Biology subjects

Schiffer, L.

Publications and source records attributed to Schiffer, L..

10 recordsLinked to original sources

bioRxiv2026

Optimisation and Validation of a UHPLC-MS/MS Assay Using Timed Post-Column Ammonium Fluoride Infusion for Sensitive Serum Steroids Quantitation

Steroid metabolomics has potential for identifying biomarkers and improving disease diagnosis. However, poor ionisation efficiency and low circulating concentrations of some steroids limit the broader clinical applications. Here, we developed a ultra-high-performance liquid chromatography - tandem mass spectrometry (UHPLC-MS/MS) assay for quantitation of 18 steroids in serum encompassing the mineralocorticoid, glucocorticoid and androgen biosynthesis pathways, including both classic and 11-oxygenated androgens. Steroid extraction from 100 L of serum was performed using supported liquid extraction with ethyl acetate elution. Ionisation efficiency of steroids was investigated using formic acid in the mobile phase and post-column infusion of ammonium fluoride. Ammonium fluoride enhanced the signal intensity of 3-keto-{Delta}4 steroids up to ninefold, however A-ring reduced steroids exhibited greater ionisation with formic acid. Thus, to optimise sensitivity, a timed post-column infusion of 2 mmol/L ammonium fluoride was employed during chromatographic separation. The lower limits of quantitation ranged from 0.04 to 2.1 nmol/L. Accuracy ranged from 82 to 113% and intra- and inter-assay imprecision was <10% and <13%, respectively. The assay was applied to analyse morning serum steroid concentrations in healthy individuals (55 women, 44 men; age-matched). Serum progesterone, 11-deoxycorticosterone, dehydroepiandrosterone and androstenedione were significantly higher in women; in contrast, testosterone and 5-dihydrotestosterone were higher in men. In summary, we have successfully developed and validated a UHPLC-MS/MS assay for multi-steroid profiling ad applied it to healthy individuals of both sexes.

biochemistry↗

bioRxiv2026

Inhibition of the androgen-activating enzyme AKR1C3 selectively decreases systemic and intra-adipose 11-oxygenated androgens in women

Androgen excess drives metabolic and reproductive complications in polycystic ovary syndrome (PCOS), affecting 10-15% of women globally. Aldo-keto reductase 1C3 (AKR1C3) converts inactive precursors from both the classic and the recently identified 11-oxygenated androgen pathways, generating testosterone and 11-ketotestosterone, respectively, which exert comparable androgen receptor activation. Both circulate in similar concentrations in premenopausal women while 11-ketotestosterone is predominant after menopause and in PCOS. Here, we show that adipocytes are a major site of AKR1C3 and androgen receptor expression, with increased expression in women and individuals with obesity. Using human female adipose tissue explants, we find a much higher activation of 11-oxygenated over classic androgens, observing a decrease in 11-oxygenated but not classic androgen activation by AKR1C3 inhibition. Correspondingly, we demonstrate that AKR1C3 inhibitor treatment in premenopausal women selectively disrupts the activation of 11-oxygenated androgens. Pharmacological targeting of AKR1C3 provides a novel strategy to alleviate systemic and intra-adipose 11-oxygenated androgen excess. One Sentence SummaryInhibition of the androgen-activating enzyme AKR1C3 results in a major decrease in 11-oxygenated but not classic androgens in women.

physiology↗

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↗

bioRxiv2020

Predominant activation of 11-oxygenated androgens by peripheral blood mononuclear cells

ContextAndrogens are important modulators of immune cell function impacting proliferation, differentiation and cytokine production. The local generation of active androgens from circulating androgen precursors is an important mediator of androgen action in peripheral androgen target cells or tissue. ObjectiveTo characterize the activation of classic and 11-oxygenated androgens in human peripheral blood mononuclear cells (PBMCs). MethodsPBMCs were isolated from healthy male donors and incubated ex vivo with precursors and active androgens of the classic and 11-oxygenated androgen pathways. Steroids were quantified by liquid chromatography-tandem mass spectrometry. The expression of genes encoding steroid-metabolizing enzymes was assessed by quantitative PCR. ResultsPBMCs generated 8-fold higher amounts of the active 11-oxygenated androgen 11-ketotestosterone than the classic androgen testosterone from their respective precursors. We identified the enzyme AKR1C3 as the major reductive 17{beta}-hydroxysteroid dehydrogenase in PBMCs responsible for both conversions and found that within the PBMC compartment natural killer cells are the major site of AKRC13 expression and activity. Steroid 5-reductase type 1 catalyzed the 5-reduction of classic but not 11-oxygenated androgens in PBMCs. Lag time prior to the separation of cellular components from whole blood increased 11KT serum concentrations in a time-dependent fashion, with significant increases detected from two hours after blood collection. Conclusions11-oxygenated androgens are the preferred substrates for androgen activation by AKR1C3 in PBMCs, primarily conveyed by natural killer cell AKR1C3 activity, yielding 11KT the major active androgen in PBMCs. Androgen metabolism by PBMCs can affect the measurement results of serum 11-ketotestosterone concentrations, if samples are not separated in a timely fashion.

biochemistry↗

bioRxiv2020

Testosterone enhances GLP-1 efficacy at the plasma membrane and endosomes to augment insulin secretion in male pancreatic β cells

Male mice with elimination of the androgen receptor (AR) in islet {beta} cells ({beta}ARKO) exhibit blunted glucose-stimulated insulin secretion (GSIS), leading to hypoinsulinemia and hyperglycemia when challenged with a Western diet. Testosterone activation of an extranuclear AR in {beta} cells potentiates GSIS by amplifying the insulinotropic action of glucagon-like peptide-1 (GLP-1). Here, using a combination of {beta}ARKO and {beta} cell-selective GLP-1 receptor knockout mice and their islets, we show that AR activation in {beta} cells amplifies the insulinotropic effect of islet-derived GLP-1. In {beta} cell models expressing cAMP sensors, testosterone enhances the ability of GLP-1, but not that of glucose-dependent insulinotropic polypeptide or glucagon, to produce cAMP. Accordingly, testosterone selectively enhances the ability of GLP-1 to potentiate GSIS. Notably, testosterone enhances GLP-1 production of cAMP at the plasma membrane and endosomes. In male mouse and human islets, the insulinotropic effect of testosterone is abolished following inhibition of the membrane and endosomal cAMP-dependent protein kinase A and exchange protein activated by cAMP islet 2 pathways. Thus, membrane localization of AR enhances the ability of the GLP-1 receptor to produce cAMP, thus increasing glucose-stimulated insulin exocytosis. Significance StatementThis study reveals that testosterone, acting on the androgen receptor (AR) in insulin-producing {beta} cells amplifies the insulinotropic action of glucagon-like peptide-1 (GLP-1) by increasing GLP-1-mediated production of cAMP at the plasma membrane and endosomal compartments, to promote insulin vesicles exocytosis in human {beta} cells. This study establishes a novel biological paradigm in which membrane location of a steroid nuclear receptor enhances the ability of a G protein-coupled receptor to produce cAMP. It has exceptional clinical significance for targeted delivery of testosterone to {beta} cells in the large population of aging and androgen-deficient men who are at increased risk of diabetes.

physiology↗

bioRxiv2019

Towards a gold standard for benchmarking gene set enrichment analysis

BackgroundAlthough gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected data sets and biological reasoning on the relevance of resulting enriched gene sets. However, this is typically incomplete and biased towards the goals of individual investigations.\n\nResultsWe present a general framework for standardized and structured benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization, and detection of relevant processes. This framework incorporates a curated compendium of 75 expression data sets investigating 42 different human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GO/KEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods on the benchmark compendium, identifying significant differences in (i) runtime and applicability to RNA-seq data, (ii) fraction of enriched gene sets depending on the type of null hypothesis tested, and (iii) recovery of the a priori defined relevance rankings. Based on these findings, we make practical recommendations on (i) how methods originally developed for microarray data can efficiently be applied to RNA-seq data, (ii) how to interpret results depending on the type of gene set test conducted, and (iii) which methods are best suited to effectively prioritize gene sets with high relevance for the phenotype investigated.\n\nConclusionWe carried out a systematic assessment of existing enrichment methods, and identified best performing methods, but also general shortcomings in how gene set analysis is currently conducted. We provide a directly executable benchmark system for straightforward assessment of additional enrichment methods.\n\nAvailabilityhttp://bioconductor.org/packages/GSEABenchmarkeR

bioinformatics↗

bioRxiv2019

Multi-omic analysis supports a developmental hierarchy of molecular subtypes in high-grade serous ovarian carcinoma

Multiple studies have identified transcriptome subtypes of high-grade serous ovarian carcinoma (HGSOC), but these have yet to impact clinical practice. Interpretation and translation of HGSOC subtypes are complicated by tumor evolution and polyclonality accompanied by accumulation of somatic aberrations, varying cell type admixtures, and different tissues of origin. The chronology of HGSOC subtype evolution was examined in the context of these factors by a novel integrative analysis of bulk absolute somatic copy number analysis and gene expression in The Cancer Genome Atlas, complemented by single-cell RNA-seq analysis of six independent tumors. The approach was validated by contrast to soft-tissue sarcoma. Genomic lesions associated with HGSOC subtypes tend to be subclonal, implying subtype divergence at later stages of tumor evolution. Subclonality of recurrent HGSOC alterations is particularly evident for proliferative tumors, characterized by extreme genomic instability, absence of immune infiltration, and greater patient age. In contrast, differentiated tumors are characterized by largely intact genome integrity, high immune infiltration, and younger patient age. We propose an alternative model to discrete subtypes of HGSOC, in which tumors develop from an early differentiated spectrum to a late proliferative spectrum, along a timeline characterized by increasing genomic instability and subclonal expansion. The proposed methods provide a new approach to investigating tumor evolution through multi-omic analysis. Statement of SignificanceThis study proposes a method to infer whether transcriptome-based groupings of tumors differentiate early in carcinogenesis and are therefore potentially appropriate targets for therapy, and demonstrates that this is not the case for high-grade serous ovarian carcinoma (HGSOC). Significant findings for HGSOC include: O_LITumor purity, ploidy, and subclonality can be reliably inferred from different genomic platforms and show marked differences between subtypes C_LIO_LIRecurrent DNA alterations are associated with subtypes and tend to occur more frequently in subclones C_LIO_LISingle-cell sequencing of 42,000 tumor cells reveals widespread heterogeneity in tumor cell type composition that drives bulk subtype calls, but demonstrates a lack of intrinsic subtypes among tumor epithelial cells C_LIO_LIFindings prompt the dismissal of discrete transcriptome subtypes for HGSOC and replacement by a more realistic model of continuous tumor development that includes mixtures of subclones, accumulation of somatic aberrations, infiltration of immune and stromal cells in proportions correlated with tissue of origin and tumor stage, and evolution between properties previously associated with discrete subtypes C_LI

cancer biology↗

bioRxiv2018

HMP16SData: Efficient Access to the Human Microbiome Project through Bioconductor

Phase 1 of the NIH Human Microbiome Project (HMP) investigated 18 body subsites of 239 healthy American adults, to produce the first comprehensive reference for the composition and variation of the \"healthy\" human microbiome. Publicly-available data sets from amplicon sequencing of two 16S rRNA variable regions, with extensive controlled-access participant data, provide a reference for ongoing microbiome studies. However, utilization of these data sets can be hindered by the complex bioinformatic steps required to access, import, decrypt, and merge the various components in formats suitable for ecological and statistical analysis. The HMP16SData package provides count data for both 16S variable regions, integrated with phylogeny, taxonomy, public participant data, and controlled participant data for authorized researchers, using standard integrative Bioconductor data objects. By removing bioinformatic hurdles of data access and management, HMP16SData enables epidemiologists with only basic R skills to quickly analyze HMP data.

bioinformatics↗

bioRxiv2017

Software For The Integration Of Multi-Omics Experiments In Bioconductor

Multi-omics experiments are increasingly commonplace in biomedical research, and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multi-omics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide all of the multiple omics data for each cancer tissue in The Cancer Genome Atlas (TCGA) as ready-to-analyze MultiAssayExperiment objects, and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable and reproducible statistical analysis of multi-omics data and enhances data science applications of multiple omics datasets.

bioinformatics↗

bioRxiv2017

Accessible, curated metagenomic data through ExperimentHub

We present curatedMetagenomicData, a Bioconductor and command-line interface to thousands of metagenomic profiles from the Human Microbiome Project and other publicly available datasets, and ExperimentHub, a platform for convenient cloud-based distribution of data to the R desktop. The resource provides standardized per-participant metadata linked to bacterial, fungal, archaeal, and viral taxonomic abundances, as well as quantitative metabolic functional profiles. The datasets can be immediately analyzed in R or other software with a minimum of bioinformatic expertise and no preprocessing of data. We demonstrate identification of taxonomic/functional correlations, an investigation of gut \"enterotypes\", and a comparison of the accuracy of disease classification from different data types. These documented analyses can be reproduced efficiently on a laptop, without the barriers of working with large-scale, raw sequencing data. The building and expansion of curatedMetagenomicData is based entirely on open source software and pipelines, to facilitate the addition of new microbiome datasets and methods.

bioinformatics↗