Supervisors info:
Ιωάννης Τρουγκάκος, Καθηγητής, Τμήμα Βιολογίας, ΕΚΠΑ (Επιβλέπων)
Βασιλική Οικονομίδου, Αναπληρώτρια Καθηγήτρια, Τμήμα Βιολογίας, ΕΚΠΑ
Τιμοκράτης Καραμήτρος, Εντεταλμένος Ερευνητής, Μονάδα Βιοπληροφορικής και Εφαρμοσμένης Γενωμικής, Ελληνικό Ινστιτούτο Παστέρ
Summary:
SARS-CoV-2 is a new beta coronavirus, which appeared in late 2019, in
Hubei Province, China. It causes coronavirus disease 2019 (COVID-19),
which was declared a pandemic by the World Health Organization (WHO) on
March 11, 2020. The clinical picture of COVID-19 is highly dependent on the
immune response. Both non-specific immunity and specific immunity-humoral
and cellular- are involved in the immune response to COVID-19. Following the
entry of SARS-Cov-2 into human cells, viral RNAs are released and act as
Pathogen-associated molecular patterns (PAMPs), which are recognized by
Pattern recognition receptors (PRRs), such as Toll-like receptors (TLRs). As a
result, among others, proinflammatory cytokines, chemokines (e.g., IL-1, IL-6)
and soluble factors associated with Interferon-Stimulated Genes (ISGs) are
expressed. Furthermore, parts of the virus, such as the spike protein, can be
identified by B lymphocytes or presented by MHC complexes in T
lymphocytes, resulting in the production of antibodies and cytolytic activity
during the acute phase of the infection. RNA sequencing (RNA-SEQ) and
specialized bioinformatics analysis of the transcriptome of patients with
COVID-19 is a key tool for studying the differential expression of genes
between groups of patients. The general workflow in a gene expression
analysis consists of two parts, the experimental part and the computational
part. The experimental part consists of the following steps: RNA extraction,
library preparation, quality control and next generation sequencing, while the
bioinformatics analysis consists of the following steps: reads mapping, reads
quantification, normalization, gene identification by differential expression
analysis. QuantSeq is an RNA sample preparation method for the precise
determination of gene expression. QuantSeq provides an easy-to-use
protocol for creating next-generation sequencing libraries with high strand
specificity near the 3 ′ end of polyadenylated RNA. Only one fragment is
generated per transcript, directly linking the number of reads corresponding to
a gene to its expression.
In this study, the QuantSeq protocol was applied to study the
immune response of 8 patients with COVID-19, who differed in the duration of positivity of their RT-PCR SARS-CoV-2 test. Specifically, differential
expression analysis was performed between 4 patients with COVID-19
disease who tested positive for RT-PCR SARS-CoV-2 one month after initial
positive, and 4 patients who tested negative, one week after the first positive
test. For this purpose, after performing the QuantSeq sequencing experiment,
bioinformatics analysis was performed using various tools for each step,
BBduk for trimming, STAR and Salmon for alignment, DESeq2 and edgeR for
differential expression analysis, DAVID and g: Profiler for functional analysis
and CIBERSORTx and xCell for digital flow cytometry. The bioinformatics
pipeline selected was the following one: STAR ⇛ DESeq2 ⇛ DAVID.
Differential expression analysis found 4464 differentially expressed genes at a
significance level of 5%, with |log2foldchange|>1. Also, the functional analysis,
showed that the statistically significant enriched gene ontologies were: cell
adhesion, chemical synaptic transmission, interferon-gamma-mediated and
the pathways: calcium signaling pathway, morphine addiction, axon guidance,
cell adhesion molecules (CAMs ), osteoclast differentiation.
