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The Kinase Chemogenomic Set (KCGS): An open science resource for kinase vulnerability identification
(2019)
We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS) is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.
Men and women differ substantially regarding height, weight, and body fat. Interestingly, previous work detecting genetic effects for waist-to-hip ratio, to assess body fat distribution, has found that many of these showed sex-differences. However, systematic searches for sex-differences in genetic effects have not yet been conducted. Therefore, we undertook a genome-wide search for sexually dimorphic genetic effects for anthropometric traits including 133,723 individuals in a large meta-analysis and followed promising variants in further 137,052 individuals, including a total of 94 studies. We identified seven loci with significant sex-difference including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were significant in women, but not in men. Of interest is that sex-difference was only observed for waist phenotypes, but not for height or body-mass-index. We found no evidence for sex-differences with opposite effect direction for men and women. The PPARG locus is of specific interest due to its link to diabetes genetics and therapy. Our findings demonstrate the importance of investigating sex differences, which may lead to a better understanding of disease mechanisms with a potential relevance to treatment options.
The Kinase Chemogenomic Set (KCGS): an open science resource for kinase vulnerability identification
(2021)
We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS), current Version 1.0, is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.
A current challenge in life sciences is to image cell membrane receptors while characterizing their specific interactions with various ligands. Addressing this issue has been hampered by the lack of suitable nanoscopic methods. Here we address this challenge and introduce multifunctional high-resolution atomic force microscopy (AFM) to image human protease-activated receptors (PAR1) in the functionally important lipid membrane and to simultaneously localize and quantify their binding to two different ligands. Therefore, we introduce the surface chemistry to bifunctionalize AFM tips with the native receptor-activating peptide and a tris-N-nitrilotriacetic acid (tris-NTA) group binding to a His10-tag engineered to PAR1. We further introduce ways to discern between the binding of both ligands to different receptor sites while imaging native PAR1s. Surface chemistry and nanoscopic method are applicable to a range of biological systems in vitro and in vivo and to concurrently detect and localize multiple ligand-binding sites at single receptor resolution.
Aims: Cardio-oncology is a growing interdisciplinary field which aims to improve cardiological care for cancer patients in order to reduce morbidity and mortality. The impact of cardiac biomarkers, echocardiographic parameters, and cardiological assessment regarding risk stratification is still unclear. We aimed to identify potential parameters that allow an early risk stratification of cancer patients. Methods and results: In this cohort study, we evaluated 930 patients that were admitted to the cardio-oncology outpatient clinic of the University Hospital Heidelberg from January 2016 to January 2019. We performed echocardiography, including Global Longitudinal Strain (GLS) analysis and measured cardiac biomarkers including N-terminal pro brain-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T levels (hs-cTnT). Most patients were suffering from breast cancer (n = 450, 48.4%), upper gastrointestinal carcinoma (n = 99, 10.6%) or multiple myeloma (n = 51, 5.5%). At the initial visit, we observed 86.7% of patients having a preserved left ventricular ejection fraction (LVEF >50%). At the second follow up, still 78.9% of patients showed a preserved LVEF. Echocardiographic parameters or elevation of NT-proBNP did not significantly correlate with all-cause mortality (ACM) (logistic regression LVEF <50%: P = 0.46, NT-proBNP: P = 0.16) and failed to identify high-risk patients. In contrast, hs-cTnT above the median (≥7 ng/L) was an independent marker to determine ACM (multivariant logistic regression, OR: 2.21, P = 0.0038) among all included patients. In particular, hs-cTnT levels before start of a chemotherapy were predictive for ACM. Conclusions: Based on our non-selected cohort of cardio-oncological patients, hs-cTnT was able to identify patients with high mortality by using a low cutoff of 7 ng/L. We conclude that measurement of hs-cTnT is an important tool to stratify the risk for mortality of cancer patients before starting chemotherapy.
A new global synthesis and biomization of long (> 40 kyr) pollen-data records is presented, and used with simulations from the HadCM3 and FAMOUS climate models to analyse the dynamics of the global terrestrial biosphere and carbon storage over the last glacial–interglacial cycle. Global modelled (BIOME4) biome distributions over time generally agree well with those inferred from pollen data. The two climate models show good agreement in global net primary productivity (NPP). NPP is strongly influenced by atmospheric carbon dioxide (CO2) concentrations through CO2 fertilization. The combined effects of modelled changes in vegetation and (via a simple model) soil carbon result in a global terrestrial carbon storage at the Last Glacial Maximum that is 210–470 Pg C less than in pre-industrial time. Without the contribution from exposed glacial continental shelves the reduction would be larger, 330–960 Pg C. Other intervals of low terrestrial carbon storage include stadial intervals at 108 and 85 kaBP, and between 60 and 65 kaBP during Marine Isotope Stage 4. Terrestrial carbon storage, determined by the balance of global NPP and decomposition, influences the stable carbon isotope composition (δ 13C) of seawater because terrestrial organic carbon is depleted in 13C. Using a simple carbon-isotope mass balance equation we find agreement in trends between modelled ocean δ 13C based on modelled land carbon storage, and palaeo-archives of ocean δ 13C, confirming that terrestrial carbon storage variations may be important drivers of ocean δ 13 C changes.
A new global synthesis and biomization of long (> 40 kyr) pollen-data records is presented and used with simulations from the HadCM3 and FAMOUS climate models and the BIOME4 vegetation model to analyse the dynamics of the global terrestrial biosphere and carbon storage over the last glacial–interglacial cycle. Simulated biome distributions using BIOME4 driven by HadCM3 and FAMOUS at the global scale over time generally agree well with those inferred from pollen data. Global average areas of grassland and dry shrubland, desert, and tundra biomes show large-scale increases during the Last Glacial Maximum, between ca. 64 and 74 ka BP and cool substages of Marine Isotope Stage 5, at the expense of the tropical forest, warm-temperate forest, and temperate forest biomes. These changes are reflected in BIOME4 simulations of global net primary productivity, showing good agreement between the two models. Such changes are likely to affect terrestrial carbon storage, which in turn influences the stable carbon isotopic composition of seawater as terrestrial carbon is depleted in 13C.