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.

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 could download/clone if you wish to start not at the beginning but at a later step of this tutorial.

Preparations¶

Download required files and rename assembly filed for the ease of this tutorial.

mkdir yoghurt
cd yoghurt

# Download genome assemblies from NCBI (RefSeq)
wget ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/056/065/GCF_000056065.1_ASM5606v1/GCF_000056065.1_ASM5606v1_genomic.fna.gz
wget ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/010/120/595/GCF_010120595.1_ASM1012059v1/GCF_010120595.1_ASM1012059v1_genomic.fna.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_genomic.fna.gz ldel.fna.gz
mv GCF_010120595.1_ASM1012059v1_genomic.fna.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 Bacteria $modelA.fna.gz
gapseq find -p all -b 200 -m Bacteria $modelB.fna.gz

# (2) Transporter prediction
gapseq find-transport -b 200 $modelA.fna.gz 
gapseq find-transport -b 200 $modelB.fna.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 -c $modelA.fna.gz -u 200 -l 100
gapseq draft -r $modelB-all-Reactions.tbl -t $modelB-Transporter.tbl -p $modelB-all-Pathways.tbl -c $modelB.fna.gz -u 200 -l 100

# (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
EX_cpd00159_e0      L-Lactate-e0 Exchange 0.000000000 4.590532201 4.534222474
EX_cpd00067_e0             H+-e0 Exchange 4.367216531 4.449087650 4.423526022
EX_cpd00108_e0      Galactose-e0 Exchange 2.499998627 2.500000000 2.500000000
EX_cpd00011_e0            CO2-e0 Exchange 0.244403823 0.326272196 0.244409448
EX_cpd00371_e0       Propanal-e0 Exchange 0.072654477 0.072661894 0.072658583
EX_cpd00130_e0       L-Malate-e0 Exchange 0.000000000 0.056304091 0.056301321
EX_cpd00024_e0 2-Oxoglutarate-e0 Exchange 0.000000000 0.025561534 0.025545066
EX_cpd00239_e0            H2S-e0 Exchange 0.010040013 0.035604294 0.010042926
EX_cpd00100_e0       Glycerol-e0 Exchange 0.002313069 0.002314900 0.002313070
EX_cpd00036_e0      Succinate-e0 Exchange 0.001375019 0.001380513 0.001375019

Output for S. thermophilus (st):

                ex                          rxn.name            l            u     mtf.flux
EX_cpd00221_e0          D-Lactate-e0-e0 Exchange 0.0000000000 9.491132e+00 7.8019798365
EX_cpd00047_e0            Formate-e0-e0 Exchange 0.0000000000 8.351311e+00 0.8016333022
EX_cpd00029_e0               Acetate-e0 Exchange 0.0000000000 1.000469e+01 0.5878410530
EX_cpd00239_e0                   H2S-e0 Exchange 0.0866659611 8.825721e-02 0.0882571474
EX_cpd00363_e0               Ethanol-e0 Exchange 0.0000000000 9.491132e+00 0.0666843262
EX_cpd00011_e0                   CO2-e0 Exchange 0.0000000000 9.919249e+00 0.0440374731
EX_cpd00036_e0             Succinate-e0 Exchange 0.0023811124 3.649335e+00 0.0190511474
EX_cpd00100_e0              Glycerol-e0 Exchange 0.0000000000 4.005551e-03 0.0040055505
EX_cpd03091_e0     5'-Deoxyadenosine-e0 Exchange 0.0023811124 2.381661e-03 0.0023811254
EX_cpd01981_e0 5-Methylthio-D-ribose-e0 Exchange 0.0007937041 7.937085e-04 0.0007937085

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).