Figures and data in Essential metabolism for a minimal cell

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Additional files

19 figures, 10 tables and 12 additional files

Figures

Figure 1

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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

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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

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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

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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

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Distribution of transposon insertion counts for $P_{1}$ (panel a) and $P_{4}$ (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 $P_{1}$ to $P_{4}$ , $k$ -means clustering was used on the ratios of transposon insertion rates in $P_{1}$ and $P_{4}$ 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

Figure 3

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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

Figure 4

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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

Figure 5

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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⁺, PP_i and P_i 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

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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

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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

Figure 7

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Nucleotide metabolism in JCVI-syn3A.

Map components and labels as in Figure 5.

https://doi.org/10.7554/eLife.36842.014

Figure 8

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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

Figure 9

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Cofactor metabolism in JCVI-syn3A.

Map components and labels as in Figure 5.

https://doi.org/10.7554/eLife.36842.017

Figure 10

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Lipid and capsule metabolism in JCVI-syn3A.

Map components and labels as in Figure 5.

https://doi.org/10.7554/eLife.36842.018

Figure 11

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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

Figure 12

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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-tRNA_Gln requires transamidation of initially mischarged Glu-tRNA $^{Gln}$ 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

Figure 13

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Ion transport reactions in JCVI-syn3A.

Map components and labels as in Figure 5.

https://doi.org/10.7554/eLife.36842.021

Figure 14

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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 ( $t_{d}$ ) 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 $R^{2}$ values of 0.9986 and 0.9976, respectively.

https://doi.org/10.7554/eLife.36842.022

Figure 15 with 1 supplement

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Comparison of FBA steady-state fluxes $ν$ to maximal fluxes $V_{max}$ 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 $V_{max}$ and $ν$ : Blue indicates $V_{max} > ν$ , red indicates $V_{max} < ν$ and green indicates that no $V_{max}$ 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

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Statistics of FBA steady-state fluxes $ν$ vs. maximal fluxes $V_{max}$ comparison (see Figure 15).

(A) Summary of $V_{max}$ 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 $V_{max}$ 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 $V_{max} > ν$ always fulfilled). B: Histogram of $V_{max} / ν$ over the blue and red subset in panel A.

https://doi.org/10.7554/eLife.36842.024

Figure 16 with 1 supplement

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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

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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

Figure 17

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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

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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., 1996; Eargle 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

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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

		Protein		Genes		Essentiality
Functional hierarchy		%	# unique	%	# unique	# E	# Q	# N	# model
Cellular processes	Cell Growth	1.02	4	0.88	4	1	0	3	0
	Defense	0.23	2	0.44	2	1	0	1	1
	Subtotal	1.25	6	1.33	6	2	0	4	1
Genetic information processing	DNA Maintenance	5.07	38	8.41	38	25	9	4	3
	Folding, Sorting and Degradation	9.58	25	5.53	25	18	7	0	7
	Transcription	3.92	14	3.32	15	8	5	2	0
	Translation	39.5	129	29.7	134	95	28	11	25
	Subtotal	58.1	206	46.9	212	146	49	17	35
Metabolism	Biosynthesis	4.27	29	6.86	31	26	4	1	27
	Central Carbon Metabolism	16.4	46	10.4	47	26	10	11	44
	Energy Metabolism	0.47	4	0.88	4	2	1	1	1
	Membrane Transport	9.37	54	12.6	57	37	16	4	46
	Other Enzymes	1.12	4	0.88	4	2	1	1	1
	Subtotal	31.6	137	31.6	143	93	32	18	119
Unclear	Kegg ortholog defined	1.04	8	1.77	8	3	2	3	0
	No Kegg ortholog	7.98	71	18.4	83	27	30	26	0
	Subtotal	9.02	79	20.1	91	30	32	29	0
Total		100.	428	100.	452	271	113	68	155

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 Ess_Tn5 contains a $\cdot$ 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 Ess_FBA contains a $\cdot$ 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

Locus	Reaction	Ess_Tn5	Ess_FBA	Locus	Reaction	Ess_Tn5	Ess_FBA	Locus	Reaction	Ess_Tn5	Ess_FBA
Amino acid metabolism				Cofactor metabolism				0798	UPPRT	■	■
0381*	AHCi	$\cdot$	$\cdot$	0823	5FTHFPGS	$□$	■	0330	dAdn kinase 1	$\cdot$	$\cdot$
0163	ALATRS	■	■	0390	FMETTRS	$□$	■	0382	dAdn kinase 2	$\cdot$	$\cdot$
0535	ARGTRS	■	■	0291	FMNAT	■	■	Transport
0076	ASNTRS	■	■	0443	FTHFCL	■	■	0822	5FTHFabc	$□$	■
0287	ASPTRS	■	■	0799	GHMT	$□$	■	0876	AA permease 1	$\cdot$	$\cdot$
0837	CYSTRS	■	■	0684	MTHFC	$□$	■	0878	AA permease 2	$□$	$\cdot$
0687	GLNTRAT	■	■	0259	NADK	■	■	0789	ATPase	■^†	■
0688	GLNTRAT	■	■	0378	NADS	■	■	0790	ATPase	■	■
0689	GLNTRAT	■	■	0614	NCTPPRT	■	■	0791	ATPase	■	■
0126	GLUTRS_Gln	■	■	0380	NNATr	■	■	0792	ATPase	■	■
0405	GLYTRS	■	■	Lipid metabolism				0793	ATPase	■	■
0288	HISTRS	■	■	0621	ACP	■	■	0794	ATPase	■	■
0519	ILETRS	■	■	0419	ACPPAT	■	■	0795	ATPase	■	■
0634	LEUTRS	■	■	0513	ACPS	■	■	0796	ATPase	■	■
0064	LYSTRS	■	■	0512	AGPAT	■	■	0879	CA2abc	■	■
0432 $^{*}$	MAT	■	$\cdot$	0117	APG3PAT	■	■	0836	COAabc	■	■
0012	METTRS	■	■	0139	BPNT	$□$	■	0642	EcfA	■	■
0528	PHETRS	■	■	0147	CLPNS	■	■	0643	EcfA	■	■
0529	PHETRS	■	■	0697	DAGGALT	$□$	$\cdot$	0641	EcfT	■	■
0282	PROTRS	■	■	0114	DAGPST/DAGGALT	■	■	0233	GLCpts	■	■
0133	Peptidase 1	$\cdot$	$\cdot$	0304	DASYN	■	■	0234	GLCpts	■	■
0305	Peptidase 2	$□$	$\cdot$	0420	FAKr	■	■	0694	GLCpts	■	■
0444	Peptidase 3	$□$	$\cdot$	0616	FAKr	■	■	0779	GLCpts	■	■
0479	Peptidase 4	$□$	$\cdot$	0617	FAKr	$□$	■	0886	GltP	$□$	$\cdot$
0061	SERTRS	■	■	0115	GALU	■	■	0685	Kt6	■	■
0222	THRTRS	■	■	0218	GLYK	■	■	0686	Kt6	■	■
0308	TRPTRS	■	■	0733	PGMT/PPM	■	■	0401	LIPTA	$\cdot$	$\cdot$
0613	TYRTRS	■	■	0214	PGPP	$□$	■	0787	MG2abc	■	■
0260	VALTRS	■	■	0875	PGSA	■	■	0314	NACabc	■	■
Central metabolism				0113	PSSYN	■	■	0165	Opp	$□$	$\cdot$
0230	ACKr	■	$\cdot$	0813	UDPG4E	■	■	0166	Opp	$□$	$\cdot$
0493	AGDC	$□$	$\cdot$	0814	UDPGALM	■	■	0167	Opp	$□$	$\cdot$
0495	AMANK	$\cdot$	$\cdot$	Macromolecules				0168	Opp	$□$	$\cdot$
0494	AMANPEr	$\cdot$	$\cdot$	0394	Lon	$□$	■	0169	Opp	$□$	$\cdot$
0732	DRPA	$\cdot$	$\cdot$	0650	Met peptidase	■	■	0345	P5Pabc	■	■
0213	ENO	■	■	0201	Pept. deformylase	■	■	0425	PIabc	■	■
0131	FBA	■	■	Nucleotide metabolism				0426	PIabc	■	■
0726	G6PDA	$\cdot$	$\cdot$	0651	(D)ADK	■	■	0427	PIabc	■	■
0607	GAPD	■	■	0413	ADPT	■	■	0877	RIBFLVabc	■	■
0451	GAPDP	■	$\cdot$	0129	CTPS	■	■	0008	RNS	■	■
0475	LDH_L	■	$\cdot$	0347	CYTK	$□$	■	0009	RNS	■	■
0435	MAN6PI	$\cdot$	$\cdot$	0515	DCMPDA	$□$	$\cdot$	0010	RNS	■	■
0227	PDH_E2/_acald	$\cdot$	$\cdot$	0447	DUTPDP	■	■	0011	RNS	■	■
0228	PDH_E3	$\cdot$	$\cdot$	0203	GK	■	■	0195	SPRMabc	$□$	■
0220	PFK	■	■	0216	GUAPRT	$□$	■	0196	SPRMabc	$□$	■
0445	PGI	■	■	0747	PNP	$□$	$\cdot$	0197	SPRMabc	$□$	■
0606	PGK	■	■	0344	PPA	■	■	0706	THMPPabc	$□$	■
0729	PGM	■	■	0771	RNDR	$□$	■	0707	THMPPabc	$□$	■
0831	PRPPS	■	■	0772	RNDR	■	■	0708	THMPPabc	$□$	■
0229	PTAr	■	$\cdot$	0773	RNDR	$□$	■
0221	PYK	■	■	0140	TMDK1/DURIK1	■	■
0262	RPE	$□$	■	0045	TMPK	■	■
0800	RPI	$□$	■	0065	TRDR	■	■
0316	TKT	$□$	■	0819	TRDR	■	■
0727	TPI	■	■	0537	UMPK	■	■

Table 3

Cellular ATP expenses by category (in percent of total ATP consumption).

https://doi.org/10.7554/eLife.36842.025

Category	ATP expense [%]
Nucleotide metabolism	3.6
Pentose phosphate pathway	1.7
Lipid metabolism	0.7
Cofactor metabolism	0.1
Transport	3.4
GAM_{Macromolecules}	18
GAM_{tRNA charging}	16
GAM_Nonquant	41
NGAM_Turnover	13
NGAM_ATPase	2.7

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 genes		Excluding AA
Exp. / Model	Essential	Non-essential	Essential	Non-essential
Essential	101	4	101	4
Quasi-essential	22	14	22	4
Non-essential	0	12	0	10

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

	Accuracy	Sensitivity	Specificity	MCC
QE as E	88%	87%	100%	0.59
QE as NE	83%	96%	54%	0.59
No QE genes	97%	96%	100%	0.85
QE as E, no AA gene	94%	94%	100%	0.72
QE as NE, no AA genes	82%	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 overview	Genes	155
	Genome coverage	31%
	Metabolites	304
	Reactions (total)	338
	Reactions (non-pseudo)	244
Reaction breakdown (% of non-pseudo)	Annotation-supported	209	86%
	Passive	14	6%
	Gap fills with exp. evidence	17	7%
	Gap fills without exp. evidence	4	2%
	Supported (annotation/exp./passive)	240	98%
Essentiality insilico	Essential genes	123	79%
	Non-essential genes	30	19%
	‘Technical' non-essential genes	2	1%
Essentiality invivo	Essential genes	308	62%
	Quasi-essential genes	114	23%
	Non-essential genes	76	15%

Table 7

List of suggested gene removal experiments.

https://doi.org/10.7554/eLife.36842.032

Gene(s)	Description	Remark
manA/0435	Mannose 6-phosphate isomerase
deoC/0732	Deoxyribose phosphate aldolase
nanE/0494, 0495, nagB/0726	N-acetylmannosamine 6-phosphate branch
pdhC/0227, pdhD/0228, 0401	pdhCD and proposed lipoate importer
dak1/0330, dak2/0382	Deoxynucleoside kinases	Synthetic lethality possible
ietS/0133	Peptidase
0876	Amino acid permease
folT in JCVI-syn3A	Folate uptake and usage	Competition experiment with wild type to probe fitness cost in JCVI-syn3A
folT in JCVI-syn1.0	Folate uptake and usage	Competition 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

Experiment	Rationale
Nutrient utilization
Detect lipoylpeptide uptake	Lipoate is cofactor for PDH, whose functionality is unclear after E1 subunit deletion.
Detect nucleotide uptake	Activity reported for M. mycoides capri without gene assignment; alternative routes present in JCVI-syn3A, but activity not ruled out.
Detect nucleobase uptake	Activity reported for M. mycoides capri, and uracil uptake essential in model.
Demonstrate growth on thiamine diphosphate	Structural 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 phosphate	With no pyridoxal kinase identified, growth on pyridoxal phosphate (P5P) assumed; inability to grow on P5P would imply unidentified kinase activity.
Demonstrate growth on acetylmannosamine	Reconstruction 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 glucosamine	Literature suggests mannose and glucosamine import through PtsG; and downstream enzymes are present.
Metabolite production/secretion
Detect production of acetate	Acetate pathway has been partially removed, but several of the remaining enzymes remain essential in transposon mutagenesis experiments.
Investigate production of lipogalactan capsule	Genetic evidence suggests capsule is still being produced.
Detect secretion of deoxyuridine or dUMP	Deoxyuridine/dUMP is currently dead-end; secretion would be one possible solution.
Enzymatic activity
Detect pyruvate oxidation	Conversion 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 acetaldehyde	Conversion 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 activity	NADH oxidase (NOX) has been deleted but activity would be necessary for PDH activity against both pyruvate and acetaldehyde.
Detect transaldolase activity	Activity 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 activity	Reaction would provide bypass to transaldolase reaction.
Detect phosphatidate phosphatase activity	Gene 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, dCTP	Activities observed in M. mycoides capri but no gene identified in JCVI-syn3A.
Detect deoxyuridine phosphorylase activity	Gene 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 activity	Extremely 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 dak2/0382	Presence of dak2/0382 in addition to the characterized dak1/0330 suggests different substrate profile.
Verify CTPS activity against dUMP	CTPS (pyrG/0129) converting dUMP to dCMP seems most plausible solution to deoxyuridine/dUMP dead-end.
Check PGPP activity against phosphatidate	Activity 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 dCMP	Substrate 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 RNDR	RNDR 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 simultaneously	RNDR 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 resource	Designation	Source or reference	Identifiers	Additional information
Strain, strain background	JCVI-syn3A	JCVI; this article	GenBank accession number:CP016816.2	[1]
Strain, strain background	JCVI-syn1.0	doi:10.1126/science.1190719	GenBank accession number:CP002027.1	[1]
Genetic reagent	terTufPuro transposome	doi:10.1126/science.aad6253		Constructed by the JCVI
Genetic reagent	Yeast tRNA	Life Technologies, Carlsbad, CA, USA	15-401-029
Genetic reagent	EZ-Tn5-Transposase	Lucigen, Madison, WI, USA	TNP92110
Sequence-based reagent	Custom forward primer	Integrated DNA technologies, San Diego, CA, USA		[2]
Commercial assay or kit	Nextera XT DNA library preparation kit	Illumina, San Diego, CA, USA	FC-131-1024
Chemical compound, drug	Quant-iT PicoGreen	Molecular Probes, Eugene, OR, USA	P7589
Chemical compound, drug	Puromycin	Molecular Probes, Eugene, OR, USA	A1113802
Software, algorithm	COBRApy	doi:10.1186/1752-0509-7-74
Software, algorithm	CLC Genomics Workbench	QIAGEN Bioinformatics, Redwood City, CA, USA
Software, algorithm	ProteomeDiscoverer 2.1.0.81	Thermo Fisher Scientific

[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

Category	Component	Fraction [%]
Macromolecules	Protein	54.727
Total: 76.521 %	RNA	16.274
	DNA	5.5
	Acyl carrier protein	0.018
	dUTPase	0.003
Lipids & capsule	Lipogalactan capsule	6.368
Total: 17.563 %	Phosphatidylglycerol	2.944
	Cardiolipin	2.944
	Cholesterol	1.534
	Diacylglycerol	1.366
	Gal-DAG	1.31
	Triacylglycerol	0.549
	Fatty acid	0.549
Small molecules & ions	Potassium	3.285
Total: 5.916 %	Phosphate	0.375
	Chloride	0.198
	ATP	0.167
	L-lysine	0.133
	Sodium	0.131
	Spermine	0.128
	L-isoleucine	0.115
	GTP	0.115
	L-leucine	0.112
	UTP	0.106
	L-glutamate	0.085
	L-valine	0.083
	L-asparagine	0.083
Small molecules	L-alanine	0.077
& ions (cont’d)	L-aspartate	0.072
	L-threonine	0.071
	L-serine	0.071
	Glycine	0.068
	CTP	0.053
	L-phenylalanine	0.049
	L-glutamine	0.048
	L-arginine	0.043
	L-tyrosine	0.041
	L-proline	0.035
	L-methionine	0.026
	Magnesium	0.019
	L-histidine	0.019
	Calcium	0.019
	FAD	0.016
	5,10-meTHF(Glu)₃	0.015
	CoA	0.012
	Thiamin diphosphate	0.009
	NADP+	0.008
	L-tryptophan	0.008
	L-cysteine	0.008
	Pyridoxal phosphate	0.005
	dTTP	0.003
	dATP	0.003
	dCTP	0.002
	dGTP	0.001

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
Download elife-36842-supp1-v2.docx
Supplementary file 2 Transposon insertion nucleotide positions.: https://doi.org/10.7554/eLife.36842.035
Download elife-36842-supp2-v2.xlsx
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
Download elife-36842-supp3-v2.xlsx
Supplementary file 4 Reactions and metabolites included in the metabolic reconstruction.: https://doi.org/10.7554/eLife.36842.037
Download elife-36842-supp4-v2.xlsx
Supplementary file 5 Data from proteomics experiments.: https://doi.org/10.7554/eLife.36842.038
Download elife-36842-supp5-v2.xlsx
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
Download elife-36842-supp6-v2.xlsx
Supplementary file 7 Flux constraints derived from proteomics and turnover numbers and comparison to FBA fluxes.: https://doi.org/10.7554/eLife.36842.040
Download elife-36842-supp7-v2.xlsx
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
Download elife-36842-supp8-v2.xlsx
Supplementary file 9 FBA model in sbml format.: https://doi.org/10.7554/eLife.36842.042
Download elife-36842-supp9-v2.zip
Supplementary file 10 FBA model in json format.: https://doi.org/10.7554/eLife.36842.043
Download elife-36842-supp10-v2.zip
Supplementary file 11 ESCHER network map in json format.: https://doi.org/10.7554/eLife.36842.044
Download elife-36842-supp11-v2.zip
Transparent reporting form: https://doi.org/10.7554/eLife.36842.045
Download elife-36842-transrepform-v2.pdf

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Marian Breuer
Tyler M Earnest
Chuck Merryman
Kim S Wise
Lijie Sun
Michaela R Lynott
Clyde A Hutchison
Hamilton O Smith
John D Lapek
David J Gonzalez
Valérie de Crécy-Lagard
Drago Haas
Andrew D Hanson
Piyush Labhsetwar
John I Glass
Zaida Luthey-Schulten

(2019)

Essential metabolism for a minimal cell

eLife 8:e36842.

https://doi.org/10.7554/eLife.36842

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Classification of gene essentiality from transposon insertion data using a Poisson mixture model for a representative region of the JCVI-syn3A genome.

Classification of gene essentiality from transposon insertion data using a Poisson mixture model for 0–275,000 bp.

Classification of gene essentiality from transposon insertion data using a Poisson mixture model for 275,000–543,379 bp.

Distribution of transposon insertion counts for P1 (panel a) and P4 (panel b) compared to the distribution inferred through the Poisson mixture model.

Essential, quasi-essential, and non-essential protein coding genes in JCVI-syn3A across four functional classes.

Biomass reaction equation for JCVI-syn3A.

Overview of the metabolic reconstruction of JCVI-syn3A, drawn with Escher (King et al., 2015).

Central metabolism in JCVI-syn3A.

Steady-state fluxes through central metabolism in JCVI-syn3A.

Nucleotide metabolism in JCVI-syn3A.

Apparent dead-end of dUMP/deoxyuridine and possible solutions.

Cofactor metabolism in JCVI-syn3A.

Lipid and capsule metabolism in JCVI-syn3A.

Macromolecule metabolism in JCVI-syn3A.

Amino acid metabolism in JCVI-syn3A.

Ion transport reactions in JCVI-syn3A.

Comparison of growth curves of JCVI-syn1.0 and JCVI-syn3A.

Comparison of FBA steady-state fluxes ν to maximal fluxes Vmax obtained from protein abundances and turnover numbers from BRENDA and the literature.

Statistics of FBA steady-state fluxes ν vs. maximal fluxes Vmax comparison (see Figure 15).

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’).

In silico double-gene knockouts between genes that are non-essential in single-gene knockouts.

Distributions of absolute protein abundances (number of molecules per average cell) in JCVI-syn3A.

Thiamin diphosphate (ThDP) from the Mycoplasma hyorhinis Cypl crystal structure (pdb: 3EKI) overlaid onto the crystal structure of MG289 (pdb: 3MYU).

Sensitivity analysis of model doubling time with respect to model constraints.

Breakdown of protein coding genes in JCVI-syn3A into functional classes.

Genes modeled in the metabolic reconstruction.

Cellular ATP expenses by category (in percent of total ATP consumption).

Confusion matrices for gene essentiality prediction.

Accuracy, sensitivity, specificity and Matthews correlation coefficient calculated for several scenarios.

Summary of features of the metabolic model for JCVI-syn3A.

List of suggested gene removal experiments.

List of suggested experiments, with rationale behind each suggestion.

Reconstructed biomass composition of JCVI-syn3A, listing the fraction of each component as percent of cellular dry mass.

Supplementary file 1

Supplementary file 2

Supplementary file 3

Supplementary file 4

Supplementary file 5

Supplementary file 6

Supplementary file 7

Supplementary file 8

Supplementary file 9

Supplementary file 10

Supplementary file 11

Transparent reporting form

Downloads (link to download the article as PDF)

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Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Distribution of transposon insertion counts for $P_{1}$ (panel a) and $P_{4}$ (panel b) compared to the distribution inferred through the Poisson mixture model.

Comparison of FBA steady-state fluxes $ν$ to maximal fluxes $V_{max}$ obtained from protein abundances and turnover numbers from BRENDA and the literature.

Statistics of FBA steady-state fluxes $ν$ vs. maximal fluxes $V_{max}$ comparison (see Figure 15).