Reliability and reproducibility of clinical phenotypes developed during the first wave of COVID-19: A validation study in critically ill patients from the second and third wave (preprint)

Background: During the first wave of the COVID-19 pandemic, different clinical phenotypes were published. However, none of them have been validated in subsequent waves, so their current validity is unknown. The aim of the study is to validate the unsupervised cluster model developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves. Methods: Retrospective, multicentre, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 74 Intensive Care Units (ICU) in Spain. To validate our original phenotypes model, we assigned a phenotype to each patient of the validation cohort using the same medoids, the same number of clusters (n= 3), the same number of variables (n= 25) and the same discretisation used in the development cohort. The performance of the classification was determined by Silhouette analysis and general linear modelling. The prognostic models were validated, and their performance was measured using accuracy test and area under curve (AUC)ROC. Results: The database included a total of 2,033 patients (mean age 63[53-92] years, 1643(70.5%) male, median APACHE II score (12[9-16]) and SOFA score (4[3-6]) points. The ICU mortality rate was 27.2%. Although the application of unsupervised cluster analysis classified patients in the validation population into 3 clinical phenotypes. Phenotype A (n=1,206 patients, 59.3%), phenotype B (n=618 patients, 30.4%) and phenotype C (n=506 patients, 24.3%), the characteristics of patients within each phenotype were significantly different from the original population. Furthermore, the silhouette coefficients were close to or below zero and the inclusion of phenotype classification in a regression model did not improve the model performance (accuracy =0.78, AUC=0.78) with respect to a standard model (accuracy = 0.79, AUC=0.79) or even worsened when the model was applied to patients within each phenotype (accuracy = 0.80, AUC 0.77 for Phenotype A, accuracy=0.73, AUC= 0.67 for phenotype B and accuracy= 0.66 , AUC= 0.76 for phenotype C ) Conclusion:  Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation. Trial Registration: The study was retrospectively registered (NCT 04948242) on June 30, 2021

Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2,022 critically ill patients with COVID-19 in Spain (preprint)

Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 Intensive Care Units(ICU) in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results: : The database included a total of 2,022 patients (mean age 64[IQR5-71] years, 1423(70.4%) male, median APACHE II score (13[IQR10-17]) and SOFA score (5[IQR3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A(mild) phenotype (537;26.7%) included older age (<65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623,30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C(severe) phenotype was the most common (857;42.5%) and was characterized by the interplay of older age (>65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. Conclusion: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice .

Detection of SARS-Cov-2 RNA in serum is associated with increased mortality risk in hospitalized COVID-19 patients. (preprint)

BackgroundCOVID-19 has overloaded national health services worldwide. Thus, early identification of patients at risk of poor outcomes is critical. Our objective was to analyse SARS-CoV-2 RNA detection in serum as a severity biomarker in COVID-19. Methods and FindingsRetrospective observational study including 193 patients admitted for COVID-19. Detection of SARS-CoV-2 RNA in serum (CoVemia) was performed with samples collected at 48-72 hours of admission by two techniques from Roche and Thermo Fischer Scientific (TFS). Main outcome variables were mortality and need for ICU admission during hospitalization for COVID-19. CoVemia was detected in 50-60% of patients depending on technique. The correlation of Ct in serum between both techniques was good (intraclass correlation coefficient: 0.612; p < 0.001). Patients with CoVemia were older (p = 0.006), had poorer baseline oxygenation (PaO2/FiO2; p < 0.001), more severe lymphopenia (p < 0.001) and higher LDH (p < 0.001), IL-6 (p = 0.021), C-reactive protein (CRP; p = 0.022) and procalcitonin (p = 0.002) serum levels. We defined "relevant CoVemia" when detection Ct was < 34 with Roche and < 31 for TFS. These thresholds had 95% sensitivity and 35 % specificity. Relevant CoVemia predicted death during hospitalization (OR 9.2 [3.8 - 22.6] for Roche, OR 10.3 [3.6 - 29.3] for TFS; p < 0.001). Cox regression models, adjusted by age, sex and Charlson index, identified increased LDH serum levels and relevant CoVemia (HR = 9.87 [4.13-23.57] for TFS viremia and HR = 7.09 [3.3-14.82] for Roche viremia) as the best markers to predict mortality. ConclusionsCoVemia assessment at admission is the most useful biomarker for predicting mortality in COVID-19 patients. CoVemia is highly reproducible with two different techniques (TFS and Roche), has a good consistency with other severity biomarkers for COVID-19 and better predictive accuracy. AUTHOR SUMMARYCOVID-19 shows a very heterogeneous clinical picture. In addition, it has overloaded national health services worldwide. Therefore, early identification of patients with poor prognosis is critical to improve the use of limited health resources. In this work, we evaluated whether baseline SARS-CoV2 RNA detection in blood (CoVemia) is associated with worse outcomes. We studied almost 200 patients admitted to our hospital and about 50-60% of them showed positive CoVemia. Patients with positive CoVemia were older and had more severe disease; CoVemia was also more frequent in patients requiring admission to the ICU. Moreover, we defined "relevant CoVemia", as the amount of viral load that better predicted mortality obtaining 95% sensitivity and 35% specificity. In addition, relevant CoVemia was a better predictor than other biomarkers such as LDH, lymphocyte count, interleukin-6, and indexes used in ICU such as qSOFA and CURB65. In summary, detection of CoVemia is the best biomarker to predict death in COVID-19 patients. Furthermore, it is easy to be implemented and is reproducible with two techniques (Roche and Thermo Fisher Scientific) that are currently used for diagnosis in nasopharyngeal swabs samples.

In vivo structure and dynamics of the RNA genome of SARS-Cov-2 (preprint)

The SARS-CoV-2 coronavirus, which causes the COVID-19 pandemic, is one of the largest positive strand RNA viruses. Here we developed a simplified SPLASH assay and comprehensively mapped the in vivo RNA-RNA interactome of SARS-CoV-2 RNA during the viral life cycle. We observed canonical and alternative structures including 3-UTR and 5-UTR, frameshifting element (FSE) pseudoknot and genome cyclization in cells and in virions. We provide direct evidence of interactions between Transcription Regulating Sequences (TRS-L and TRS-Bs), which facilitate discontinuous transcription. In addition, we reveal alternative short and long distance arches around FSE, forming a "high-order pseudoknot" embedding FSE, which might help ribosome stalling at frameshift sites. More importantly, we found that within virions, while SARS-CoV-2 genome RNA undergoes intensive compaction, genome cyclization is weakened and genome domains remain stable. Our data provides a structural basis for the regulation of replication, discontinuous transcription and translational frameshifting, describes dynamics of RNA structures during life cycle of SARS-CoV-2, and will help to develop antiviral strategies.

Structural characterization of cocktail-like targeting polysaccharides from Ecklonia kurome Okam and their anti-SARS-CoV-2 activities in vitro (preprint)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent responsible for the worldwide coronavirus disease 2019 (COVID-19) outbreak. Investigation has confirmed that polysaccharide heparan sulfate can bind to the spike protein and block SARS-CoV-2 infection. Theoretically, similar structure of nature polysaccharides may also have the impact on the virus. Indeed, some marine polysaccharide has been reported to inhibit SARS-Cov-2 infection in vitro, however the convinced targets and mechanism are still vague. By high throughput screening to target 3CLpro enzyme, a key enzyme that plays a pivotal role in the viral replication and transcription using nature polysaccharides library, we discover the mixture polysaccharide 375 from seaweed Ecklonia kurome Okam completely block 3Clpro enzymatic activity (IC50, 0.48 {micro}M). Further, the homogeneous polysaccharide 37502 from the 375 may bind to 3CLpro molecule well (kD value : 4.23 x 10-6). Very interestingly, 37502 also can potently disturb spike protein binding to ACE2 receptor (EC50, 2.01 {micro}M). Importantly, polysaccharide 375 shows good anti-SARS-CoV-2 infection activity in cell culture with EC50 values of 27 nM (99.9% inhibiting rate at the concentration of 20 {micro}g/mL), low toxicity (LD50: 136 mg/Kg on mice). By DEAE ion-exchange chromatography, 37501, 37502 and 37503 polysaccharides are purified from native 375. Bioactivity test show that 37501 and 37503 may impede SARS-Cov-2 infection and virus replication, however their individual impact on the virus is significantly less that of 375. Surprisingly, polysaccharide 37502 has no inhibition effect on SARS-Cov-2. The structure study based on monosaccharide composition, methylation, NMR spectrum analysis suggest that 375 contains guluronic acid, mannuronic acid, mannose, rhamnose, glucouronic acid, galacturonic acid, glucose, galactose, xylose and fucose with ratio of 1.86 : 9.56 : 6.81 : 1.69 : 1.00 : 1.75 : 1.19 : 11.06 : 4.31 : 23.06. However, polysaccharide 37502 is an aginate which composed of mannuronic acid (89.3 %) and guluronic acid (10.7 %), with the molecular weight (Mw) of 27.9 kDa. These results imply that mixture polysaccharides 375 works better than the individual polysaccharide on SARS-Cov-2 may be the cocktail-like polysaccharide synergistic function through targeting multiple key molecules implicated in the virus infection and replication. The results also suggest that 375 may be a potential drug candidate against SARS-CoV-2.

Distinct Patterns of Emergence of SARS-CoV-2 Spike Variants including N501Y in Clinical Samples in Columbus Ohio (preprint)

Following the worldwide emergence of the p.Asp614Gly shift in the Spike (S) gene of SARS-CoV-2, there have been few recurring pathogenic shifts occurring during 2020, as assessed by genomic sequencing. This situation has evolved in the last several months with the emergence of several distinct variants (first identified in the United Kingdom and South Africa, respectively) that illustrate multiple changes in the S gene, particularly p.Asn501Tyr (N501Y), that likely have clinical impact. We report here the emergence in Columbus, Ohio in December 2020 of two novel SARS-CoV-2 clade 20C/G variants. One isolate, that has become the predominant virus found in nasopharyngeal swabs in the December 2020-January 2021 period, harbors S p.Gln677His, membrane glycoprotein (M) p.Ala85Ser (Q677H) and nucleocapsid (N) p.Asp377Tyr (D377Y) mutations. The other isolate contains S N501Y and ORF8 Arg52Ile (R52I), which are two markers of the UK-B.1.1.7 (clade 20I/501Y.V1) strain, but lacks all other mutations from that virus. It is also from a different clade and shares multiple mutations with the clade 20C/G viruses circulating in Ohio prior to December 2020. These two SARS-CoV-2 viruses emerging now in the United States add to the diversity of S gene shifts occurring worldwide and support multiple independent acquisition of S N501Y (in likely contrast to the unitary S D614G shift) occurring first during this period of the pandemic.

CAR-NK Cells Effectively Target the D614 and G614 SARS-CoV-2-infected Cells (preprint)

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is highly contagious presenting a significant public health issue. Current therapies used to treat coronavirus disease 2019 (COVID-19) include monoclonal antibody cocktail, convalescent plasma, antivirals, immunomodulators, and anticoagulants, though the current therapeutic options remain limited and expensive. The vaccines from Pfizer and Moderna have recently been authorized for emergency use, which are invaluable for the prevention of SARS-CoV-2 infection. However, their long-term side effects are not yet to be documented, and populations with immunocompromised conditions (e.g., organ-transplantation and immunodeficient patients) may not be able to mount an effective immune response. In addition, there are concerns that wide-scale immunity to SARS-CoV-2 may introduce immune pressure that could select for escape mutants to the existing vaccines and monoclonal antibody therapies. Emerging evidence has shown that chimeric antigen receptor (CAR)- natural killer (NK) immunotherapy has potent antitumor response in hematologic cancers with minimal adverse effects in recent studies, however, the potentials of CAR-NK cells in preventing and treating severe cases of COVID-19 has not yet been fully exploited. Here, we improve upon a novel approach for the generation of CAR-NK cells for targeting SARS-CoV-2 and its D614G mutant. CAR-NK cells were generated using the scFv domain of S309 (henceforward, S309-CAR-NK), a SARS-CoV and SARS-CoV-2 neutralizing antibody that targets the highly conserved region of SARS-CoV-2 spike (S) glycoprotein, therefore would be more likely to recognize different variants of SARS-CoV-2 isolates. S309-CAR-NK cells can specifically bind to pseudotyped SARS-CoV-2 virus and its D614G mutant. Furthermore, S309-CAR-NK cells can specifically kill target cells expressing SARS-CoV-2 S protein in vitro and show superior killing activity and cytokine production, compared to that of the recently published CR3022-CAR-NK cells. Thus, these results pave the way for generating off-the-shelf S309-CAR-NK cells for treatment in high-risk individuals as well as provide an alternative strategy for patients unresponsive to current vaccines.

Enhanced Cholesterol-dependent Hemifusion by Internal Fusion Peptide 1 of SARS Coronavirus-2 Compared to its N-terminal Counterpart (preprint)

Membrane fusion is an important step for the entry of the lipid-sheathed viruses into the host cells. The fusion process is being carried out by fusion proteins present in the viral envelope. The class I viruses contains a 20-25 amino acid sequence at its N-terminal of the fusion domain, which is instrumental in fusion, and is termed as fusion peptide. However, Severe Acute Respiratory Syndrome Coronavirus (SARS) coronaviruses contain more than one fusion peptide sequences. We have shown that the internal fusion peptide 1 (IFP1) of SARS-CoV is far more efficient than its N-terminal counterpart (FP) to induce hemifusion between small unilamellar vesicles. Moreover, the ability of IFP1 to induce hemifusion formation increases dramatically with growing cholesterol content in the membrane. Interestingly, IFP1 is capable of inducing hemifusion, but fails to open pore.

Deploying Machine Learning to Validate Clinical Phenotypes and Factors Associated with Mortality Risk in 2,022 Critically Ill Patients with COVID-19 in Spain (preprint)

Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. The objective was to analyze patient’s factors associated with mortality risk and utilize a Machine Learning(ML) to derive clinical COVID-19 phenotypes.Methods: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 Intensive Care Units(ICU) in Spain. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. An unsupervised clustering analysis was applied to determine presence of phenotypes. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves.Findings: The database included a total of 2,022 patients (mean age 64[IQR5-71] years, 1423(70·4%) male, median APACHE II score (13[IQR10-17]) and SOFA score (5[IQR3-7]) points. The ICU mortality rate was 32·6%. Of the 3 derived phenotypes, the C(severe) phenotype was the most common (857;42·5%) and was characterized by the interplay of older age (>65 years), high severity of illness and a higher likelihood of development shock. The A(mild) phenotype (537;26·7%) included older age (>65 years), fewer abnormal laboratory values and less development of complications and B (moderate) phenotype (623,30·8%) had similar characteristics of A phenotype but were more likely to present shock. Crude ICU mortality was 45·4%, 25·0% and 20·3% for the C, B and A phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.Interpretation: The presented ML model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.Funding Statement: This study was supported by the Spanish Intensive Care Society(SEMICYUC) and Ricardo Barri Casanovas Foundation.Declaration of Interests: All authors declare that they have no conflicts of interest.Ethics Approval Statement: The study was approved by the reference institutional review board at Joan XXIII University Hospital (IRB# CEIM/066/2020) and each participating site with a waiver of informed consent. All data values were anonymized prior to the phenotyping which consisted of clustering clinical variables on their association with COVID-19 mortality.