BUSCADOR RECOLECTA

In this work, the use of a hydrophobic natural deep eutectic solvent (NADES) as supported liquid membrane (SLM) for hollow fiber liquid-phase microextraction (HF-LPME) of triazines is proposed. The combination of formic acid:L-menthol (2:1) was selected as optimum. FT-IR spectra of selected NADES and its components associated to Fig. S1 of paper submitted to Microchemical Journal are provided as csv files as follow: FigS1 (menthol); FigS1 (formic acid); FigS1 /NADES)., "Hacia un analisis medioambiental verde: nuevos disolventes, materiales y dispositivos (GREENNESS)" (Grant PID2021-122327OB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”), FigS1 (menthol)_Díaz-Álvarez, Turiel and Martín-Esteban.csv, FigS1 (formic acid)_Díaz-Álvarez, Turiel and Martín-Esteban.csv, FigS1 (NADES)_Díaz-Álvarez, Turiel and Martín-Esteban.csv, Peer reviewed

In this work, the use of a hydrophobic natural deep eutectic solvent (NADES) as supported liquid membrane (SLM) for hollow fiber liquid-phase microextraction (HF-LPME) of triazines is proposed. The combination of formic acid:L-menthol (2:1) was selected as optimum. The optimized HF-LPME procedure was applied to the analysis of an artificial water containing humic acids, tap and river water, and urine samples by HPLC with UV detection at 268 nm. The chromatographic data associated to Fig.6 and 7 of paper submitted to Microchemical Journal are provided as csv files as follows: Fig6a: Milli-Q water; Fig6b; artificial water; Fig7a: Tap water; Fig7b: River water; Fig7c: Urine river. All the samples were spiked with selected sulfonamides at 5 µg/L concentration level., "Hacia un analisis medioambiental verde: nuevos disolventes, materiales y dispositivos (GREENNESS)" (Grant PID2021-122327OB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”)., Fig6a_Díaz-Álvarez_Turiel and Martín-Esteban.csv, Fig6b_Díaz-Álvarez_Turiel and Martín-Esteban.csv, Fig7a_Díaz-Álvarez_Turiel and Martín-Esteban.csv, Fig7b_Díaz-Álvarez_Turiel and Martín-Esteban.csv, Fig7c_Díaz-Álvarez_Turiel and Martín-Esteban.csv, Peer reviewed

Additional file 2: Table S1. Regulated genes in siJUNB-transfected cells compared to siControl-transfected cells. Additional file 3: Table S2. High-confidence JUNB regulated genes involved in cell cycle regulation and E2F pathway. Additional file 4: Table S3. High-confidence JUNB regulated genes involved in EMT and TGFB signalling. Additional file 5: Table S4. Genes assigned to JunB binding sites. Each peak was assigned to the nearest TSS. Additional file 6: Table S5. High-confidence JUNB regulated genes containing JUNB binding sites of ChIP-seq analysis. Additional file 7: Table S6. List of antibodies used for western blot, immunofluorescence and immunohistochemistry. Additional file 8: Table S7. List of primers used for real-time PCR. Additional file 9: Table S8. List of siRNA. Additional file 10. Review history., Additional file 1: Figure S1. Depletion of JUNB inhibits cell proliferation in epithelial cancer cells. Figure S2. Validation by RT-qPCR of a panel of JUNB targets genes identified in the transcriptome approach and involved in cell cycle regulation and cell proliferation. Figure S3. JUNB binding at the TGFB2, RBPJ, ERCC2 and RPTOR locus. Figure S4. JUNB binds to and regulates the expression of TGFB2 gene. Figure S5. JUNB binds to and regulates the expression of CCNE1 gene. Figure S6. Overexpression of JUNB promotes TGFB2 signaling-induced EMT. Figure S7. H&E images and IHC images of GFP expression in UTA6-Control, UTA6-JUNB primary tumors and metastatic lung and liver lesions. Figure S8. JUNB and GFP expression in UTA6-UTA6-JUNB primary tumors and metastatic lung and liver lesions. Figure S9. IHC images of JUNB, TGFB2, CCNE1, Fibronectin and Integrin β1 in UTA6-Control and UTA6-JUNB primary tumors, and of JUNB, TGFB2 and CCNE1 in metastatic lung and liver lesions. Figure S10. JUNB amplification in breast and ovarian cancers is associated with poor survival. Figure S11. Expression of JUNB protein in breast cancer is associated with poor survival. Supplemental methods for cell proliferation by living cell imaging. Figure S12. Uncropped western blot gel images in Fig. 1B. The dotted line boxes highlight lanes used in figures. Figure S13. Uncropped western blot gel images in Fig. 2D. The dotted line boxes highlight lanes used in figures. Figure S14. Uncropped western blot gel images in Fig. 4E. The dotted line boxes highlight lanes used in figures. Figure S15. Uncropped western blot gel images in Fig. 5 A-B. The dotted line boxes highlight lanes used in figures. Figure S16. Uncropped western blot gel images in Fig. 6 B and F. The dotted line boxes highlight lanes used in figures. Figure S17. Uncropped western blot gel images in Fig. S1A and S1E. The dotted line boxes highlight lanes used in figures. Figure S18. Uncropped western blot gel images in Fig. S2E. The dotted line boxes highlight lanes used in figures. Figure S19. Uncropped western blot gel images in Figure S4C and S4D. The dotted line boxes highlight lanes used in figures. Figure S20. Uncropped western blot gel images in Figure S5A. The dotted line boxes highlight lanes used in figures. Figure S21. Uncropped western blot gel images in Figure S6A and S6C. The dotted line boxes highlight lanes used in figures., Peer reviewed

Supplementary Material 2: Script for the application of the Filtering Method of the Predictive Characterization., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

1. metadata_BLASTOCYSTIS Contains relevant clinical and cognitive information about the subjects: age, BMI, sex, years of ecudation and measures of executive function (total digit Span, trail making test part B, stroop interference). 2. Metabolomics_Plasma_HPLC_MS_positive_mode Contains the plasma metabolomic raw data (intensities) measured by HPLC-ESI-MS/MS in positive mode for each subjects according to the mass and retention time (mass@retention time). 3. Metabolomics_Plasma_HPLC_MS_negative_mode Contains the plasma metabolomic raw data (intensities) measured by HPLC-ESI-MS/MS in negative mode for each subjects according to the mass and retention time (mass@retention time). 4. Metabolomics_Plasma_NMR Contains the integrations of the plasma metabolites measures by 1H-NMR for each subject. 5. Metabolomics_Feces_HPLC_MS_positive_mode Contains the fecal metabolomic raw data (intensities) measured by HPLC-ESI-MS/MS in positive mode for each subjects according to the mass and retention time (mass@retention time). 6. Metabolomics_Feces_HPLC_MS_negative_mode Contains the fecal metabolomic raw data (intensities) measured by HPLC-ESI-MS/MS in negative mode for each subjects according to the mass and retention time (mass@retention time). 7. Metabolomics_Feces_NMR Contains the integrations of the fecal metabolites measures by 1H-NMR for each subject., Peer reviewed

Supplementary Data 1. Database of reference human messenger RNAs and SARS-CoV-2 genomes used for mapping reads. Raw count matrices. Supplementary Data 2. Expression matrices and transcript levels in uninfected cells. Supplementary Data 3. Differential Expressed Gene analysis used in the volcano plots analysis. Datasets and results used in the Gene Ontology analyses. Classification of outliers. Supplementary Data 4. Transcriptional profiles for all genes in each cell type. Supplementary Data 5. Networks used in the analyses presented in Figs. 5 and 6. Supplementary Data 6. Transcriptional analysis of human astrovirus 1 infection in ileum organoids., Peer reviewed

From inbreeding avoidance to kin-selected cooperation, social behaviours are frequently reliant on kin recognition. However, kin recognition mechanisms are costly to evolve and currently not very well understood. Recent evidence suggests that, by altering their host’s odour, gut and other host-associated microorganisms may provide a promising avenue for understanding kin recognition. In Drosophila melanogaster, kin recognition can mediate mate choice, sexual conflict and larval competition/cooperation, underscoring its important functional role. As is commonly the case, kin recognition in this species depends on both familiarity (i.e. shared rearing environment) and relatedness, and seems to rely mainly on body odours determined by cuticular hydrocarbons. Here, we investigated the degree to which larval rearing environment and relatedness (full-sibs vs. unrelated) determine co-variation between gut microbiota and cuticular hydrocarbons in D. melanogaster. We found that rearing environment strongly determined both microbiota and cuticular hydrocarbon composition, but that these effects were independent from each other. In contrast, relatedness did not influence microbiota composition, but had a strong influence on microbiota diversity, which in turn covaried significantly with cuticular hydrocarbon composition. Our results show that, in D. melanogaster, odours may convey information about both familiarity and relatedness via an interaction between: a) direct effects of the rearing environment on cuticular hydrocarbons and b) indirect effects of relatedness on cuticular hydrocarbons via gut microbiota diversity., Peer reviewed

[Description of methods used for collection/generation of data]: This dataset includes records of the monitoring of the distribution and relative abundance of the Mediterranean Spur-Thighed Tortoise (Testudo graeca) in Doñana since 2005 as part of the monitoring program of natural resources and processes in Doñana. One of the aims of this project was to obtain a temporal and continuous series of data of the distribution and abundance of this species to detect changes and trends in the population within the protected area. The study area is Doñana National Park, south-west of Spain, which is one of the two areas of distribution of this species in the south of Spain. The tortoise population of Doñana has been monitored since 1973 for other research studies (Andreu, A.C., 2000; Díaz-Paniagua, C. et al., 2001). Individuals are usually present along the border of temporal marshes and between dunes with vegetation composed by mediterranean scrub, scattered oaks and pine trees of medium and low cover. The method used to monitor long-term changes throughout time is the transect survey looking for tortoise tracks, which can be easily detected in the sandy substrates up to 3 days if there are good weather conditions (moderate temperatures, absence of rains or strong wind). The transects (n=10) are distributed within the whole study area (see decimal coordinates of the starting and end of the survey in the dataset as “verbatimCoordinate”) and runs along linear sand trails (n=8) across mediterranean vegetation and throughout circular dunes transects (n=2). Transects have different length ranged between 2-10 kms. The length of the transect is established since the beginning of the study, however it may be variable when a section of the trails is inaccessible because of flooding or when it overlaps with the breeding area of a protected bird species. In these cases, alternative sections of the trail are established to complete the survey. In total, approximately 60 kms are sampled in Doñana Natural Area. Surveys are designed to be performed every two years, however we include data for every year only for dunes transects since these transects overlap with those corresponding to annual surveys of lizards and geckos in dunes, which are included in the same monitoring program of natural resources and processes in Doñana. The transects are conducted by members of the monitoring team usually three times by year in spring or two times in spring (March-May) and once in autumn (October), when the active period of the species is optimal. The census is performed from the front part of the car, at 5-8 km/h, which permits a correct identification of the tortoise tracks. However, the transects in dunes are always performed on foot, as there are not car trails. In most censuses performed in 2021 the car was not available and were conducted on foot. The minimum length interval between census is 7 days. At least two days of good weather conditions are needed to perform the surveys (days without rain, strong wind and without night frost) to ensure the activity of the tortoises. Data recorded during the surveys include the number of individual tortoise tracks observed, life stage when a correct identification is possible (adult or juvenile), sex (recorded when the individual is observed during the survey too), as well as length transect, time and georeferenced data of the observation. Other information as weather description: sky conditions, temperature, precipitation and wind conditions. Wind speed is registered according to Beafourt scale where 0: 0-1 km/h, 1: 1-5 km/h, 2: 6-11 km/h, 3: 12-19 km/h, 4: 20-28 km/h, 5: 29 a 38 km/h. Between 2005-2007 data were registered in paper and transfer directly in an Excel file. During this period only the number of the tortoise tracks were recorded but not geographic coordinates information of each track could be taken. Since 2008 data are recorded with the app CyberTracker (see protocol) which allows that all geographic coordinates of the tortoise tracks are recorded. The protocol used has been supervised by herpetological researchers and the data have been validated by the coordinators who have also performed the transects., Dataset are structured following well-established data formats Darwing Core. Three files are provided. The first file (icts-rbd_TesGra_ev_20230710) contains the information of the project, the institution and the description each event (time of occurrence, geographical coordinates, habitat type, etc…). The eventID code has been built with the code of the transect and eventDate. The second file (icts-rbd_TesGra_occ_20230710) contains the information of the occurrences of species recorded in each transect, taxonomic classification or the geographical coordinates of its observation. The occurrenceID has been built with the eventID code plus a number regarding the number of observations recorded for this event. The third file (icts-rbd_TesGra_mof_20230710) provides information of the description of the meteorological variables measured., This dataset includes records of the monitoring of the distribution and relative abundance of the Mediterranean Spur-Thighed Tortoise (Testudo graeca) in Doñana since 2005 as part of the monitoring program of natural resources and processes in Doñana. One of the aims of this project was to obtain a temporal and continuous series of data of the distribution and abundance of this species to detect changes and trends in the population within the protected area. The study area is Doñana National Park, south-west of Spain, which is one of the two areas of distribution of this species in the south of Spain. The tortoise population of Doñana has been monitored since 1973 for other research studies (Andreu, A.C., 2000; Díaz-Paniagua, C. et al., 2001). Individuals are usually present along the border of temporal marshes and between dunes with vegetation composed by mediterranean scrub, scattered oaks and pine trees of medium and low cover. The method used to monitor long-term changes throughout time is the transect survey looking for tortoise tracks, which can be easily detected in the sandy substrates up to 3 days if there are good weather conditions (moderate temperatures, absence of rains or strong wind). The transects (n=10) are distributed within the whole study area (see decimal coordinates of the starting and end of the survey in the dataset as “verbatimCoordinate”) and runs along linear sand trails (n=8) across mediterranean vegetation and throughout circular dunes transects (n=2). Transects have different length ranged between 2-10 kms. The length of the transect is established since the beginning of the study, however it may be variable when a section of the trails is inaccessible because of flooding or when it overlaps with the breeding area of a protected bird species. In these cases, alternative sections of the trail are established to complete the survey. In total, approximately 60 kms are sampled in Doñana Natural Area. Surveys are designed to be performed every two years, however we include data for every year only for dunes transects since these transects overlap with those corresponding to annual surveys of lizards and geckos in dunes, which are included in the same monitoring program of natural resources and processes in Doñana. The transects are conducted by members of the monitoring team usually three times by year in spring or two times in spring (March-May) and once in autumn (October), when the active period of the species is optimal. The census is performed from the front part of the car, at 5-8 km/h, which permits a correct identification of the tortoise tracks. However, the transects in dunes are always performed on foot, as there are not car trails. In most censuses performed in 2021 the car was not available and were conducted on foot. The minimum length interval between census is 7 days. At least two days of good weather conditions are needed to perform the surveys (days without rain, strong wind and without night frost) to ensure the activity of the tortoises. Data recorded during the surveys include the number of individual tortoise tracks observed, life stage when a correct identification is possible (adult or juvenile), sex (recorded when the individual is observed during the survey too), as well as length transect, time and georeferenced data of the observation. Other information as weather description: sky conditions, temperature, precipitation and wind conditions. Wind speed is registered according to Beafourt scale where 0: 0-1 km/h, 1: 1-5 km/h, 2: 6-11 km/h, 3: 12-19 km/h, 4: 20-28 km/h, 5: 29 a 38 km/h. Between 2005-2007 data were registered in paper and transfer directly in an Excel file. During this period only the number of the tortoise tracks were recorded but not geographic coordinates information of each track could be taken. Since 2008 data are recorded with the app CyberTracker (see protocol) which allows that all geographic coordinates of the tortoise tracks are recorded. The protocol used has been supervised by herpetological researchers and the data have been validated by the coordinators who have also performed the transects. References: Díaz-Paniagua, C., Claudia Keller and Ana C. Andreu. 2001. Long-term demographic fluctuations of the spur-thighed tortoise Testudo graeca in SW Spain. Ecography 24: 707-721. Copenhagen 2001. Andreu, A. C., C. Díaz-Paniagua y C. Keller. 2000. La tortuga mora (Testudo graeca L.) en Doñana. Edita: Asociación Herpetológica Española. Barcelona. Monografías de Herpetología, Vol. 5: 70 pp., We acknowledge financial support from National Parks Autonomous Agency (OAPN) between 2002-2007; Singular Scientific and Technical Infrastructures from the Spanish Science and Innovation Ministry (ICTS-MICINN); Ministry of Agriculture, Livestock, Fisheries and Sustainable Development from the Regional Government of Andalusia (CAGPDES-JA) since 2007; Plan de Recuperación, Transformación y Resilencia and NextGeneration project EU/PRTR supported by ICT2021-006767 of the MICINN and the European Union since 2019; and Doñana Biological Station from the Spanish National Research Council (EBD-CSIC) since 2005., 1. icts-rbd_TesGra_ev_20230710: institutionID, institutionCode, datasetName, eventID, year, month, day, eventDate, continent, country, stateProvince, county, locality, verbatimCoordinates, eventTime, habitat, sampleSizeValue, sampleSizeUnit, samplingEffort, samplingProtocol, eventRemarks. 2. icts-rbd_TesGra_occ_20230710: eventID, occurrenceID, decimalLatitude, decimalLongitude, organismQuantity, organismQuantityType, occurrenceStatus, lifeStage, sex, occurrenceRemarks, kingdom, phylum, class, order, family, genus, specificEpithet, scientificName, scientificNameAuthorship, taxonRank, dynamicProperties, recordedBy, basisOfRecord. 3. icts-rbd_TesGra_mof_20230710: eventID, measurementID, measurementType, measurementValue, measurementUnit, measurementAccuracy, measurementMethod., Peer reviewed

Additional file 1: Supplementary Figure 1. Schematic flow chart representation of the genomic and proteomic approaches used to identify cell surface proteins in TNBC. Supplementary Figure 2. A) The table shows the data generated form the microarray analyses to identify cell surface proteins upregulated in TNBC. B) Venn diagram showing the number of genes specifically identified in each array and those that are common among them. Supplementary Figure 3. A) Procedure used to obtain enriched plasma membrane microsomal fraction, used to identify plasma membrane proteins in TNBC cell lines. B) The table shows the total number of proteins identified, as well as those that correspond to plasma membrane proteins. C) Venn diagram showing the number of proteins specifically identified in each cell line and those that are common among them. Supplementary Figure 4. A) Schematic representation of the protocol used in cell surface biotinylation experiments. B) The table shows the proteins identified and those that correspond to plasma membrane proteins. C) Venn diagram showing the number of proteins identified in each cell line and those that are common among them. Supplementary Figure 5. BT549 (A and B) and MDA-MB231 (C and D) cells were infected with lentivirus containing the shRNA control (sh-Control) or the shRNA sequences targeting GLUT1 or LAT1. Knockdown efficiency was verified by western (A and C), and the effect of the knockdowns on cell proliferation was analyzed by MTT metabolization (B and D). GAPDH was used as a loading control. Supplementary Figure 6. BT549 and HCC3153 cells were seeded on coverslips and treated with 10 nM of anti-CD98hc for the indicated times. Cells were fixed and stained for CD98hc (red), LAMP1 (green) and DNA (blue). Scale bar = 25 μm. Magnification of one cell at 24 hours of treatment is shown. Scale bar = 10 and 7.5 μm. Supplementary Figure 7. A) Dose-response analyses of the anti-proliferative effect of anti-CD98hc-DM1 in MDA-MB231 CD98hc CRISPR #B3, #G3 and parental MDA-MB231 cells. Cells were treated with anti-CD98hc-DM1 for four days at the indicated doses. Results are shown as the mean ± SD of quadruplicates of an experiment repeated three times. B and D) BT549 (B) and MDA-MB231 (D) cells were infected with lentivirus containing the shRNA control (sh-Control) or two shRNA sequences targeting CD98hc (sh-CD98hc #3 and #7). To verify the knockdown efficiency, levels of CD98hc were analyzed by Western blot. Calnexin was used as a loading control. C and E) BT549 (C) and MDA-MB231 (E) cells infected with lentivirus containing the shRNA control (sh-Control) or two shRNA sequences targeting CD98hc were plated and the MTT metabolization was measured at the times indicated. Supplementary Figure 8. Cell cycle profiles of TNBC cells treated with CD98hc-DM1. Cells were treated for one day with CD98hc-DM1 (10 nM), and then harvested and stained with propidium iodide for cell cycle analysis, following the procedure described in the materials and methods section. Supplementary Figure 9. Graphical representation of the surfaceome and the strategy to develop ADCs against differentially expressed proteins. Genomic as well as proteomic strategies allow the identification of proteins overexpressed or newly expressed by tumors with respect to normal tissue. That information may be used to develop an antibody that targets the differentially expressed protein, and that may be used as a backbone for the preparation of an ADC. Once prepared, in vitro and in vivo models can be used to define the antitumoral activity of the ADC as well as its mechanism of action., Instituto de Salud Carlos III Consejo Superior de Investigaciones Científicas Consejería de Educación, Junta de Castilla y León CRIS Cancer Foundation ACMUMA UCCTA ALMOM, Peer reviewed

Instituto de Salud Carlos III Consejo Superior de Investigaciones Científicas Consejería de Educación, Junta de Castilla y León CRIS Cancer Foundation ACMUMA UCCTA ALMOM, Peer reviewed