RESULTS: The results showed that lipid content of cell dry weight in Snf-β knockout strain was increased by 32 % (from 19 to 25 %). However, in Snf-β overexpressing strain, lipid content of cell dry weight was decreased about 25 % (from 19 to 14.2 %) compared to the control strain. Total fatty acid analysis revealed that the expression of the Snf-β gene did not significantly affect the fatty acid composition of the strains. However, GLA content in biomass was increased from 2.5 % in control strain to 3.3 % in Snf-β knockout strain due to increased lipid accumulation and decreased to 1.83 % in Snf-β overexpressing strain. AMPK is known to inactivate acetyl-CoA carboxylase (ACC) which catalyzes the rate-limiting step in lipid synthesis. Snf-β manipulation also altered the expression level of the ACC1 gene which may indicate that Snf-β control lipid metabolism by regulating ACC1 gene.
CONCLUSIONS: Our results suggested that Snf-β gene plays an important role in regulating lipid accumulation in M. circinelloides WJ11. Moreover, it will be interesting to evaluate the potential of other key subunits of AMPK related to lipid metabolism. Better insight can show us the way to manipulate these subunits effectively for upscaling the lipid production. Up to our knowledge, it is the first study to investigate the role of Snf-β in lipid accumulation in M. circinelloides.
METHODS: Here, an advanced search of articles was conducted using PubMed, Scopus, EBSCOhost, and Web of Science databases using terms from Medical Subject Heading (MeSH) like SARS-CoV-2, lipid metabolism and transcriptomic as the keywords. From 428 retrieved studies, only clinical studies using next-generation sequencing as a gene expression method in COVID-19 patients were accepted. Study design, study population, sample type, the method for gene expression and differentially expressed genes (DEGs) were extracted from the five included studies. The DEGs obtained from the studies were pooled and analyzed using the bioinformatics software package, DAVID, to determine the enriched pathways. The DEGs involved in lipid metabolic pathways were selected and further analyzed using STRING and Cytoscape through visualization by protein-protein interaction (PPI) network complex.
RESULTS: The analysis identified nine remarkable clusters from the PPI complex, where cluster 1 showed the highest molecular interaction score. Three potential candidate genes (PPARG, IFITM3 and APOBEC3G) were pointed out from the integrated bioinformatics analysis in this systematic review and were chosen due to their significant role in regulating lipid metabolism. These candidate genes were significantly involved in enriched lipid metabolic pathways, mainly in regulating lipid homeostasis affecting the pathogenicity of SARS-CoV-2, specifically in mechanisms of viral entry and viral replication in COVID-19 patients.
CONCLUSIONS: Taken together, our findings in this systematic review highlight the affected lipid-metabolic pathways along with the affected genes upon SARS-CoV-2 invasion, which could be a potential target for new therapeutic strategies study in the future.