Abstract
Atlantis II is the largest brine pool in the Red Sea. It lies at 2,200m deep with an area of about 60km2. The lower convective layer of the Atlantis II brine pool (ATII-LCL) is characterized by extreme conditions, the temperature reaches 68.2°C, salinity of 270 psu, and there is high concentration of heavy metals. Microbial communities inhabiting this harsh environment are expected to have enzymes and proteins that are adapted to these conditions. Such proteins and enzymes would be very attractive candidates not just to understand the structural alteration that lead to their adaptation to these abiotic factors, but also for their potential use in industrial and biotechnological applications. In this work we established an ATII-LCL metagenomics dataset of potential biomass and cell wall degrading enzymes. Out of 1,337,597 pyrosequencing reads, a total of 28,547 contigs were assembled using Newbler GS assembler version 2.6. A total of 58,124 predicted open reading frames (ORFs) were identified using Metagene Annotator program. We searched the 58,124 ORFs for domains that matched to cell wall and biomass degrading enzymes using the Pfam database Version 26. The 53 matched sequences were confirmed by BLASTx search against NCBI nr database. Upstream regulatory elements, ribosome binding sequence, and secretory signal peptide sequences for secretion were checked for their presence. Additionally, halophilicity based on high prevalence of aspartic and glutamic acids was checked. Out of the 53 potential ORFs, only 14 presented a full-length coding sequence. We selected 4 ORFs with high similarities to cellulases, alpha galactosidase and cell wall lytic enzyme for further investigation. The four proteins have traditional signal peptide for secretion, and high occurrences of aspartic and glutamic amino acids when compared with non-halophilic orthologues. Moreover, the 3D structures of the four proteins were predicted and the relevant acidic amino acid residues were located on the surface of the molecules. Based on these features, we believe that the four proteins should have unique properties regarding stability in high saline solution, high temperature, and elevated concentration of heavy metals. Thus, this work established a dataset of the most abundant glycosyl hydrolases present in the microbial community of the ATII-LCL environment, and selected the most promising candidates for further molecular and catalytic characterization.
Department
Biotechnology Program
Degree Name
MS in Biotechnology
Graduation Date
6-1-2012
Submission Date
June 2012
First Advisor
El Dorry, Hamza
Extent
NA
Document Type
Master's Thesis
Library of Congress Subject Heading 1
Biotechnology -- Red Sea -- Egypt.
Library of Congress Subject Heading 2
Microbiology -- Red Sea -- Egypt.
Rights
The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy.
Institutional Review Board (IRB) Approval
Not necessary for this item
Recommended Citation
APA Citation
Mofeed, N.
(2012).In silico identification of potential biomass and cell wall degrading enzymes in the microbial community of the Red Sea Atlantis-II brine pool using metagenomic approach [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/1185
MLA Citation
Mofeed, Norhan Mohammed Magdy. In silico identification of potential biomass and cell wall degrading enzymes in the microbial community of the Red Sea Atlantis-II brine pool using metagenomic approach. 2012. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/1185
Comments
I would like to thank my mentor, Professor Dr. Hamza El Dorry for advising me and supervising my thesis, Dr. Mohammed Ghazy for his continuous help and support throughout my work. I would like as well to thank Dr. Rania Siam for her support and guidance throughout my studying. I would also like to thank Dr. Ari Ferreira, Mr. Hazem Sharaf, Mr. Mustafa Adel and Ms Mariam RizkAllah for their help in the computatiol work. And I would like to extend my thanks to Mr. Mohamed Maged who helped me with everything. I would also like to thank KAUST for providing us with the funds necessary to do this work. And I would extend my thanks to the Alfi foundation which provided me with a fellowship and ebled me to complete my thesis.