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Team:GDSYZX - 2021.igem.org

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Feather Bioconversion


Reconstruction of a highly efficient feather degrading Streptomyces sp. SCUT-3 for waste recycling

Feather is one of the highest protein-containing resource in nature but is poorly reused. Previous reports have showed that Streptomyces sp. SCUT-3 can efficiently degrade feather. Although co-overexpression of CDO1 and protease Sep39 improved SCUT-3’s feather degradtion efficiency distinctly, SCUT-Ocdo1-sep39 yield fewer cells than SCUT-3. In this study, we revealed how to use different promotesr such as pro22610, pro24880 and pro1380 to increase SCUT-3’s feather degradtion efficiency without influence the growth of Streptomyces sp. SCUT-3.
Key Words: Feather; Bioconversion; SCUT-3; CDO1; Sep39


Genetic modification of feather degrading bacteria

To further improve the degradation rate of SCUT-3, genetic engineering is used in our program. Scientists have shown that Sep39 and CDO1 are the main protease involved in the hydrolysis of keratin, and their overexpression can increase the keratinase activity of SCUT-3. To further improve the degradation efficiency without influencing the growth rate, we attempt to modify SCUT-3 through replacing the constitutive promoter with well-characterized promoters. Finally, we found that the SCUT-Ocdo-p24880-Sep39 strain showed stronger keratinase activity, higher production of peptides and amino acids and great changes in feather degradation rate.


qtWGCNA: a user-friendly Qt interfact for WGCNA analyses

To facilitate and automatize the analyses of Weight Gene Co-expression Network Analyses (WGCNA), we initiatively use Qt technology to compile a C++ coded interface for iterativeWGCNA python script. With the help of qtWGCNA, we are able to run WGCNA analyses on different RNA-seq or other genomics data, which may greatly save the time of research specialist staff and relieve them from tedious bioinformatics coding. In this study, we qtWGCNA to analyze RNA-seq data from feather/chitin treatment samples, among which we found the important modules consisting of the promoter genes that we mentioned before and help to infer more congeneric promoter like p24880. It is worth noting that our software is also useful for data mining of other types of engineered bacteria data.