Morphological and Genetic Characterization of Economically Important Aspergilli

Naureen Akhtar

Author address: 

Institute of Agricultural Sciences, University of the Punjab, Lahore, Pakistan

Abstract: 

Aspergillus is the best-known and most-studied fungal genus due to the importance of several of its species being mycotoxins producers, animal and plant pathogens as well as their wide use in industry and research. For present study, eighteen strains of Aspergillus were characterized using polyphasic taxonomic approach. Initially, species were identified on the basis of morphological characters and then identification was confirmed using sequence analysis of the Internal Transcribed Spacer (ITS) region of rDNA and partial calmodulin gene (CAL). Phylogenetic analysis of strains was also carried out using the nucleotide sequence of both ITS and CAL gene. Based on morphology as well as nucleotide sequences, strains of Aspergillus were categorized into five different taxonomic groups. Five species of A. niger group that are characterized for present study are A. niger, A. awamorii, A. tubingensis, A. welwitschiae, A. neoniger and A. phoenicis. Two isolates each of A. flavus from Aspergillus flavus group and A. fumigatus from Aspergillus fumigatus group; one isolate each of A. terreus from Aspergillus terreus group and A. tamarii from Aspergillus tamarii group were characterized. BLAST results using ITS and CAL nucleotide sequences revealed 99-100% identity with the many of their respective strains deposited to GenBank. Phylogenetic analysis of ITS based results revealed lack of clear distinction amongst morphologically similar isolates however nucleotide sequence of CAL gene grouped morphologically similar strains in same clade. Present study concludes that proper identification using polyphasic approach is indeed requirement that will contribute information in stable taxonomy and nomenclature of Aspergillus group.

2017

abstract No: 

541

Full conference title: 

Microbiology Society Annual Conference 2017
    • MS 2017