Directing in Vitro Selection towards G-quadruplex-forming Aptamers to Inhibit HMGB1 Pathological Activity.
Ettore NapolitanoAndrea CriscuoloClaudia RiccardiCarla L EspositoSilvia CatuognoGabriele CoppolaGiovanni N RovielloDaniela MontesarchioDomenica MusumeciPublished in: Angewandte Chemie (International ed. in English) (2024)
In the search for novel, effective inhibitors of High-Mobility Group Box1 (HMGB1)-a protein involved in various inflammatory and autoimmune diseases as well as in cancer-we herein discovered a set of anti-HMGB1 G-quadruplex(G4)-forming aptamers by using an in vitro selection procedure applied to a doped library of guanine-rich oligonucleotides. The selected DNA sequences were then studied in a pseudo-physiological buffer mimicking the extracellular medium, where HMGB1 exerts its pathological activity, using spectroscopic, electrophoretic, and chromatographic techniques. All the oligonucleotides proved to fold into monomeric G4s and in some cases also dimeric species, stable at physiological temperature. Remarkably, the protein preferentially recognized the sequences forming dimeric parallel G4 structures, as evidenced by a properly designed chemiluminescent binding assay which also highlighted a good selectivity of these aptamers for HMGB1. Moreover, all aptamers showed anti-HMGB1 activity, inhibiting protein-induced cell migration. The acquired data allowed identifying L12 as the best anti-HMGB1 aptamer, featured by high thermal and enzymatic stability, no toxicity at least up to 5 μM concentration on healthy cells, along with potent anti-HMGB1 activity (IC 50 ca. 28 nM) and good binding affinity for the protein, thus indicating it as a very promising lead candidate for in vivo studies.
Keyphrases
- nucleic acid
- binding protein
- cell migration
- protein protein
- oxidative stress
- gold nanoparticles
- induced apoptosis
- transcription factor
- quantum dots
- hydrogen peroxide
- machine learning
- small molecule
- cell proliferation
- mass spectrometry
- nitric oxide
- high resolution
- cell death
- big data
- molecular docking
- young adults
- cell cycle arrest
- deep learning
- genetic diversity
- drug induced
- anti inflammatory
- solid state