LDL's unexpected travel partners in the road to atherosclerosis.

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High-density lipoprotein remodelled in hypercholesterolaemic blood induce epigenetically driven down-regulation of endothelial HIF-1α expression in a preclinical animal model

Altres ajuts: This work was supported by the Plan Nacional de Salud (PNS) from the Spanish Ministry of Science and Innovation and funds FEDER 'Una Manera de Hacer Europa'; a grant from the Spanish Society of Cardiology (Beca FEC Investigacio'n Ba'sica/2016 to G.V); the Instituto de Salud Carlos III (ISCIII); and CIBERCV (to L.B). We thank the support of the Generalitat of Catalunya (Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat,) and the Fundación Investigación Cardiovascular-Fundación Jesus Serra for their continuous support.High-density lipoproteins (HDLs) are circulating micelles that transport proteins, lipids, and miRNAs. HDL-transported miRNAs (HDL-miRNAs) have lately received attention but their effects on vascular cells are not fully understood. Additionally, whether cardiovascular risk factors affect HDL-miRNAs levels and miRNA transfer to recipient cells remains equally poorly known. Here, we have investigated the changes induced by hypercholesterolaemia on HDL-miRNA levels and its effect on recipient endothelial cells (ECs). Pigs were kept on a high-fat diet (HC; n = 10) or a normocholesterolaemic chow (NC; n = 10) for 10 days reaching cholesterol levels of 321.0 (229.7-378.5) mg/dL and 74.0 (62.5-80.2) mg/dL, respectively. HDL particles were isolated, purified, and quantified. HDL-miRNA profiling (n = 149 miRNAs) of HC- and NC-HDLs was performed by multipanel qPCR. Cell cultures of porcine aortic ECs were used to determine whether HDL-miRNAs were delivered to ECs. Potential target genes modulated by miRNAs were identified by bioinformatics and candidate miRNAs were validated by molecular analysis. In vivo effects in the coronary arteries of normocholesterolaemic swine administered HC- or NC-HDLs were analysed. Among the HDL-miRNAs, four were found in different amounts in HC- and NC-HDL (P < 0.05). miR-126-5p and -3p and miR-30b-5p (2.7×, 1.7×, and 1.3×, respectively) were found in higher levels and miR-103a-3p and miR-let-7g-5p (−1.6×, −1.4×, respectively) in lower levels in HC-HDL. miR-126-5p and -3p were transferred from HC-HDL to EC (2.5×; P < 0.05), but not from NC-HDL, by a SRB1-mediated mechanism. Bioinformatics revealed that HIF1α was the miR-126 target gene with the highest predictive value, which was accordingly found to be markedly reduced in HC-HDL-treated ECs and in miR126 mimic transfected ECs. In vivo validation confirmed that HIF1α was diminished in the coronary endothelial layer of NC pigs administered HC-HDL vs. those administered NC-HDL (P < 0.05). Hypercholesterolaemia induces changes in the miRNA content of HDL enhancing miR126 and its delivery to ECs with the consequent down-regulation of its target gene HIF1α

Assessment of in vivo versus in vitro biofilm formation of clinical methicillin-resistant Staphylococcus aureus isolates from endotracheal tubes

Our aim was to demonstrate that biofilm formation in a clinical strain of methicillin-resistant Staphylococcus aureus (MRSA) can be enhanced by environment exposure in an endotracheal tube (ETT) and to determine how it is affected by systemic treatment and atmospheric conditions. Second, we aimed to assess biofilm production dynamics after extubation. We prospectively analyzed 70 ETT samples obtained from pigs randomized to be untreated (controls, n = 20), or treated with vancomycin (n = 32) or linezolid (n = 18). A clinical MRSA strain (MRSA-in) was inoculated in pigs to create a pneumonia model, before treating with antibiotics. Tracheally intubated pigs with MRSA severe pneumonia, were mechanically ventilated for 69 ± 16 hours. All MRSA isolates retrieved from ETTs (ETT-MRSA) were tested for their in vitro biofilm production by microtiter plate assay. In vitro biofilm production of MRSA isolates was sequentially studied over the next 8 days post-extubation to assess biofilm capability dynamics over time. All experiments were performed under ambient air (O2) or ambient air supplemented with 5% CO2. We collected 52 ETT-MRSA isolates (placebo N = 19, linezolid N = 11, and vancomycin N = 22) that were clonally identical to the MRSA-in. Among the ETT-MRSA isolates, biofilm production more than doubled after extubation in 40% and 50% under 5% CO2 and O2, respectively. Systemic antibiotic treatment during intubation did not affect this outcome. Under both atmospheric conditions, biofilm production for MRSA-in was at least doubled for 9 ETT-MRSA isolates, and assessment of these showed that biofilm production decreased progressively over a 4-day period after extubation. In conclusion, a weak biofilm producer MRSA strain significantly enhances its biofilm production within an ETT, but it is influenced by the ETT environment rather than by the systemic treatment used during intubation or by the atmospheric conditions used for bacterial growth

Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes

The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related disease outcomes. Here, we introduce multi-response Mendelian randomization (MR2), an MR method specifically designed for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses. MR2 uses a sparse Bayesian Gaussian copula regression framework to detect causal effects while estimating the residual correlation between summary-level outcomes, i.e., the correlation that cannot be explained by the exposures, and vice versa. We show both theoretically and in a comprehensive simulation study how unmeasured shared pleiotropy induces residual correlation between outcomes irrespective of sample overlap. We also reveal how non-genetic factors that affect more than one outcome contribute to their correlation. We demonstrate that by accounting for residual correlation, MR2 has higher power to detect shared exposures causing more than one outcome. It also provides more accurate causal effect estimates than existing methods that ignore the dependence between related responses. Finally, we illustrate how MR2 detects shared and distinct causal exposures for five cardiovascular diseases in two applications considering cardiometabolic and lipidomic exposures and uncovers residual correlation between summary-level outcomes reflecting known relationships between cardiovascular diseases

The science behind soft skills: Do’s and Don’ts for early career researchers and beyond. A review paper from the EU-CardioRNA COST Action CA17129

peer reviewedSoft skills are the elementary management, personal, and interpersonal abilities that are vital for an individual to be efficient at workplace or in their personal life. Each work place requires different set of soft skills. Thus, in addition to scientific/technical skills that are easier to access within a short time frame, several key soft skills are essential for the success of a researcher in today’s international work environment. In this paper, the trainees and trainers of the EU-CardioRNA COST Action CA17129 training school on soft skills present basic and advanced soft skills for early career researchers. Here, we particularly emphasize on the importance of transferable and presentation skills, ethics, literature reading and reviewing, research protocol and grant writing, networking, and career opportunities for researchers. All these skills are vital but are often overlooked by some scholars. We also provide tips to ace in aforementioned skills that are crucial in a day-to-day life of early and late career researchers in academia and industry.</ns4:p

Post-Genomic Methodologies and Preclinical Animal Models: Chances for the Translation of Cardioprotection to the Clinic

Although many cardioprotective strategies have demonstrated benefits in animal models of myocardial infarction, they have failed to demonstrate cardioprotection in the clinical setting highlighting that new therapeutic target and treatment strategies aimed at reducing infarct size are urgently needed. Completion of the Human Genome Project in 2001 fostered the post-genomic research era with the consequent development of high-throughput &#8220;omics&#8222; platforms including transcriptomics, proteomics, and metabolomics. Implementation of these holistic approaches within the field of cardioprotection has enlarged our understanding of ischemia/reperfusion injury with each approach capturing a different angle of the global picture of the disease. It has also contributed to identify potential prognostic/diagnostic biomarkers and discover novel molecular therapeutic targets. In this latter regard, &#8220;omic&#8222; data analysis in the setting of ischemic conditioning has allowed depicting potential therapeutic candidates, including non-coding RNAs and molecular chaperones, amenable to pharmacological development. Such discoveries must be tested and validated in a relevant and reliable myocardial infarction animal model before moving towards the clinical setting. Moreover, efforts should also focus on integrating all &#8220;omic&#8222; datasets rather than working exclusively on a single &#8220;omic&#8222; approach. In the following manuscript, we will discuss the power of implementing &#8220;omic&#8222; approaches in preclinical animal models to identify novel molecular targets for cardioprotection of interest for drug development

Advances in HDL : Much More than Lipid Transporters

High Density Lipoprotein (HDL) particles, beyond serving as lipid transporters and playing a key role in reverse cholesterol transport, carry a highly variable number of proteins, micro-RNAs, vitamins, and hormones, which endow them with the ability to mediate a plethora of cellular and molecular mechanisms that promote cardiovascular health. It is becoming increasingly evident, however, that the presence of cardiovascular risk factors and co-morbidities alters HDLs cargo and protective functions. This concept has led to the notion that metrics other than HDL-cholesterol levels, such as HDL functionality and composition, may better capture HDL cardiovascular protection. On the other hand, the potential of HDL as natural delivery carriers has also fostered the design of engineered HDL-mimetics aiming to improve HDL efficacy or as drug-delivery agents with therapeutic potential. In this paper, we first provide an overview of the molecules known to be transported by HDL particles and mainly discuss their functions in the cardiovascular system. Second, we describe the impact of cardiovascular risk factors and co-morbidities on HDL remodeling. Finally, we review the currently developed HDL-based approaches

Post-Genomic Methodologies and Preclinical Animal Models : Chances for the Translation of Cardioprotection to the Clinic

Although many cardioprotective strategies have demonstrated benefits in animal models of myocardial infarction, they have failed to demonstrate cardioprotection in the clinical setting highlighting that new therapeutic target and treatment strategies aimed at reducing infarct size are urgently needed. Completion of the Human Genome Project in 2001 fostered the post-genomic research era with the consequent development of high-throughput "omics" platforms including transcriptomics, proteomics, and metabolomics. Implementation of these holistic approaches within the field of cardioprotection has enlarged our understanding of ischemia/reperfusion injury with each approach capturing a different angle of the global picture of the disease. It has also contributed to identify potential prognostic/diagnostic biomarkers and discover novel molecular therapeutic targets. In this latter regard, "omic" data analysis in the setting of ischemic conditioning has allowed depicting potential therapeutic candidates, including non-coding RNAs and molecular chaperones, amenable to pharmacological development. Such discoveries must be tested and validated in a relevant and reliable myocardial infarction animal model before moving towards the clinical setting. Moreover, efforts should also focus on integrating all "omic" datasets rather than working exclusively on a single "omic" approach. In the following manuscript, we will discuss the power of implementing "omic" approaches in preclinical animal models to identify novel molecular targets for cardioprotection of interest for drug development

Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes.

The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related disease outcomes. Here, we introduce multi-response Mendelian randomization (MR2), an MR method specifically designed for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses. MR2 uses a sparse Bayesian Gaussian copula regression framework to detect causal effects while estimating the residual correlation between summary-level outcomes, i.e., the correlation that cannot be explained by the exposures, and vice versa. We show both theoretically and in a comprehensive simulation study how unmeasured shared pleiotropy induces residual correlation between outcomes irrespective of sample overlap. We also reveal how non-genetic factors that affect more than one outcome contribute to their correlation. We demonstrate that by accounting for residual correlation, MR2 has higher power to detect shared exposures causing more than one outcome. It also provides more accurate causal effect estimates than existing methods that ignore the dependence between related responses. Finally, we illustrate how MR2 detects shared and distinct causal exposures for five cardiovascular diseases in two applications considering cardiometabolic and lipidomic exposures and uncovers residual correlation between summary-level outcomes reflecting known relationships between cardiovascular diseases