride, and 60.3 (55.1 to 65.5) for tamsulosin. There was a modest increased risk of type 2 diabetes for dutasteride (adjusted hazard ratio 1.32, 95% confidence interval 1.08 to 1.61) and finasteride (1.26, 1.10 to 1.45) compared with tamsulosin. Results for the NHIRD were consistent with the findings for the CPRD (adjusted hazard ratio 1.34, 95% confidence interval 1.17 to 1.54 for dutasteride, and 1.49, 1.38 to 1.61 for finasteride compared with tamsulosin). Propensity score matched analyses showed similar results.Conclusions: The risk of developing new onset type 2 diabetes appears to be higher in men with benign prostatic hyperplasia exposed to 5α-reductase inhibitors than in men receiving tamsulosin, but did not differ between men receiving dutasteride and those receiving finasteride. Additional monitoring might be required for men starting these drugs, particularly in those with other risk factors for type 2 diabetes.

Bioengineered tissues have become increasingly more sophisticated owing to recent advancements in the fields of biomaterials, microfabrication, microfluidics, genetic engineering, and stem cell and developmental biology. In the coming years, the ability to engineer artificial constructs that accurately mimic the compositional, architectural, and functional properties of human tissues, will profoundly impact the therapeutic and diagnostic aspects of the healthcare industry. In this regard, bioengineered cardiac tissues are of particular importance due to the extremely limited ability of the myocardium to self-regenerate, as well as the remarkably high mortality associated with cardiovascular diseases worldwide. As novel microphysiological systems make the transition from bench to bedside, their implementation in high throughput drug screening, personalized diagnostics, disease modeling, and targeted therapy validation will bring forth a paradigm shift in the clinical management of cardiovascular diseases. Here, we will review the current state of the art in experimental in vitro platforms for next generation diagnostics and therapy validation. We will describe recent advancements in the development of smart biomaterials, biofabrication techniques, and stem cell engineering, aimed at recapitulating cardiovascular function at the tissue- and organ levels. In addition, integrative and multidisciplinary approaches to engineer biomimetic cardiovascular constructs with unprecedented human and clinical relevance will be discussed. We will comment on the implementation of these platforms in high throughput drug screening, in vitro disease modeling and therapy validation. Lastly, future perspectives will be provided on how these biomimetic platforms will aid in the transition towards patient centered diagnostics, and the development of personalized targeted therapeutics.

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.

Table S1. Genes significantly associated with risk of multiple myeloma. Table S2. New and previously implicated1-5 genes at each genome wide significant multiple myeloma locus. Table S3. Quality control filters applied to samples from the seven published GWAS. Table S4. Quality control filters applied to SNPs from each GWAS. Table S5. MM GWAS risk SNPs. Figure S1. Quantile-Quantile Plots of â log10(P-value) associations. Figure S2. TWAS power plot in EBV-transformed lymphocytes. (DOCX 1515 kb)

Equine Cushing's Disease (Pituitary pars intermedia dysfunction (PPID)) is a common condition of older horses but its pathophysiology is complex and poorly understood. In contrast to pituitary-dependent hyperadrenocorticism in other species, PPID is characterised by elevated plasma ACTH but not elevated plasma cortisol. In this study, we address this paradox and the hypothesis that PPID is a syndrome of ACTH excess in which there is dysregulation of peripheral glucocorticoid metabolism and binding. In 14 PPID horses compared with 15 healthy controls, we show that: in plasma, cortisol levels and cortisol binding to CBG were not different; in urine, glucocorticoid and androgen metabolites were increased up to four-fold; in liver, 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) expression was reduced; in peri-renal adipose tissue 11β-HSD1 and carbonyl reductase 1 expression was increased; and tissue cortisol levels were not measurably different. The combination of normal plasma cortisol with markedly enhanced urinary cortisol metabolite excretion and dysregulated tissue-specific steroid-metabolising enzymes suggests that cortisol clearance is increased in PPID horses. We infer that the ACTH excess may be compensatory and pituitary pathology and autonomous secretion may be a secondary rather than primary pathology. It is possible, that successful therapy in PPID may be targeted either at lowering ACTH or, paradoxically, at reducing cortisol clearance.

Individuals with psychiatric disorders have elevated rates of autoimmune comorbidity and altered immune signaling. It is unclear whether these altered immunological states have a shared genetic basis with those psychiatric disorders. The present study sought to use existing summary-level data from previous genome-wide association studies to determine if commonly varying single nucleotide polymorphisms are shared between psychiatric and immune-related phenotypes. We estimated heritability and examined pair-wise genetic correlations using the linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics methods. Using LDSC, we observed significant genetic correlations between immune-related disorders and several psychiatric disorders, including anorexia nervosa, attention deficit-hyperactivity disorder, bipolar disorder, major depression, obsessive compulsive disorder, schizophrenia, smoking behavior, and Tourette syndrome. Loci significantly mediating genetic correlations were identified for schizophrenia when analytically paired with Crohn's disease, primary biliary cirrhosis, systemic lupus erythematosus, and ulcerative colitis. We report significantly correlated loci and highlight those containing genome-wide associations and candidate genes for respective disorders. We also used the LDSC method to characterize genetic correlations among the immune-related phenotypes. We discuss our findings in the context of relevant genetic and epidemiological literature, as well as the limitations and caveats of the study.

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.

A hallmark of chronic liver injury is fibrosis, with accumulation of extracellular matrix orchestrated by activated hepatic stellate cells (HSCs). Glucocorticoids limit HSC activation in vitro, and tissue glucocorticoid levels are amplified by 11beta‐hydroxysteroid dehydrogenase‐1 (11βHSD1). Although 11βHSD1 inhibitors have been developed for type 2 diabetes mellitus and improve diet‐induced fatty liver in various mouse models, effects on the progression and/or resolution of liver injury and consequent fibrosis have not been characterized. We have used the reversible carbon tetrachloride‐induced model of hepatocyte injury and liver fibrosis to show that in two models of genetic 11βHSD1 deficiency (global, Hsd11b1⁻/⁻, and hepatic myofibroblast‐specific, Hsd11b1fl/fl/Pdgfrb‐cre) 11βHSD1 pharmacological inhibition in vivo exacerbates hepatic myofibroblast activation and liver fibrosis. In contrast, liver injury and fibrosis in hepatocyte‐specific Hsd11b1fl/fl/albumin‐cre mice did not differ from that of controls, ruling out 11βHSD1 deficiency in hepatocytes as the cause of the increased fibrosis. In primary HSC culture, glucocorticoids inhibited expression of the key profibrotic genes Acta2 and Col1α1, an effect attenuated by the 11βHSD1 inhibitor [4‐(2‐chlorophenyl‐4‐fluoro‐1‐piperidinyl][5‐(1H‐pyrazol‐4‐yl)‐3‐thienyl]‐methanone. HSCs from Hsd11b1–/– and Hsd11b1fl/fl/Pdgfrb‐cre mice expressed higher levels of Acta2 and Col1α1 and were correspondingly more potently activated. In vivo [4‐(2‐chlorophenyl‐4‐fluoro‐1‐piperidinyl][5‐(1H‐pyrazol‐4‐yl)‐3‐thienyl]‐methanone administration prior to chemical injury recapitulated findings in Hsd11b1–/– mice, including greater fibrosis. Conclusion: 11βHSD1 deficiency enhances myofibroblast activation and promotes initial fibrosis following chemical liver injury; hence, the effects of 11βHSD1 inhibitors on liver injury and repair are likely to be context‐dependent and deserve careful scrutiny as these compounds are developed for chronic diseases including metabolic syndrome and dementia. (Hepatology 2018;67:2167‐2181).

Glucocorticoids are potent inhibitors of angiogenesis in the rodent in vivo and in vitro but the mechanism by which this occurs has not been determined. Administration of glucocorticoids is used to treat a number of conditions in horses but the angiogenic response of equine vessels to glucocorticoids and, therefore, the potential role of glucocorticoids in pathogenesis and treatment of equine disease, is unknown. This study addressed the hypothesis that glucocorticoids would be angiostatic both in equine and murine blood vessels.The mouse aortic ring model of angiogenesis was adapted to assess the effects of cortisol in equine vessels. Vessel rings were cultured under basal conditions or exposed to: foetal bovine serum (FBS; 3%); cortisol (600 nM), cortisol (600nM) plus FBS (3%), cortisol (600nM) plus either the glucocorticoid receptor antagonist RU486 or the mineralocorticoid receptor antagonist spironolactone. In murine aortae cortisol inhibited and FBS stimulated new vessel growth. In contrast, in equine blood vessels FBS alone had no effect but cortisol alone, or in combination with FBS, dramatically increased new vessel growth compared with controls. This effect was blocked by glucocorticoid receptor antagonism but not by mineralocorticoid antagonism. The transcriptomes of murine and equine angiogenesis demonstrated cortisol-induced down-regulation of inflammatory pathways in both species but up-regulation of pro-angiogenic pathways selectively in the horse. Genes up-regulated in the horse and down-regulated in mice were associated with the extracellular matrix. These data call into question our understanding of glucocorticoids as angiostatic in every species and may be of clinical relevance in the horse.

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. © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

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. CA extern

Gas chromatography mass spectrometry has been the lynchpin of clinical assessment of steroid profiles for ~3 decades. The improvements in assay performance offered by tandem mass spectrometry were assessed. Across the spectrum of glucocorticoid and androgen analytes tested, limits of detection and quantitation were ~20 fold lower with triple than single quadrupole systems, but the more noticeable improvement was that signal to noise was substantially improved and the linear range wider. These benefits allowed more reliable and concomitant measurement of steroids with substantially different abundances and in smallervolumes of urine.

Global deficiency of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1), an enzyme that regenerates glucocorticoids within cells, promotes angiogenesis, and reduces acute infarct expansion after myocardial infarction (MI), suggesting that 11β-HSD1 activity has an adverse influence on wound healing in the heart after MI. The present study investigated whether 11β-HSD1 deficiency could prevent the development of heart failure after MI and examined whether 11β-HSD1 deficiency in cardiomyocytes and vascular smooth muscle cells confers this protection. Male mice with global deficiency in 11β-HSD1, or with Hsd11b1 disruption in cardiac and vascular smooth muscle (via SM22α-Cre recombinase), underwent coronary artery ligation for induction of MI. Acute injury was equivalent in all groups. However, by 8 weeks after induction of MI, relative to C57Bl/6 wild type, globally 11β-HSD1-deficient mice had reduced infarct size (34.7 ± 2.1% left ventricle [LV] vs 44.0 ± 3.3% LV, P = .02), improved function (ejection fraction, 33.5 ± 2.5% vs 24.7 ± 2.5%, P = .03) and reduced ventricular dilation (LV end-diastolic volume, 0.17 ± 0.01 vs 0.21 ± 0.01 mL, P = .01). This was accompanied by a reduction in hypertrophy, pulmonary edema, and in the expression of genes encoding atrial natriuretic peptide and β-myosin heavy chain. None of these outcomes, nor promotion of periinfarct angiogenesis during infarct repair, were recapitulated when 11β-HSD1 deficiency was restricted to cardiac and vascular smooth muscle. 11β-HSD1 expressed in cells other than cardiomyocytes or vascular smooth muscle limits angiogenesis and promotes infarct expansion with adverse ventricular remodeling after MI. Early pharmacological inhibition of 11β-HSD1 may offer a new therapeutic approach to prevent heart failure associated with ischemic heart disease.

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.

BACKGROUND AND PURPOSE: Reducing glucocorticoid exposure in the brain via intracellular inhibition of the cortisol-regenerating enzyme 11β-hydroxysteroid dehydrogenase (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 optimisation of a series of amido-thiophene analogues was performed to identify potent and selective 11β-HSD1 inhibitors with optimised 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 (ACTH) was elevated (a marker of systemic enzyme inhibition) at doses of 10mg and above, but plasma cortisol levels were unchanged. Following multiple doses of UE2343, plasma levels were approximately dose proportional and the terminal half-life ranged from 10-14h. The urinary tetrahydrocortisols (THFs)/tetrahydrocortisone (THE) ratio was reduced at doses of 10mg and above, indicating maximal 11β-HSD1 inhibition in the liver. Concentrations of UE2343 in the cerebrospinal fluid (CSF) were 33% of free plasma levels and the peak concentration in CSF was 9-fold 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.

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.

5-Reductases irreversibly catalyse A-ring reduction of pregnene steroids, including glucocorticoids and androgens. Genetic disruption of 5-reductase 1 in male mice impairs glucocorticoid clearance and predisposes to glucose intolerance and hepatic steatosis upon metabolic challenge. However it is unclear whether this is driven by changes in androgen and/or glucocorticoid action.Female mice with transgenic disruption of 5-reductase 1 (5R1-KO) were studied, representing a “low androgen” state. Glucocorticoid clearance and stress responses were studied in mice aged 6m. Metabolism was assessed in mice on normal chow (aged 6 and 12m) and also in a separate cohort following 1m high-fat diet (aged 3m).Female 5R1-KO mice had adrenal suppression (44% lower AUC corticosterone following stress), and upon corticosterone infusion accumulated hepatic glucocorticoids (~27% increased corticosterone). Female 5R1-KO mice aged 6m fed normal chow demonstrated insulin resistance (~35% increased area under curve (AUC) for insulin upon glucose tolerance testing) and hepatic steatosis (~33% increased hepatic triglycerides) compared with controls. This progressed to obesity (~12% increased body weight) and sustained insulin resistance (~38% increased AUC insulin) by age 12m. Hepatic transcript profiles supported impaired lipid -oxidation and increased triglyceride storage. Female 5R1-KO mice were also predisposed to develop high-fat diet-induced insulin resistance. Exaggerated predisposition to metabolic disorders in female mice, compared with that seen in male mice, following disruption of 5R1 suggests phenotypic changes may be underpinned by altered metabolism of glucocorticoids rather than androgens.

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.

Constitutive NF ?B signaling promotes survival in multiple myeloma (MM) and other cancers; however current NF ?B targeting strategies lack cancer cell specificity. Here we identify the interaction between the NF ?B regulated antiapoptotic factor GADD45ß 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 GADD45ß/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 myeloma xenografts in mice with no apparent side effects at the effective doses. Hence cancer selective targeting of the NF ?B pathway is possible and at least for myeloma patients promises a profound benefit.

Androgens are essential for male development and reproductive function. They are transported to their site of action as blood-borne endocrine hormones, but can also be produced within tissues to act in intracrine and paracrine fashions. Because of this, circulating concentrations may not accurately reflect the androgenic influence within specific tissue microenvironments. Mass spectrometry imaging permits regional analysis of small molecular species directly from tissue surfaces. However, due to poor ionization and localized ion suppression, steroid hormones are difficult to detect. Here derivatization with Girard T reagent was used to charge-tag testosterone and 5α-dihydrotestosterone allowing direct detection of these steroids in mouse testes, in both basal and maximally-stimulated states and in rat prostate. Limits of detection were ~0.1 pg for testosterone. Exemplary detection of endogenous steroids was achieved by matrix-assisted laser desorption ionization and either Fourier transform ion cyclotron resonance detection (at 150 μm spatial resolution) or quadrupole-time of flight detection (at 50 μm spatial resolution). Structural confirmation was achieved by collision induced fragmentation following liquid extraction surface desorption and electrospray ionization. This application broadens the scope for derivatization strategies on tissue surfaces to elucidate local endocrine signaling in health and disease

BackgroundMaternal obesity is associated with high birthweight, obesity and premature mortality in adult offspring, probably as a result of maternal hyperglycaemia and insulin resistance. We present the results of a trial designed to test the hypothesis that metformin will improve insulin sensitivity in obese pregnant women, thereby reducing the incidence of high-birthweight babies.ObjectiveTo determine the efficacy of metformin (up to 2500 mg daily) given to obese pregnant women in reducing the gestational age-, parity- and sex-adjusted birthweight centile of the baby.DesignDouble-blind, placebo-controlled, randomised controlled trial with embedded substudies.SettingFifteen NHS hospitals in the UK.ParticipantsPregnant women aged ≥ 16 years with a singleton fetus and a body mass index of ≥ 30 kg/m2.InterventionMetformin tablets (or placebo) administered between 12 and 16 weeks’ gestation until delivery of the baby.Main outcome measuresThe primary outcome measure was z-score corresponding to the gestational age-, parity- and sex-adjusted birthweight centile of live-born babies delivered at ≥ 24 weeks’ gestation. The main secondary outcome was maternal insulin resistance at 36 weeks’ gestation. Embedded substudies were included to assess the effect of metformin on insulin sensitivity using the hyperinsulinaemic–euglycaemic clamp; endothelial function; maternal and fetal fat distribution using magnetic resonance imaging; placental expression of 11β-hydroxysteroid dehydrogenase types 1 and 2 and glucocorticoid receptor; and myometrial contractility and glycogen storage.ResultsWe randomised 449 women to either placebo (n = 223) or metformin (n = 226), of whom 434 were included in the final intention-to-treat analysis. Mean birthweight at delivery was 3463 g [standard deviation (SD) 660 g] in the placebo group and 3462 g (SD 548 g) in the metformin group. The estimated effect size of metformin on the primary outcome was non-significant [adjusted mean difference in z-score –0.029, 95% confidence interval (CI) –0.217 to 0.158; p = 0.7597]. There was no evidence of a reduction in the main secondary outcome of homeostatic model assessment – insulin resistance (HOMA-IR) at 36 weeks’ gestation (mean HOMA-IR 5.98 and 6.30 molar units in the placebo and metformin groups, respectively; adjusted mean ratio 0.974, 95% CI 0.865 to 1.097). Metformin had no effect on the combined adverse outcome of miscarriage, termination of pregnancy, stillbirth or neonatal death. Subjects taking metformin demonstrated increased insulin sensitivity [glucose disposal per unit plasma insulin difference between means during high-dose insulin 0.02 mg/kg, 95% CI 0.001 to 0.03 mg/kg (fat-free mass)/minute/µIU/l; p = 0.04] compared with those taking placebo and enhanced endogenous glucose production [difference between means 0.54 mg/kg, 95% CI 0.08 to 1.00 mg/kg (fat-free mass)/minute; p = 0.02]. There were no differences in endothelial function, maternal or fetal body fat distribution, placental expression of 11β-hydroxysteroid dehydrogenase types 1 and 2 and glucocorticoid receptor, or myometrial contractility and glycogen storage.ConclusionsMetformin has no clinically significant effect on birthweight centile in obese pregnant women. Follow-up studies of the children born to participants in the trial are required to determine whether or not there are any longer-term benefits or harms of maternal metformin for offspring weight, fat mass or metabolism.

CONTEXT: The mechanism of action of metformin remains unclear. Given regulation of the cortisol-regenerating enzyme 11βHSD1 by insulin, and limited efficacy of selective 11βHSD1 inhibitors to lower blood glucose when co-prescribed with metformin, we hypothesized that metformin reduces 11βHSD1 activity.OBJECTIVE: To determine whether metformin regulates 11βHSD1 activity in vivo in obese men with and without type 2 diabetes.DESIGN: Double blind randomised placebo controlled crossover study Setting: A hospital clinical research facility Participants: Eight obese non-diabeticmen (OND) and eight obesemenwith type 2 diabetes (ODM) Intervention: Participants received 28 days of metformin (1g twice daily), placebo, or (in the ODM group) gliclazide (80mg twice daily) in random order. A deuterated cortisol infusion at the end of each phase measured cortisol regeneration by 11βHSD1. Oral cortisone was given to measure hepatic 11βHSD1 activity in the ODM group. The effect of metformin on 11βHSD1 was also assessed in human hepatocytes and SGBS adipocytes.MAIN OUTCOME MEASURES: The effect of metformin on whole body and hepatic 11βHSD1 activity.RESULTS: Whole body 11βHSD1 activity was ~25% higher in the ODM than OND group. Metformin increased whole body cortisol regeneration by 11βHSD1 in both groups compared with placebo and gliclazide, and tended to increase hepatic 11βHSD1 activity. In vitro, metformin did not increase 11βHSD1 activity in hepatocytes or adipocytes.CONCLUSIONS: Metformin increases whole body cortisol generation by 11βHSD1 probably through an indirect mechanism, potentially offsetting other metabolic benefits of metformin. Co-prescription with metformin should provide a greater target for selective 11βHSD1 inhibitors.