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Whole blood transcriptome signature predicts severe forms of COVID-19: Results from the COVIDeF cohort study

ABSTRACT: COVID-19 is associated with heterogeneous outcome. Early identification of a severe progression of the disease is essential to properly manage the patients and improve their outcome. Biomarkers reflecting an increased inflammatory response, as well as individual features including advanced age, male gender, and pre-existing comorbidities, are risk factors of severe COVID-19. Yet, these features show limited accuracy for outcome prediction. We hypothesized that whole blood transcriptome could have a prognostic value at an early stage of the disease. Blood transcriptome of patients with mild pneumonia was profiled. Patients with subsequent severe COVID-19 were compared to those with favourable outcome, and a molecular predictor based on gene expression was built.

ORGANISM(S): Homo sapiens (human)

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PROVIDER: S-BSST1135 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:The etiology of severe forms of COVID19, especially in young patients, remains a salient unanswered question. Here we build built on a 3-tier cohort where all individuals/patients were strictly below 50 years of age and where a number of comorbidities were excluded at study onset. Besides healthy controls (N=22), these included patients in the intensive care unit with Acute Respiratory Distress Syndrome (ARDS) (“critical group”; N=47), and those in a non-critical care ward under supplemental oxygen (“non-critical group”, N=25). This highly curated cohort allowed us to perform a deep multi-omics approach, which included whole genome sequencing, whole blood RNA-sequencing, plasma and peripheral-blood mononuclear cells proteomics, multiplex cytokine profiling, mass-cytometry-based immune cell profiling in conjunction with viral parameters i.e. anti-SARS-Cov-2 neutralizing antibodies and multi-target antiviral serology. Critical patients were characterized by an exacerbated inflammatory state, perturbed lymphoid and myeloid cell compartments, signatures of dysregulated blood coagulation and active regulation of viral entry into the cells. A unique gene signature that differentiates critical from non-critical patients was identified by an ensemble of machine learning, deep learning and quantum annealing approachmethods. Within this gene networksignature, Sstructural Causal causal Modeling modeling identified several genes that may potentially drivepromote ARDS driver genes etiology, among which the up-regulated metalloprotease ADAM9 seems to be a key driver. Inhibition of ADAM9 ex vivo interfered with SARS-Cov-2 uptake and replication in human epithelial cells. In brief, Hence we applyied an advanced integrated machine learning approach and probabilistic programming strategy to identify causal molecular driver geness for of severe forms of COVID-19 in a small, uncluttered tightly controlled cohort of patients, the importance of which were then validated with experiments.

Project description:BackgroundCoronaviruses can induce the production of interleukin (IL)-1β, IL-6, tumour necrosis factor, and other cytokines implicated in autoinflammatory disorders. It has been postulated that anakinra, a recombinant IL-1 receptor antagonist, might help to neutralise the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related hyperinflammatory state, which is considered to be one cause of acute respiratory distress among patients with COVID-19. We aimed to assess the off-label use of anakinra in patients who were admitted to hospital for severe forms of COVID-19 with symptoms indicative of worsening respiratory function.MethodsThe Ana-COVID study included a prospective cohort from Groupe Hospitalier Paris Saint-Joseph (Paris, France) and a historical control cohort retrospectively selected from the Groupe Hospitalier Paris Saint-Joseph COVID cohort, which began on March 18, 2020. Patients were included in the prospective cohort if they were aged 18 years or older and admitted to Groupe Hospitalier Paris Saint-Joseph with severe COVID-19-related bilateral pneumonia on chest x-ray or lung CT scan. The other inclusion criteria were either laboratory-confirmed SARS-CoV-2 or typical lung infiltrates on a lung CT scan, and either an oxygen saturation of 93% or less under oxygen 6 L/min or more, or aggravation (saturation ≤93% under oxygen 3 L/min) with a loss of 3% of oxygen saturation in ambient air over the previous 24 h. The historical control group of patients had the same inclusion criteria. Patients in the anakinra group were treated with subcutaneous anakinra (100 mg twice a day for 72 h, then 100 mg daily for 7 days) as well as the standard treatments at the institution at the time. Patients in the historical group received standard treatments and supportive care. The main outcome was a composite of either admission to the intensive care unit (ICU) for invasive mechanical ventilation or death. The main analysis was done on an intention-to-treat basis (including all patients in the anakinra group who received at least one injection of anakinra).FindingsFrom March 24 to April 6, 2020, 52 consecutive patients were included in the anakinra group and 44 historical patients were identified in the Groupe Hospitalier Paris Saint-Joseph COVID cohort study. Admission to the ICU for invasive mechanical ventilation or death occurred in 13 (25%) patients in the anakinra group and 32 (73%) patients in the historical group (hazard ratio [HR] 0·22 [95% CI 0·11-0·41; p<0·0001). The treatment effect of anakinra remained significant in the multivariate analysis (HR 0·22 [95% CI 0·10-0·49]; p=0·0002). An increase in liver aminotransferases occurred in seven (13%) patients in the anakinra group and four (9%) patients in the historical group.InterpretationAnakinra reduced both need for invasive mechanical ventilation in the ICU and mortality among patients with severe forms of COVID-19, without serious side-effects. Confirmation of efficacy will require controlled trials.FundingGroupe Hospitalier Paris Saint-Joseph.

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Whole blood transcriptome signature predicts severe forms of COVID-19: Results from the COVIDeF cohort study

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