1. Computational and Systems Biology
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Essential metabolism for a minimal cell

  1. Marian Breuer
  2. Tyler M Earnest
  3. Chuck Merryman
  4. Kim S Wise
  5. Lijie Sun
  6. Michaela R Lynott
  7. Clyde A Hutchison
  8. Hamilton O Smith
  9. John D Lapek
  10. David J Gonzalez
  11. Valérie de Crécy-Lagard
  12. Drago Haas
  13. Andrew D Hanson
  14. Piyush Labhsetwar
  15. John I Glass
  16. Zaida Luthey-Schulten  Is a corresponding author
  1. University of Illinois at Urbana-Champaign, United States
  2. J Craig Venter Institute, United States
  3. University of California at San Diego, United States
  4. University of Florida, United States
Research Article
Cite this article as: eLife 2019;8:e36842 doi: 10.7554/eLife.36842
19 figures, 10 tables, 6 data sets and 12 additional files

Figures

Comparison of protein coding genes in the genomes of JCVI-syn3A (NCBI GenBank: https://www.ncbi.nlm.nih.gov/nuccore/CP016816.2 (Glass, 2017)), M. pneumoniae (NCBI GenBank: https://www.ncbi.nlm.nih.gov/nuccore/U00089.2 (Himmelreich et al., 2014)), and E. coli (NCBI GenBank: https://www.ncbi.nlm.nih.gov/nuccore/NC_012967.1 (Jeong et al., 2017)) with 452, 688, and 4637 coding genes, respectively.

Each color represents a primary functional class, each contiguous shaded region corresponds to a secondary functional class, within each of the shaded regions the bold lines separate tertiary functional classes, finally each polygonal cell represents a single gene. The functional class hierarchy is presented in Supplementary file 1A. The ratio of metabolic to genetic information processing genes—0.67, 0.79, and 2.23 respectively—is smallest for JCVI-syn3A. The JCVI-syn3A genome contains both the smallest absolute number of genes of unclear function and the smallest percentage, 91 (20 %), compared to M. pneumoniae with 311 (45 %) and E. coli with 1780 (38 %).

https://doi.org/10.7554/eLife.36842.003
Figure 2 with 3 supplements
Classification of gene essentiality from transposon insertion data using a Poisson mixture model for a representative region of the JCVI-syn3A genome.

Coding regions are colored by their predicted class: red (essential), yellow (quasi-essential), blue (non-essential). Lavender regions denote RNA and light brown regions are pseudogenes. The distributions of transposon insertions in passage 1 and passage 4 are represented by yellow and dark green histograms, respectively (bin size of 50 bp). The overlap of the two histograms is highlighted in blue. When a common gene name is not available, the four-digit locus tag for JCVI-syn1.0 is used instead. Locus number identifiers with the (3A) suffix represent newly identified open reading frames in JCVI-syn3A which are missing from the JCVI-syn1.0 annotation. Asterisks mark genes with unknown functionality.

https://doi.org/10.7554/eLife.36842.004
Figure 2—figure supplement 1
Classification of gene essentiality from transposon insertion data using a Poisson mixture model for 0–275,000 bp.

Coding regions are colored by their predicted class: red (essential), yellow (quasi-essential), blue (non-essential). Lavender regions denote RNA, light brown regions are pseudogenes, and green regions are markers used to construct and implant the genome. The distributions of transposon insertions in passage 1 and passage 4 are represented by yellow and dark green histograms respectively (bin size of 50 bp). The overlap of the two histograms is highlighted in blue. When a common gene name is not available, the four-digit locus tag for JCVI-syn1.0 is used instead. Locus number identifiers with the (3A) suffix represent represent newly identified open reading frames in JCVI-syn3A which are missing from the JCVI-syn1.0 annotation. Asterisks mark genes with unknown functionality.

https://doi.org/10.7554/eLife.36842.005
Figure 2—figure supplement 2
Classification of gene essentiality from transposon insertion data using a Poisson mixture model for 275,000–543,379 bp.
https://doi.org/10.7554/eLife.36842.006
Figure 2—figure supplement 3
Distribution of transposon insertion counts for P1 (panel a) and P4 (panel b) compared to the distribution inferred through the Poisson mixture model.

To separate genes labeled ‘non-essential’ by the mixture model, but that showed a significant decrease in insertion counts from P1 to P4k-means clustering was used on the ratios of transposon insertion rates in P1 and P4 for the genes labeled ‘non-essential’. Panel c shows how the genes were divided into two clusters such that the first cluster (blue) contains quasi-essential genes and the second contains truly non-essential genes.

https://doi.org/10.7554/eLife.36842.007
Essential, quasi-essential, and non-essential protein coding genes in JCVI-syn3A across four functional classes.

(a) Distribution across genome (cell areas all equal). (b) Distribution across proteome (cell areas proportional to protein copy number in an average cell). Among non-essential proteins, the three most abundant ones are ftsZ/0522, the peptidase 0305 and 0538 (unclear function). A detailed breakdown of the JCVI-syn3A genome into these classes is available in Table 1.

https://doi.org/10.7554/eLife.36842.008
Biomass reaction equation for JCVI-syn3A.

This reaction consumes biomass precursors (macromolecules, lipids, capsule, small molecules) (black) and consumes energy in the form of ATP (red) to produce biomass (blue). Values in parentheses are the stoichiometric coefficients in mmol compound per gram cellular dry weight (mmol gDW−1). The macromolecular compositions are highlighted in green (stoichiometric coefficients within the macromolecule, unitless) and the compositions of lipids and small molecule pools are highlighted in gray (mmol gDW−1). ATP expenses within green boxes denote total macromolecular synthesis costs (based on macromolecular fractions in the biomass) and the ATP expense in the main equation denotes the nonquantifiable part of the growth-associated maintenance cost (GAM; see Section 'GAM/NGAM').

https://doi.org/10.7554/eLife.36842.011
Overview of the metabolic reconstruction of JCVI-syn3A, drawn with Escher (King et al., 2015).

Orange nodes represent metabolites, labeled by their short names in the model (black); the suffixes ‘_c’ and ‘_e’ denote cytoplasmic and extracellular compartments, respectively. For clarity, H2O, H+, PPi and Pi are generally omitted as reactants. Blue edges represent (enzymatic or spontaneous) reactions, labeled by reaction name (gray labels) and associated gene loci (gene-protein-reaction (GPR) rules, turquoise; omitting ‘MMSYN1_’ prefix). Blue parenthesized numbers denote reactants (products) which are consumed (produced) in stoichiometric quantities greater than one. In this map and subsequent maps, the following color scheme for highlighted reactions is used—blue: reaction based on new annotation, light green: reaction based on suggested annotation refinement, cyan: specific reaction assumed for generic annotation, light violet: non-enzymatic reaction, orange: reaction not accounted for by gene yet but supported by experimental evidence, and red: reaction included based on gap filling. Small boxes list metabolites that can be taken up (green boxes) or secreted (brown boxes) under physiological conditions.

https://doi.org/10.7554/eLife.36842.015
Figure 6 with 1 supplement
Central metabolism in JCVI-syn3A.

Map components and labels as in Figure 5. Big arrows denote incoming or outgoing connections to other parts of the metabolic network. For context, the node representing glucose transport has been labeled explicitly and glycolysis has been highlighted in gray.

https://doi.org/10.7554/eLife.36842.012
Figure 6—figure supplement 1
Steady-state fluxes through central metabolism in JCVI-syn3A.

Map components and labels as in Figure 5, with gene loci/gene-protein-reaction rules omitted. Numbers after reaction labels denote steady-state reaction fluxes in mmol gDW−1 h-1; edge color corresponds to the absolute value of the carried flux—gray to blue to purple to red, from low to high flux. For reversible reactions, the reaction progresses from the white to the filled arrowhead.

https://doi.org/10.7554/eLife.36842.013
Nucleotide metabolism in JCVI-syn3A.

Map components and labels as in Figure 5.

https://doi.org/10.7554/eLife.36842.014
Apparent dead-end of dUMP/deoxyuridine and possible solutions.

Internal metabolites are highlighted with cyan boxes, external ones with red boxes. Blue arrows denote reactions incorporated during model reconstruction—no reaction leads away from the dUMP/deoxyuridine pair. Red arrows denote hypothetical reactions that could possibly solve this dead-end. In the model, we have adopted the hypothetical CTP synthase reaction converting dUMP to dCMP (see also Figure 7; CTPSDUMP).

https://doi.org/10.7554/eLife.36842.016
Cofactor metabolism in JCVI-syn3A.

Map components and labels as in Figure 5.

https://doi.org/10.7554/eLife.36842.017
Lipid and capsule metabolism in JCVI-syn3A.

Map components and labels as in Figure 5.

https://doi.org/10.7554/eLife.36842.018
Macromolecule metabolism in JCVI-syn3A.

Map components and labels as in Figure 5. The detailed (amino acid-specific) stoichiometry of the protein synthesis and degradation reactions can be found in Supplementary file 4. Protein synthesis reactions for the proteins explicitly included in the model (apo-ACP, dUTPase and PdhC) are analogous to the translation reaction shown and are therefore not included in the map.

https://doi.org/10.7554/eLife.36842.019
Amino acid metabolism in JCVI-syn3A.

Map components and labels as in Figure 5. As amino acid metabolism in JCVI-syn3A constitutes sets of analogous reactions (for each amino acid or peptide), we use generic reactions in the upper right part of the map. The ABC importer Opp catalyzes tetrapeptide uptake reactions in the model ([amino acid]4abc in Supplementary file 4); the AA permeases (incl. GltP) catalyze amino acid proton symport reactions ([amino acid]t2[p]r in Supplementary file 4). The peptidases catalyze peptide hydrolysis reactions ([amino acid]4P in Supplementary file 4). The aminoacyl tRNA synthetases (‘aaRS’s’ in the map) catalyze charging of tRNAs ([amino acid]TRS in Supplementary file 4). Synthesis of Gln-tRNAGln requires transamidation of initially mischarged Glu-tRNAGln and the corresponding reactions are shown on the lower left. In the S-adenosylmethionine pathway on the lower right, we note that nucleic acid modification reactions (indicated by the edge labeled ‘DNA/RNA modification’) were not included in the model due to lack of sufficient information on kind and abundance of nucleic acid modifications in JCVI-syn3A.

https://doi.org/10.7554/eLife.36842.020
Ion transport reactions in JCVI-syn3A.

Map components and labels as in Figure 5.

https://doi.org/10.7554/eLife.36842.021
Comparison of growth curves of JCVI-syn1.0 and JCVI-syn3A.

JCVI-syn1.0 has a doubling time of 66 min (blue; ‘×' markers), whereas JCVI-syn3A has a doubling time of 105 min (orange; ‘+' markers). Doubling times (td) were calculated as described in Section 'Materials and methods', plotting fluorescence staining of cellular DNA vs. time, fitted by exponential regression curves. The regression curves for JCVI-syn1.0 and JCVI-syn3A have R2 values of 0.9986 and 0.9976, respectively.

https://doi.org/10.7554/eLife.36842.022
Figure 15 with 1 supplement
Comparison of FBA steady-state fluxes ν to maximal fluxes Vmax obtained from protein abundances and turnover numbers from BRENDA and the literature.

Map components and labels as in Figure 5, with reaction highlighting and gene loci/gene-protein-reaction rules omitted. Each edge is colored according to the ratio between Vmax and ν: Blue indicates Vmax>ν, red indicates Vmax<ν and green indicates that no Vmax could be obtained (because of either missing turnover number or missing protein abundance; or because reaction is not enzymatic to begin with).

https://doi.org/10.7554/eLife.36842.023
Figure 15—figure supplement 1
Statistics of FBA steady-state fluxes ν vs. maximal fluxes Vmax comparison (see Figure 15).

(A) Summary of Vmax vs. ν comparison over all 253 non-exchange reactions in the model. Red, blue, green: Meaning as in Figure 15. Green-striped: Subset of green set—reactions without Vmax that pertain to transport, which usually do not have an EC number associated with them. Gray: Reactions with ν=0 in the FBA solution (thus Vmax>ν always fulfilled). B: Histogram of Vmax/ν over the blue and red subset in panel A.

https://doi.org/10.7554/eLife.36842.024
Figure 16 with 1 supplement
Partitioning of genes classified as essential, quasi-essential, and non-essential by transposon mutagenesis experiments into those which are in silico essential, in silico non-essential, and not modeled (‘Non-metabolic’).

All genes are included (i.e. also RNA genes and pseudogenes).

https://doi.org/10.7554/eLife.36842.026
Figure 16—figure supplement 1
In silico double-gene knockouts between genes that are non-essential in single-gene knockouts.

Among the individually non-essential genes, a double knockout of the gene pair (0876, 0878) is the only lethal combination (red). This knockout corresponds to simultaneously removing both amino acid permeases, thus preventing cysteine uptake. Simultaneous knockout of the glutamate/aspartate permease gltP/0886 and any Opp gene (oppB/0165 through oppA/0169) is non-lethal in silico, as the model will under these circumstances produce glutamate through the hypothesized dUMP breakdown reaction CTPSDUMP and, to a lesser extent, through the reaction CTPS2 (both catalyzed by pyrG/0129). Glutamate production through pyrG/0129 is not expected to be able to meet cellular demands in vivo. If flux through CTPSDUMP is set to zero in the model, a double knockout of gltP/0886 and Opp becomes lethal in silico.

https://doi.org/10.7554/eLife.36842.027
Distributions of absolute protein abundances (number of molecules per average cell) in JCVI-syn3A.

(a) Breakdown of the JCVI-syn3A proteome into functional classes. The area of each cell is proportional to its relative abundance. (b,c) Histograms of absolute protein abundances. (b) Absolute abundances of model-included metabolic proteins essential or non-essential in silico compared to all protein abundances. ‘Technical non-essential’ proteins are not included (see Section 'In silico gene knockouts and mapping to in vivo essentiality'). (c) Absolute abundances for proteins classified by in vivo essentiality from transposon mutagenesis experiments. (d,e) Exceedence plots of absolute abundances for proteins classified by in silico or in vivo essentiality. The exceedence at a given protein abundance value x is the fraction of the protein set displaying an abundance higher than x. (d) Model-included proteins (classified by in silico essentiality) compared to all proteins. (e) Proteins classified by in vivo (transposon-based) essentiality.

https://doi.org/10.7554/eLife.36842.030
Appendix 1—figure 1
Thiamin diphosphate (ThDP) from the Mycoplasma hyorhinis Cypl crystal structure (pdb: 3EKI) overlaid onto the crystal structure of MG289 (pdb: 3MYU).

(Structures aligned using STAMP (Russell and Barton, 1992) in VMD (Humphrey et al., 1996Eargle et al., 2006).) a): Space-filling view, with MG289 in gray and ThDP in color. The pyrophosphate tail of ThDP from the Cypl structure would have an appropriate cavity in MG289 as well. b): Visualization of hydrogen bonds for the same alignment. All possible hydrogen bonds are shown between potential donor and acceptor heavy atoms within 3.5 Å or less of each other. Even in the absence of the residues involved in pyrophosphate binding in Cypl (Sippel et al., 2009), the alignment suggests other side group and backbone interactions could still allow for pyrophosphate binding.

https://doi.org/10.7554/eLife.36842.048
Appendix 1—figure 2
Sensitivity analysis of model doubling time with respect to model constraints.

In each panel, the stated parameter was varied over the indicated range and the model doubling time calculated while keeping all other constraints constant. (A:) Maximal glucose uptake. (B:) Maximal acetate secretion. (C:) ATPase ATP cost. D: GAM ATP cost. (E:) Protein degradation rate. (F:) RNA degradation rate. (G:) Imposed NADPH consumption. The blue circle marks the value used in the FBA model and resulting doubling time; the orange circle indicates the parameter that would yield the experimental doubling time. If there is no value of the parameter which would yield the experimental doubling time, a horizontal line is plotted.

https://doi.org/10.7554/eLife.36842.049

Tables

Table 1
Breakdown of protein coding genes in JCVI-syn3A into functional classes.
https://doi.org/10.7554/eLife.36842.009
ProteinGenesEssentiality
Functional hierarchy%# unique%# unique# E# Q# N# model
Cellular processesCell Growth1.0240.8841030
Defense0.2320.4421011
Subtotal1.2561.3362041
Genetic information processingDNA Maintenance5.07388.413825943
Folding, Sorting and Degradation9.58255.532518707
Transcription3.92143.32158520
Translation39.512929.713495281125
Subtotal58.120646.9212146491735
MetabolismBiosynthesis4.27296.8631264127
Central Carbon Metabolism16.44610.44726101144
Energy Metabolism0.4740.8842111
Membrane Transport9.375412.6573716446
Other Enzymes1.1240.8842111
Subtotal31.613731.6143933218119
UnclearKegg ortholog defined1.0481.7783230
No Kegg ortholog7.987118.4832730260
Subtotal9.027920.1913032290
Total100.428100.45227111368155
Table 2
Genes modeled in the metabolic reconstruction.

The ‘MMSYN1_’ prefix on the locus tags has been omitted for brevity. The reaction column provides the specific reaction name or general description of the gene (if involved in multiple reactions). Reaction names may appear multiple times if there are multiple gene products that can catalyze that reaction. Column EssTn5 contains a if the gene is non-essential, a if it is quasi-essential, or a ■ if it has been determined to be essential through the transposon mutagenesis experiments. A dagger in this column indicates that the automatic essentiality assignment required manual intervention. Column EssFBA contains a if the gene is non-essential or a ■ if it has been determined to be essential through FBA. Loci marked with an asterisk are genes that are non-essential only ‘technically’ with respect to FBA (see Section 'In silico gene knockouts and mapping to in vivo essentiality'). The doubling times predicted by FBA for non-essential genes were all 2.02 hr, with the exception of single knockouts of loci pdhC/0227 through ackA/0230, which all had doubling times of 3.22 hr; locus punA/0747 with a doubling time of 2.04 hr; and locus gltP/0886 with a doubling time of 2.03 hr.

https://doi.org/10.7554/eLife.36842.010
LocusReactionEssTn5EssFBALocusReactionEssTn5EssFBALocusReactionEssTn5EssFBA
Amino acid metabolismCofactor metabolism0798UPPRT
0381*AHCi08235FTHFPGS0330dAdn kinase 1
0163ALATRS0390FMETTRS0382dAdn kinase 2
0535ARGTRS0291FMNATTransport
0076ASNTRS0443FTHFCL08225FTHFabc
0287ASPTRS0799GHMT0876AA permease 1
0837CYSTRS0684MTHFC0878AA permease 2
0687GLNTRAT0259NADK0789ATPase
0688GLNTRAT0378NADS0790ATPase
0689GLNTRAT0614NCTPPRT0791ATPase
0126GLUTRS_Gln0380NNATr0792ATPase
0405GLYTRSLipid metabolism0793ATPase
0288HISTRS0621ACP0794ATPase
0519ILETRS0419ACPPAT0795ATPase
0634LEUTRS0513ACPS0796ATPase
0064LYSTRS0512AGPAT0879CA2abc
0432MAT0117APG3PAT0836COAabc
0012METTRS0139BPNT0642EcfA
0528PHETRS0147CLPNS0643EcfA
0529PHETRS0697DAGGALT0641EcfT
0282PROTRS0114DAGPST/DAGGALT0233GLCpts
0133Peptidase 10304DASYN0234GLCpts
0305Peptidase 20420FAKr0694GLCpts
0444Peptidase 30616FAKr0779GLCpts
0479Peptidase 40617FAKr0886GltP
0061SERTRS0115GALU0685Kt6
0222THRTRS0218GLYK0686Kt6
0308TRPTRS0733PGMT/PPM0401LIPTA
0613TYRTRS0214PGPP0787MG2abc
0260VALTRS0875PGSA0314NACabc
Central metabolism0113PSSYN0165Opp
0230ACKr0813UDPG4E0166Opp
0493AGDC0814UDPGALM0167Opp
0495AMANKMacromolecules0168Opp
0494AMANPEr0394Lon0169Opp
0732DRPA0650Met peptidase0345P5Pabc
0213ENO0201Pept. deformylase0425PIabc
0131FBANucleotide metabolism0426PIabc
0726G6PDA0651(D)ADK0427PIabc
0607GAPD0413ADPT0877RIBFLVabc
0451GAPDP0129CTPS0008RNS
0475LDH_L0347CYTK0009RNS
0435MAN6PI0515DCMPDA0010RNS
0227PDH_E2/_acald0447DUTPDP0011RNS
0228PDH_E30203GK0195SPRMabc
0220PFK0216GUAPRT0196SPRMabc
0445PGI0747PNP0197SPRMabc
0606PGK0344PPA0706THMPPabc
0729PGM0771RNDR0707THMPPabc
0831PRPPS0772RNDR0708THMPPabc
0229PTAr0773RNDR
0221PYK0140TMDK1/DURIK1
0262RPE0045TMPK
0800RPI0065TRDR
0316TKT0819TRDR
0727TPI0537UMPK
Table 3
Cellular ATP expenses by category (in percent of total ATP consumption).
https://doi.org/10.7554/eLife.36842.025
CategoryATP expense [%]
Nucleotide metabolism3.6
Pentose phosphate pathway1.7
Lipid metabolism0.7
Cofactor metabolism0.1
Transport3.4
GAMMacromolecules18
GAMtRNA charging16
GAMNonquant41
NGAMTurnover13
NGAMATPase2.7
Table 4
Confusion matrices for gene essentiality prediction.

‘All genes’ denotes agreement/disagreement between the model prediction and the transposon mutagenesis experiment considering all genes in the metabolic reconstruction (excluding the two ‘technical non-essential’ genes). ‘Excluding AA’ repeats the same comparison as ‘All genes’, with genes for amino acid utilization (uptake and peptidase genes) excluded. See Table 2 for individual gene essentialities in silico and in vivo.

https://doi.org/10.7554/eLife.36842.028
All genesExcluding AA
Exp. / ModelEssentialNon-essentialEssentialNon-essential
Essential10141014
Quasi-essential2214224
Non-essential012010
Table 5
Accuracy, sensitivity, specificity and Matthews correlation coefficient calculated for several scenarios.

QE as E: Treating in vivo quasi-essentials as essentials; QE as NE: Treating quasi-essentials as non-essentials; No QE genes: Excluding all genes quasi-essential in vivo; QE as E, no AA genes: Excluding genes related to amino acid utilization, and treating quasi-essentials as essentials; QE as NE, no AA genes: Excluding genes related to amino acid utilization, and treating quasi-essentials as non-essentials.

https://doi.org/10.7554/eLife.36842.029
AccuracySensitivitySpecificityMCC
QE as E88%87%100%0.59
QE as NE83%96%54%0.59
No QE genes97%96%100%0.85
QE as E, no AA gene94%94%100%0.72
QE as NE, no AA genes82%96%39%0.46
Table 6
Summary of features of the metabolic model for JCVI-syn3A.

‘Non-pseudo’ reactions exclude exchange, biomass and macromolecule reactions. ‘Annotation-supported’ includes all non-pseudo reactions that have a gene assigned. ‘Passive’ reactions are transport processes assumed to take place without protein mediation. The meaning of ‘technical’ non-essential genes is explained in Section 'In silico gene knockouts and mapping to in vivo essentiality'.

https://doi.org/10.7554/eLife.36842.031
Model overviewGenes155
Genome coverage31%
Metabolites304
Reactions (total)338
Reactions (non-pseudo)244
Reaction breakdown
(% of non-pseudo)


Annotation-supported20986%
Passive146%
Gap fills with exp. evidence177%
Gap fills without exp. evidence42%
Supported (annotation/exp./passive)24098%
Essentiality insilicoEssential genes12379%
Non-essential genes3019%
‘Technical' non-essential genes21%
Essentiality invivoEssential genes30862%
Quasi-essential genes11423%
Non-essential genes7615%
Table 7
List of suggested gene removal experiments.
https://doi.org/10.7554/eLife.36842.032
Gene(s)DescriptionRemark
manA/0435Mannose 6-phosphate isomerase
deoC/0732Deoxyribose phosphate aldolase
nanE/0494, 0495, nagB/0726N-acetylmannosamine 6-phosphate branch
pdhC/0227, pdhD/0228, 0401pdhCD and proposed lipoate importer
dak1/0330, dak2/0382Deoxynucleoside kinasesSynthetic lethality possible
ietS/0133Peptidase
0876Amino acid permease
folT in JCVI-syn3AFolate uptake and usageCompetition experiment with wild type to probe fitness cost in JCVI-syn3A
folT in JCVI-syn1.0Folate uptake and usageCompetition experiment with wild type to probe fitness cost in JCVI-syn1.0
Table 8
List of suggested experiments, with rationale behind each suggestion.
https://doi.org/10.7554/eLife.36842.033
ExperimentRationale
Nutrient utilization
Detect lipoylpeptide uptakeLipoate is cofactor for PDH, whose functionality is unclear after E1 subunit deletion.
Detect nucleotide uptakeActivity reported for M. mycoides capri without gene assignment; alternative routes present in JCVI-syn3A, but activity not ruled out.
Detect nucleobase uptakeActivity reported for M. mycoides capri, and uracil uptake essential in model.
Demonstrate growth on thiamine diphosphateStructural data and deletion of thiamine diphosphokinase suggest thiamine diphosphate (ThDP) uptake. Inability to grow on ThDP would imply unidentified kinase activity.
Demonstrate growth on pyridoxal phosphateWith no pyridoxal kinase identified, growth on pyridoxal phosphate (P5P) assumed; inability to grow on P5P would imply unidentified kinase activity.
Demonstrate growth on acetylmannosamineReconstruction suggests operon 0493 through 0495 to be nagA/nanE/nagC; growth on acetylmannosamine would support assignment and imply uptake capability.
Demonstrate growth on mannose or glucosamineLiterature suggests mannose and glucosamine import through PtsG; and downstream enzymes are present.
Metabolite production/secretion
Detect production of acetateAcetate pathway has been partially removed, but several of the remaining enzymes remain essential in transposon mutagenesis experiments.
Investigate production of lipogalactan capsuleGenetic evidence suggests capsule is still being produced.
Detect secretion of deoxyuridine or dUMPDeoxyuridine/dUMP is currently dead-end; secretion would be one possible solution.
Enzymatic activity
Detect pyruvate oxidationConversion of pyruvate to acetyl-CoA by truncated PDH complex has been assumed for the time being but is not supported by experiment.
Detect oxidation of acetaldehydeConversion of acetaldehyde to acetyl-CoA would provide alternative explanation for presence of truncated PDH complex in JCVI-syn3A in spite of deletion of first subunit.
Detect NOX activityNADH oxidase (NOX) has been deleted but activity would be necessary for PDH activity against both pyruvate and acetaldehyde.
Detect transaldolase activityActivity is essential in model and has been detected in M. mycoides capri; no known gene in any mycoplasma though.
Detect sedoheptulose-1,7-bisphosphate phosphatase activityReaction would provide bypass to transaldolase reaction.
Detect phosphatidate phosphatase activityGene present in M. pneumoniae and reaction is missing link to diacylglycerol, but no gene identified in JCVI-syn3A.
Assess phosphatase activity against dCTP, dCDP, GMP, dAMP, dGMP, dUMP, dTMP; pyrophosphatase activity against CTP, dCTPActivities observed in M. mycoides capri but no gene identified in JCVI-syn3A.
 Detect deoxyuridine phosphorylase activityGene has been removed in JCVI-syn3A (MMSYN1_0734), but deoxyuridine/dUMP is currently dead-end, raising the question whether function is carried out by some other gene.
Detect thymidylate synthase activityExtremely low activity detected in M. mycoides mycoides SC, but no gene identified. Reaction would be alternative solution to deoxyuridine/dUMP dead-end.
Specific gene function
Determine substrates for deoxynucleoside kinase dak2/0382Presence of dak2/0382 in addition to the characterized dak1/0330 suggests different substrate profile.
Verify CTPS activity against dUMPCTPS (pyrG/0129) converting dUMP to dCMP seems most plausible solution to deoxyuridine/dUMP dead-end.
Check PGPP activity against phosphatidateActivity observed for PgpB in E. coli; activity for PGPP (pgpA/0214) would provide missing link to diacylglycerol in apparent absence of phosphatidate phosphatase gene.
Check UMPK activity against CMP and dCMPSubstrate profile for UMP kinase similar to eukaryotic enzyme could explain quasi-essentiality of CMP kinase.
Change of expression levels
Knock out deoxynucleoside kinases and over express RNDRRNDR and deoxynucleoside kinases provide redundant routes to deoxydinucleotides, suggesting one pathway might be sufficient if expression increased.
Reduce expression of RNDR and knock out putative dUTPase simultaneouslyRNDR is currently only known source of dUTP. If RNDR knockdown would make the putative dUTPase gene dut/0447 nonessential as well, this would corroborate the putative assignment.
Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain,
strain background
JCVI-syn3AJCVI; this articleGenBank accession
number:CP016816.2
[1]
Strain, strain backgroundJCVI-syn1.0doi:10.1126/science.1190719GenBank accession
number:CP002027.1
[1]
Genetic reagentterTufPuro transposomedoi:10.1126/science.aad6253Constructed by the JCVI
Genetic reagentYeast tRNALife Technologies, Carlsbad, CA, USA15-401-029
Genetic reagentEZ-Tn5-TransposaseLucigen, Madison, WI, USATNP92110
Sequence-based reagentCustom forward primerIntegrated DNA technologies, San Diego, CA, USA[2]
Commercial assay or kitNextera XT DNA library
preparation kit
Illumina, San Diego, CA, USAFC-131-1024

Chemical compound, drug

Quant-iT PicoGreen

Molecular Probes, Eugene, OR, USA

P7589
Chemical compound, drugPuromycinMolecular Probes, Eugene, OR, USAA1113802
Software, algorithmCOBRApydoi:10.1186/1752-0509-7-74
Software, algorithmCLC Genomics WorkbenchQIAGEN
Bioinformatics, Redwood City, CA, USA
Software, algorithmProteomeDiscoverer 2.1.0.81Thermo Fisher Scientific
  1. [1] Bacterial strains JCVI-syn3A and JCVI-syn1.0 will be made available to qualified researchers by the JCVI and Synthetic Genomics, Inc under a material transfer agreement. Note that United States scientists must obtain a United States Veterinary Permit for Importation and Transportation of Controlled Materials and Organisms and Vectors from the U.S. Department of Agriculture Animal and Plant Health Inspection Service. The organisms require Biosafety Level 2 containment.

    [2] Used for marker-specific sequencing with PCR; sequence under ‘Tn5 mutagenesis–Experimental method'.

Appendix 1—table 1
Reconstructed biomass composition of JCVI-syn3A, listing the fraction of each component as percent of cellular dry mass.
https://doi.org/10.7554/eLife.36842.047
CategoryComponentFraction [%]
MacromoleculesProtein54.727
Total: 76.521 %RNA16.274
DNA5.5
Acyl carrier protein0.018
dUTPase0.003
Lipids & capsuleLipogalactan capsule6.368
Total: 17.563 %Phosphatidylglycerol2.944
Cardiolipin2.944
Cholesterol1.534
Diacylglycerol1.366
Gal-DAG1.31
Triacylglycerol0.549
Fatty acid0.549
Small molecules & ionsPotassium3.285
Total: 5.916 %Phosphate0.375
Chloride0.198
ATP0.167
L-lysine0.133
Sodium0.131
Spermine0.128
L-isoleucine0.115
GTP0.115
L-leucine0.112
UTP0.106
L-glutamate0.085
L-valine0.083
L-asparagine0.083
Small moleculesL-alanine0.077
& ions (cont’d)L-aspartate0.072
L-threonine0.071
L-serine0.071
Glycine0.068
CTP0.053
L-phenylalanine0.049
L-glutamine0.048
L-arginine0.043
L-tyrosine0.041
L-proline0.035
L-methionine0.026
Magnesium0.019
L-histidine0.019
Calcium0.019
FAD0.016
5,10-meTHF(Glu)30.015
CoA0.012
Thiamin diphosphate0.009
NADP+0.008
L-tryptophan0.008
L-cysteine0.008
Pyridoxal phosphate0.005
dTTP0.003
dATP0.003
dCTP0.002
dGTP0.001

Data availability

Proteomics: data were uploaded to MassIVE (massive.ucsd.edu) with dataset identifier MSV000081687 and ProteomeXchange with dataset identifier PXD008159. All other new data are included in the manuscript and supporting files.

The following data sets were generated
  1. 1
    ProteomeXchange
    1. JD Jr Lapek
    2. DJ Gonzalez
    (2018)
    Data from Essential Metabolism for a Minimal Cell.
The following previously published data sets were used
  1. 1
    NCBI Nucleotide
    1. I Glass John
    (2017)
    ID CP016816.2. Synthetic bacterium JCVI-Syn3.0 strain 6d, complete genome.
  2. 2
  3. 3
    NCBI Nucleotide
    1. CA III Hutchison
    2. R-Y Chuang
    3. VN Noskov
    4. N Assad-Garcia
    5. TJ Deerinck
    6. MH Ellisman
    7. J Gill
    8. K Kannan
    9. BJ Karas
    (2016)
    ID CP014940.1. Synthetic bacterium JCVI-Syn3.0, complete genome.
  4. 4
  5. 5

Additional files

Supplementary file 1

Gene classification hierarchy, model reaction breakdown and protein gene breakdowns for M. pneumoniae and E. coli.

https://doi.org/10.7554/eLife.36842.034
Supplementary file 2

Transposon insertion nucleotide positions.

https://doi.org/10.7554/eLife.36842.035
Supplementary file 3

Transposon insertion counts and assignment of gene essentiality from both transposon mutagenesis and FBA.

https://doi.org/10.7554/eLife.36842.036
Supplementary file 4

Reactions and metabolites included in the metabolic reconstruction.

https://doi.org/10.7554/eLife.36842.037
Supplementary file 5

Data from proteomics experiments.

https://doi.org/10.7554/eLife.36842.038
Supplementary file 6

Comparison of the JCVI-syn3A metabolic reconstruction to that of M. pneumoniae published by Wodke et al. (2013).

https://doi.org/10.7554/eLife.36842.039
Supplementary file 7

Flux constraints derived from proteomics and turnover numbers and comparison to FBA fluxes.

https://doi.org/10.7554/eLife.36842.040
Supplementary file 8

Known metabolic reactions removed during genome minimization from JCVI-syn1.0 to JCVI-syn3A.

https://doi.org/10.7554/eLife.36842.041
Supplementary file 9

FBA model in sbml format.

https://doi.org/10.7554/eLife.36842.042
Supplementary file 10

FBA model in json format.

https://doi.org/10.7554/eLife.36842.043
Supplementary file 11

ESCHER network map in json format.

https://doi.org/10.7554/eLife.36842.044
Transparent reporting form
https://doi.org/10.7554/eLife.36842.045

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