Bringing self assessment home: repository profiling and key lines of enquiry within DRAMBORA

Digital repositories are a manifestation of complex organizational, financial, legal, technological, procedural, and political interrelationships. Accompanying each of these are innate uncertainties, exacerbated by the relative immaturity of understanding prevalent within the digital preservation domain. Recent efforts have sought to identify core characteristics that must be demonstrable by successful digital repositories, expressed in the form of check-list documents, intended to support the processes of repository accreditation and certification. In isolation though, the available guidelines lack practical applicability; confusion over evidential requirements and difficulties associated with the diversity that exists among repositories (in terms of mandate, available resources, supported content and legal context) are particularly problematic. A gap exists between the available criteria and the ways and extent to which conformity can be demonstrated. The Digital Repository Audit Method Based on Risk Assessment (DRAMBORA) is a methodology for undertaking repository self assessment, developed jointly by the Digital Curation Centre (DCC) and DigitalPreservationEurope (DPE). DRAMBORA requires repositories to expose their organization, policies and infrastructures to rigorous scrutiny through a series of highly structured exercises, enabling them to build a comprehensive registry of their most pertinent risks, arranged into a structure that facilitates effective management. It draws on experiences accumulated throughout 18 evaluative pilot assessments undertaken in an internationally diverse selection of repositories, digital libraries and data centres (including institutions and services such as the UK National Digital Archive of Datasets, the National Archives of Scotland, Gallica at the National Library of France and the CERN Document Server). Other organizations, such as the British Library, have been using sections of DRAMBORA within their own risk assessment procedures. Despite the attractive benefits of a bottom up approach, there are implicit challenges posed by neglecting a more objective perspective. Following a sustained period of pilot audits undertaken by DPE, DCC and the DELOS Digital Preservation Cluster aimed at evaluating DRAMBORA, it was stated that had respective project members not been present to facilitate each assessment, and contribute their objective, external perspectives, the results may have been less useful. Consequently, DRAMBORA has developed in a number of ways, to enable knowledge transfer from the responses of comparable repositories, and incorporate more opportunities for structured question sets, or key lines of enquiry, that provoke more comprehensive awareness of the applicability of particular threats and opportunities

Plasma cortisol-linked gene networks in hepatic and adipose tissues implicate corticosteroid-binding globulin in modulating tissue glucocorticoid action and cardiovascular risk

Genome-wide association meta-analysis (GWAMA) by the Cortisol Network (CORNET) consortium identified genetic variants spanning the SERPINA6/SERPINA1 locus on chromosome 14 associated with morning plasma cortisol, cardiovascular disease (CVD), and SERPINA6 mRNA expression encoding corticosteroid-binding globulin (CBG) in the liver. These and other findings indicate that higher plasma cortisol levels are causally associated with CVD; however, the mechanisms by which variations in CBG lead to CVD are undetermined. Using genomic and transcriptomic data from The Stockholm Tartu Atherosclerosis Reverse Networks Engineering Task (STARNET) study, we identified plasma cortisol-linked single-nucleotide polymorphisms (SNPs) that are trans-associated with genes from seven different vascular and metabolic tissues, finding the highest representation of trans-genes in the liver, subcutaneous fat, and visceral abdominal fat, [false discovery rate (FDR) = 15%]. We identified a subset of cortisol-associated trans-genes that are putatively regulated by the glucocorticoid receptor (GR), the primary transcription factor activated by cortisol. Using causal inference, we identified GR-regulated trans-genes that are responsible for the regulation of tissue-specific gene networks. Cis-expression Quantitative Trait Loci (eQTLs) were used as genetic instruments for identification of pairwise causal relationships from which gene networks could be reconstructed. Gene networks were identified in the liver, subcutaneous fat, and visceral abdominal fat, including a high confidence gene network specific to subcutaneous adipose (FDR = 10%) under the regulation of the interferon regulatory transcription factor, IRF2. These data identify a plausible pathway through which variation in the liver CBG production perturbs cortisol-regulated gene networks in peripheral tissues and thereby promote CVD

Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy

Cystatin C (CyC), a secreted cysteine protease inhibitor, has unclear biological functions. Many patients exhibit elevated plasma CyC levels, particularly during glucocorticoid (GC) treatment. This study links GCs with CyC’s systemic regulation by utilizing genome-wide association and structural equation modeling to determine CyC production genetics in the UK Biobank. Both CyC production and a polygenic score (PGS) capturing predisposition to CyC production were associated with increased all-cause and cancer-specific mortality. We found that the GC receptor directly targets CyC, leading to GC-responsive CyC secretion in macrophages and cancer cells. CyC-knockout tumors displayed significantly reduced growth and diminished recruitment of TREM2+ macrophages, which have been connected to cancer immunotherapy failure. Furthermore, the CyC-production PGS predicted checkpoint immunotherapy failure in 685 patients with metastatic cancer from combined clinical trial cohorts. In conclusion, CyC may act as a GC effector pathway via TREM2+ macrophage recruitment and may be a potential target for combination cancer immunotherapy.publishedVersio

A Study of Pi Aquarii During a Quasi-normal Star Phase: Refined Fundamental Parameters and Evidence for Binarity

We present the results of recent multicolor photometric and high-resolution spectroscopic observations of the bright Be star Pi Aquarii. Observational data collected from the literature were used to study the star's variations over the last four decades. The star is identified with the IR sources F22227+0107 in the IRAS Faint Point Source catalog and MSX5_G066.0066-44.7392 in the MSX catalog. The variations in near-IR brightness of Pi Aqr are found to be among the largest reported for Be stars. Since 1996, the star has shown only weak signs of circumstellar emission, which has allowed us to refine the fundamental stellar parameters: A_V=0.15 mag., T_eff=24000K, log g=3.9, and M_V=-2.95 mag. A weak emission component of the H-alpha line has been detected during the recent quasi-normal star phase. From analysis of the H-alpha line profiles, we find anti-phased radial velocity variations of the emission component and the photospheric absorption, with a period of 84.1 days and semi-amplitudes of 101.4 and 16.7 km/s, respectively. This result suggests that Pi Aqr may be a binary system consisting of stars with masses of M_1 sin^{3}i = 12.4 M_sun, M_2 sin^{3}i = 2.0 M_sun. We also estimate the orbital inclination angle to be between 50 and 75 degrees. We suggest that the photometric, spectroscopic, and polarimetric variations observed during the second half of the 20th century may be due to variable mass transfer between the binary components.Comment: 26 pages (including 8 figs, 2 tables), accepted by Ap

Variation in the SERPINA6SERPINA1 locusalters morning plasma cortisol, hepatic corticosteroid binding globulin expression, gene expressionin peripheral tissues, and risk of cardiovascular disease

The stress hormone cortisol modulates fuel metabolism, cardiovascular homoeostasis, mood, inflammation and cognition. The CORtisol NETwork (CORNET) consortium previously identified a single locus associated with morning plasma cortisol. Identifying additional genetic variants that explain more of the variance in cortisol could provide new insights into cortisol biology and provide statistical power to test the causative role of cortisol in common diseases. The CORNET consortium extended its genome-wide association meta-analysis for morning plasma cortisol from 12,597 to 25,314 subjects and from ~2.2 M to ~7 M SNPs, in 17 population-based cohorts of European ancestries. We confirmed the genetic association with SERPINA6/SERPINA1. This locus contains genes encoding corticosteroid binding globulin (CBG) and α1-antitrypsin. Expression quantitative trait loci (eQTL) analyses undertaken in the STARNET cohort of 600 individuals showed that specific genetic variants within the SERPINA6/SERPINA1 locus influence expression of SERPINA6 rather than SERPINA1 in the liver. Moreover, trans-eQTL analysis demonstrated effects on adipose tissue gene expression, suggesting that variation

Systematic Evaluation of Pleiotropy Identifies 6 Further Loci Associated With Coronary Artery Disease

Background: Genome-wide association studies have so far identified 56 loci associated with risk of coronary artery disease (CAD). Many CAD loci show pleiotropy; that is, they are also associated with other diseases or traits. Objectives: This study sought to systematically test if genetic variants identified for non-CAD diseases/traits also associate with CAD and to undertake a comprehensive analysis of the extent of pleiotropy of all CAD loci. Methods: In discovery analyses involving 42,335 CAD cases and 78,240 control subjects we tested the association of 29,383 common (minor allele frequency >5%) single nucleotide polymorphisms available on the exome array, which included a substantial proportion of known or suspected single nucleotide polymorphisms associated with common diseases or traits as of 2011. Suggestive association signals were replicated in an additional 30,533 cases and 42,530 control subjects. To evaluate pleiotropy, we tested CAD loci for association with cardiovascular risk factors (lipid traits, blood pressure phenotypes, body mass index, diabetes, and smoking behavior), as well as with other diseases/traits through interrogation of currently available genome-wide association study catalogs. Results: We identified 6 new loci associated with CAD at genome-wide significance: on 2q37 (KCNJ13-GIGYF2), 6p21 (C2), 11p15 (MRVI1-CTR9), 12q13 (LRP1), 12q24 (SCARB1), and 16q13 (CETP). Risk allele frequencies ranged from 0.15 to 0.86, and odds ratio per copy of the risk allele ranged from 1.04 to 1.09. Of 62 new and known CAD loci, 24 (38.7%) showed statistical association with a traditional cardiovascular risk factor, with some showing multiple associations, and 29 (47%) showed associations at p < 1 × 10−4 with a range of other diseases/traits. Conclusions: We identified 6 loci associated with CAD at genome-wide significance. Several CAD loci show substantial pleiotropy, which may help us understand the mechanisms by which these loci affect CAD risk