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Network reconstruction of the mouse secretory pathway applied on CHO cell transcriptome data

Overview of attention for article published in BMC Systems Biology, March 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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6 X users
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1 patent
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1 Facebook page

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Title
Network reconstruction of the mouse secretory pathway applied on CHO cell transcriptome data
Published in
BMC Systems Biology, March 2017
DOI 10.1186/s12918-017-0414-4
Pubmed ID
Authors

Anne Mathilde Lund, Christian Schrøder Kaas, Julian Brandl, Lasse Ebdrup Pedersen, Helene Faustrup Kildegaard, Claus Kristensen, Mikael Rørdam Andersen

Abstract

Protein secretion is one of the most important processes in eukaryotes. It is based on a highly complex machinery involving numerous proteins in several cellular compartments. The elucidation of the cell biology of the secretory machinery is of great importance, as it drives protein expression for biopharmaceutical industry, a 140 billion USD global market. However, the complexity of secretory process is difficult to describe using a simple reductionist approach, and therefore a promising avenue is to employ the tools of systems biology. On the basis of manual curation of the literature on the yeast, human, and mouse secretory pathway, we have compiled a comprehensive catalogue of characterized proteins with functional annotation and their interconnectivity. Thus we have established the most elaborate reconstruction (RECON) of the functional secretion pathway network to date, counting 801 different components in mouse. By employing our mouse RECON to the CHO-K1 genome in a comparative genomic approach, we could reconstruct the protein secretory pathway of CHO cells counting 764 CHO components. This RECON furthermore facilitated the development of three alternative methods to study protein secretion through graphical visualizations of omics data. We have demonstrated the use of these methods to identify potential new and known targets for engineering improved growth and IgG production, as well as the general observation that CHO cells seem to have less strict transcriptional regulation of protein secretion than healthy mouse cells. The RECON of the secretory pathway represents a strong tool for interpretation of data related to protein secretion as illustrated with transcriptomic data of Chinese Hamster Ovary (CHO) cells, the main platform for mammalian protein production.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Denmark 1 1%
Unknown 95 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 28%
Researcher 20 21%
Student > Master 11 11%
Student > Bachelor 6 6%
Student > Doctoral Student 5 5%
Other 11 11%
Unknown 17 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 29 30%
Agricultural and Biological Sciences 19 20%
Engineering 10 10%
Chemical Engineering 6 6%
Pharmacology, Toxicology and Pharmaceutical Science 4 4%
Other 9 9%
Unknown 20 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 22 February 2018.
All research outputs
#4,166,338
of 22,959,818 outputs
Outputs from BMC Systems Biology
#126
of 1,144 outputs
Outputs of similar age
#75,039
of 308,059 outputs
Outputs of similar age from BMC Systems Biology
#3
of 32 outputs
Altmetric has tracked 22,959,818 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,144 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 88% 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 308,059 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.