Unsupervised clustering reveals phenotypes of AKI in ICU Covid19 patients

BackgroundAcute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors. MethodsWe adopted a three-step pipeline of analyses. Firstly, we looked for factors associated with AKI using a generalized additive model. Secondly, we calculated the importance of each identified AKI related factor in the estimated AKI risk to find the main risk factor for AKI, at the single patient level. Lastly, we clusterized AKI patients according to their profile of risk factors and compared the clinical characteristics and outcome of every cluster. We applied this method to a cohort of severe Covid19 patients hospitalized in the ICU of Geneva University Hospitals. ResultsAmong the 250 patients analyzed, we found ten factors associated with AKI development. Using the individual expression of these factors, we identified three groups of AKI patients, based on the use of Lopinavir/Ritonavir, a prior history of diabetes mellitus and baseline eGFR and ventilation. The three clusters expressed distinct characteristic in terms of AKI severity and recovery, metabolic patterns and ICU mortality. ConclusionWe propose here a new method to phenotype AKI patients according to their most important individual risk factors for AKI development. When applied to an ICU cohort of Covid19 patients, we were able to differentiate three groups of patients. Each expressed specific AKI characteristics and outcomes, which probably reflects a distinct pathophysiology.

Excessive inflammatory and metabolic responses to acute SARS-CoV-2 infection are associated with a distinct gut microbiota composition

Protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and associated clinical sequelae requires well-coordinated metabolic and immune responses that limit viral spread and promote recovery of damaged systems. In order to understand potential mechanisms and interactions that influence coronavirus disease 2019 (COVID-19) outcomes, we performed a multi-omics analysis on hospitalised COVID-19 patients and compared those with the most severe outcome (i.e. death) to those with severe non-fatal disease, or mild/moderate disease, that recovered. A distinct subset of 8 cytokines and 140 metabolites in sera identified those with a fatal outcome to infection. In addition, elevated levels of multiple pathobionts and lower levels of protective or anti-inflammatory microbes were observed in the faecal microbiome of those with the poorest clinical outcomes. Weighted gene correlation network analysis (WGCNA) identified modules that associated severity-associated cytokines with tryptophan metabolism, coagulation-linked fibrinopeptides, and bile acids with multiple pathobionts. In contrast, less severe clinical outcomes associated with clusters of anti-inflammatory microbes such as Bifidobacterium or Ruminococcus, short chain fatty acids (SCFAs) and IL-17A. Our study uncovered distinct mechanistic modules that link host and microbiome processes with fatal outcomes to SARS-CoV-2 infection. These features may be useful to identify at risk individuals, but also highlight a role for the microbiome in modifying hyperinflammatory responses to SARS-CoV-2 and other infectious agents.

Single-cell profiling in COVID-19 associated acute kidney injury reveals patterns of tubule injury and repair in human

The cellular mechanisms of kidney tubule repair are poorly characterized in human. Here, we applied single-nucleus RNA sequencing to analyze the kidney in the first days after acute injury in 5 critically ill patients with COVID-19. We identified abnormal proximal tubule cell states associated with injury, characterized by altered functional and metabolic profiles and by pro-fibrotic properties. Tubule repair involved the plasticity of mature tubule cells in a process of cell de-differentiation and re-differentiation, which displayed substantial similarities between mouse and man. In addition, in man we identified a peculiar tubule reparative response determining the expansion of progenitor-like cells marked by PROM1 and following a differentiation program characterized by the sequential activation of the WNT, NOTCH and HIPPO signaling pathways. Taken together, our analyses reveal cell state transitions and fundamental cellular hierarchies underlying kidney injury and repair in critically ill patients.

Long COVID neuropsychological deficits after severe, moderate or mild infection

BackgroundThere is growing awareness that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can include long-term neuropsychological deficits, even in its mild or moderate respiratory forms. MethodsStandardized neuropsychological, psychiatric, neurological and olfactory tests were administered to 45 patients (categorized according to the severity of their respiratory symptoms during the acute phase) 236.51 {+/-} 22.54 days post-discharge following SARS-CoV-2 infection. ResultsDeficits were found in all the domains of cognition and the prevalence of psychiatric symptoms was also high in the three groups. The severe performed more poorly on long-term episodic memory and exhibited greater anosognosia. The moderate had poorer emotion recognition, which was positively correlated with persistent olfactory dysfunction. The mild were more stressed, anxious and depressed. ConclusionThe data support the hypothesis that the virus targets the central nervous system (and notably the limbic system), and support the notion of different neuropsychological phenotypes.

SARS-CoV2- infection as a trigger of humoral response against apolipoprotein A-1

AimsUnravelling autoimmune targets triggered by SARS-CoV-2 infection may provide crucial insights in the physiopathology of the disease and foster the development of potential therapeutic candidate targets and prognostic tools. We aimed at determining i) the association between anti-SARS-CoV-2 and anti-apoA-1 humoral response, ii) their relationship to prognosis, and iii) the degree of linear homology between SARS-CoV-2, apoA-1, and Toll-like receptor-2 (TLR2) epitopes. Methods and ResultsImmunoreactivity against different engineered peptides as well as cytokines were assessed by immunoassays, on a case-control (n=101), an intensive care unit (ICU; n=126) with a 28-days follow-up, and a general population cohort (n=663) with available samples in the pre and post-pandemic period. Using bioinformatics modelling a linear sequence homologies between apoA-1, TLR2, and Spike epitopes were identified. Overall, anti-apoA-1IgG levels were higher in COVID-19 patients or anti-SARS-CoV-2 seropositive individuals than in healthy donors or anti-SARS-CoV-2 seronegative individuals (p<0.0001). Significant and similar associations were noted between anti-apoA-1, anti-SARS-CoV-2IgG, cytokines, and lipid profile. In ICU patients, anti-SARS-CoV-2 and anti-apoA-1 seroconversion rates displayed similar 7-days kinetics, reaching 82% for anti-apoA-1 seropositivity. C-statistics (CS) indicated that anti-Spike/TLR2 mimic-peptide IgGs displayed a significant prognostic accuracy for overall mortality at 28 days (CS: 0.64; p=0.02). In the general population, SARS-CoV-2 exposure increased baseline anti-apoA-1 IgG levels. ConclusionCOVID-19 induces a marked humoral response against the major protein of high-density lipoproteins. As a correlate of poorer prognosis in other clinical settings, such autoimmunity signatures may relate to long-term COVID-19 prognosis assessment and warrant further scrutiny in the current COVID-19 pandemic.

Validation of a commercially available SARS-CoV-2 serological Immunoassay

ObjectivesTo validate the diagnostic accuracy of a Euroimmun SARS-CoV-2 IgG and IgA immunoassay for COVID-19. MethodsIn this unmatched (1:1) case-control validation study, we used sera of 181 laboratory-confirmed SARS-CoV-2 cases and 176 controls collected before SARS-CoV-2 emergence. Diagnostic accuracy of the immunoassay was assessed against a whole spike protein-based recombinant immunofluorescence assay (rIFA) by receiver operating characteristic (ROC) analyses. Discrepant cases between ELISA and rIFA were further tested by pseudo-neutralization assay. ResultsCOVID-19 patients were more likely to be male and older than controls, and 50.3% were hospitalized. ROC curve analyses indicated that IgG and IgA had high diagnostic accuracies with AUCs of 0.992 (95% Confidence Interval [95%CI]: 0.986-0.996) and 0.977 (95%CI: 0.963-0.990), respectively. IgG assays outperformed IgA assays (p=0.008). Taking an assessed 15% inter-assay imprecision into account, an optimized IgG ratio cut-off > 1.5 displayed a 100% specificity (95%CI: 98-100) and a 100% positive predictive value (95%CI: 97-100). A 0.5 cut-off displayed a 97% sensitivity (95%CI: 93-99) and a 97% negative predictive value (95%CI: 93-99). Substituting these thresholds for the manufacturers, improved assay performance, leaving 12% of IgG ratios indeterminate between 0.5-1.5. ConclusionsThe Euroimmun assay displays a nearly optimal diagnostic accuracy using IgG against SARS-CoV-2 in patient samples, with no obvious gains from IgA serology. The optimized cut-offs are fit for rule-in and rule-out purposes, allowing determination of whether individuals in our study population have been exposed to SARS-CoV-2 or not. IgG serology should however not be considered as a surrogate of protection at this stage.