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Additional file 4: Figure S2. METABRIC mRNA expression data and patient survival. (a) Spearman correlations among GYS1 mRNA and GYS isoform 2 (GYS2), glycogen branching enzyme 1 (GBE1), glycogen breakdown enzymes glycogen phosphorylase muscle isoform (PYGM), brain isoform (PYGB) and liver isoform (PYGL), the hypoxic marker carbonic anhydrase 9 (CA9), and Marker of Proliferation Ki67 (MKI67). Numbers indicate Spearman’s p, only significant correlations are displayed. (b) GYS1 mRNA log2 expression levels across breast cancer clinical subtypes (left panel), tumor stage (middle) and tumor grade (right). Boxes represent the interquartile range and median, whiskers 1.5*interquartile range. Only significant Wilcoxon P-values are annotated. *P

Additional file 2: Table S2. Antibody and RNA specifications.

Additional file 8: Figure 6. (a) Clonogenic agarose assay of HCC1806 cells transfected with siCtrl or siGYS1, cultured in 5.6 mM glucose complete medium for 10 days in normoxia. (b) Ki67 proliferation index in spheroids of HCC1806 cells transfected with siRNA-mediated GYS1 knockdown or scrambled control. (c) Well confluency of HCC1806-shCtrl and -shGYS1 cells treated with different concentrations of GTPP, cultured in 5.6 mM glucose complete DMEM, was measured by Incucyte every 3h.

Additional file 3: Figure S1. METABRIC mRNA expression data (27) and patient overall survival. (a) Overall survival of breast cancer patients with high or low mRNA expression of the respective glycogen enzymes in their primary tumor. Curves are separated by the median mRNA expression. (b) Overall survival curves of triple-negative patients only. (c) Overall survival of respectively ER-/HER2+, ER+/HER2+ or ER+/HER2- breast cancer patients with high or low GYS1 mRNA expression in their primary tumor. Curves are separated by the median GYS1 expression. (d) Overall survival multivariate Cox regression analyses correcting for Nottingham Prognostic Index (NPI) for all (left) and triple-negative breast cancer patients (right) with respectively high and low GYS1 mRNA in their primary tumors. GBE1 = glycogen branching enzyme 1; GYS = glycogen synthase; PYGB = brain glycogen phosphorylase; PYGL = liver glycogen phosphorylase; PYGM = muscle glycogen phosphorylase

Additional file 6: Figure 4. (a) Western blot of GYS1 demonstrates differential expression in breast cancer cell lines. (b) Knock-down of GYS1 in a broad panel of breast cancer cell lines, cultured in 5.6 mM glucose complete DMEM medium for 5 days, reduces cell growth in most breast cancer cell lines.

Additional file 5: Figure S3. Patient selection, survival curves and scoring examples of the primary breast tumor tissue micro-array. (a) Flowchart of the included tumor samples. (b) Corresponding PAS, PAS+diastase and CA9 staining of the areas depicted in Figure 1c. (c) Representative examples of staining intensity per scoring intensity and corresponding output of the analysis app in tumor cores and positive controls. (d) Additional boxplots of GYS1 and glycogen tumor H-score stratified by subtypes and Ki67 low vs. high subgroups within subtypes (top), and scores per tumor grade (bottom). (e) Heatmap of individual primary breast tumor samples (one sample per column) after unsupervised hierarchical clustering based on GYS1, glycogen and Ki67 tumor scores. White boxes in the annotation bars indicate missing values. (f) Overall survival curves of all patients included in the TMA analysis, stratified by respectively GYS1 or glycogen tertiles. Patients with more than one primary breast tumor (n = 9) were excluded from survival analyses.

Additional file 7: Figure 5. (a) Western blot confirmation of siRNA GYS1 knockdown, belonging to Figure 2c. (b) Clonogenic agarose assay of MDA-MB-231 cells with siCtrl or siGYS1, cultured in 5.6 mM glucose complete medium + 0.3% agarose for 14 days in normoxia. (c) Background intracellular glucose levels of MDA-MB-231 with scrambled control or GYS1 shRNA, after 48h culture in 5.6 mM glucose complete DMEM. (d) Proliferation of MDA-MB-231 cells with scrambled control or GYS1 shRNA, after 5 days culture in 10 mM complete DMEM.

Dit inrichtings- en beheersadvies wordt opgesteld als onderdeel van het wetenschapswinkelproject “Verbindend Landschap Heelsums Beekdal” in opdracht van de gelijknamige burgerwerkgroep. Op basis van de informatie van studenten groepen (ACT-groepen) en een scriptie van een MSc studente is dit advies voor de inrichting en beheer van het beekdal opgesteld. Dit inrichtings- en beheersadvies is als volgt opgebouwd: in hoofdstuk 1 wordt de het beheersperspectief uitgewerkt, het toekomstbeeld waarnaar gestreefd moet worden op de middellange termijn (10-15 jaar). Dit perspectief, dat gebaseerd is op 3 belangrijke elementen, wordt vervolgens uitgewerkt in wat meer detail in par. 1.2. In hoofdstuk 2 volgen ‘inrichtingsmaatregelen’, hiermee wordt bedoeld het doen van eenmalige aanpassingen of investeringen om in een korte tijd te komen tot een betere uitgangspositie voor het Heelsums beekdal. Naast zulke investeringen zijn er beheersmaatregelen die voor de langere termijn gelden, en bepaalde kwaliteiten verder ontwikkelen of in stand houden. Die worden besproken in hoofdstuk 3, waarbij ook de rol van de betrokkenen benoemd wordt. Tenslotte wordt in hoofdstuk 4 de strategie besproken met suggesties voor bv. financiering van bepaalde maatregelen, en de wijze van uitvoering en coördinatie van activiteiten.

The aim of Dutch public authorities is to create a river system in which all habitats indicative for a robust river system as well as the characteristic species of the system are present. The focus is on four hotspots of large-scale coherent nature along the branches of the major Dutch rivers Rhine and Meuse. In addition to the ongoing de-velopment of a string of natural areas along the major rivers (corridors), the aspiration in these hotspots is to arrive at large contiguous areas with high nature qual-ity, to serve as robust core areas and source populations for the surrounding parts of the river area. For a num-ber of indicator species (animal species representative of other species with similar habitat requirements) we have modelled with the metapopulation model LARCH whether they can develop sustainable populations. Subsequently, an estimate was made for 2050 of the habitat distribution in the hotspots that would result if all agricultural land on the riverside of the flood levees were given a nature destination, with or without shared agricultural use. Finally, based on modelling outcome additional measures were defined to realize sustaina-ble populations of indicator species, increasing riparian forests and wet grasslands. From this semi-quantitative approach it appears that for most river-bound species significant habitat improve-ments can be achieved when focussing on nature reha-bilitation in a limited number of promising hotspots. De uiterwaarden van de grote rivieren vertegenwoordigen een succesverhaal van natuurontwikkeling. Toch kunnen we voor veel kenmerkende diersoorten van het rivierecosysteem nog niet rekenen op duur-zame populaties, zeker niet bij de te verwachten klimaatverandering. Het aanpassen van het landgebruik tot grote, aaneengesloten natuurgebieden (hotspots) langs de grote rivieren kan mogelijk aanzienlijke winst opleveren. Dit artikel verkent de bijdrage van vier van deze hotspots in 2050 voor een aantal riviergebonden gidssoorten met behulp van metapopulatiemodellering.