Back

Predictive evolution of metabolic phenotypes using model-designed selection niches

Jouhten, P.; Konstantinidis, D.; Pereira, F.; Andrejev, S.; Grkovska, K.; Ghiaci, P.; Beltran, G.; Almaas, E.; Mas, A.; Warringer, J.; Gonzalez, R.; Morales, P.; Patil, K. R.

2021-05-16 systems biology
10.1101/2021.05.14.443989 bioRxiv
Show abstract

Traits lacking fitness benefit cannot be directly selected for under Darwinian evolution. Thus, features such as metabolite secretion are currently inaccessible to adaptive laboratory evolution. Here, we utilize environment-dependency of trait correlations to enable Darwinian selection of fitness-neutral or costly traits. We use metabolic models to design selection niches and to identify surrogate traits that are genetically correlated with cell fitness in the selection niche but coupled to the desired trait in the target niche. Adaptive evolution in the selection niche and subsequent return to the target niche is thereby predicted to enhance the desired trait. We experimentally validate the theory by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds in wine fermentation. Genomic, transcriptomic, and proteomic changes in the evolved strains confirmed the predicted flux re-routing to aroma biosynthesis. The use of model-designed selection niches facilitates the predictive evolution of fitness-costly traits for ecological and biotechnological applications.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Metabolic Engineering
○ 75 papers in training set
Top 0.1%
12.4%
Similar papers in this journal
2
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 3%
11.6%
Similar papers in this journal
3
Cell Systems
○ 201 papers in training set
Top 0.4%
7.7%
Similar papers in this journal
4
mSystems
● 394 papers in training set
Top 1.0%
7.7%
Similar papers in this journal
5
iScience
○ 1154 papers in training set
Top 2%
5.4%
Similar papers in this journal
6
eLife
● 5828 papers in training set
Top 28%
4.3%
7
PLOS Computational Biology
● 1863 papers in training set
Top 9%
3.9%
50% of probability mass above
8
Nature Communications
○ 5641 papers in training set
Top 34%
3.4%
9
npj Systems Biology and Applications
○ 125 papers in training set
Top 0.5%
3.1%
10
BMC Genomics
○ 406 papers in training set
Top 2%
3.1%
11
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 2%
2.3%
12
Science Advances
● 1243 papers in training set
Top 16%
2.3%
13
G3: Genes, Genomes, Genetics
◐ 252 papers in training set
Top 2%
2.1%
14
Biotechnology for Biofuels
○ 14 papers in training set
Top 0.2%
1.7%
15
mBio
● 833 papers in training set
Top 8%
1.7%
16
Molecular Systems Biology
○ 162 papers in training set
Top 2%
1.7%
17
Scientific Reports
○ 3612 papers in training set
Top 55%
1.6%
18
Cell Reports
○ 1498 papers in training set
Top 20%
1.6%
19
Applied and Environmental Microbiology
● 339 papers in training set
Top 4%
1.6%
20
Environmental Microbiology
○ 133 papers in training set
Top 2%
1.5%
21
Microbial Biotechnology
○ 34 papers in training set
Top 0.5%
1.5%
22
Microbiology Spectrum
● 469 papers in training set
Top 8%
1.5%
23
Communications Biology
○ 993 papers in training set
Top 18%
1.4%
24
PLOS Genetics
● 862 papers in training set
Top 8%
1.4%
25
Metabolic Engineering Communications
○ 22 papers in training set
Top 0.3%
1.3%
26
BMC Ecology and Evolution
○ 51 papers in training set
Top 1.0%
1.1%
27
Biotechnology and Bioengineering
○ 53 papers in training set
Top 0.8%
1.0%
28
ACS Synthetic Biology
● 287 papers in training set
Top 2%
0.8%
29
Microbial Cell Factories
○ 27 papers in training set
Top 0.6%
0.8%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.