Imidazolium-based ionic liquids disrupt <i>saccharomyces cerevisiae</i> cell membrane integrity


ERGÜDEN B., Tarlak F., ÜNVER Y.

ARCHIVES OF MICROBIOLOGY, cilt.206, sa.7, 2024 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 206 Sayı: 7
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s00203-024-04043-y
  • Dergi Adı: ARCHIVES OF MICROBIOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Chemical Abstracts Core, EMBASE, Environment Index, Food Science & Technology Abstracts, Veterinary Science Database
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Ionic liquids (ILs) are interesting chemical compounds that have a wide range of industrial and scientific applications. They have extraordinary properties, such as the tunability of many of their physical properties and, accordingly, their activities; and the ease of synthesis methods. Hence, they became important building blocks in catalysis, extraction, electrochemistry, analytics, biotechnology, etc. This study determined antifungal activities of various imidazolium-based ionic liquids against yeast Saccharomyces cerevisiae via minimum inhibitory concentration (MIC) estimation method. Increasing the length of the alkyl group attached to the imidazolium cation, enhanced the antifungal activity of the ILs, as well as their ability of the disruption of the cell membrane integrity. FTIR studies performed on the S. cerevisiae cells treated with the ILs revealed alterations in the biochemical composition of these cells. Interestingly, the alterations in fatty acid content occurred in parallel with the increase in the activity of the molecules upon the increase in the length of the attached alkyl group. This trend was confirmed by statistical analysis and machine learning methodology. The classification of antifungal activities based on FTIR spectra of S. cerevisiae cells yielded a prediction accuracy of 83%, indicating the pharmacy and medicine industries could benefit from machine learning methodology. Furthermore, synthesized ionic compounds exhibit significant potential for pharmaceutical and medical applications.