Transcriptome and Literature Mining Highlight the Differential Expression of ERLIN1 in Immune Cells during Sepsis.
Susie S Y HuangMohammed ToufiqLuis R SaraivaNicholas Van PanhuysDamien ChaussabelMathieu GarandPublished in: Biology (2021)
Sepsis results from the dysregulation of the host immune system. This highly variable disease affects 19 million people globally, and accounts for 5 million deaths annually. In transcriptomic datasets curated from public repositories, we observed a consistent upregulation (3.26-5.29 fold) of ERLIN1-a gene coding for an ER membrane prohibitin and a regulator of inositol 1, 4, 5-trisphosphate receptors and sterol regulatory element-binding proteins-under septic conditions in healthy neutrophils, monocytes, and whole blood. In vitro expression of the ERLIN1 gene and proteins was measured by stimulating the whole blood of healthy volunteers to a combination of lipopolysaccharide and peptidoglycan. Septic stimulation induced a significant increase in ERLIN1 expression; however, ERLIN1 was differentially expressed among the immune blood cell subsets. ERLIN1 was uniquely increased in whole blood neutrophils, and confirmed in the differentiated HL60 cell line. The scarcity of ERLIN1 in sepsis literature indicates a knowledge gap between the functions of ERLIN1, calcium homeostasis, and cholesterol and fatty acid biosynthesis, and sepsis. In combination with experimental data, we bring forth the hypothesis that ERLIN1 is variably modulated among immune cells in response to cellular perturbations, and has implications for ER functions and/or ER membrane protein components during sepsis.
Keyphrases
- acute kidney injury
- septic shock
- intensive care unit
- poor prognosis
- single cell
- systematic review
- healthcare
- genome wide
- rna seq
- fatty acid
- gene expression
- transcription factor
- endoplasmic reticulum
- breast cancer cells
- copy number
- emergency department
- stem cells
- peripheral blood
- estrogen receptor
- long non coding rna
- binding protein
- toll like receptor
- dna methylation
- artificial intelligence
- cell therapy
- big data
- high glucose