The S100 Protein Family as Players and Therapeutic Targets in Pulmonary Diseases.
Zeeshan SattarAlnardo LoraBakr JundiChristopher RailwahPatrick GeraghtyPublished in: Pulmonary medicine (2021)
The S100 protein family consists of over 20 members in humans that are involved in many intracellular and extracellular processes, including proliferation, differentiation, apoptosis, Ca2 + homeostasis, energy metabolism, inflammation, tissue repair, and migration/invasion. Although there are structural similarities between each member, they are not functionally interchangeable. The S100 proteins function both as intracellular Ca2+ sensors and as extracellular factors. Dysregulated responses of multiple members of the S100 family are observed in several diseases, including the lungs (asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, cystic fibrosis, pulmonary hypertension, and lung cancer). To this degree, extensive research was undertaken to identify their roles in pulmonary disease pathogenesis and the identification of inhibitors for several S100 family members that have progressed to clinical trials in patients for nonpulmonary conditions. This review outlines the potential role of each S100 protein in pulmonary diseases, details the possible mechanisms observed in diseases, and outlines potential therapeutic strategies for treatment.
Keyphrases
- pulmonary hypertension
- chronic obstructive pulmonary disease
- idiopathic pulmonary fibrosis
- cystic fibrosis
- clinical trial
- lung function
- pulmonary artery
- oxidative stress
- end stage renal disease
- protein protein
- pulmonary arterial hypertension
- chronic kidney disease
- amino acid
- prognostic factors
- ejection fraction
- signaling pathway
- endoplasmic reticulum stress
- pseudomonas aeruginosa
- binding protein
- randomized controlled trial
- reactive oxygen species
- patient reported outcomes
- interstitial lung disease
- small molecule
- combination therapy
- risk assessment
- systemic sclerosis
- coronary artery
- protein kinase
- bioinformatics analysis