BUSCADOR RECOLECTA

Appendix of Emerging Microbes & Infections 13: 2295389 (2024), Rat hepatitis E virus (ratHEV; species Rocahepevirus ratti) is considered a newly emerging cause of acute hepatitis of zoonotic origin. ratHEV infection of people living with HIV (PLWH) might portend a worse, as with hepatitis E virus (HEV; species Paslahepevirus balayani), and consequently this group may constitute a high-risk population. We aimed to evaluate the prevalence of ratHEV by measuring viral RNA and specific IgG antibodies in a large Spanish cohort of PLWH. Multicentre study conducted in Spain evaluating PLWHIV included in the Spanish AIDS Research Network (CoRIS). Patients were evaluated for ratHEV infection using PCR at baseline and anti-ratHEV IgG by dot blot analysis to evaluate exposure to ratHEV strains. Patients with detectable ratHEV RNA were followed-up to evaluate persistence of viremia and IgG seroconversion. Eight-hundred and forty-two individuals were tested. A total of 9 individuals showed specific IgG antibodies against ratHEV, supposing a prevalence of 1.1 (95% CI; 0.5%−2.1%). Of these, only one was reactive to HEV IgG antibodies by ELISA. One sample was positive for ratHEV RNA (prevalence of infection: 0.1%; 95% CI: 0.08%−0.7%). The case was a man who had sex with men exhibiting a slightly increased alanine transaminase level (49 IU/L) as only biochemical alteration. In the follow-up, the patients showed undetectable ratHEV RNA and seroconversion to specific ratHEV IgG antibodies. Our study shows that ratHEV is geographical broadly distributed in Spain, representing a potential zoonotic threat., This work was supported by the Andalusian General Secretariat for Research, Development, and Innovation in Health (PI-0287-2019), the Spanish Ministry of Health (RD12/0017/0012), co-financed by European Regional Development Fund (ERDF), and the Carlos III Health Institute (Research Project grant numbers: PI21/00793 and PI22/01098). Projects “PI21/00793” and “PI22/01098” were funded by Carlos III Health Institute (ISCIII) and co-funded by the European Union. ARJ is the recipient of a “Miguel Servet” Research Contract by the Spanish Ministry of Sciences (CP18/00111). JCG is supported by the CIBERINFEC (CB21/13/00083), Carlos III Health Institute, Spanish Ministry os Science and NextGenerationEU. MCJ is the recipient of a PFIS predoctoral grant (FI22/00180) from the Carlos III Health Institute and co-funded by the European Union. DCM is the recipient of a “Rio-Hortega” (CM22/00176) grant from the Carlos III Health Institute and co-funded by the European Union. PLL is the recipient of a “Margarita Salas” contract funded by NextGeneration EU. TGG is recipient of a “Ramon y Cajal” contract funded by MCIN/AEI/10.13039/501100011033 and NextGeneration EU/PRTR. The laboratory of R.G.U. is supported by DZIF Thematic Translational Unit (TTU) “Emerging Infections” (grant number 01.808; awarded to R.G.U.). The HIV BioBank is supported by Carlos III Health Institute (PT20/00138) and Networking Research Center on Bioengineering, Biomaterials and Nanomedicine, CIBER-BBN (CB22/01/00041). CoRIS cohort is supported by CIBER (CB21/13/00091), Carlos III Health Institute, Spanish Ministry of Sciences NextGenerationEU., Peer reviewed

Table S1. Data used in the analysis and data sources. Table S2. Sample sizes for SSD and explanatory variables: mating system, sexual display, clutch size, and diet overlap. Table S3. Phylogenetic generalized least squares (PGLS) testing sexual size dimorphism (SSD) in body mass in relation to diet overlap (Morisita's index) as explanatory variable. Table S4. Phylogenetic generalized least squares (PGLS) testing sexual size dimorphism (SSD) in beak length in relation to diet overlap (Morisita's index) as explanatory variable. Table S5. Phylogenetic generalized least squares (PGLS) testing sexual size dimorphism (SSD) in wing length in relation to diet overlap (Morisita's index) as explanatory variable. Table S6. Phylogenetic generalized least squares (PGLS) testing sexual size dimorphism (SSD) in tarsus in relation to diet overlap (Morisita's index) as explanatory variable. Table S7. Candidates PGLS "full models" for SSD in body mass, beak length, wing length and tarsus length. Table S8. Ranking of all path models using the R packages ‘piecewiseSEM’ and ‘lavaan’. Table S9. Ranking of all path models tested based on CICc values using the R package ‘phylopath’. Figure S1. Distribution of SSD, calculated as log 10 (male trait / female trait) in four morphological traits: body mass, beak length, wing length and tarsus length. Figure S2. Sexual size dimorphism (SSD) according to mating systems: monogamy (MG, greeen) and polygyny (PG, orange). Monogamy corresponded to mating system values of 2 and 3 (i.e., monogamy and <5% polygyny) and polygyny to values of 4 and 5 (i.e., <15 polygyny and lek)., Peer reviewed

Table S1. Reagents used in this work. Table S2: Patient Cohort characteristics. Figure S1. PLD2 expression in OC patients and patient survival in OC public databases. (A) PLD2 expression in the OC patient databases GSE12172, GSE9891, GSE63885 and GSE2109 indicating OC subtype. (B) PLD2 expression in the OC patient databases GSE18520, GSE40595, and GSE38666, indicating OC subtype. (C) Kaplan-Meier plots showing overall survival (OS) of patients with high (red) or low (green) PLD2 expression levels in three OC databases with survival data: GSE13876 (advanced HGSOC); GSE23554 (advanced serous epithelial OC); and GSE31245 (92% serous, 2% endometroid, 6% clear cell). Data were analyzed with the log-rank test, and the associated P-values are shown in the graphs. (D) Kaplan-Meier plots showing overall survival (OS) of patients with high (red) or low (green) risk. PLD2 expression for each group is shown on the right of each graph. (E) Kaplan-Meier plots generated with Kaplan-Meier Plotter showing PFS (left column), PPS (middle column) and OS (right column) by splitting patients according to PLD2 expression in all OC patients (top row) or only HGSOC patients (bottom row). Expression levels are shown as log2 transformed values from the R2 database. Data were analyzed using Student’s t-test. *, P <0.05; **, P < 0.01; ***, P < 0.001. Figure S2. HIF1a levels in OC cell lines in response to hypoxia. (A) Representative images of HIF-1a protein levels by immunofluorescence in SKOV3, OVCAR8 and ES-2 cells under normoxia and hypoxia or in the presence of the HIF-hydroxylase inhibitor DMOG. (B) Western blot showing HIF-1a and alpha-tubulin protein levels in SKOV3, OVCAR8 and ES-2 cells under normoxia and hypoxia or in the presence of the HIF-hydroxylase inhibitor DMOG. A minimum of three independent experiments were performed. Figure S3. Differential analyses of chromatin accessibility in OC cells. (A) Volcano plot showing differential analyses of chromatin accessibility between SKOV3 cells carrying Ev and plasmid overexpressing PLD2 in normoxia. (B) Heatmaps and average profiles plotting normalized ATAC-seq signal in SKOV3 cells carrying Ev and overexpressing PLD2 for the differentially accessible regions (DARs) in (A). (C) Gene Ontology term enrichment analyses of biological processes for the genes associated with DARs in SKOV3 cells carrying EV in normoxia versus hypoxia. (D) Gene Ontology term enrichment analyses of biological processes for the genes associated with DARs in SKOV3 cells carrying Ev versus overexpressing PLD2 in normoxia. Figure S4. Motif and footprint analyses of transcription factor binding in OC cells. (A-B) Motif enrichment analyses of the increased and decreased ATAC peaks in OC cells carrying Ev in hypoxia vs. normoxia (A) and +/- PLD2 expression in normoxia (B). The three motifs with the lowest p values are shown in each case. (C) Venn diagrams plotting the overlap between TFs with increased binding in hypoxia and expressing PLD2 in normoxia (top) or the overlap between TFs with increased binding in hypoxia and expressing shPLD2 in hypoxia (bottom). (D) Distribution of fold changes in the ATAC peaks containing the motifs FOS::JUN in Ev normoxia vs. Ev hypoxia (top) or in Ev hypoxia vs. shPLD2 hypoxia. (E) Aggregate footprint signal of the peaks containing the motifs in (D). Figure S5. Clustering of differentially accessible regions in OC cells. (A) Heatmaps plotting normalized ATAC-seq signal at peaks associated with stemness genes in SKOV3 cells carrying Ev or plasmid expressing PLD2 in normoxia and carrying Ev or plasmid expressing shPLD2 in hypoxia, for the differentially accessible regions (DARs) clustered using k-means method in 4 clusters. (B) Tracks with ATAC-seq in SKOV3 cells carrying Ev or expressing PLD2 in normoxia and carrying Ev or shPLD2 in hypoxia, at the JAG1 locus. Figure S6. Expression of stemness and hypoxia genes correlated with PLD2 in OC patients. Heatmaps showing the expression z-scores of stemness-associated genes or hypoxia-response genes whose expression correlated with PLD2 in GSE40595 and GSE38666 OC patient databases. Figure S7. Hypoxia induces CSCs in ovarian cancer cells. (A) Top, Representative images of tumorspheres formed by SKOV3, OVCAR8 and ES-2 cells in normoxia or hypoxia. Bottom, quantification of the number and size of tumorspheres. Scale bars: 250 μm. (B) Percentage of holoclones formed by SKOV3, OVCAR8 and ES-2 cells in normoxia or hypoxia. At least 200 individual clones were analyzed. (C) Analysis of the expression of NANOG, SOX2, CD44 and EPCAM stemness-associated genes by RT-qPCR in SKOV3, OVCAR8 and ES-2 cells in normoxia or hypoxia. The mRNA expression was calculated as 2-∆Ct relative to the ACTB gene. (D) Percentage of CD133 positive cells measured by FACS in SKOV3, OVCAR8 and ES-2 cells in normoxia and hypoxia The average and SD of three independent experiments are shown in all cases. A minimum of three independent experiments were performed and the data were compared using Student’s t tests. Asterisks indicate statistical significance with respect to normoxia. *p < 0.05; **p < 0.01; ***p < 0.001. 13 Figure S8. Relative protein quantification of PLD2 normalized to alpha-tubulin from the western blots in Figure 4B. Figure S9. PLD2 expression and clone analyses. (A) Western blot showing PLD2 and alpha-tubulin protein levels in SKOV3, OVCAR8 and ES-2 OC cells carrying Ev, a plasmid expressing shPLD2 or plasmids expressing shPLD2 and PLD2. (B) Percentage of paraclones, meroclones and holoclones formed by SKOV3, OVCAR8 and ES-2 cells carrying Ev or plasmids expressing PLD2, shPLD2 or both in hypoxia or normoxia. At least 200 individual clones were analyzed. The average of three independent experiments is shown. A dotted line represents the percentage of holoclones in Ev carrying cells as a reference. Data were compared using Student’s t tests. Asterisks indicate statistical significance with respect to Ev carrying cells. *p< 0.05. (C) Left, determination of PLD2 protein levels by immunofluorescence in tumorspheres formed by OC cells carrying Ev and expressing PLD2 or shPLD2. Right, quantification of the percentage of cells with PLD2 expression in tumorspheres. Scale bars: 100 μm. Figure S10. Analyses of the EMT in OC cells in response to hypoxia and/or PLD2 expression. (A) Heatmaps showing the expression z-scores of epithelial-to-mesenchymal transition-associated genes obtained from TaqMan Arrays. Genes are sorted according to decreasing z-scores in the Ev-carrying cells under normoxia. (B) Heatmaps showing the z-scores of EMT genes expression levels in SKOV3 cells carrying EV or plasmid overexpressing PLD2 under normoxia conditions and carrying EV or plasmid expressing shPLD2 under hypoxia condition. Hierarchical clustering of the samples is shown. (C) Expression levels of SNAI1, VIM, CDH1 and CDH2 EMT-associated genes in cells carrying Ev or plasmids expressing PLD2 under normoxia or shPLD2 under hypoxia conditions. (D) Left, representative images of the Boyden chamber migration assays in SKOV3 and OVCAR8 cells carrying Ev or plasmids expressing PLD2 or shPLD2 under normoxic or hypoxic conditions. Right, quantification of the Boyden chamber migration assays. A minimum of three independent experiments were performed and the data were analyzed using Student’s t-test. *, P < 0.05; **, P < 0.01; ***, P < 0.001., Supplementary Material 1: Essential role of PLD2 in hypoxia-induced stemness and therapy resistance in ovarian tumors., Funding: Consejo Superior de Investigaciones Cientificas (CSIC)., Peer reviewed

Experimental studies and clinical trials of nanoparticles for treating diseases are increasing continuously. However, the reach to the market does not correlate with these efforts due to the enormous cost, several years of development, and off-target effects like cardiotoxicity. Multicellular organisms such as the Caenorhabditis elegans (C. elegans) can bridge the gap between in vitro and vertebrate testing as they can provide extensive information on systemic toxicity and specific harmful effects through facile experimentation following 3R EU directives on animal use. Since the nematodes’ pharynx shares similarities with the human heart, we assessed the general and pharyngeal effects of drugs and polypyrrole nanoparticles (Ppy NPs) using C. elegans. The evaluation of FDA-approved drugs, such as Propranolol and Racepinephrine reproduced the arrhythmic behavior reported in humans and supported the use of this small animal model. Consequently, Ppy NPs were evaluated due to their research interest in cardiac arrhythmia treatments. The NPs’ biocompatibility was confirmed by assessing survival, growth and development, reproduction, and transgenerational toxicity in C. elegans. Interestingly, the NPs increased the pharyngeal pumping rate of C. elegans in two slow-pumping mutant strains, JD21 and DA464. Moreover, the NPs increased the pumping rate over time, which sustained up to a day post-excretion. By measuring pharyngeal calcium levels, we found that the impact of Ppy NPs on the pumping rate could be mediated through calcium signaling. Thus, evaluating arrhythmic effects in C. elegans offers a simple system to test drugs and nanoparticles, as elucidated through Ppy NPs., Peer reviewed

Coordinators: Natividad Benito (GEIO-SEIMC), Juan Carlos Martínez-Pastor (SECOT), Jesús Saavedra-Lozano (SEIP).-- Methods: Eighteen clinical questions were formulated under the three major headings of Diagnostics, Therapeutics and Follow-up. The coordinators assigned each clinical question to a subgroup of panellists. For each clinical question, all significant scientific literature was reviewed and summarised in comprehensive tables following the PICO system (P– Populations/People/Patient/Problem; I– Intervention(s); C-Comparison; O– Outcome). The criteria used to evaluate the strength of the recommendation and the quality of the evidence are summarised in Table 1. The coordinators wrote the first draft based on the sections submitted by each subgroup of panellists. The draft was reviewed by all members of the panel of experts, controversial issues were debated, and a final version was prepared. Before its final approval, the document was posted on the SEIMC intranet and left open for suggestions and comments from members. All authors have approved the contents of the document and the final recommendations. Possible conflicts of interest associated with all members of the panel of experts are listed at the end of the document., Infectious arthritis is an infection of one or more joints caused by bacteria (including mycobacteria), fungi and viruses. Only a small number of viruses directly infect the joint, mainly as part of a self-limiting systemic infection. While the term septic arthritis (SA) usually refers to bacterial or fungal arthritis, bacteria are by far the most common agents of these infections. The annual incidence of SA is between 2 and 7 cases per 100,000 inhabitants in most Western European series. 2–7 Higher rates have been observed in other areas, such as Australasia. 8–10 Even though SA remains relatively rare in the general population, the overall incidence is increasing, due to the rising rate among older patients, who have more underlying co-morbidities and joint disorders, undergo more invasive procedures with increased use of immunosuppressive treatments., Peer reviewed

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Microglial cells are recognized as very dynamic brain cells, screening the environment and sensitive to signals from all other cell types in health and disease. Apolipoprotein D (ApoD), a lipid-binding protein of the Lipocalin family, is required for nervous system optimal function and proper development and maintenance of key neural structures. ApoD has a cell and state-dependent expression in the healthy nervous system, and increases its expression upon aging, damage or neurodegeneration. An extensive overlap exists between processes where ApoD is involved and those where microglia have an active role. However, no study has analyzed the role of ApoD in microglial responses. In this work, we test the hypothesis that ApoD, as an extracellular signal, participates in the intercellular crosstalk sensed by microglia and impacts their responses upon physiological aging or damaging conditions. We find that a significant proportion of ApoD-dependent aging transcriptome are microglia-specific genes, and show that lack of ApoD in vivo dysregulates microglial density in mouse hippocampus in an age-dependent manner. Murine BV2 and primary microglia do not express ApoD, but it can be internalized and targeted to lysosomes, where unlike other cell types it is transiently present. Cytokine secretion profiles and myelin phagocytosis reveal that ApoD has both long-term pre-conditioning effects on microglia as well as acute effects on these microglial immune functions, without significant modification of cell survival. ApoD-triggered cytokine signatures are stimuli (paraquat vs. Aβ oligomers) and sex-dependent. Acute exposure to ApoD induces microglia to switch from their resting state to a secretory and less phagocytic phenotype, while long-term absence of ApoD leads to attenuated cytokine induction and increased myelin uptake, supporting a role for ApoD as priming or immune training factor. This knowledge should help to advance our understanding of the complex responses of microglia during aging and neurodegeneration, where signals received along our lifespan are combined with damage-triggered acute signals, conditioning both beneficial roles and limitations of microglial functions., Peer reviewed

S1 Fig. Workflow for the manual and automatic text-mining searches. A starting list of 6034 mammal viruses was used to obtain, which were then reviewed using both manual and PubmedKB-based automated strategies. The resulting virus-receptor pairs were combined with known databases and manually curated (see text for full description). M, manual strategy; PKB, PubmedKB strategy., S2 Fig. Roles of known receptors according to viral family. Only families with at least 10 known virus-host interactions are represented. Families of enveloped and non-enveloped viruses are shown, and within each group, families are sorted by the fraction of known receptors that are sufficient for viral entry (main plus alternative receptors)., S3 Fig. Research effort versus the known number of host proteins used as receptors for different viral families. Data points correspond to individual viruses. Those corresponding to the indicated family are shown in color (blue for non-enveloped viruses; yellow for enveloped viruses), and grey points correspond to all other viruses. The colored and grey dashed lines show the GLM prediction obtained specifically for the family and all viruses, respectively. Only families with at least 5 viral species in the dataset were considered., S1 Table. Database of virus receptors generated in this study. The following information is provided: the viral species, number of PubMed records and Genbank sequences available for each virus, viral family, presence of an envelope, number of host species, receptor symbol, receptor nature, functional role of the receptor, corresponding gene symbol, original publication PMID, year of discovery, and whether the virus-receptor interaction was reported in previous reviews and databases., S2 Table. Scores obtained from the GBM. The gene symbol, assigned score (probability of being a receptor), and whether the corresponding protein is a known receptor are indicated., S3 Table. Relevant features identified by the GBM. Gain represents the relative contribution of each variable to the model prediction. Cover indicates the relative number of observations that are related to a given variable., Peer reviewed

S1 Fig. Time of exposure. We tested the effect of the exposure time to CPC. To do so, IAV/WSN/33 was incubated with CPC at 0.1% for 2 minutes, 1 minute, or 30 seconds. Next, the virus was diluted and used to infect MDCK cells as previously described. After 48 hours of infection, the viral load was assessed by TCID50. We used a 2-minute treatment with SDS at 0.05% as a positive control and PBS solution as a negative control. No differences in the viral load were observed between the 2 minutes, 1 minute, or 30 seconds exposure., Peer reviewed

Figure S1: Differences in the composition of each donor attributed to protocol variations; Table S1: Abundance matrix displaying viral counts across samples. Table S2: Relative abundance of the viral families identified for each sample in the study, used for Figure 3., Peer reviewed