Back

Bayesian multi-model-based 13C15N-metabolic flux analysis quantifies carbon-nitrogen metabolism in mycobacteria

Borah, K.; Beyss, M.; Xu, Y.; Barber, J.; Costa, C.; Newcombe, J.; Theorell, A.; Bailey, M.; Beste, D. J.; McFadden, J.; Noh, K.

2022-03-08 systems biology
10.1101/2022.03.08.483448 bioRxiv
Show abstract

Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the pathogen Mycobacterium tuberculosis (Mtb), this is particularly important in informing the development of effective drugs targeting Mtbs metabolism. Here we performed 13C15N dual isotopic labelling of mycobacterial steady state cultures and quantified intracellular carbon-nitrogen (CN) and nitrogen (N) fluxes in addition to carbon (C) fluxes and inferred their reaction bidirectionalities. The combination of 13C15N-MFA with a Bayesian multi-model approach allowed us to resolve C and N fluxes simultaneously which was not possible with classical 13C-MFA. We quantified CN fluxes for amino acid and, for the first time, nucleotide biosynthesis. Our analysis identified glutamate as the central CN and N node in mycobacteria, and improved resolution of the anaplerotic node. Our study describes a powerful platform to measure carbon and nitrogen metabolism in any biological system with statistical rigor.

Matching journals

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

1
mSystems
● 394 papers in training set
Top 0.2%
18.1%
Similar papers in this journal
2
Science Advances
● 1243 papers in training set
Top 3%
6.6%
Similar papers in this journal
3
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 6%
6.6%
Similar papers in this journal
4
Microbiology Spectrum
● 469 papers in training set
Top 2%
6.6%
Similar papers in this journal
5
mBio
● 833 papers in training set
Top 3%
5.4%
Similar papers in this journal
6
PLOS Computational Biology
● 1863 papers in training set
Top 7%
5.3%
7
iScience
○ 1154 papers in training set
Top 5%
3.9%
50% of probability mass above
8
Nature Communications
○ 5641 papers in training set
Top 33%
3.9%
9
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 1%
3.1%
10
npj Systems Biology and Applications
○ 125 papers in training set
Top 0.7%
2.3%
11
Communications Biology
○ 993 papers in training set
Top 10%
2.3%
12
PNAS Nexus
◐ 159 papers in training set
Top 0.8%
2.1%
13
Cell Reports Methods
○ 165 papers in training set
Top 1%
2.1%
14
Metabolic Engineering
○ 75 papers in training set
Top 0.4%
1.9%
15
Applied and Environmental Microbiology
● 339 papers in training set
Top 4%
1.7%
16
PLOS ONE
● 5266 papers in training set
Top 47%
1.7%
17
Journal of Biological Chemistry
○ 690 papers in training set
Top 6%
1.7%
18
Nucleic Acids Research
◐ 1281 papers in training set
Top 9%
1.6%
19
Biophysical Journal
○ 631 papers in training set
Top 3%
1.5%
20
Bioinformatics
◐ 1204 papers in training set
Top 7%
1.5%
21
ISME Communications
◐ 120 papers in training set
Top 1%
1.5%
22
eLife
● 5828 papers in training set
Top 57%
1.3%
23
Scientific Reports
○ 3612 papers in training set
Top 65%
1.1%
24
PLOS Biology
● 486 papers in training set
Top 9%
1.0%
25
Molecular Systems Biology
○ 162 papers in training set
Top 3%
1.0%
26
Science
● 477 papers in training set
Top 9%
0.8%
27
The EMBO Journal
○ 309 papers in training set
Top 8%
0.6%
28
Analytical Chemistry
● 218 papers in training set
Top 3%
0.6%
29
Journal of Molecular Biology
○ 232 papers in training set
Top 5%
0.6%
30
Frontiers in Microbiology
○ 427 papers in training set
Top 9%
0.6%

"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.