Markers of NETosis and DAMPs are altered in critically ill COVID-19 patients

Background Coronavirus disease 19 (COVID-19) is known to present with disease severities of varying degree. In its most severe form, infection may lead to respiratory failure and multi-organ dysfunction. Here we study the levels of extracellular histone H3 (H3), neutrophil elastase (NE) and cfDNA in relation to other plasma parameters, including the immune modulators GAS6 and AXL, ICU scoring systems and mortality in patients with severe COVID-19. Methods We measured plasma H3, NE, cfDNA, GAS6 and AXL concentration in plasma of 83 COVID-19-positive and 11 COVID-19-negative patients at admission to the Intensive Care Unit (ICU) at the Uppsala University hospital, a tertiary hospital in Sweden and a total of 333 samples obtained from these patients during the ICU-stay. We determined their correlation with disease severity, organ failure, mortality and other blood parameters. Results H3, NE, cfDNA, GAS6 and AXL were increased in plasma of COVID-19 patients compared to controls. cfDNA and GAS6 decreased in time in in patients surviving to 30 days post ICU admission. Plasma H3 was a common feature of COVID-19 patients, detected in 40% of the patients at ICU admission. Although these measures were not predictive of the final outcome of the disease, they correlated well with parameters of tissue damage (H3 and cfDNA) and neutrophil counts (NE). A subset of samples displayed H3 processing, possibly due to proteolysis. Conclusions Elevated H3 and cfDNA levels in COVID-19 patients illustrate the severity of the cellular damage observed in critically ill COVID-19 patients. The increase in NE indicates the important role of neutrophil response and the process of NETosis in the disease. GAS6 appears as part of an early activated mechanism of response in Covid-19.The study was supported through grants from the dedSciLifeLab/KAW national COVID-19 research program project grant (MH), by Scilifelab, the Knut and Alice Wallenberg Foundation and in part by the Swedish Research Council (RF, grant no 2014-02569 and 2014-07606), and the Netherlands Thrombosis Foundation (GN).N

Presence and evolution of NET markers and DAMPS in critically ill COVID-19 patients

Resumen del trabajo presentado en el 4th European Congress on Thrombosis and Haemostasis, celebrado en Gante (Bélgica), los días 14 y 15 de octubre de 2021Background: The coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection presents with a wide range of disease symptoms. In the more severe patients, COVID-19 is associated with respiratory failure, neutrophil extracellular trap (NET) formation, and multiple organ failure (MOF). Aims: We investigated the presence and evolution of several damage associated molecular patterns (DAMPs) neutrophil markers and immune modulators in a group of 100 COVID-19-positive ICU patients. Methods: Citrated plasma was collected from adult patients with confirmed COVID-19 by PCR detection of SARS-CoV-2 E and N-genes in nasopharyngeal swabs admitted to the intensive care unit (ICU) at Uppsala University hospital, Sweden. Written informed consent was obtained from the patients, or next of kin if the patient was unable to give consent. The Declaration of Helsinki and its subsequent revisions were followed. Plasma concentration of cell free DNA (cfDNA), extracellular histone H3 (H3), neutrophil elastase (NE), myeloperoxidase (MPO) and the cfDNA-MPO complex, and the immune modulators GAS6, and sAXL were measured in all COVID-19-positive and in COVID-19-negative patients and healthy controls. We determined marker levels upon admission, of their evolution, and correlation with disease severity, organ failure, thromboembolic events, mortality, and other blood parameters. Results: The level of cfDNA, H3, NE, MPO, cfDNA-MPO complex, GAS6, and sAXL were all significantly increased in plasma of COVID-19 patients compared to controls. Importantly, a diminution of cfDNA and GAS6 levels over time was observed in patients surviving 30 days after ICU admission. Histone H3 levels were detected in 40% of the COVID-19 patient plasma at ICU admission and the presence of histone H3 during ICU stay was associated with an increased risk of thromboembolic events and secondary infection. Though NET markers were not predictive of 30-day mortality, they correlated with several parameters of tissue damage and neutrophil counts. Summary/Conclusion: The increased presence of cfDNA, H3 and NE, MPO, and MPO-DNA illustrates the severity of cellular damage and indicates activation of NETosis in severe COVID-19 ICU patients. The evolution of cfDNA and Gas6 is able to predict disease prognosis of severely ill COVID-19 patients, where GAS6 appears to be part of an early activated mechanism in response to COVID-19. These data support treatment aimed at the reduction of NET formation in severe COVID-19 patients

Decreased renal perfusion during acute kidney injury in critical COVID-19 assessed by magnetic resonance imaging: a prospective case control study

Abstract: Background: Renal hypoperfusion has been suggested to contribute to the development of acute kidney injury (AKI) in critical COVID-19. However, limited data exist to support this. We aim to investigate the differences in renal perfusion, oxygenation and water diffusion using multiparametric magnetic resonance imaging in critically ill COVID-19 patients with and without AKI. Methods: A prospective case–control study where patients without prior kidney disease treated in intensive care for respiratory failure due to COVID-19 were examined. Kidney Disease: Improving Global Outcomes Creatinine criteria were used for group allocation. Main comparisons were tested using Mann–Whitney U test. Results: Nineteen patients were examined, ten with AKI and nine without AKI. Patients with AKI were examined in median 1 [0–2] day after criteria fulfillment. Age and baseline Plasma-Creatinine were similar in both groups. Total renal blood flow was lower in patients with AKI compared with patients without (median 645 quartile range [423–753] vs. 859 [746–920] ml/min, p = 0.037). Regional perfusion was reduced in both cortex (76 [51–112] vs. 146 [123–169] ml/100 g/min, p = 0.015) and medulla (28 [18–47] vs. 47 [38–73] ml/100 g/min, p = 0.03). Renal venous saturation was similar in both groups (72% [64–75] vs. 72% [63–84], ns.), as was regional oxygenation (R2*) in cortex (17 [16–19] vs. 17 [16–18] 1/s, ns.) and medulla (29 [24–39] vs. 27 [23–29] 1/s, ns.). Conclusions: In critically ill COVID-19 patients with AKI, the total, cortical and medullary renal blood flows were reduced compared with similar patients without AKI, whereas no differences in renal oxygenation were demonstrable in this setting. Trial registration ClinicalTrials ID: NCT02765191, registered May 6 2014 and updated May 7 2020. Graphic Abstract

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Multi-Site Benchmark Classification of Major Depressive Disorder Using Machine Learning on Cortical and Subcortical Measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects