Dataset Information

Drivers genes in severe forms of COVID-19

ABSTRACT: The etiology of severe forms of COVID19, especially in young patients, remains a salient unanswered question. Here we build 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 include 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 machine learning, deep learning and quantum computing approach. Within this gene network, Structural Causal Modeling identified several ARDS driver genes, 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. Hence we apply a machine learning approach to identify driver genes for severe forms of COVID-19 in a small, uncluttered cohort of patients.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma, Cell Culture

SUBMITTER: Aurélie Hirschler

LAB HEAD: Christine Carapito

PROVIDER: PXD025265 | Pride | 2021-11-25

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 176

Similar Datasets

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:SARS-CoV-2 is a novel coronavirus that causes acute respiratory distress syndrome (ARDS), death and long-term sequelae. Innate immune cells are critical for host defense but are also the primary drivers of ARDS. The relationships between innate cellular responses in ARDS resulting from COVID-19 compared to other causes of ARDS, such as bacterial sepsis is unclear. Moreover, the beneficial effects of dexamethasone therapy during severe COVID-19 remain speculative, but understanding the mechanistic effects could improve evidence-based therapeutic interventions. To interrogate these relationships, we developed an scRNAseq atlas that is freely accessible (biernaskielab.ca/COVID_neutrophil). We discovered that compared to bacterial ARDS, COVID-19 was associated with distinct neutrophil polarization characterized by either interferon (IFN) or prostaglandin (PG) active states. Neutrophils from bacterial ARDS had higher expression of antibacterial molecules such as PLAC8 and CD83. Dexamethasone therapy in COVID patients rapidly altered the IFNactive state, downregulated interferon responsive genes, and activated IL1R2+ve neutrophils. Dexamethasone also induced the emergence of immature neutrophils expressing immunosuppressive molecules ARG1 and ANXA1, which were not present in healthy controls. Moreover, dexamethasone remodeled global cellular interactions by changing neutrophils from information receivers into information providers. Importantly, male patients had higher proportions of IFNactive neutrophils and a greater degree of steroid-induced immature neutrophil expansion. Indeed, the highest proportion of IFNactive neutrophils was associated with mortality. These results define neutrophil states unique to COVID-19 when contextualized to other life-threatening infections, thereby enhancing the relevance of our findings at the bedside. Furthermore, the molecular benefits of dexamethasone therapy are also defined. The identified molecular pathways can now be targeted to develop improved therapeutics.

			Action	DRS
	HumanBovin_CpSP_LMU_20200908.fasta	Fasta
	HumanMQ_pSP_LMU_20200511.fasta	Fasta
	QE31335LMU.raw	Raw
	QE31338LMU.raw	Raw
	QE31341LMU.raw	Raw

Dataset Information

Drivers genes in severe forms of COVID-19

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets