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Snoep, J.

Publications and source records attributed to Snoep, J..

2 recordsLinked to original sources
bioRxiv2023

Inhibition of the glucocorticoid-activating enzyme 11β-hydroxysteroid dehydrogenase type 1 drives concurrent 11-oxygenated androgen excess

Aldo-keto reductase 1C3 (AKR1C3) is a key enzyme in the activation of both classic and 11-oxygenated androgens. In adipose tissue, AKR1C3 is co-expressed with 11{beta}-hydroxysteroid dehydrogenase type 1 (HSD11B1), which catalyses the local activation of glucocorticoids but also the inactivation of 11-oxygenated androgens, and thus has the potential to counteract AKR1C3. Using a combination of in vitro assays and in silico modelling we show that HSD11B1 attenuates the biosynthesis of the potent 11-oxygenated androgen, 11-ketotestosterone, by AKR1C3. Employing ex vivo incubations of human female adipose tissue samples we show that inhibition of HSD11B1 results in the increased peripheral biosynthesis of 11-ketotestosterone. Moreover, circulating 11KT increased 2-3 fold in individuals with type 2 diabetes after receiving the selective oral HSD11B1 inhibitor AZD4017 for 35 days, thus confirming that HSD11B1 inhibition results in systemic increases in 11KT concentrations. Our findings show that HSD11B1 protects against excess 11KT production by adipose tissue, a finding of particular significance when considering the evidence for adverse metabolic effects of androgens in women. Therefore, when targeting glucocorticoid activation by HSD11B1 inhibitor treatment in women, the consequently increased generation of 11-ketotestosterone may offset beneficial effects of decreased glucocorticoid activation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=135 HEIGHT=200 SRC="FIGDIR/small/543687v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@f68f0forg.highwire.dtl.DTLVardef@12a5070org.highwire.dtl.DTLVardef@107231borg.highwire.dtl.DTLVardef@191ca58_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗
bioRxiv2017

Identifiers for the 21st century:How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data

In many disciplines, data is highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline ten lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers; we also outline important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

bioinformatics↗