Antimicrobial resistance is one of the serious global challenges in the current century. The fact that resistance genes transfer between bacteria, coupled with the fact that the world is connected through complex dynamics. Studying microbial behavior and understanding the different factors coffering microbial resistance to a broad spectrum of the available drug classes, parallel with a comprehensive analysis of the natural microbial products as the primary source of the novel antibiotics, might shed some light on solutions for this problem. Microbial environments harbor a wide range of secondary metabolites (SM) with different functional groups. SMs are not directly involved in vital microbial processes such as reproduction, growth, and development. However, these organic compounds, which exist in many different chemical structures, carry out a broad range of functions. Some bioactive SMs are widely used in drug development of various therapeutic classes such as antibacterial, anticancer, immunosuppressant, diabetic, and cholesterol-lowering agents. These bioactive compounds’ metabolic pathways are encoded by co-localized genes collectively called Biosynthetic Gene Clusters (BGCs). The majority of the discovered bioactive natural products are from microbial strains that are cultivatable. However, the advancement in sequencing techniques, bioinformatics, and metagenomics opened unlimited opportunities to reach and study the uncultivatable microbial communities, which represent the more significant fraction of the underexplored microbial ecology. In this study, selected samples of seven selected metatranscriptomic/metagenomic datasets were subjected to assembly, taxonomic assignment to the reads, and assembled contigs. The aim of this study is two-fold. Firstly, the assembled contigs were then investigated by two primary distinct computational methods, namely antibiotics and Secondary Metabolite Analysis Shell (antiSMASH) and deep-learning (deepBGC) methods. A comparative study was performed to determine the biosynthetic gene clusters (BGCs) present in each of the included samples and compare their taxonomic differences. Secondly, the assembled contigs were also analyzed to determine the antimicrobial resistance (AMR) genes present in each sample by using the Resistance Gene Identifier (RGI) algorithm, which is a part of the Comprehensive Antibiotic Resistance Database (CARD). A total of 65 samples from the seven selected v metagenomic and metatranscriptomic datasets were investigated by antiSMASH, deepBGC pipelines, and CARD in the present study. The different classes of detected BGCs and their corresponding microbial taxa and the antimicrobial resistance gene families and their corresponding resistance mechanisms against specific drug classes were reported. In the current study, we reported that the datasets with a large extent of variability (i.e. sex, age and illness state) due to the nature of their environments, such as host microbiome samples of patients in two ecosystems (COVID-19 & Atopic Dermatitis), gave the most variable number of BGC classes detected by antiSMASH, where 19 different classes detected in skin microbiome of AD patients and 16 different classes detected in gut microbiome of COVID-19 patients. On the other hand and due to the selection pressure on the microbial ecosystems by the wide use of antibiotics, gut microbiome of COVID-19 patients’ and water sewage samples had more than 70% of the detected AMR gene families where gut microbiome of COVID-19 patients’ sample alone reported to had more than 50% of AMR genes detected by CARD. In conclusion, ecological characteristics and microbial diversity in terms of composition and relative abundance dramatically affect the dynamics of secondary metabolites’ production and transferring antimicrobial resistance genes between bacteria. Microbial strains with higher biosynthetic and antimicrobial resistance potentials were enriched in environments with a rich microbial diversity such as host microbiome (i.e., COVID-19 patients), with patterns of abundance of biosynthetic gene clusters and AMR genes fluctuating by taxonomy.
MS in Biotechnology
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Committee Member 2
Khaled Abou Aisha
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(2021).Mining Selected Metagenomes/metatranscriptomes for Biosynthetic Gene Clusters and Antimicrobial Resistance Genes [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
Yamany, Ahmed. Mining Selected Metagenomes/metatranscriptomes for Biosynthetic Gene Clusters and Antimicrobial Resistance Genes. 2021. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.