• Screening new antimicrobial candidates, using Differential Multivariate Analysis of Proteomic data against ecological stressors
  • Naghmeh Roohi-Shalmaee1,1 Rezvan Mousavi-Nadushan,2,* Kamran Pooshang Bagheri,3
    1. Venom and Biotherapeutics Molecules Lab, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran. Tehran, Iran.
    2. Department of Marine Science, Faculty of Natural Resources and Environment, Tehran North Branch, Islamic Azad University Tehran, Iran.
    3. Venom and Biotherapeutics Molecules Lab, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran. Tehran, Iran.


  • Introduction: Arthropods including terrestrial/aquatic insects and marine polychaetes produce the largest and most diverse inventory of Anti-Microbial Peptides (AMPs). Since invertebrates do not have the innate immune system, they are just dependent upon their adaptive immunity. This has provoked intense researches of marine invertebrates such as polychaetes, which has led to the discovery of molecular immune response. The production of these antimicrobial peptides/proteins can be innate or can be inducible by bacterial pollution. As a routine in many researches complete peptide/protein expression profiling of organism would be explored for antimicrobial ones. So in this research, we try to suggest a new high-performance technique, for selection of effective antibacterial molecules via integrating partial adaptive response (ecological adaptation) and multivariate molecular profiling analysis in Primer software, rather than antibacterial scanning of the whole profiling. So that, we analyzed all proteome detected by HPLC from polluted and control area’s samples for their ecological plasticity by DistLM analysis.
  • Methods: At first, the coelomic fluid of collected samples (sessile bristle worms, Bandar Abbas port- city, Persian Gulf) from polluted and control locations were injected to a C18 column (5-µm 100-Å - 250×4.6 mm, Beckman, USA) in a HPLC instrument (Knauer Co., Berlin, Germany). The fractions collected in polypropylene tubes, and dried in a freeze dryer system at -56 °C overnight. Then, Distance-based linear modeling (DistLM) was implemented to examine an ordination of fitted values from a given model and is constrained to find linear combinations of predictor variables of HPLC profiles/fractions that explain the significant variation in the percentage area compositions. This analysis was done using the PERMANOVA+ add-on package to the PRIMER v6. All extractive fractions were screened for antimicrobial/anticancer efficacy by Microbial inhibition tests and tetrazolium-based MTT assay.
  • Results: Twenty six fractions, 16 major and 10 minor, were seen in the coelomic fluid of the samples collected from polluted area and seventeen fractions were seen in the coelomic fluid collected from the samples lived in control area. The results of BIC criterion showed that 10 fractions (F1,F2,F3,F4,F5, F8, F11,F13,F20 F23) accounted for 100% of proteome variability and from these 10 fractions, 6 were detected as determinant or essential fractions for proteome plasticity or proteome differentiation of contaminated and control areas sample. Consequently, in vitro tests for antibacterial/anticancer activity of all 47 fraction validate the results of DistLM.
  • Conclusion: In this research we tried to combine protein biotechnology and multivariate ecological techniques together in order to scan the effective proteome. Using the ecological multivariate analysis we detect just 6 fractions that showed different expression instead of scanning all 47 obtained fractions. Finally among 6 detected fractions as effective fractions by DistLM, 3 factions were considered as antibiotic candidates based on their positive antibacterial effects and low cytotoxicity.
  • Keywords: Biotechnology, Ecology; HPLC; Primer software, DistLM analysis.