BUSCADOR RECOLECTA

Chronic lymphocytic leukemia (CLL) is a lymphoid neoplasm characterized by the accumulation of mature B cells. The diagnosis is established by the detection of monoclonal B lymphocytes in peripheral blood, even in early stages [monoclonal B-cell lymphocytosis (MBLhi)], and its clinical course is highly heterogeneous. In fact, there are well-characterized multiple prognostic factors that are also related to the observed genetic heterogenicity, such as immunoglobulin heavy chain variable region (IGHV) mutational status, del17p, and TP53 mutations, among others. Moreover, a dysregulation of the immune system (innate and adaptive immunity) has been observed in CLL patients, with strong impact on immune surveillance and consequently on the onset, evolution, and therapy response. In addition, the tumor microenvironment is highly complex and heterogeneous (i.e., matrix, fibroblast, endothelial cells, and immune cells), playing a critical role in the evolution of CLL. In this study, a quantitative profile of 103 proteins (cytokines, chemokines, growth/regulatory factors, immune checkpoints, and soluble receptors) in 67 serum samples (57 CLL and 10 MBLhi) has been systematically evaluated. Also, differential profiles of soluble immune factors that discriminate between MBLhi and CLL (sCD47, sCD27, sTIMD-4, sIL-2R, and sULBP-1), disease progression (sCD48, sCD27, sArginase-1, sLAG-3, IL-4, and sIL-2R), or among profiles correlated with other prognostic factors, such as IGHV mutational status (CXCL11/I-TAC, CXCL10/IP-10, sHEVM, and sLAG-3), were deciphered. These results pave the way to explore the role of soluble immune checkpoints as a promising source of biomarkers in CLL, to provide novel insights into the immune suppression process and/or dysfunction, mostly on T cells, in combination with cellular balance disruption and microenvironment polarization leading to tumor escape., Peer reviewed

Peer reviewed

Chronic lymphocytic leukemia (CLL) is a lymphoid neoplasm characterized by the accumulation of mature B cells. The diagnosis is established by the detection of monoclonal B lymphocytes in peripheral blood, even in early stages [monoclonal B-cell lymphocytosis (MBLhi)], and its clinical course is highly heterogeneous. In fact, there are well-characterized multiple prognostic factors that are also related to the observed genetic heterogenicity, such as immunoglobulin heavy chain variable region (IGHV) mutational status, del17p, and TP53 mutations, among others. Moreover, a dysregulation of the immune system (innate and adaptive immunity) has been observed in CLL patients, with strong impact on immune surveillance and consequently on the onset, evolution, and therapy response. In addition, the tumor microenvironment is highly complex and heterogeneous (i.e., matrix, fibroblast, endothelial cells, and immune cells), playing a critical role in the evolution of CLL. In this study, a quantitative profile of 103 proteins (cytokines, chemokines, growth/regulatory factors, immune checkpoints, and soluble receptors) in 67 serum samples (57 CLL and 10 MBLhi) has been systematically evaluated. Also, differential profiles of soluble immune factors that discriminate between MBLhi and CLL (sCD47, sCD27, sTIMD-4, sIL-2R, and sULBP-1), disease progression (sCD48, sCD27, sArginase-1, sLAG-3, IL-4, and sIL-2R), or among profiles correlated with other prognostic factors, such as IGHV mutational status (CXCL11/I-TAC, CXCL10/IP-10, sHEVM, and sLAG-3), were deciphered. These results pave the way to explore the role of soluble immune checkpoints as a promising source of biomarkers in CLL, to provide novel insights into the immune suppression process and/or dysfunction, mostly on T cells, in combination with cellular balance disruption and microenvironment polarization leading to tumor escape., Peer reviewed

Glucocorticoids (GCs) are metabolic hormones that regulate physiological and behavioural responses to environmental change and mediate homeostasis maintenance in vertebrates. Despite the assumption that GCs covary with energy metabolism, we yet lack a mechanistic understanding of how environmental factors such as temperature modulate GC variation through their effect on organismal energy balance. In particular, the mechanisms linking temperature-dependent metabolic rate and GCs at broad spatial scales and across species remain poorly investigated., Here we used biophysical models to calculate thermoregulatory costs (i.e., the amount of heat required to keep body temperature in homeostasis) of free-living birds as a function of environmental conditions, body size, shape, and insulating layer of feathers. We then investigated the link between ambient temperature, cost of thermoregulation and baseline plasma GC concentrations in a comparative study including GC data from 94 bird species from HormoneBase., We found a significant, positive association between thermoregulatory costs and baseline GC concentrations. Interestingly, models including thermoregulatory costs better explained GC variation when compared to those including ambient temperature as a predictor variable. This result suggests that body size and shape fundamentally modulate energy requirements for thermoregulation and thereby GC concentrations in the wild., By providing a mechanistic description of the link between ambient temperature and thermoregulatory metabolism, biophysical models provide a tool to predict the impact of environmental conditions on energy metabolism. Our work demonstrates that differences in thermoregulation modulate variation in GC concentrations across a broad climatic gradient., Peer reviewed

Multimedia component 1: Fig. S1. Espinal biome, La Pampa Province, Argentina, characterized by a) open Caldén (Prosopis caldenia) forest, b) closed Caldén forest with shrubs, and c) and d) Caldén forest with grassland. Fig. S2. Location of sites sampled to evaluate arthropod prey abundance of Vermilion Flycatchers and Fork-tailed Flycatchers in the Espinal biome, La Pampa Province, Argentina, during a) 2016–17, and b) 2017-18 and 2018-19 breeding seasons. Fig. S3. Location of sites sampled to evaluate diet of Vermilion Flycatchers (VEFL) and Fork-tailed Flycatchers (FTFL) in the Espinal biome, La Pampa Province, Argentina, during a) 2015–16, b) 2016–17, c) 2017–18 and d) 2018-19 breeding seasons. Fig. S4. Location of control and nest sites sampled to evaluate the effect of arthropod prey abundance on breeding habitat selection of Vermilion Flycatchers (VEFL) and Fork-tailed Flycatchers (FTFL) in the Espinal biome, La Pampa Province, Argentina. All arthropod abundance samples were obtained during the 2016-17 breeding season., Peer reviewed

Dataset 1: this is the dataset used for the eggshell coloration analysis. The dataset presents 145 columns and 197 lines. Each line represents the data relative to a single starlin egg considered for the analysis. Column 1 is the original ID of the egg in the field noterbook. Column 2 is the individual code for each breeding attempt (i.d. every time a first egg is found in a nest). Column 3 is the nest in which each egg has been found. Col. 4 is the tratment to which the female laying the eggs has been subjected: H, handicap; C, control. Col. 5 contains the date of measurement of the eggs. Col. 6 contains the date of laying of each egg (date of April 2020). Col. 7 the clutch size of each breeding event. Cols. 8-10 the lenght (mm), widht (mm) and the calculated volume (mm3) of each egg. Please refer to the main text for more information about egg size calculation. Cols. 11-15 contain the informations about the laying female (IDs and measurements). Cols. 16 - 135 contain the repeated measurements of eggshell coloration as stored in the spectrophotometer and the relative values of brightness, BGC, hue and chroma. The x and y are used to distinguish the first from the second spectra values. Cols. 136-139 contain the values of the mean value of brightness, hue, chroma and blue-green chroma for the repeated measures (x and y). Cols. 140-142 contain the main, the standard deviation and the maximum values of BGC for each nest. Cols. 143-145 contain the main, the standard deviation and the maximum values of BGC x100. Dataset 2: this dataset contains the data of the females recorded before and after the laying. Col. 1 and 2 contain the ID of the female (ring number and chip code, respectively) Col.3 the tratment to which the female laying the eggs has been subjected: H, handicap; C, control. Col. 4 the nest in which the eggs have been laid. Cols. 5 and 6 contain the dates in which the female has been captured (before and after laying the eggs, respectively). Cols. 7 and 8 contain the day counts of the corrispective date, starting the day of the laying (April 11th). Col. 9 and 10 contain the initials of the observer of the first and second capture. Col. 11 contains the coded age of the oldest nestling: P5, chick of 5 days; P6, chick of 6 days... Cols. 12 and 13 contain the weights recorded in the first and in the second capture (pre and post laying, respectively). Cols. 14 and 15 contain the wing (mm) and the tarsus (mm) measurements of the females, recorded in the first capture., It has been proposed that blue-green egg coloration is a condition-dependent female sexual trait that may modify paternal care in a post-mating sexual selection scenario. This pattern may arise because the pigment responsible for eggshell colouration (biliverdin) may be a costly and limited resource, whose availability is linked to female health state. Thus, it can be predicted that females whose condition is compromised should be constrained in their capacity to deposit biliverdin in the eggshell, thus producing paler clutches. To test this hypothesis, we performed a handicapping experiment by clipping some feathers of female spotless starlings before egg laying and measuring the colour of their clutches. We expected the handicapping treatment to increase flying costs, impairing female overall condition and resulting in paler clutches. Our experiment was successful in lowering the weight gain of handicapped with respect to control females. However, in contrast to our expectations, we found no effect of the treatment on eggshell colouration. Eggshell colour varied along the laying order, with initial eggs of the laying sequence being relatively paler than the rest of the clutch, but this pattern was not different between experimental groups. Despite a very similar methodology, our results differ from a previous study and offers no support to the post mating sexual selected hypothesis, questioning the general applicability of the sexual selection role of eggshell coloration., Peer reviewed

Fig. S1. Ddiagram of flow of participants used in this study., Identification as a study of diagnostic accuracy using at least one measure of accuracy (such as sensitivity, specificity, predictive values, or AUC). Structured summary of study design, methods, results, and conclusions (for specific guidance, see STARD for Abstracts)., Peer reviewed

Table S1. List of DEGs in Sfb-treated HepG2 and SNU423 cells. See supplementary Table S1 Excel file. Table S2. List of DEGs with log2(FC) higher than 1.5 or lower than -1.5 in Sfb-treated HepG2 and SNU423 cells. See supplementary Table S2 Excel file. Table S3. List of pathways activated or inhibited upon a Sfb treatment in HepG2 cells. See supplementary Table S3 Excel file. Figure S1. Quantitative RT-PCR validation of RNA-Seq data for HepG2 cells. A selection of down-regulated and up-regulated genes were assessed for gene expression through qPCR. Cells were grown for 24 h and then treated with 10 μM Sorafenib for 12 h before RNA extraction. The table compared the fold expression obtained by RNA-Seq versus the relative expression calculated by RT-PCR. Note that the corresponding mRNA levels quantified by RT-PCR from untreated cells (control) were arbitrarily set at 1.0. The ACTB, BAX, BIRC3, CEBP, CPEB4, DUSP1, EIF4E2, EPOP, FEN1, GADD45B, IDI1, PCNA, SMAD7, TPI and VEGFA genes were analysed. Expression levels were relativized to levels of 28S rRNA of each sample. Values are the mean ± S.D. values of at least three independent experiments performed in triplicate. Statistical significances were analysed by the Student's test (*p< 0.05, **p< 0.01, *** p< 0.001, ****p< 0.0001). Values for upregulated genes are shown in red, while those for downregulated genes are shown in green. Figure S2. Quantitative RT-PCR validation of RNA-Seq data for SNU423 cells. A selection of down-regulated and up-regulated genes were assessed for gene expression through qPCR. Cells were grown for 24 h and then treated with 10 μM Sorafenib for 12 h before RNA extraction. The table compared the fold expression obtained by RNA-Seq versus the relative expression calculated by RT-PCR. Note that the corresponding mRNA levels quantified by RT-PCR from untreated cells (control) were arbitrarily set at 1.0. The BIM, BOP1, BIRC3, CPEB4, DUSP1, EIF4E2, EPOP, GADD45B, IDI1, PHB, PCNA, SMAD7 and TPI genes were analysed. Expression levels were relativized to levels of 28S rRNA of each sample. Values are the mean ± S.D. values of at least three independent experiments performed in triplicate. Statistical significances were analysed by the Student's test (*p< 0.05, **p< 0.01, ***p< 0.001, ****p< 0.0001). Values for upregulated genes are shown in red, while those for downregulated genes are shown in green. Figure S3. Sorafenib lead to translation inhibition in HCC cells. Polysome profiles in HepG2 and SNU423 cells treated or not with Sorafenib (10 μM, 12 h). Cell extracts and polysome profile analysis were performed following the procedure described in Material and Methods. Ten A260 units of each extract were resolved in 7 to 50% sucrose gradients. The A254 was continuously monitored. Sedimentation is from left to right. The identity of the different peaks is indicated. Figure S4. Cholesterol biosynthesis pathway in humans. This outline shows the cholesterol biosynthesis process with those enzymes whose genes were down-regulated by Sfb in red as suggested by our RNA-Seq analysis. The log2(FC) for each one is indicated in brackets. Figure S5. Categories with opposite NES values in HepG2 and SNU423 cell lines. Reactome categories which show NES in opposite directions in the two cell lines analysed with the GSEA. NES and False Discovery Rate (FDR) q-Value are shown for each category. Panel (A) shows categories with FDR lower than 0.3 for HepG2 cells and its value in SNU423 cell line whereas panel (B) shows categories with FDR more significative for SNU423 cells and its respective value for HepG2 cells. Figure S6. Uncropped Western Blots., Peer reviewed

[Objectives] CARB-ES-19 is a comprehensive, multicenter, nationwide study integrating whole-genome sequencing (WGS) in the surveillance of carbapenemase-producing K. pneumoniae (CP-Kpn) and E. coli (CP-Eco) to determine their incidence, geographical distribution, phylogeny, and resistance mechanisms in Spain., [Methods] In total, 71 hospitals, representing all 50 Spanish provinces, collected the first 10 isolates per hospital (February to May 2019); CPE isolates were first identified according to EUCAST (meropenem MIC > 0.12 mg/L with immunochromatography, colorimetric tests, carbapenem inactivation, or carbapenem hydrolysis with MALDI-TOF). Prevalence and incidence were calculated according to population denominators. Antibiotic susceptibility testing was performed using the microdilution method (EUCAST). All 403 isolates collected were sequenced for high-resolution single-nucleotide polymorphism (SNP) typing, core genome multilocus sequence typing (cgMLST), and resistome analysis., [Results] In total, 377 (93.5%) CP-Kpn and 26 (6.5%) CP-Eco isolates were collected from 62 (87.3%) hospitals in 46 (92%) provinces. CP-Kpn was more prevalent in the blood (5.8%, 50/853) than in the urine (1.4%, 201/14,464). The cumulative incidence for both CP-Kpn and CP-Eco was 0.05 per 100 admitted patients. The main carbapenemase genes identified in CP-Kpn were blaOXA–48 (263/377), blaKPC–3 (62/377), blaVIM–1 (28/377), and blaNDM–1 (12/377). All isolates were susceptible to at least two antibiotics. Interregional dissemination of eight high-risk CP-Kpn clones was detected, mainly ST307/OXA-48 (16.4%), ST11/OXA-48 (16.4%), and ST512-ST258/KPC (13.8%). ST512/KPC and ST15/OXA-48 were the most frequent bacteremia-causative clones. The average number of acquired resistance genes was higher in CP-Kpn (7.9) than in CP-Eco (5.5)., [Conclusion] This study serves as a first step toward WGS integration in the surveillance of carbapenemase-producing Enterobacterales in Spain. We detected important epidemiological changes, including increased CP-Kpn and CP-Eco prevalence and incidence compared to previous studies, wide interregional dissemination, and increased dissemination of high-risk clones, such as ST307/OXA-48 and ST512/KPC-3., Peer reviewed

Datasets used in the analysis of the manuscript entitled: Sexual differences in phenotypical predictors of floating status: body condition influences male but not female reproductive status in a wild passerine. In Oecologia., Peer reviewed