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Supplementary Table S1. Amplification system for the genes of interest and for the genes used for normalization in the qPCR validation. Supplementary Table S2. Normalized counts of all mapped genes from the RNA-Seq assay. Supplementary Table S3. Differentially expressed genes according to the differential expression analysis with DESeq2. Supplementary Table S4. diaPASEF-based protein quantification data for all the samples. A total of 6839 proteins were quantified by diaPASEF LC-MS/MS after processing of the runs as described in the Section 2 and Section 2.5.4. Supplementary Table S5. Differential abundance test for comparison of the LPS + I1 vs. LPS groups. Proteins with CV ≤ 20.0% and quantified in at least 50% of the samples of each group were considered. Fold changes, resulting p-values, and Benjamini–Hochberg corrected p-values for each of the 6065 considered proteins are shown. Supplementary Figure S1. Effect of inulin on the lysosome pathway (KEGG: 04142) from RAW 264.7 LPS-induced inflammation model cells. Pathway diagrams overlayed with the measured protein fold change showing coherent cascades. Differentially expressed genes are represented with positive values in red. Supplementary Figure S2. Effect of inulin on the NF-κB signaling pathway showing integrated transcriptomics data. Genes overexpressed (in red) or underexpressed (green) according to the transcriptomic analysis are highlighted. Supplementary Figure S3. Effect of Inulin on the IL-17 signaling pathway (KEGG: 04657) from RAW 264.7 LPS-induced inflammation model cells. Pathway diagram, overlayed with the measured protein perturbation showing coherent cascades. Differentially expressed genes are represented with negative fold-change values in blue and positive values in red., Peer reviewed

[EN] Herein, we use two exemplary superparamagnetic iron oxide multicore nanoparticles (SPIONs) to illustrate the significant influence of slightly different physicochemical properties on the cellular and molecular processes that define SPION interplay with primary neural cells. Particularly, we have designed two different SPION structures, NFA (i.e., a denser multicore structure accompanied by a slightly less negative surface charge and a higher magnetic response) and NFD (i.e., a larger surface area and more negatively charged), and identified specific biological responses dependent on SPION type, concentration, exposure time, and magnetic actuation. Interestingly, NFA SPIONs display a higher cell uptake, likely driven by their less negative surface and smaller protein corona, more significantly impacting cell viability and complexity. The tight contact of both SPIONs with neural cell membranes results in the significant augmentation of phosphatidylcholine, phosphatidylserine, and sphingomyelin and the reduction of free fatty acids and triacylglycerides for both SPIONs. Nonetheless, NFD induces greater effects on lipids, especially under magnetic actuation, likely indicating a preferential membranal location and/or a tighter interaction with membrane lipids than NFA, in agreement with their lower cell uptake. From a functional perspective, these lipid changes correlate with an increase in plasma membrane fluidity, again larger for more negatively charged nanoparticles (NFD). Finally, the mRNA expression of iron-related genes such as Ireb-2 and Fth-1 remains unaltered, while Tf R-1 is only detected in SPION-treated cells. Taken together, these results demonstrate the substantial impact that minor physicochemical differences of nanomaterials may exert in the specific targeting of cellular and molecular processes. A denser multicore structure generated by autoclave-based production is accompanied by a slight difference in surface charge and magnetic properties that become decisive for the biological impact of these SPIONs. Their capacity to markedly modify the lipidic cell content makes them attractive as lipid-targetable nanomedicines., This work has received funding from the European Union’s Horizon Europe research and Innovation program under grant agreement No. 101098597. It has also been supported by Grant PID2020-113480RB-I00 funded by MCIN/AEI/ 10.13039/501100011033/. A.E. acknowledges grant RYC2020-029282-I from the Spanish MCIN., EDX_NFA (dat); EDX_NFD (dat); FTIR_NFA_NFD (dat); NFA_Mag_RT (dat); NFD_Mag_RT (dat); Results_CultureAfterSuspension (xls); Results_Hyperthermia (xls); Results_Lipidomics (xls); Results_qPCR (xls); RX_NFA (png); Flow cytometry dataset (pdf); ReportFile (word), Peer reviewed

29 Pags.-, 24 Figs.- 15 Tabls. © Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License., This article describes the development of a monthly precipitation dataset for the Spanish mainland, covering the period between December 1915 and December 2020. The dataset combines ground observational data from the National Climate Data Bank (NCDB) of the Spanish meteorological service (AEMET) and new data rescued from meteorological yearbooks published prior to 1951 that were never incorporated into the NCDB. The yearbooks’ data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century, the period when the data were more scarce. The final dataset contains records from 11 312 stations, although the number of stations with data in a given month varies largely between 674 in 1939 and a maximum of 5234 in 1975. Spatial interpolation was used on the resulting dataset to create monthly precipitation grids. The process involved a two-stage process: estimation of the probability of zero precipitation (dry month) and estimation of precipitation magnitude. Interpolation was carried out using universal kriging, using anomalies (ratios with respect to the 1961–2000 monthly climatology) as dependent variables and several geographic variates as independent variables. Cross-validation results showed that the resulting grids are spatially and temporally unbiased, although the mean error and the variance deflation effect are highest during the first decades of the 20th century, when the observational data were more scarce. The dataset is available at https://doi.org/10.20350/digitalCSIC/15136 under an open license and can be cited as Beguería et al. (2023)., . This research has been supported by research grants PID2020-116860RB-C22, CGL2017-83866-C1-3-R, and CGL2017-83866-C3-3-R by the Spanish Ministry of Science and Innovation and by the European Commission – NextGenerationEU (Regulation EU 2020/2094), through CSIC’s Interdisciplinary Thematic Platform Clima (PTI Clima)/Development of Operational Climate Services., Peer reviewed

7 Pág., Update notice Author Correction: The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe (Scientific Data, (2020), 7, 1, (1), 10.1038/s41597-019-0340-y) Scientific Data, Volume 7, Issue 1, 1 December 2020, Article number 114, The dataset presented here was collected by the GenTree project (EU-Horizon 2020), which aims to improve the use of forest genetic resources across Europe by better understanding how trees adapt to their local environment. This dataset of individual tree-core characteristics including ring-width series and whole-core wood density was collected for seven ecologically and economically important European tree species: silver birch (Betula pendula), European beech (Fagus sylvatica), Norway spruce (Picea abies), European black poplar (Populus nigra), maritime pine (Pinus pinaster), Scots pine (Pinus sylvestris), and sessile oak (Quercus petraea). Tree-ring width measurements were obtained from 3600 trees in 142 populations and whole-core wood density was measured for 3098 trees in 125 populations. This dataset covers most of the geographical and climatic range occupied by the selected species. The potential use of it will be highly valuable for assessing ecological and evolutionary responses to environmental conditions as well as for model development and parameterization, to predict adaptability under climate change scenarios., This publication is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676876 (GenTree). This work was also supported by the Swiss Secretariat for Education, Research and Innovation (SERI) under contract No. 6.0032. We want to thank to Olivier Ambrosio, Francisco Auñón, Virgilijus Baliuckas, Eduardo Ballesteros, Giorgina Beffa, Sabine Brodbeck, William Brunetto, Jurata Buchovska, Fernando del Caño, Andreas Fera, Rene Graf, Berit Gregorsson, Markus Hartmann, Andreas Helmersson, Enja Hollenbach, Jan Philipp Kappner, Johannes Lambertz, David Lopez-Quiroga, Jérémy Marchon, David Matter, Benjamin Meier, Helge Meischner, Pekka Närhi, Daniel Nievergelt, Juri Nievergelt, Anne Eskild Nilsen, Hans Nyeggen, Geir Østreng, Sebastian Richter, Christoph Rieckmann, Marcus Stefsky, Sergio San Segundo, Ivan Scotti, Jørn Henrik Sønstebø, Arne Steffenrem, Jussi Tiainen, Anne Verstege and Mikael Westerlund for their support during the field campaigns. We are also grateful to all the forest owners and national administrations for providing sampling permissions., Peer reviewed

All images are projections of confocal sections, of super-resolution microscopy. (A, B) Kkv localises apically in the trachea of wild-type embryos (A) and in absence of exp reb (B). (C, D) The localisation of Kkv is apical also in presence of exp ΔMH2 in trachea (C) and in presence of MH2-exp in salivary glands (D). (E, F) At stage 14, in wild-type embryos (E) and in embryos deficient for exp and reb (F), Kkv is present in the apical membrane and in many intracellular vesicles (yellow arrowheads). (G) At stage 16, in wild-type embryo, Kkv apical distribution follows the pattern of taenidial folds and intracellular vesicles are mostly absent. (H) At stage 16, in exp reb mutant embryos, Kkv is apical but shows altered distribution pattern. (I, J) At stage 15, in control embryos, Kkv pattern is apical and covers the whole membrane leaving minimal spatial gaps (I); instead, in exp reb mutant embryos, Kkv distribution changes to a less organised pattern at the apical membrane (J). (K) Three different types of spatial distribution within a selected area. The positions of the defined objects can be random and exhibit characteristics of attraction (clustered pattern) or repulsion (regular pattern). The F-Function tends to be larger (≈1) for clustered patterns and smaller (≈0) for regular. The G-Function tends to be smaller (≈0) for clustered and larger (≈1) for regular patterns. (L) Kkv punctae (magenta) on the apical cell area marked by Armadillo (green) in the trachea of a control embryo. (L’) Positions of Kkv punctae on the selected area marked by black dots. (L”) Random pattern of distribution for the same area created by the spatial statistics 2D/3D image analysis plugin. (M) The corresponding observed F and G functions (blue) are displayed above and below the reference simulated random distributions (black) and the 95% confidence interval (light gray), respectively, indicating a nonrandom spatial pattern. (N) SDI histogram for the F-Function of the control (blue) and the Df(exp reb) samples. A significant difference between the frequency distributions for each group of individuals has been observed. (Kolmogorov–Smirnov D = 0.5833, p < 0.05) (N’) SDI histogram for the G-Function of the control (blue) and the Df(exp reb) samples. Statistical analysis of the distributions did not reveal significant differences between the two groups of individuals for this parameter (Kolmogorov–Smirnov D = 0.25, p > 0.05). (O) Kkv punctae (magenta) on the apical cell area marked by Armadillo (green) in the trachea of a exp reb mutant embryo. (O’) Positions of Kkv punctae on the selected area marked by black dots. (O”) Random pattern of distribution for the same area created by the spatial statistics 2D/3D image analysis plugin. (P) The corresponding observed F and G functions (blue) are displayed above and below the reference simulated random distributions (black), respectively. Both curves largely overlap with the 95% confidence interval (light gray), indicating a tendency towards a random spatial pattern. (Q) Frequency distribution histograms for the nearest neighbour distances between Kkv punctae in control (blue) and exp reb mutant samples. The distribution of values between the two groups is found significantly different (Kolmogorov–Smirnov D = 0.2036, p < 0.005). The underlying data for quantifications can be found in the S1 Data. Scale bars A-J: 10 μm; L, O: 2 μm., Peer reviewed

(A) Schematic representation of Kkv protein (CD, catalytic domain; WGTRE; CC, coiled-coil domain). (B, C, G, I, J) Show projections of confocal sections and (D-F, H, K-N) show single confocal sections. (B) The overexpression of GFP-kkvΔWGTRE in a kkv mutant background does not rescue the absence of extracellular chitin deposition (white arrow, note the absence of CBP) and the protein accumulates in a generalised pattern. (C-D) The overexpression of GFP-KkvΔWGTRE does not produce intracellular chitin punctae, neither in trachea at early stages (C-C’) nor in salivary glands (D). (E-E”‘) GFP-kkvΔWGTRE colocalise with the ER marker KDEL. (F) GFP-KkvΔWGTRE does not colocalise with the marker FK2. (G) The overexpression of GFP-kkvΔCC in a kkv mutant background rescues the lack of extracellular chitin deposition in the trachea (note the presence of CBP staining). (H, I) The simultaneous expression of reb and GFP-kkvΔCC in salivary glands produces ectopic extracellular chitin (H), and no defects in trachea (I). (J) The overexpression of reb in trachea leads to morphogenetic defects. (K, L) Overexpressed GFP-Kkv localises mainly apically (orange arrowheads) although a bit of the protein can be detected in the basal region (yellow arrowheads). (M, N) Apical accumulation of overexpressed GFP-KkvΔCC is less conspicuous. (O) Quantifications of accumulation of GFP-Kkv and GFP-KkvΔCC in apical versus basal region. n corresponds to the number of ratios analysed (apical/basal ratio per cell), and brackets indicate the number of embryos used. Ratios were obtained from the apical (orange line in K) and basal (yellow line in K) domains of single cells in trachea and salivary glands. The underlying data for quantifications can be found in the S1 Data. Scale bars B, C, G, I, J: 25 μm; D-F, H, K-N: 10 μm., Peer reviewed

(A) Alignment of amino acids (aa) sequences of the isoform B of Exp (aa 356–433) and homologs; the blue square indicates 8 aa highly conserved, the red square includes 9 aa less conserved. (B-D) Show projections of confocal sections and (E-F) show single confocal sections. (B) The overexpression of expΔCM2 in an exp reb mutant background rescues the lack of extracellular chitin deposition. (C, D) The simultaneous expression of expΔCM2 and GFP-kkv produces morphogenetic defects in the trachea (arrowheads in C) and ectopic chitin deposition in the lumen of salivary glands (D). (E, F) Overexpressed Exp localises mainly in the apical region (orange arrowheads) with respect the basal domain (yellow arrowheads), while the apical accumulation of overexpressed ExpΔCM2 is less conspicuous (F). (G) Quantifications of accumulation of Exp and ExpΔCM2 in apical versus basal region. n corresponds to the number of ratios analysed (apical/basal ratio per cell), and brackets indicate the number of embryos used. Ratios were obtained from the apical (orange line in E) and basal (yellow line in E) domains of single cells in trachea and salivary glands. The underlying data for quantifications can be found in the S1 Data. Scale bars B, C: 25 μm; D-F: 10 μm., Peer reviewed

The excerpted portion of the immunoblot shown in the figures is highlighted by a black box., Peer reviewed

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Salivary gland of a stage 15 embryo carrying GFP-kkv, ChtVisTomato, and expΔMH2 visualised from a lateral view using Drangofly 505 (Andor) with 63× oil objective and a 2× zoom. Images were taken every 3 s in one single Z-stack during 2 min. The movie shows a chitin particle detaching from a GFP-kkv vesicle., Peer reviewed