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Integrative transcriptome analysis of SARS-CoV-2 human-infected cells combined with deep learning algorithms
Ricardo Lemes Gonçalves1, Gabriel Augusto Pires de Souza2,3, Mateus de Souza Terceti2
1Núcleo de Pesquisas em Ciências Biológicas, NUPEB, Universidade Federal de Ouro Preto, Ouro Preto, 35400-000, Brazil.
Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) quickly spread worldwide, leading coronavirus disease 2019 (COVID-19) to hit pandemic level less than 4 months after the first official cases. Hence, the search for drugs and vaccines that could prevent or treat infections by SARS-CoV-2 began, intending to reduce a possible collapse of health systems. After 2 years, efforts to find therapies to treat COVID-19 continue. However, there is still much to be understood about the virus' pathology. Tools such as transcriptomics have been used to understand the impact of SARS-CoV-2 on different cells isolated from various tissues, leaving datasets in the databases that integrate genes and differentially expressed pathways during SARS-CoV-2 infection. After retrieving transcriptome datasets from different human cells infected with SARS-CoV-2 available in the database, we performed an integrative analysis associated with deep learning algorithms to determine differentially expressed targets mainly after infection. The targets found represented a fructose transporter (GLUT5) and a component of proteasome 26s. These targets were then molecularly modeled, followed by molecular docking that identified potential inhibitors for both structures. Once the inhibition of structures that have the expression increased by the virus can represent a strategy for reducing the viral replication by selecting infected cells, associating these bioinformatics tools, therefore, can be helpful in the screening of molecules being tested for new uses, saving financial resources, time, and making a personalized screening for each infectious disease.
Insights
Researchers used deep learning to analyze transcriptomics data from SARS-CoV-2 infected cells, identifying key targets like GLUT5 and proteasome components. This approach aids in discovering potential COVID-19 treatments by finding inhibitors for virus-upregulated cellular structures.
Area of Science:
- Bioinformatics
- Computational Biology
- Infectious Disease Research
Background:
- The COVID-19 pandemic, caused by SARS-CoV-2, necessitated rapid development of treatments.
- Understanding the virus's impact on host cells is crucial for identifying therapeutic targets.
- Transcriptomics data offers insights into cellular responses to SARS-CoV-2 infection.
Purpose of the Study:
- To identify differentially expressed genes and pathways in human cells infected with SARS-CoV-2.
- To discover potential therapeutic targets and inhibitors for SARS-CoV-2 infection using bioinformatics.
- To evaluate the utility of integrative analysis and deep learning in drug discovery for infectious diseases.
Main Methods:
- Retrieved and integrated transcriptome datasets from various human cells infected with SARS-CoV-2.
- Applied deep learning algorithms to identify differentially expressed targets post-infection.
- Performed molecular modeling and docking to identify potential inhibitors for the discovered targets.
Main Results:
- Identified a fructose transporter (GLUT5) and a proteasome 26S component as significantly upregulated targets.
- Discovered potential molecular inhibitors for both GLUT5 and the proteasome component through molecular docking.
- Demonstrated that inhibiting virus-upregulated cellular structures could be a strategy to reduce viral replication.
Conclusions:
- Integrative bioinformatics analysis combined with deep learning can effectively identify therapeutic targets in viral infections.
- Targeting GLUT5 and proteasome components presents a potential strategy for developing novel COVID-19 therapies.
- This computational approach can accelerate drug screening, saving resources and enabling personalized therapeutic strategies.
