Back

Effect of EBV-transformation on Oxidative Phosphorylation Physiology in Human Cell lines

Crawford, D. L.; Weinstein, R. N.

2019-12-16 evolutionary biology
10.1101/2019.12.16.878025 bioRxiv
Show abstract

AO_SCPLOWBSTRACTC_SCPLOWDo the immortalized and cryopreserved white blood cells that are part of the 1,000 Human Genomes Project represent a valuable cellular physiological resource to investigate the importance of genome wide sequence variation? While much research exists on the nucleotide variation in the 1,000 Human Genomes, there are few quantitative measures of these humans physiologies. Fortunately, physiological measures can be done on the immortalized and preserved cells from each of the more than 1,000 individuals that are part of Human Genome project. However, these human white blood cells were immortalized by transforming them with the Epstein-Barr virus (EBV-transformed lymphoblastoid cell lines (LCL)). This transformation integrates the viral genome into the human genome and potentially affects important biological differences among individuals. The questions we address here are whether EBV transformations significantly alters the cellular physiology so that 1) replicate transformations within an individual are significantly different, and 2) whether the variance among replicates obscures the variation among individuals. To address these questions, we quantified oxidative phosphorylation (OxPhos) metabolism in LCLs from six individuals with 4 separate and independent EBV-transformations. We examined OxPhos because it is critical for energy production, and mutations in this pathway are responsible for most inborn metabolic diseases. The data presented here demonstrate that there are small but significant effects of EBV-transformations on some OxPhos parameters. In spite of significant variation due to transformations, there is greater and significant variation among individuals in their OxPhos metabolism. Thus, the LCLs from the 1,000 Human Genome project could provide valuable insights into the natural variation of cellular physiology because there is statistically significant variation among individuals when using these EBV-transformed cells.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
PLOS ONE
5266 papers in training set
Top 6%
23.4%
2
Genes
144 papers in training set
Top 0.1%
10.2%
3
International Journal of Molecular Sciences
494 papers in training set
Top 0.5%
7.6%
4
Medical Research Archives
11 papers in training set
Top 0.1%
5.7%
5
Scientific Reports
3612 papers in training set
Top 19%
5.1%
50% of probability mass above
6
Viruses
332 papers in training set
Top 1%
3.4%
7
Advanced Biology
29 papers in training set
Top 0.2%
1.8%
8
The Journal of Steroid Biochemistry and Molecular Biology
11 papers in training set
Top 0.1%
1.6%
9
Cells
249 papers in training set
Top 3%
1.6%
10
Virology
61 papers in training set
Top 0.7%
1.5%
11
iScience
1154 papers in training set
Top 22%
1.4%
12
Vaccines
198 papers in training set
Top 2%
1.2%
13
Free Radical Biology and Medicine
36 papers in training set
Top 0.5%
1.2%
14
Open Biology
106 papers in training set
Top 0.9%
1.2%
15
Biology Letters
76 papers in training set
Top 0.8%
1.2%
16
Microbiology
65 papers in training set
Top 1%
1.1%
17
Proceedings of the Royal Society B: Biological Sciences
393 papers in training set
Top 5%
1.1%
18
Cancers
213 papers in training set
Top 4%
1.1%
19
mBio
833 papers in training set
Top 10%
1.1%
20
The FEBS Journal
93 papers in training set
Top 1%
1.0%
21
Journal of Cellular Biochemistry
11 papers in training set
Top 0.2%
0.9%
22
Virus Research
37 papers in training set
Top 0.7%
0.9%
23
BMC Genomics
406 papers in training set
Top 8%
0.9%
24
Molecular Genetics and Genomics
12 papers in training set
Top 0.2%
0.6%
25
Placenta
22 papers in training set
Top 0.4%
0.6%
26
Biochemical Journal
91 papers in training set
Top 2%
0.6%
27
The Journal of Nutrition
25 papers in training set
Top 0.7%
0.5%
28
BMC Genomic Data
13 papers in training set
Top 0.3%
0.5%
29
Frontiers in Cell and Developmental Biology
233 papers in training set
Top 6%
0.5%
30
PeerJ
308 papers in training set
Top 14%
0.5%