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Objective: Hormone secretion from metabolically active tissues, such as pancreatic islets, is governed by specific and highly regulated signaling pathways. Defects in insulin secretion are among the major causes of diabetes. The molecular mechanisms underlying regulated insulin secretion are, however, not yet completely understood. In this work, we studied the role of the GTPase ARFRP1 on insulin secretion from pancreatic 13-cells. <br /> Methods: A 13-cell-specific Arfrp1 knockout mouse was phenotypically characterized. Pulldown experiments and mass spectrometry analysis were employed to screen for new ARFRP1-interacting proteins. Co-immunoprecipitation assays as well as super-resolution microscopy were applied for validation. <br /> Results: The GTPase ARFRP1 interacts with the Golgi-associated PDZ and coiled-coil motif-containing protein (GOPC). Both proteins are co localized at the trans-Golgi network and regulate the first and second phase of insulin secretion by controlling the plasma membrane localization of the SNARE protein SNAP25. Downregulation of both GOPC and ARFRP1 in Min6 cells interferes with the plasma membrane localization of SNAP25 and enhances its degradation, thereby impairing glucose-stimulated insulin release from 13-cells. In turn, overexpression of SNAP25 as well as GOPC restores insulin secretion in islets from 13-cell-specific Arfrp1 knockout mice. <br /> Conclusion: Our results identify a hitherto unrecognized pathway required for insulin secretion at the level of trans-Golgi sorting. (c) 2020 The Authors. Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Adverse environmental conditions are detrimental to plant growth and development. Acclimation to abiotic stress conditions involves activation of signaling pathways which often results in changes in gene expression via networks of transcription factors (TFs). Mediator is a highly conserved co-regulator complex and an essential component of the transcriptional machinery in eukaryotes. Some Mediator subunits have been implicated in stress-responsive signaling pathways; however, much remains unknown regarding the role of plant Mediator in abiotic stress responses. Here, we use RNA-seq to analyze the transcriptional response of Arabidopsis thaliana to heat, cold and salt stress conditions. We identify a set of common abiotic stress regulons and describe the sequential and combinatorial nature of TFs involved in their transcriptional regulation. Furthermore, we identify stress-specific roles for the Mediator subunits MED9, MED16, MED18 and CDK8, and putative TFs connecting them to different stress signaling pathways. Our data also indicate different modes of action for subunits or modules of Mediator at the same gene loci, including a co-repressor function for MED16 prior to stress. These results illuminate a poorly understood but important player in the transcriptional response of plants to abiotic stress and identify target genes and mechanisms as a prelude to further biochemical characterization.
Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected "normal" behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the "stable" principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement.