COVID-19 can result in severe lung injury. It remained to be determined why diabetic individuals with uncontrolled glucose levels are more prone to develop the severe form of COVID-19. The molecular mechanism underlying SARS-CoV-2 infection and what determines the onset of the cytokine storm found in severe COVID-19 patients are unknown. Monocytes and macrophages are the most enriched immune cell types in the lungs of COVID-19 patients and appear to have a central role in the pathogenicity of the disease. These cells adapt their metabolism upon infection and become highly glycolytic, which facilitates SARS-CoV-2 replication. The infection triggers mitochondrial ROS production, which induces stabilization of hypoxia-inducible factor-1α (HIF-1α) and consequently promotes glycolysis. HIF-1α-induced changes in monocyte metabolism by SARS-CoV-2 infection directly inhibit T cell response and reduce epithelial cell survival. Targeting HIF-1ɑ may have great therapeutic potential for the development of novel drugs to treat COVID-19.

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Two in silico methodologies were implemented to reveal the molecular signatures of inorganic hydroxyapatite and β‐TCP materials from a transcriptome database to compare biomaterials. To test this new methodology, we choose the array E‐MTAB‐7219, which contains the transcription profile of osteoblastic cell line seeded onto 15 different biomaterials up to 48 hr. The expansive potential of the methodology was tested from the construction of customized signatures. We present, for the first time, a methodology to compare the performance of different biomaterials using the transcriptome profile of the cell through the Gene set variation analysis (GSVA) score. To test this methodology, we implemented two methods based on MSigDB collections, using all the collections and sub‐collections except the Hallmark collection, which was used in the second method. The result of this analysis provided an initial understanding of biomaterial grouping based on the cell transcriptional landscape. The comparison using GSVA score combined efforts and expand the potential to compare biomaterials using transcriptome profile. Altogether, our results provide a better understanding of the comparison of different biomaterials and suggest a possibility of the new methodology be applied to the prospection of new biomaterials.