Diversity and Antimicrobial Activities of Actinobacteria Isolated from Mining Soils in Midelt Region, Morocco.
Soumia Ait AssouJaouad AnissiKhalid SendideMohammed El HassouniPublished in: TheScientificWorldJournal (2023)
Multidrug-resistant bacteria have emerged as a serious global health threat that requires, more than ever before, an urgent need for novel and more effective drugs. In this regard, the present study sheds light on the diversity and antimicrobial potential of Actinobacteria isolates in mining ecosystems. We have indeed investigated the production of bioactive molecules by the Actinobacteria isolated from abandoned mining areas in Midelt, Morocco, where average contents of lead (Pb) and cadmium (Cd) are higher than normal world levels. One hundred and forty-five Actinobacteria isolates were isolated and characterized based on morphological, chemotaxonomical, biochemical, and molecular data. Most of the 145 isolates were identified as Streptomyces . Isolates affiliated to the genera Amycolatopsis , Lentzea , Actinopolymorpha , and Pseudonocardia were also found. Antimicrobial producing potentials of Actinobacteria isolates were assessed against eight test microorganisms Gram + and Gram - bacteria and yeast. Out of 145 isolates, 51 showed antimicrobial activities against at least one test microorganism. 31 isolates inhibited only bacteria, 7 showed activity against bacteria and Candida albicans , and 13 displayed activity against C . albicans solely. Our findings suggest that Actinobacteria isolated from natural heavy metal ecosystems may be a valuable source of novel secondary metabolites and therefore of new biotechnologically promising antimicrobial compounds.
Keyphrases
- heavy metals
- candida albicans
- genetic diversity
- staphylococcus aureus
- multidrug resistant
- global health
- gram negative
- climate change
- biofilm formation
- public health
- risk assessment
- mass spectrometry
- machine learning
- human health
- ms ms
- drug resistant
- pseudomonas aeruginosa
- cystic fibrosis
- drinking water
- data analysis
- artificial intelligence
- sewage sludge