Biology subjects

Navarro, M.

Publications and source records attributed to Navarro, M..

2 recordsLinked to original sources

bioRxiv2018

The Notch and EGFR signaling regulate caspase inhibitor Diap1 to match supply with intestinal demand

The regenerative activity of adult stem cells carries a high risk of cancer, particularly in highly renewable tissues. To guarantee that the correct organ size is attained and to cope with the continual risk of cancer, developing tissues often use programmed cell death (PCD) as an adaptive mechanism to cull excess and abnormal cells. Members of the family of Inhibitor of Apoptosis Proteins (IAPs) inhibit caspases and cell death and are often overexpressed in cancer. Here, we show that Diap1 is expressed in committed progenitor (enteroblast) cells in the adult Drosophila intestine. Blocking endogenous caspases uncovered that more than half of enteroblasts produced by intestinal stem cells (ISCs) are actively eliminated by apoptosis in the physiological intestine and also led to tumorigenesis. We find that antagonistic interplay between the Notch and EGFR signaling on Diap1 regulation governs the enteroblast cell death or survival decision via the conserved Klumpfuss/WT1-Lozenge/RUNX axis, which also regulates of differentiation plasticity of enteroblasts. These data provide new insights into how apoptosis drives adult tissue renewal and protection against tumors.

cell biology↗

bioRxiv2018

Maximizing the quality of NMR automatic metabolite profiling by a machine learning based prediction of signal parameters

The quality of automatic metabolite profiling in NMR datasets in complex matrices can be compromised by the multiple sources of variability in the samples. These sources cause uncertainty in the metabolite signal parameters and the presence of multiple low-intensity signals. Lineshape fitting approaches might produce suboptimal resolutions or distort the fitted signals to adapt them to the complex spectrum lineshape. As a result, tools tend to restrict their use to specific matrices and strict protocols to reduce this uncertainty. However, the analysis and modelling of the signal parameters collected during a first profiling iteration can further reduce the uncertainty by the generation of narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted and the performance of automatic profiling can be maximized. Thanks to the ability of our workflow to learn and model the sample properties, restrictions in the matrix or protocol and limitations of lineshape fitting approaches can be overcome.

bioinformatics↗