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Historien og de kollektive minnene om andre verdenskrig har mange forskjellige vinkler og tilnærminger, og museet har en helt sentral plass som formidler av disse. Røde Kors Krigsminnemuseum i Narvik åpnet i mars 1964. Gjennom en utvikling på over 50 år, har en institusjon for historisk formidling vært et bevaringssted for krigshistoriske gjenstander som har blitt presentert for et bredt utvalg av publikum. Fra 2016 gikk museet inn i en ny fase som innebar overgangen og utviklingen til det som nå er kjent som Narviksenteret. Museet som institusjon blir i seg selv et historisk åsted, med fortellinger som bidrar til å utvikle et minne om krigen. Denne studien ser på Røde Kors Krigsminnemuseum og Narviksenteret som forvalter og formidler av en fortelling om krigen i Narvik under andre verdenskrig. Ved å undersøke hvordan andre verdenskrig i Norge og Narvik har blitt fremstilt gjennom etterkrigstiden, kaster dette prosjektet lys på de mange prosessene og fasene Røde Kors Krigsminnemuseum og Narviksenteret har gjennomgått, og hvordan det kollektive minnet kan ha blitt påvirket av dette. Det kollektive minnet og fortellingen om andre verdenskrig har vært viktig for at nye generasjoner kan forstå å lære av tidligere hendelser. Et sentralt funn i denne undersøkelsen er at det har vært en klar utvikling bort fra det patriotiske narrativet som ble fremstilte i de tidlige fasene etter krigen. Det har gått fra å sette søkelys på motgang og seier hos Røde Kors Krigsminnemuseum, til mer generelle moralske spørsmål hos Narviksenteret.

Additional file 1: Fig. S1. FACS-plot of D. melanogaster eye-antennal disc cells after live/dead cell staining. (A) Counterstaining of Propidium Iodide to label dead cells (y-axis, Q1) and Calcein violet to label live cells (x-axis, Q4). Double positive signals might indicate dying cells or incompletely separated cells (Q2). This method allows removing debris (Q3) efficiently. (B) The cell population P4 (i.e. 16,208 living cells) were isolated and used for the scRNAseq run using 10x Genomics. Fig. S2. Quality and quantity of cDNA after reverse transcription of mRNA fraction (polyA-based enrichment) and full-length cDNA amplification from cell lysate of 30 cells sorted from cell suspension of eye-antennal discs run on Fragment Analyzer (Agilent). Size distribution of all fragments shows little impact on degradation (almost no cDNA detectable below 400 bp). Fig. S3. Contribution of mitochondrial gene expression to scRNAseq and snRNAseq datasets. (A) Total amount of genes (features) over percentage of mitochondrial reads, per cell each. The dashed line indicates a threshold of 10% of reads attributed to mitochondrial genes. In scRNAseq data, approximately 14% of cells show a high (>10%) proportion of mitochondrial gene reads on the total number of reads. (B) Total amount of genes (features) over Percentage of mitochondrial reads, per cell each in snRNAseq data. The dashed line indicates a threshold of 10% of reads attributed to mitochondrial genes. In most nuclei, only a low percentage of reads is attributed to mitochondrial genes. Fig. S4. Clustering and cluster annotation for scRNAseq data. (A) The heatmap shows the score for each potential cell type (Y-axis) in each cluster (X-axis). The cell types are annotated based on the highest scoring identity in the heatmap. The clusters are grouped based on their transcriptional similarity to each other. For clusters which express an equal number of marker genes for two different identities both identities were assigned (e.g. cluster 3:Antenna|EAB). Clusters with unresolved identities (i.e. more than two equal assignments) are called “Other”. The colors of the cluster names correspond to the colors in UMAP in (B). The marker score is calculated using a matrix of published marker genes (see Additional file Table S2). (B) UMAP of scRNAseq data. The clusters were annotated based on the heatmap in (A). This UMAP is identical to the UMAP with combined cluster annotation shown in Fig. 3A. Note that the color code is not comparable to the one used in Additional file Figs. S7 and S10. Fig. S5. Fluorescence intensity curves from Bioanalyzer for fresh- and cryopreserved nuclei obtained by different nuclei extraction protocols. (A) The RNA was extracted directly from a fresh sample (36 eye-antennal discs), which was dissociated using the 10x Genomics protocol with 0.1% IGEPAL as a detergent. (B) RNA isolated from a cryopreserved sample, which was dissociated using a protocol based on Triton X-100 as a detergent and a variety of RNAse inhibitors [1]. Note that the sample was thawed for 3.5h before being frozen again. Both curves are close to the expectation of RNA isolated from D. melanogaster [2]. Fig. S6. BioAnalyzer results comparing different nuclei isolation protocols for frozen samples. Samples 1, 2, 4 and 5 were dissociated using the protocol based on Triton X-100 as a detergent and a variety of RNAse inhibitors [1]. Samples 1 and 2 were dissociated by pipetting up and down and samples 4 and 5 were dissociated using a Dounce homogenizer. Samples 7 and 8 were dissociated using the protocol “10x Genomics® Isolation of Nuclei for Single Cell RNA Sequencing” [3]. Samples 7 and 8 were dissociated using only citric acid buffer and samples 10 und 11 were dissociated using only a detergent. Note that for D. melanogaster, intense bands are expected at about 40s and weaker bands at 25s and 45-50s [2]. Each run was repeated once. Fig. S7. Clustering and cluster annotation for snRNAseq data. (A) The heatmap shows the score for each potential cell type (Y-axis) in each cluster (X-axis). The cell types are annotated based on the highest scoring identity in the heatmap. The clusters are grouped based on their transcriptional similarity to each other. For clusters which express an equal number of marker genes for two different identities both identities were assigned (e.g. cluster 15:MFurrow|SMW). Clusters with unresolved identities (i.e. more than two equal assignments) are called “Other”. The colors of the cluster names correspond to the colors in UMAP in (B). The marker score is calculated using a matrix of published marker genes (see Additional file Table S2). (B) UMAP of snRNAseq data. The clusters were annotated based on the heatmap in (A). This UMAP is identical to the UMAP with combined cluster annotation shown in Fig. 3C. Note that the color code is not comparable to the one used in Additional file Figs. S4 and S10. Fig. S8. Gene ontology enrichment analysis for genes with most variable expression. (A) Top 3000 genes unique to scRNAseq (i.e. 1520 genes). (B) Top 3000 genes unique to snRNAseq (i.e. 1520 genes). (C) Top 3000 genes shared between scRNAseq and snRNAseq (i.e. 1480 genes). See also Supplementary Table S9 for a full list of enriched GO terms. Fig. S9. Comparison of dataset specific and shared differentially expressed genes for each cell type. The radar plot shows for each cell type the percentage of cluster specific differentially expressed genes unique for the scRNAseq and snRNAseq data, respectively (red and blue lines), as well as the percentage of differentially expressed genes shared between both datasets (black line). The total number of genes fulfilling the differential expression criteria (FDR 0.05 and log2-fold change > 0.25) for each cell type is shown in brackets. Fig. S10. Clustering and cluster annotation of integrated scRNAseq and snRNAseq dataset. (A) The heatmap shows the score for each potential cell type (Y-axis) in each cluster (X-axis). The cell types are annotated based on the highest scoring identity in the heatmap. The clusters are grouped based on their transcriptional similarity to each other. For clusters which express an equal number of marker genes for two different identities both identities were assigned (e.g. cluster 14:Dorsal|Ocelli). Clusters with unresolved identities (i.e. more than two equal assignments) are called “Other”. The colors of the cluster names correspond to the colors in the UMAP in (B). The marker score is calculated using a matrix of published marker genes (see Additional file Table S2). (B) UMAP of integrated scRNAseq and snRNAseq data. Cells are colored by clusters identified based on the (A). Note that the color code in A and B is not comparable to the one used in Additional file Figs. S4 and S7. Table S1. Overview of different dissociation conditions. Samples within blocks (highlighted in grey and white) were prepared in parallel. The Flow Cytometer only provides percentages of survival because it stops after a defined number of events (i.e. ~50,000 cells) and therefore absolute numbers are not meaningful. “Pipetting” refers to the number of strokes during and after incubation. The cells obtained by experiment/block 12 were subjected to 10X Genomics scRNAseq.

Additional file 1: Figure S1. A kinome-wide CRISPR screen in pancreatic cancer cells. Figure S2. Targeted inhibition of CDK7 enhances gemcitabine and paclitaxel chemotherapy response in pancreatic cancer in vitro and in vivo. Figure S3. CDK7 inhibition induces cell cycle arrest, apoptotic cell death, and DNA damage. Figure S4. Targeted inhibition of CDK7 reverses chemoresistance in pancreatic cancer. Table S1. Cell lines. Table S2. Plasmids and primers list. Table S3. Antibody list. Table S4. List of CRISPR-Cas9 screening results of gemcitabine and paclitaxel in TB32047 cells.

BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) is initiated by steatosis and can progress via fibrosis and cirrhosis to hepatocellular carcinoma (HCC). The RNA binding protein HuR controls RNAs at the posttranscriptional level; hepatocyte HuR has been implicated in the regulation of diet-induced hepatic steatosis. The present study aimed to understand the role of hepatocyte-HuR in NAFLD development and progression to fibrosis and HCC. APPROACH AND RESULTS: Hepatocyte-specific HuR-deficient mice and control HuR-sufficient mice were fed either a normal diet or a NAFLD-inducing diet. Hepatic lipid accumulation, inflammation, fibrosis and HCC development were studied by histology, flow cytometry, quantitative PCR and RNA sequencing. The liver lipidome was characterized by lipidomics analysis and the HuR-RNA interactions in the liver were mapped by RNA immunoprecipitation-sequencing. Hepatocyte-specific HuR-deficient mice displayed spontaneous hepatic steatosis and fibrosis predisposition, compared to control HuR-sufficient mice. On a NAFLD-inducing diet, hepatocyte-specific HuR-deficiency resulted in exacerbated inflammation, fibrosis and HCC-like tumor development. A multi-omic approach, including lipidomics, transcriptomics and RNA-immunoprecipitation sequencing revealed that HuR orchestrates a protective network of hepatic-metabolic and lipid homeostasis-maintaining pathways. Consistently, HuR-deficient livers accumulated, already at steady-state, a triglyceride signature resembling that of NAFLD livers. Moreover, upregulation of Spp1 and its product osteopontin mediated, at least partially, the fibrosis development in hepatocyte-specific HuR deficiency on a NAFLD-inducing diet, as shown by experiments utilizing antibody blockade of osteopontin. CONCLUSIONS: HuR is a gatekeeper of liver homeostasis preventing NAFLD-related fibrosis and HCC, suggesting that the HuR-dependent network could be exploited therapeutically.