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Custom made inclusion bodies: impact of classical process parameters and physiological parameters on inclusion body quality attributes

Overview of attention for article published in Microbial Cell Factories, September 2018
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  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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73 Mendeley
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Title
Custom made inclusion bodies: impact of classical process parameters and physiological parameters on inclusion body quality attributes
Published in
Microbial Cell Factories, September 2018
DOI 10.1186/s12934-018-0997-5
Pubmed ID
Authors

Christoph Slouka, Julian Kopp, Stefan Hutwimmer, Michael Strahammer, Daniel Strohmer, Elisabeth Eitenberger, Andreas Schwaighofer, Christoph Herwig

Abstract

The bacterium E. coli is a major host for recombinant protein production of non-glycosylated products. Depending on the expression strategy, the recombinant protein can be located intracellularly. In many cases the formation of inclusion bodies (IBs), protein aggregates inside of the cytoplasm of the cell, is favored in order to achieve high productivities and to cope with toxic products. However, subsequent downstream processing, including homogenization of the cells, centrifugation or solubilization of the IBs, is prone to variable process performance or can be characterized by low extraction yields as published elsewhere. It is hypothesized that variations in IB quality attributes (QA) are responsible for those effects and that such attributes can be controlled by upstream process conditions. This contribution is aimed at analyzing how standard process parameters, such as pH and temperature (T) as well as different controlled levels of physiological parameters, such as specific substrate uptake rates, can vary IB quality attributes. Classical process parameters like pH and T influence the expression of analyzed IB. The effect on the three QAs titer, size and purity could be successfully revealed. The developed data driven model showed that low temperatures and low pH are favorable for the expression of the two tested industrially relevant proteins. Based on this knowledge, physiological control using specific substrate feeding rate (of glucose) qs,Glu is altered and the impact is tested for one protein. Time dependent monitoring of IB QA-titer, purity, IB bead size-showed a dependence on classical process parameters pH and temperature. These findings are confirmed using a second industrially relevant strain. Optimized process conditions for pH and temperature were used to determine dependence on the physiological parameters, the specific substrate uptake rate (qs,Glu). Higher qs,Glu were shown to have a strong influence on the analyzed IB QAs and drastically increase the titer and purity in early time stages. We therefore present a novel approach to modulate-time dependently-quality attributes in upstream processing to enable robust downstream processing.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 18%
Student > Master 12 16%
Student > Bachelor 11 15%
Researcher 8 11%
Student > Postgraduate 4 5%
Other 7 10%
Unknown 18 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 33 45%
Agricultural and Biological Sciences 8 11%
Chemistry 4 5%
Chemical Engineering 3 4%
Engineering 3 4%
Other 4 5%
Unknown 18 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 September 2018.
All research outputs
#7,576,264
of 23,103,903 outputs
Outputs from Microbial Cell Factories
#539
of 1,618 outputs
Outputs of similar age
#134,250
of 342,063 outputs
Outputs of similar age from Microbial Cell Factories
#9
of 34 outputs
Altmetric has tracked 23,103,903 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,618 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 53% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 342,063 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.