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

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. The aim was to evaluate the prognostic value of whole blood transcriptome 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. Unsupervised classification discriminated patients who would later develop a COVID-19-related severe pneumonia. The corresponding gene expression signature reflected the immune response to the viral infection dominated by a prominent type I interferon, with IFI27 among the most over-expressed genes. A 48-genes transcriptome signature predicting the risk of severe COVID-19 was built on a training cohort, then validated on an external independent cohort, showing an accuracy of 81% for predicting severe outcome. These results identify an early transcriptome signature of severe COVID-19 pneumonia, with a possible relevance to improve COVID-19 patient management.

Whole blood transcriptomic signature of Cushing's syndrome.

OBJECTIVE: Cushing's syndrome is characterized by high morbidity and mortality with high interindividual variability. Easily measurable biomarkers, in addition to the hormone assays currently used for diagnosis, could reflect the individual biological impact of glucocorticoids. The aim of this study is to identify such biomarkers through the analysis of whole blood transcriptome. DESIGN: Whole blood transcriptome was evaluated in 57 samples from patients with overt Cushing's syndrome, mild Cushing's syndrome, eucortisolism, and adrenal insufficiency. Samples were randomly split into a training cohort to set up a Cushing's transcriptomic signature and a validation cohort to assess this signature. METHODS: Total RNA was obtained from whole blood samples and sequenced on a NovaSeq 6000 System (Illumina). Both unsupervised (principal component analysis) and supervised (Limma) methods were used to explore the transcriptome profile. Ridge regression was used to build a Cushing's transcriptome predictor. RESULTS: The transcriptomic profile discriminated samples with overt Cushing's syndrome. Genes mostly associated with overt Cushing's syndrome were enriched in pathways related to immunity, particularly neutrophil activation. A prediction model of 1500 genes built on the training cohort demonstrated its discriminating value in the validation cohort (accuracy .82) and remained significant in a multivariate model including the neutrophil proportion (P = .002). Expression of FKBP5, a single gene both overexpressed in Cushing's syndrome and implied in the glucocorticoid receptor signaling, could also predict Cushing's syndrome (accuracy .76). CONCLUSIONS: Whole blood transcriptome reflects the circulating levels of glucocorticoids. FKBP5 expression could be a nonhormonal marker of Cushing's syndrome.