Evidence map›Paper›PMID 38112968›Full record

ArticleDoklady. Biochemistry and biophysics2024

Enhancing Human Glycoprotein Hormones Production in CHO Cells Using Heterologous Beta-Chain Signal Peptides.

M V Sinegubova, D E Kolesov, L K Dayanova, I I Vorobiev, N A Orlova

Open access · hybridAbstract read
In one paragraph

Article in Doklady. Biochemistry and biophysics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

M V SinegubovaFederal Research Center "Fundamentals of Biotechnology," Russian Academy of Sciences, Moscow, Russia. mvsineg@gmail.com.
D E KolesovFederal Research Center "Fundamentals of Biotechnology," Russian Academy of Sciences, Moscow, Russia.
L K DayanovaFederal Research Center "Fundamentals of Biotechnology," Russian Academy of Sciences, Moscow, Russia.
I I VorobievFederal Research Center "Fundamentals of Biotechnology," Russian Academy of Sciences, Moscow, Russia.
N A OrlovaFederal Research Center "Fundamentals of Biotechnology," Russian Academy of Sciences, Moscow, Russia.
Russian Academy of Sciences · RUFederal Research Centre «Fundamentals of Biotechnology» of the Russian Academy of Sciences · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We studied the influence of heterologous signal peptides in the β-chains of glycoprotein hormones on the biosynthesis of these hormones in a transiently transfected culture of Chinese hamster ovary cells CHO S. When the natural signal peptides of the β-chains were replaced with the heterologous signal peptide of human serum albumin, cell productivity was increased 2-2.5 times for human luteinizing hormone, human chorionic gonadotropin, and human thyroid-stimulating hormone, but not for human follicle-stimulating hormone. No significant increase in cell productivity was observed for human azurocidin signal peptide and human glycoprotein hormone α-chain signal peptide. The used approach allows quick assessing the effect of heterologous signal peptides on the biosynthesis of heterodimeric proteins of various classes.

Indexed as

GlycoproteinsProtein Sorting SignalsAnimalsCHO CellsChorionic GonadotropinCricetinaeCricetulusHumansChorionic GonadotropinGlycoproteinsProtein Sorting SignalsChinese hamster ovary cellsglycoprotein hormonessignal peptidestransient expression

Identifiers

PMID38112968
PMCPMC11021241
OpenAlexW4389933279

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.