Lactic acid bacteria in yogurt production
Lactobacillus delbrueckii subsp. bulgaricus and Streptococcus thermophilus are both lactic acid bacteria, which are frequently used for the production of yogurt. The organisms differ in their lactose degradation and their fermentation end products. In this tutorial, genome-scale metabolic models will be reconstructed using gapseq starting from the organisms’ genomes in multi-protein sequences fasta file (translated sequences of protein-coding genes).
Required gapseq version: 1.2 0d0123e (or later)
Input files
Genome assemblies:
Lactobacillus delbrueckii subsp. bulgaricus ATCC 11842 = JCM 1002. RefSeq:
GCF_000056065.1
Streptococcus thermophilus ATCC 19258. RefSeq:
GCF_010120595.1
Growth media (milk) file:
milk.csv
This growth media is based on the main ingredients described for whole milk (without fatty acids), as listed here.
NOTE: All intermediate files produced by the commands below are stored at the github repository gapseq.tutorial.data, which you can download/clone if you wish to start not at the beginning but at a later step of this tutorial or want to cross-check your results.
Preparations
Download required files and rename assembly filed for the ease of this tutorial.
mkdir yoghurt
cd yoghurt
# Download genome assemblies from NCBI (RefSeq) in protein sequences
wget ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/056/065/GCF_000056065.1_ASM5606v1/GCF_000056065.1_ASM5606v1_protein.faa.gz
wget ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/010/120/595/GCF_010120595.1_ASM1012059v1/GCF_010120595.1_ASM1012059v1_protein.faa.gz
# Download gapfill-medium file from github
wget https://github.com/Waschina/gapseq.tutorial.data/raw/master/yogurt/milk.csv
# Rename genomes
mv GCF_000056065.1_ASM5606v1_protein.faa.gz ldel.fna.gz
mv GCF_010120595.1_ASM1012059v1_protein.faa.gz sthe.fna.gz
gapseq reconstruction pipeline
(1) Reaction & pathway prediction (2) Transporter prediction (3) Draft model reconstruction (4) Gapfilling
modelA="ldel"
modelB="sthe"
# (1) Reaction & Pathway prediction
gapseq find -p all -b 200 -m auto -t auto $modelA.faa.gz
gapseq find -p all -b 200 -m auto -t auto $modelB.faa.gz
# (2) Transporter prediction
gapseq find-transport -b 200 $modelA.faa.gz
gapseq find-transport -b 200 $modelB.faa.gz
# (3) Building Draft Model - based on Reaction-, Pathway-, and Transporter prediction
gapseq draft -r $modelA-all-Reactions.tbl -t $modelA-Transporter.tbl -p $modelA-all-Pathways.tbl -u 200 -l 100 -c $modelA.faa.gz
gapseq draft -r $modelB-all-Reactions.tbl -t $modelB-Transporter.tbl -p $modelB-all-Pathways.tbl -u 200 -l 100 -c $modelB.faa.gz
# (4) Gapfilling
gapseq fill -m $modelA-draft.RDS -n milk.csv -c $modelA-rxnWeights.RDS -g $modelA-rxnXgenes.RDS -b 100
gapseq fill -m $modelB-draft.RDS -n milk.csv -c $modelB-rxnWeights.RDS -g $modelB-rxnXgenes.RDS -b 100
FBA and FVA prediction of metabolic by-products
Here, we will use the R-Package sybil
(Gelius-Dietrich et al. (2013) BMC Syst Biol) to perform Flux-Balance-Analysis (FBA) and Flux-Variability-Analysis (FVA) with the two reconstructed network models.
First, we define a function, that automatically performs FBA with the minimalization of total flux (MTF) as secondary objective and FVA for all exchange reactions. The function also summarizes the results in a sorted data.table
.
getMetaboliteProduction <- function(mod) {
require(sybil)
require(data.table)
# MTF
sol.mtf <- optimizeProb(mod, algorithm = "mtf")
dt.mtf <- data.table(ex = mod@react_id,
mtf.flux = sol.mtf@fluxdist@fluxes[1:mod@react_num])
dt.mtf.tmp <- copy(dt.mtf[grepl("^EX_cpd[0-9]+_e0", ex)])
# FVA
sol.fv <- fluxVar(mod, react = mod@react_id[grep("^EX_cpd[0-9]+_e0", mod@react_id)])
dt <- data.table(ex = rep(mod@react_id[grep("^EX_cpd[0-9]+_e0", mod@react_id)],2),
rxn.name = rep(mod@react_name[grep("^EX_cpd[0-9]+_e0", mod@react_id)],2),
dir = c(rep("l",length(grep("^EX_cpd[0-9]+_e0", mod@react_id))),
rep("u",length(grep("^EX_cpd[0-9]+_e0", mod@react_id)))),
fv = sol.fv@lp_obj)
dt <- dcast(dt, ex + rxn.name ~ dir, value.var = "fv")[(u>1e-6 & l >= 0)]
dt <- merge(dt, dt.mtf, by = "ex")
return(dt[order(-mtf.flux)])
}
Now, we can apply this function to the network models of L. delbrueckii and S. thermophilus to predict the top 10 produced metabolic by-products.
library(sybil)
sybil::SYBIL_SETTINGS("SOLVER","cplexAPI") # (optional)
ld <- readRDS("ldel.RDS") # for L. delbrueckii
st <- readRDS("sthe.RDS") # for S. thermophilus
getMetaboliteProduction(ld)[1:10]
getMetaboliteProduction(st)[1:10]
Output for L. delbrueckii (ld
):
ex rxn.name l u mtf.flux
1: EX_cpd00221_e0 D-Lactate-e0 Exchange 0.00000000 4.66688283 4.57527357
2: EX_cpd00067_e0 H+-e0 Exchange 4.41375845 4.56576030 4.53889042
3: EX_cpd00108_e0 Galactose-e0 Exchange 2.49998944 2.50000000 2.50000000
4: EX_cpd00011_e0 CO2-e0 Exchange 0.32602433 0.47806843 0.35293482
5: EX_cpd00371_e0 Propanal-e0 Exchange 0.16765414 0.16773865 0.16769626
6: EX_cpd00047_e0 Formate-e0 Exchange 0.09438168 0.16513265 0.16509025
7: EX_cpd00013_e0 NH3-e0 Exchange 0.13880177 0.26199257 0.16445716
8: EX_cpd00239_e0 H2S-e0 Exchange 0.09297787 0.09302013 0.09302010
9: EX_cpd00324_e0 MTTL-e0 Exchange 0.08651320 0.08655546 0.08655540
10: EX_cpd00029_e0 Acetate-e0 Exchange 0.07586589 0.17284734 0.07654518
Output for S. thermophilus (st
):
ex rxn.name l u mtf.flux
1: EX_cpd00221_e0 D-Lactate-e0 Exchange 0.0000000000 9.2094468818 8.6588710458
2: EX_cpd00011_e0 CO2-e0 Exchange 0.2558426738 9.9070817239 0.5225485126
3: EX_cpd00239_e0 H2S-e0 Exchange 0.0868496916 0.0884168597 0.0868510077
4: EX_cpd00281_e0 GABA-e0 Exchange 0.0644172061 0.0644198887 0.0644184793
5: EX_cpd00092_e0 Uracil-e0 Exchange 0.0469340783 0.0469354195 0.0469353179
6: EX_cpd00036_e0 Succinate-e0 Exchange 0.0023487402 0.3596882237 0.0454283729
7: EX_cpd00029_e0 Acetate-e0 Exchange 0.0000000000 0.2101233068 0.0268075147
8: EX_cpd00100_e0 Glycerol-e0 Exchange 0.0000000000 0.0039510794 0.0039510794
9: EX_cpd00363_e0 Ethanol-e0 Exchange 0.0000000000 9.2094468818 0.0023487447
10: EX_cpd01981_e0 5-Methylthio-D-ribose-e0 Exchange 0.0007829134 0.0007829149 0.0007829149
As expected, both organisms produce Lactate in the FBA+MTF solution. The FVA further predicted a lower bound for L-Lactate or D-Lactate production of zero. This is due to the fact, that the models harbour also the capability to produce the respective other Lactate enantiomer. In contrast to S. thermophilus, the FBA simulation predicted a release of Galactose by L. debrueckii. In fact, L. debrueckii is usually reported to be Galactose negative; i.e. does not produce acid from this hexose (https://bacdive.dsmz.de/strain/6449) and utilized only the glucose part of lactose, while S. thermophilus has been reported to be Galactose positive (https://bacdive.dsmz.de/strain/14786).