Recombinant Production of Bifidobacterial Endoglycosidases for N-glycan Release.

Clicks: 245
ID: 270046
2021
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #47 of 62 articles by views in Journal of visualized experiments : JoVE

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Protein glycosylation is a diverse and common post-translational modification that has been associated with many important roles such as protein function, including protein folding, stability, enzymatic protection, and biological recognition. N-glycans attached to glycoproteins (such as lactoferrin, lactadherin, and immunoglobulins) cannot be digested by the host and reach the large intestine, where they are consumed by certain beneficial microbes. Therefore, they are considered next-generation prebiotic compounds that can selectively stimulate the gut microbiome's beneficial microorganisms. However, the isolation of these new classes of prebiotics requires novel enzymes. Here, we describe the recombinant production of novel glycosidases from different Bifidobacteria strains (isolated from infants, rabbits, chicken, and bumblebee) for improved N-glycan isolation from glycoproteins. The method presented in this study includes the following steps: molecular cloning of Bifidobacterial genes by an in vivo recombinational cloning strategy, control of transformation success, protein induction, and protein purification.
Reference Key
sucu2021recombinantjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sucu, Berfin;Bayraktar, Ayşe;Duman, Hatice;Arslan, Ayşenur;Kaplan, Merve;Karyelioğlu, Melda;Ntelitze, Eda;Taştekin, Taner;Yetkin, Seray;Ertürk, Melih;Frese, Steven A;Henrick, Bethany M;Kayili, Hacı Mehmet;Salih, Bekir;Karav, Sercan;
Journal Journal of visualized experiments : JoVE
Year 2021
DOI
10.3791/62804
URL
Keywords
prediction model casualties pandemic covid-19 linear dynamics non-linear dynamics

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.