On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers

Abstract

COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses. On the other hand, understanding how the immune system orchestrates its responses in this spectrum of disease severities is a fundamental issue required to design new and optimized therapeutic strategies. In this work, using single-cell RNAseq of bronchoalveolar lavage fluid of nine patients with COVID-19 and three healthy controls, we contribute to both aspects. First, we presented computational supervised machine-learning models with high accuracy in classifying the disease severity (moderate and severe) in patients with COVID-19 starting from single-cell data from bronchoalveolar lavage fluid. Second, we identified regulatory mechanisms from the heterogeneous cell populations in the lungs microenvironment that correlated with different clinical responses. Given the results, patients with moderate COVID-19 symptoms showed an activation/inactivation profile for their analyzed cells leading to a sequential and innocuous immune response. In comparison, severe patients might be promoting cytotoxic and pro-inflammatory responses in a systemic fashion involving epithelial and immune cells without the possibility to develop viral clearance and immune memory. Consequently, we present an in-depth landscape analysis of how transcriptional factors and pathways from these heterogeneous populations can regulate their expression to promote or restrain an effective immune response directly linked to the patients prognosis.

SARS-CoV-2 potential drugs, drug targets, and biomarkers: a viral-host interaction network-based analysis

“…Intriguingly, cell cycle progression pathways are also enriched in SARS-CoV-2-infected human gastrointestinal cells 33 . In addition to our study, the aforementioned studies confirm that the impact of SARS-CoV-2 on host cell cycle regulation is yet not fully understood 32 and requires further in vivo testing and validation. miRNAs are key players in gene expression regulation through interaction with genes, transcription factors, and other regulatory miRNAs.…”

“…This is further supported by other studies on peripheral blood samples of infected patients in which cell cycle progression was the highest signal among other cellular, metabolic, and signaling pathways [29][30][31] . This may be attributed to the fact that virally infected B cells signal cell cycle progression 32 . Intriguingly, cell cycle progression pathways are also enriched in SARS-CoV-2-infected human gastrointestinal cells 33 .…”

Macrophage Boolean networks in the time of SARS-CoV-2

“…In our opinion, two factors will contribute to unveil the genetic mechanism that guides the response of macrophages facing the SARS-CoV-2 infection: the high-throughput technologies and the computational/mathematical model of regulatory networks. On one hand, the importance of macrophages in COVID-19 has been evidenced thanks to the transcriptional profiles of thousands of single cells obtained from bronchoalveolar samples in COVID-19-infected patients ( 7 , 20 ). Currently, these massive amounts of data can integrate the gene expression of thousands of genes in thousands of cells from a sample in time and space simultaneously ( 48 ).…”

“…At this stage, the analysis of these data through machine learning (ML) algorithms can identify how the abundance of macrophages and other immunological cells could be associated with the progression and outcome of the disease. For example, by combining ML algorithms and single-cell RNASeq data from bronchoalveolar samples, we classified COVID-19 patients with different degrees of severity and found that genes associated with a pro-inflammatory response are implicated in severe COVID-19 patients ( 20 ). Besides, it has been reported that unsupervised hierarchical clustering is a proper method to stratify and classify patients through their severity progression determined by the pro-inflammatory, anti-inflammatory, and antiviral cytokines abundance data ( 49 ).…”

“…Besides, monocytes have a lower expression of interferon-stimulated genes in severe COVID-19 patients, resulting in the delay of the interferon response against SARS-CoV-2 ( 19 ). Furthermore, macrophages in severe COVID-19 patients are associated with overexpression of pro-inflammatory genes when compared with moderate COVID-19 patients ( 20 ). Altogether, these and other findings highlight the remarkable role that monocytes and macrophage polarization have in the progression of the disease.…”