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Seiler, R.

Publications and source records attributed to Seiler, R..

3 recordsLinked to original sources

Interaction between time-of-day and oxytocin efficacy in mice and humans with and without gestational diabetes

Management of labor in women with diabetes is challenging due to the high risk of peri- and postpartum complications. To avoid cesarean section and assist with labor progression, Pitocin, a synthetic oxytocin, is frequently used to induce and augment labor. However, the efficacy of Pitocin is often compromised in diabetic pregnancies, leading to increased cesarian delivery. As diabetes deregulates the bodys circadian timekeeping system and the time-of-day of the first Pitocin administration contributes to labor duration, our objective was to determine how the time of day and the circadian clock gene, Bmal1, gates oxytocin efficacy. Our studies in mice show that, compared to the rest phase of the day (lights on), the uterotonic efficacy of oxytocin is significantly increased during the active phase (lights off). Using in vitro studies, a myometrium-specific Bmal1 conditional knockout mouse model, and a mouse model of food-induced gestational diabetes, we find that Bmal1 is crucial for maintaining oxytocin receptor expression and response in the myometrium in mice. These findings also translate to humans, where oxytocin-induced human myometrial cell contraction is time-of-day dependent, and retrospective clinical data suggest that administration of Pitocin in the morning should be considered for pregnant women with gestational diabetes.

physiology↗

Personalized in-silico drug response prediction based on the genetic landscape of muscle-invasive bladder cancer

In bladder cancer (BLCA) there are, to date, no reliable diagnostics available to predict the potential benefit of a therapeutic approach. The extraordinarily high molecular heterogeneity of BLCA might explain its wide range of therapy responses to empiric treatments. To better stratify patients for treatment response, we present a highly automated workflow for in-silico drug response prediction based on a tumors individual multi-omic profile. Within the TCGA-BLCA cohort, the algorithm identified a panel of 21 genes and 72 drugs, that suggested personalized treatment for 94,7% of patients - including five genes not yet reported as biomarkers for clinical testing in BLCA. The automated predictions were complemented by manually curated data, thus allowing for accurate sensitivity- or resistance-directed drug response predictions. Manual curation revealed pitfalls of current, and potential of future drug-gene interaction databases. Functional testing in patient derived models and/or clinical trials are next steps to validate our in-silico drug predictions.

bioinformatics↗

The consensus molecular classification of muscle-invasive bladder cancer

Muscle-Invasive Bladder Cancer (MIBC) is a molecularly diverse disease with heterogeneous clinical outcomes. Several molecular classifications have been proposed, yielding diverse sets of subtypes, which hampers the clinical implications of such knowledge. Here, we report the results of a large international effort to reach a consensus on MIBC molecular subtypes. Using 1750 MIBC transcriptomes and a network-based analysis of six independent MIBC classification systems, we identified a consensus set of six molecular classes: Luminal Papillary (24%), Luminal Non-Specified (8%), Luminal Unstable (15%), Stroma-rich (15%), Basal/Squamous (35%), and Neuroendocrine-like (3%). These consensus classes differ regarding underlying oncogenic mechanisms, infiltration by immune and stromal cells, and histological and clinical characteristics. This consensus system offers a robust framework that will enable testing and validating predictive biomarkers in future clinical trials.

cancer biology↗