Identification of LPS-Activated Endothelial Subpopulations With Distinct Inflammatory Phenotypes and Regulatory Signaling Mechanisms

Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Endothelial cells (EC) are actively involved in sepsis-associated (micro)vascular disturbances and subsequent organ dysfunction. Lipopolysaccharide (LPS), a Gram-negative bacterial product, can activate EC leading to the expression of pro-inflammatory molecules. This process is molecularly regulated by specific receptors and distinct, yet poorly understood intracellular signaling pathways. LPS-induced expression of endothelial adhesion molecules E-selectin and VCAM-1 in mice was previously shown to be organ- and microvascular-specific. Here we report that also within renal microvascular beds the endothelium expresses different extents of E-selectin and VCAM-1. This heterogeneity was recapitulated in vitro in LPS-activated human umbilical vein EC (HUVEC). Within 2 h after LPS exposure, four distinct HUVEC subpopulations were visible by flow cytometric analysis detecting E-selectin and VCAM-1 protein. These encompassed E-selectin−/VCAM-1− (–/–), E-selectin+/VCAM-1− (E-sel+), E-selectin+/VCAM-1+ (+/+), and E-selectin−/VCAM-1+ (VCAM-1+) subpopulations. The formation of subpopulations was a common response of endothelial cells to LPS challenge. Using fluorescence-activated cell sorting (FACS) we demonstrated that the +/+ subpopulation also expressed the highest levels of inflammatory cytokines and chemokines. The differences in responsiveness of EC subpopulations could not be explained by differential expression of LPS receptors TLR4 and RIG-I. Functional studies, however, demonstrated that the formation of the E-sel+ subpopulation was mainly TLR4-mediated, while the formation of the +/+ subpopulation was mediated by both TLR4 and RIG-I. Pharmacological blockade of NF-κB and p38 MAPK furthermore revealed a prominent role of their signaling cascades in E-sel+ and +/+ subpopulation formation. In contrast, the VCAM-1+ subpopulation was not controlled by any of these signaling pathways. Noteworthy is the existence of a “quiescent” subpopulation that was devoid of the two adhesion molecules and did not express cytokines or chemokines despite LPS exposure. Summarizing, our findings suggest that LPS activates different signaling mechanisms in EC that drive heterogeneous expression of EC inflammatory molecules. Further characterization of the signaling pathways involved will enhance our understanding of endothelial heterogeneous responses to sepsis related stimuli and enable the future design of effective therapeutic strategies to interfere in these processes to counteract sepsis-associated organ dysfunction

Mediators of Obesity Do Not Influence SARS-CoV-2 Infection or Activation of Primary Human Lung Microvascular Endothelial Cells In Vitro

Clinical observations have shown that obesity is associated with the severe outcome of SARS-CoV-2 infection hallmarked by microvascular dysfunction in the lungs and other organs. Excess visceral fat and high systemic levels of adipose tissue (AT) derived mediators such as leptin and other adipokines have also been linked to endothelial dysfunction. Consequently, we hypothesized that AT-derived mediators may exacerbate microvascular dysfunction during of SARS-CoV-2 infection and tested this in a primary human lung microvascular endothelial (HLMVEC) cell model. Our results indicate that HLMVEC are not susceptible to SARS-CoV-2 infection since no expression of viral proteins and no newly produced virus was detected. In addition, exposure to the virus did not induce endothelial activation as evidenced by a lack of adhesion molecule, E-selectin, VCAM-1, ICAM-1, and inflammatory cytokine IL-6 induction. Incubation of endothelial cells with the pro-inflammatory AT-derived mediator, leptin, prior to virus inoculation, did not alter the expression of endothelial SARS-CoV-2 entry receptors and did not alter their susceptibility to infection. Furthermore, it did not induce inflammatory activation of endothelial cells. To verify if the lack of activated phenotype in the presence of adipokines was not leptin-specific, we exposed endothelial cells to plasma obtained from critically ill obese COVID-19 patients. Plasma exposure did not result in E-selectin, VCAM-1, ICAM-1, or IL-6 induction. Together our results strongly suggest that aberrant inflammatory endothelial responses are not mounted by direct SARS-CoV-2 infection of endothelial cells, even in the presence of leptin and other mediators of obesity. Instead, endothelial activation associated with COVID-19 is likely a result of inflammatory responses initiated by other cells. Further studies are required to investigate the mechanisms regulating endothelial behavior in COVID-19 and the mechanisms driving severe disease in obese individuals

Accelerated menopausal changes as human disease model 'FOCUM' for the development of osteoarthritis and other degenerative disorders:protocol for a prospective cohort study

INTRODUCTION: The incidence of degenerative disorders, including osteoarthritis (OA), increases rapidly in women after menopause. However, the influence of the menopause is still insufficiently investigated due to the slowness of menopausal transition. In this study, a novel human model is used in which it is expected that menopausal-related changes will occur faster. This is the Females discontinuing Oral Contraceptives Use at Menopausal age model. The ultimate aim is to link these changes to OA and other degenerative disorders, including cardiovascular diseases, diabetes, osteoporosis and tendinopathies. METHODS AND ANALYSIS: This is a pilot observational prospective cohort study with 2 years of follow-up. Fifty women aged 50–60 who use oral contraceptive (OC) and have the intention to stop are included. Measurements are performed once before stopping OC, and four times thereafter at 6 weeks, 6 months, 1 year and 2 years. At every time point, a questionnaire is filled in and a sample of blood is drawn. At the first and final time points, a physical examination, hand radiographs and a MRI scan of one knee are performed. The primary OA outcome is progression of the MRI Osteoarthritis Knee Score. Secondary OA outcomes are the development of clinical knee and hand OA, development of knee OA according to the MRI definition, and progression of radiographic features for hand OA. Principal component analysis will be used to assess which changes occur after stopping OC. Univariate and multivariate generalised estimating equation models will be used to test for associations between these components and OA. ETHICS AND DISSEMINATION: The study has been approved by the Medical Ethics Committee of the Erasmus MC University Medical Center Rotterdam (MEC-2019-0592). All participants must give informed consent before data collection. Results will be disseminated in national and international journals. TRIAL REGISTRATION NUMBER: NL70796.078.19

Machine learning for determining lateral flow device results for testing of SARS-CoV-2 infection in asymptomatic populations

Rapid antigen tests, in the form of lateral flow devices (LFD) allow testing of a large population for SARS-CoV-2. To reduce the variability seen in device interpretation, we show the design and testing of an AI algorithm based on machine learning. The machine learning (ML) algorithm is trained on a combination of artificially hybridised LFDs and LFD data linked to RT-qPCR result. Participants are recruited from assisted test sites (ATS) and health care workers undertaking self-testing and images analysed using the ML algorithm. A panel of trained clinicians are used to resolve discrepancies. In total, 115,316 images are returned. In the ATS sub study, sensitivity increased from 92.08% to 97.6% and specificity from 99.85% to 99.99%. In the self-read sub-study, sensitivity increased from 16.00% to 100%, and specificity from 99.15% to 99.40%. An ML-based classifier of LFD results outperforms human reads in asymptomatic testing sites and self-reading

Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2x)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted

Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking

Refining Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Genetic Loci by Integrating Summary Data From Genome-wide Association, Gene Expression, and DNA Methylation Studies

Background: Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels. Methods: We applied summary-data–based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data–based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels. Results: We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before. Conclusions: Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies

DNA methylation signatures of aggression and closely related constructs : A meta-analysis of epigenome-wide studies across the lifespan

DNA methylation profiles of aggressive behavior may capture lifetime cumulative effects of genetic, stochastic, and environmental influences associated with aggression. Here, we report the first large meta-analysis of epigenome-wide association studies (EWAS) of aggressive behavior (N = 15,324 participants). In peripheral blood samples of 14,434 participants from 18 cohorts with mean ages ranging from 7 to 68 years, 13 methylation sites were significantly associated with aggression (alpha = 1.2 x 10(-7); Bonferroni correction). In cord blood samples of 2425 children from five cohorts with aggression assessed at mean ages ranging from 4 to 7 years, 83% of these sites showed the same direction of association with childhood aggression (r = 0.74, p = 0.006) but no epigenome-wide significant sites were found. Top-sites (48 at a false discovery rate of 5% in the peripheral blood meta-analysis or in a combined meta-analysis of peripheral blood and cord blood) have been associated with chemical exposures, smoking, cognition, metabolic traits, and genetic variation (mQTLs). Three genes whose expression levels were associated with top-sites were previously linked to schizophrenia and general risk tolerance. At six CpGs, DNA methylation variation in blood mirrors variation in the brain. On average 44% (range = 3-82%) of the aggression-methylation association was explained by current and former smoking and BMI. These findings point at loci that are sensitive to chemical exposures with potential implications for neuronal functions. We hope these results to be a starting point for studies leading to applications as peripheral biomarkers and to reveal causal relationships with aggression and related traits.Peer reviewe

Riverine flood risk screening with a simple network-based approach: A proof of concept in the Ganghes-Brahmaputra basin

Floods cause major problems around the world. Over 35 million people were affected by floods in 2018. They have a growing worldwide impact on life and property. Changes in climate conditions lead to unanticipated variations in glacial runoffs, snowmelt and precipitation, which all significantly changing river flows. An imbalance in river network equilibrium leads to flooding and often ends up causing tremendous damage to society and the environment. Regions that are perceived to be downstream from the source of flooding may end up taking the brunt of the river force due to flood cascades. Floods account for about a third of all natural catastrophes worldwide, they cause more than half of all fatalities and are responsible for a third of the overall economic loss.Modelling approaches are often used to determine flood consequences. Two types of flood models are commonly used: statistical models and flow simulation models. Statistical methods are easy to use but provide limited insight into flood problems. Flow simulation models’ results can be very accurate, especially for hydraulic simulation models. However, these models are expensive to use and develop, and they require a lot of data. These requirements make them unsuitable for application in developing countries and analysing large watersheds. Flood risk screening models try to solve these problems. They are suitable for use in data-sparse regions and are efficient in terms of omputational costs. However, there is a lack of knowledge between river structure and cascading flood effects, and there is a lack of models that are efficient, easy to understand, use topological data and have the purpose of risk screening. In this research, we show a flood model based on complex network theory to efficiently study the cascading effects of floods in riverine systems. Cascading effects are defined as floods that occur as a result of water waves through the system that originate from upstream sources. The developed model uses the hydrological Muskingum routing method. We found that it was possible, notwithstanding many assumptions and a lack of data, to reproduce system behaviour during an extreme flood event in the Ganges-Brahmaputra Basin. Satellite elevation data were used to construct the river network, and satellite precipitation data was used to feed the model. The model can indicate high risk reaches based on the simulated overflow, the flow exceeding a predefined capacity. No existing models are known that can do this, on a laptop, within seven min- utes per simulated day, with limited data for a watershed that exceeds the size of one million square kilometres. The network structure of the model makes it possible to achieve a better understanding between river typology and cascading flood effects. The model is not without its limitations. It cannot pinpoint when and where floods will occur, because it only calculates overflow. Moreover, flood failure mechanisms are not yet included in this model. Failure mechanisms will change model behaviour: when a flood occurs water temporarily leaves the system, which reduces downstream risk. Overflow cascades, therefore, would be shorter in reality than in this model. The model is a proof of concept that shows the potential of a network theory-based risk screening method in flood simulation context. Its properties make it suitable for analysing the effects of changing precipitation patterns, which, for example, could originate from climate change studies. Another use case is real-time forecasting of discharge levels if the mode is combined with real-time discharge levels and precipitations forecasts. The model can be used as an early warning system: alerting when and where high discharge levels are expected. We anticipate our model to be a starting point for policy screening and scenario analysis. Sugges- tions are made to include policy options within the model. Policy analysts can then use the model to compare different policy interventions for all kinds of (future) scenarios. The model should not be seen as a replacement of the advanced hydraulic simulation models, but as a complementary tool useful at an earlier moment in a design process with the purpose of screening options. Ultimately it can become a framework with the aim to support informed decision-making.https://github.com/bcvanmeurs/rnaEngineering and Policy Analysi

Identification of LPS-Activated Endothelial Subpopulations With Distinct Inflammatory Phenotypes and Regulatory Signaling Mechanisms

Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Endothelial cells (EC) are actively involved in sepsis-associated (micro) vascular disturbances and subsequent organ dysfunction. Lipopolysaccharide (LPS), a Gram-negative bacterial product, can activate EC leading to the expression of pro-inflammatory molecules. This process is molecularly regulated by specific receptors and distinct, yet poorly understood intracellular signaling pathways. LPS-induced expression of endothelial adhesion molecules E-selectin and VCAM-1 in mice was previously shown to be organ-andmicrovascular-specific. Here we report that also within renal microvascular beds the endothelium expresses different extents of E-selectin and VCAM-1. This heterogeneity was recapitulated in vitro in LPS-activated human umbilical vein EC (HUVEC). Within 2 h after LPS exposure, four distinct HUVEC subpopulations were visible by flow cytometric analysis detecting E-selectin and VCAM-1 protein. These encompassed E-selectin(-)/VCAM-1(-) (-/-), E-selectin(+)/VCAM-1(-) (E-sel+), E-selectin(+)/VCAM-1(+) (+/+), and E-selectin(-)/VCAM-1(+) (VCAM-1+) subpopulations. The formation of subpopulations was a common response of endothelial cells to LPS challenge. Using fluorescence-activated cell sorting (FACS) we demonstrated that the +/+ subpopulation also expressed the highest levels of inflammatory cytokines and chemokines. The differences in responsiveness of EC subpopulations could not be explained by differential expression of LPS receptors TLR4 and RIG-I. Functional studies, however, demonstrated that the formation of the E-sel+ subpopulation was mainly TLR4-mediated, while the formation of the +/+ subpopulation was mediated by both TLR4 and RIG-I. Pharmacological blockade of NF-kappa B and p38 MAPK furthermore revealed a prominent role of their signaling cascades in E-sel+ and +/+ subpopulation formation. In contrast, the VCAM-1+ subpopulation was not controlled by any of these signaling pathways. Noteworthy is the existence of a "quiescent" subpopulation that was devoid of the two adhesion molecules and did not express cytokines or chemokines despite LPS exposure. Summarizing, our findings suggest that LPS activates different signaling mechanisms in EC that drive heterogeneous expression of EC inflammatory molecules. Further characterization of the signaling pathways involved will enhance our understanding of endothelial heterogeneous responses to sepsis related stimuli and enable the future design of effective therapeutic strategies to interfere in these processes to counteract sepsis-associated organ dysfunction