Looking for pathways related to COVID-19 phenotypes: Confirmation of pathogenic mechanisms by SARS-CoV-2 - Host interactome. (preprint)

In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level. Host conditions and comorbidities were identified as risk factors for severe and fatal disease courses, but the mechanisms of interaction between host and SARS-CoV-2 determining the grade of COVID-19 severity, are still unknown. We provide a network analysis on protein-protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus interactome was inferred on published PPI, using an explorative algorithm (Random Walk with Restart) triggered by all the 28 proteins of SARS-CoV-2, or each single viral protein one-by-one. The functional analysis for all proteins, linked to many aspects of COVID-19 pathogenesis, allows to identify the subcellular districts, where SARS-CoV-2 proteins seem to be distributed, while in each interactome built around one single viral protein, a different response was described, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, an explorative network-based approach was applied to Bradykinin Storm, highlighting a possible direct action of ORF3a and NS7b to enhancing this condition. This network-based model for SARS-CoV-2 infection could be a framework for pathogenic evaluation of specific clinical outcomes. We identified possible host responses induced by specific proteins of SARS-CoV-2, underlining the important role of specific viral accessory proteins in pathogenic phenotypes of severe COVID-19 patients.

The Evolution of Clinical and Scientific Evidence-Based Management Guidelines for COVID-19 Disease: A Longitudinal Descriptive Analysis (preprint)

In January 2020, the novel Coronavirus Disease-2019 (COVID-19) epidemic spread to Italy. The ensuing high rates of patients with pulmonary disease due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections, overwhelmed the Italian health services. Management of inpatients was based on World Health Organization (WHO) and other public health bodies’ and specialist societies’ clinical, diagnostic and therapeutic protocols developed with very low-quality evidence base at that time. Over time, management guidelines and protocols were progressively modified and adapted based on the evolving first hand clinical management experience, and the evidence, which has slowly accumulated from clinical large cohort studies and clinical trials. As of August 9th, 2020, there have been 250.103 confirmed COVID-19 cases (with 35.203 deaths) reported from Italy. We present chronological evolution of the clinical and scientific evidence-based management guidelines to date, and their influence on the health care workers management of patients with COVID-19 disease.Funding Statement: This research was supported by funds to National Institute for Infectious Diseases ‘Lazzaro Spallanzani’ IRCCS from Line one-Ricerca Corrente ‘Infezioni Emergenti e Riemergenti’ and by Progetto COVID 2020 12371675 both funded by Italian Ministry of Health and from European Commission – Horizon 2020 (EXSCALATE4CoV).Sir Zumla and Prof Ippolito are co-PIs of the Pan-African Network on Emerging and Re-Emerging Infections (PANDORA-ID-NET – https://www.pandora-id.net/) funded by the European and Developing Countries Clinical Trials Partnership. Sir Zumla is in receipt of a National Institutes of Health Research senior investigator award.Declaration of Interests: EN received grants from Gilead science for educational purpose. Al other authors have no conflicts of interest to declareEthics Approval Statement: The authors stated that Ethical approval was not required.

COVID-19 Disease - Temporal Analyses of Complete Blood Count Parameters Over Course of Illness, and Relationship to Patient Demographics and Management Outcomes in Survivors and Non-Survivors: A Longitudinal Descriptive Cohort Study (preprint)

Background: More detailed temporal analyses of complete (Full) blood count (CBC) parameters, their evolution and relationship to patient age, gender, co-morbidities and management outcomes in survivors and non-survivors with COVID-19 disease could help identify prognostic clinical biomarkers. Methods: From 29 January 2020 until 28 March 2020, we performed a longitudinal cohort study of COVID-19 inpatients at the Italian National Institute for Infectious Diseases, Rome, Italy. Nine CBC parameters as a continuous variable were studied [neutrophils, lymphocytes, monocytes, platelets, mean platelet volume, red blood cell count, haemoglobin concentration, mean red blood cell volume and red blood cell distribution width (RDW %)]. Model-based punctual estimates and difference between survivors and non-survivors, overall, and by co-morbidities, at specific times after symptoms, with relative 95% CI and P-values were obtained by marginal prediction and ANOVA-style joint tests. All analyses were carried out by STATA 15 statistical package. Main Findings: 379 COVID-19 patients [273 (72% were male; mean age was 61.67 (SD 15.60)] were enrolled and 1,805 measures per parameter were analysed. Neutrophil counts were on average significantly higher in non-survivors than in survivors (P<0.001) and lymphocytes were on average higher in survivors (P<0.001). These differences were time dependent. Reverse temporal trends were observed for lymphocyte and neutrophil counts in survivors and non-survivors. Average platelets counts (P<0.001) and median platelets volume (P<0.001) were significantly different in survivors and in non-survivors. The differences were time dependent and consistent with acute inflammation followed either by recovery or by death. Anaemia with anisocytosis were observed in the later phase of COVID-19 disease in non-survivors only. Mortality was significantly higher in patients with diabetes (p=0.005), obesity (p=0.010), chronic renal failure (p=0.001), COPD (p=0.033) cardiovascular diseases (p=0.001) and those >60 years(p=0.001). Age (p=0.042), obesity (p=0.002), chronic renal failure (p=0.002) and cardiovascular diseases (p=0.009) were independently associated with poor patient clinical outcome at 30 day after symptom onset. Interpretation: Increased neutrophil counts, reduced lymphocyte counts, higher median platelet volume, anemia with anisocytosis, in association with obesity, chronic renal failure, COPD, cardiovascular diseases and age >60 years predict poor prognosis in COVID19 patients.Funding Statement: Ricerca Corrente e Finalizzata Italy Ministry of Health, AIRC (IG2018-21880); Regione Lazio (Gruppi di ricerca, E56C18000460002).Declaration of Interests: The authors declare no competing interest.Ethics Approval Statement: This study was approved by the IRB of Italian National Institute for Infectious Diseases “Lazzaro Spallanzani” (INMI), in Rome (Italy).