| Autor: |
Baeshen NN; Department of Biology, College of Sciences and Arts at Khulais, University of Jeddah, Jeddah 21959, Saudi Arabia., Baz L; Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia., Shami AY; Department of Biology, College of Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11617, Saudi Arabia., Ashy RA; Department of Biology, College of Science, University of Jeddah, Jeddah 21493, Saudi Arabia., Jalal RS; Department of Biology, College of Science, University of Jeddah, Jeddah 21493, Saudi Arabia., Abulfaraj AA; Biological Sciences Department, College of Science & Arts, King Abdulaziz University, Rabigh 21911, Saudi Arabia., Refai M; Department of Biochemistry, College of Science, University of Jeddah, Jeddah 21493, Saudi Arabia., Majeed MA; Department of Biology, College of Science, University of Jeddah, Jeddah 21493, Saudi Arabia., Abuzahrah SS; Department of Biology, College of Science, University of Jeddah, Jeddah 21493, Saudi Arabia., Abdelkader H; Virus Research Department, Molecular Biology Laboratory, PPRI, ARC, Giza 12613, Egypt., Baeshen NA; Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia., Baeshen MN; Department of Biology, College of Science, University of Jeddah, Jeddah 21493, Saudi Arabia. |
| Abstrakt: |
The coast of the Red Sea in Jeddah City is home to a unique microbial community that has adapted to extreme environmental conditions. Therefore, it is essential to characterize the microbial community in this unique microbiome to predict how environmental changes will affect it. The aim of this study was to conduct metagenomic sequencing of 16S rRNA and ITS rRNA genes for the taxonomic classification of the microbial community in soil samples associated with the halophytic plants Tamarix aphylla and Halopeplis perfoliata . Fifteen soil samples were collected in triplicate to enhance robustness and minimize sampling bias. Firstly, to identify novel microbial candidates, the gDNAs were isolated from the saline soil samples surrounding each plant, and then bacterial 16S (V3-V4) and fungal ITS1 regions were sequenced utilizing a high-throughput approach (next-generation sequencing; NGS) on an Illumina MiSeq platform. Quality assessment of the constructed amplicon libraries was conducted using Agilent Bioanalyzer and fluorometric quantification methods. The raw data were processed and analyzed using the Pipeline (Nova Lifetech, Singapore) for bioinformatics analysis. Based on the total number of readings, it was determined that the phylum Actinobacteriota was the most prevalent in the soil samples examined, followed by the phylum Proteobacteria . Based on ITS rRNA gene analysis, the alpha and beta fungal diversity in the studied soil samples revealed that the fungal population is structured into various groups according to the crust (c) and/or rhizosphere (r) plant parts. Fungal communities in the soil samples indicated that Ascomycota and Basidiomycota were the two most abundant phyla based on the total amount of sequence reads. Secondly, heat-map analysis of the diversity indices showed that the bacterial alpha diversity, as measured by Shannon, Simpson, and InvSimpson, was associated with soil crust (Hc and Tc enclosing H. perfoliata and T. aphylla , respectively) and that the soil rhizosphere (Hr and Tr) was strongly correlated with bacterial beta diversity. Finally, fungal-associated Tc and Hc samples clustered together, according to observations made using the Fisher and Chao1 methods, and Hr and Tr samples clustered together according to Shannon, Simpson, and InvSimpson analyses. As a result of the soil investigation, potential agents that have been identified could lead to innovative agricultural, medical, and industrial applications. |
