Simulating ice core 10Be on the glacial–interglacial timescale

10Be ice core measurements are an important tool for paleoclimate research, e.g., allowing for the reconstruction of past solar activity or changes in the geomagnetic dipole field. However, especially on multi-millennial timescales, the share of production and climate-induced variations of respective 10Be ice core records is still up for debate. Here we present the first quantitative climatological model of the 10Be ice concentration up to the glacial–interglacial timescale. The model approach is composed of (i) a coarse resolution global atmospheric transport model and (ii) a local 10Be air–firn transfer model. Extensive global-scale observational data of short-lived radionuclides as well as new polar 10Be snow-pit measurements are used for model calibration and validation. Being specifically configured for 10Be in polar ice, this tool thus allows for a straightforward investigation of production- and non-production-related modulation of this nuclide. We find that the polar 10Be ice concentration does not immediately record the globally mixed cosmogenic production signal. Using geomagnetic modulation and revised Greenland snow accumulation rate changes as model input, we simulate the observed Greenland Summit (GRIP and GISP2) 10Be ice core records over the last 75 kyr (on the GICC05modelext timescale). We show that our basic model is capable of reproducing the largest portion of the observed 10Be changes. However, model–measurement differences exhibit multi-millennial trends (differences up to 87% in case of normalized to the Holocene records) which call for closer investigation. Focusing on the (12–37) b2k (before the year AD 2000) period, mean model–measurement differences of 30% cannot be attributed to production changes. However, unconsidered climate-induced changes could likely explain the model–measurement mismatch. In fact, the 10Be ice concentration is very sensitive to snow accumulation changes. Here the reconstructed Greenland Summit (GRIP) snow accumulation rate record would require revision of +28% to solely account for the (12–37) b2k model–measurement differences

Diverse molecular causes of unsolved autosomal dominant tubulointerstitial kidney diseases

Autosomal Dominant Tubulointerstitial Kidney Disease (ADTKD) is caused by mutations in one of at least five genes and leads to kidney failure usually in mid adulthood. Throughout the literature, variable numbers of families have been reported, where no mutation can be found and therefore termed ADTKD-not otherwise specified. Here, we aim to clarify the genetic cause of their diseases in our ADTKD registry. Sequencing for all known ADTKD genes was performed, followed by SNaPshot minisequencing for the dupC (an additional cytosine within a stretch of seven cytosines) mutation of MUC1. A virtual panel containing 560 genes reported in the context of kidney disease (nephrome) and exome sequencing were then analyzed sequentially. Variants were validated and tested for segregation. In 29 of the 45 registry families, mutations in known ADTKD genes were found, mostly in MUC1. Sixteen families could then be termed ADTKD-not otherwise specified, of which nine showed diagnostic variants in the nephrome (four in COL4A5, two in INF2 and one each in COL4A4, PAX2, SALL1 and PKD2). In the other seven families, exome sequencing analysis yielded potential disease associated variants in novel candidate genes for ADTKD; evaluated by database analyses and genome-wide association studies. For the great majority of our ADTKD registry we were able to reach a molecular genetic diagnosis. However, a small number of families are indeed affected by diseases classically described as a glomerular entity. Thus, incomplete clinical phenotyping and atypical clinical presentation may have led to the classification of ADTKD. The identified novel candidate genes by exome sequencing will require further functional validation

Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals

A large-scale GWAS provides insight on diabetes-dependent genetic effects on the glomerular filtration rate, a common metric to monitor kidney health in disease.Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.</p

Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies

Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics

Viewing the early stage of metal foam formation by computed tomography using synchrotron radiation

Foamed aluminium alloy containing 7wt.-% of Si is investigated by µm-resolved X-ray computed tomography (CT) using synchrotron radiation. The foam is fabricated employing a Powder metallurgical route. The evolution of foam microstructure is characterized by studying a series of samples representing different stages of foam expansion obtained by interrupting the foaming process for each sample at different foaming times. The computer tomographic reconstruction provides a 3D image of the pore structure as well as the spatial distribution of the blowing agent and of the pores at different foaming stages

Simulating ice core ¹⁰Be on the glacial–interglacial timescale

¹⁰Be ice core measurements are an important tool for paleoclimate research, e.g., allowing for the reconstruction of past solar activity or changes in the geomagnetic dipole field. However, especially on multi-millennial timescales, the share of production and climate-induced variations of respective ¹⁰Be ice core records is still up for debate. Here we present the first quantitative climatological model of the ¹⁰Be ice concentration up to the glacial–interglacial timescale. The model approach is composed of (i) a coarse resolution global atmospheric transport model and (ii) a local ¹⁰Be air–firn transfer model. Extensive global-scale observational data of short-lived radionuclides as well as new polar ¹⁰Be snow-pit measurements are used for model calibration and validation. Being specifically configured for ¹⁰Be in polar ice, this tool thus allows for a straightforward investigation of production- and non-production-related modulation of this nuclide. We find that the polar ¹⁰Be ice concentration does not immediately record the globally mixed cosmogenic production signal. Using geomagnetic modulation and revised Greenland snow accumulation rate changes as model input, we simulate the observed Greenland Summit (GRIP and GISP2) ¹⁰Be ice core records over the last 75 kyr (on the GICC05modelext timescale). We show that our basic model is capable of reproducing the largest portion of the observed ¹⁰Be changes. However, model–measurement differences exhibit multi-millennial trends (differences up to 87% in case of normalized to the Holocene records) which call for closer investigation. Focusing on the (12–37) b2k (before the year AD 2000) period, mean model–measurement differences of 30% cannot be attributed to production changes. However, unconsidered climate-induced changes could likely explain the model–measurement mismatch. In fact, the ¹⁰Be ice concentration is very sensitive to snow accumulation changes. Here the reconstructed Greenland Summit (GRIP) snow accumulation rate record would require revision of +28% to solely account for the (12–37) b2k model–measurement differences