One way that researchers can test whether they understand a biological system is to see if they can accurately recreate it as a computer model. The more they learn about living things, the more the researchers can improve their models and the closer the models become to simulating the original. In this approach, it is best to start by trying to model a simple system.

Biologists have previously succeeded in creating ‘minimal bacterial cells’. These synthetic cells contain fewer genes than almost all other living things and they are believed to be among the simplest possible forms of life that can grow on their own. The minimal cells can produce all the chemicals that they need to survive – in other words, they have a metabolism. Accurately recreating one of these cells in a computer is a key first step towards simulating a complete living system.

Breuer et al. have developed a computer model to simulate the network of the biochemical reactions going on inside a minimal cell with just 493 genes. By altering the parameters of their model and comparing the results to experimental data, Breuer et al. explored the accuracy of their model. Overall, the model reproduces experimental results, but it is not yet perfect. The differences between the model and the experiments suggest new questions and tests that could advance our understanding of biology. In particular, Breuer et al. identified 30 genes that are essential for life in these cells but that currently have no known purpose.

Continuing to develop and expand models like these to reproduce more complex living systems provides a tool to test current knowledge of biology. These models may become so advanced that they could predict how living things will respond to changing situations. This would allow scientists to test ideas sooner and make much faster progress in understanding life on Earth. Ultimately, these models could one day help to accelerate medical and industrial processes to save lives and enhance productivity.

Establishing the core requirements of cellular life is a fundamental challenge of biology. The question of the minimal set of biochemical functions necessary for a cell to grow and replicate has been studied from a number of angles for more than 20 years. It has long been suggested (Morowitz, 1984) that a model to study the basics of cellular life would be the mycoplasmas—a group of bacteria with small genomes (580–1350 kbp (Herrmann, 1992; Fraser et al., 1995)) lacking a cell wall, which evolved via extreme genome reduction from low GC content Gram-positive ancestors (Pollack et al., 1997). Mycoplasmas exist as parasites or saprotrophs and are adapted to scavenging nutrients and cellular building blocks from their niche environments, which enabled them to lose many metabolic capabilities.

The genome of the human urogenital pathogen Mycoplasma genitalium (580 kbp, 525 genes overall, 482 for proteins, 43 for RNAs), sequenced in 1995 (Fraser et al., 1995), is the smallest genome of any autonomously replicating cell found in nature and has thus been deemed a close approximation to a minimal genome (Glass et al., 2006). In particular, different efforts have been undertaken to establish a minimal set of genes based on the near-minimal M. genitalium genome. A comparison of the first two sequenced bacterial genomes (the Gram-positive M. genitalium (Fraser et al., 1995) and the Gram-negative Haemophilus influenzae (Fleischmann et al., 1995)) yielded 256 orthologous genes that were suggested to approximate a minimal set of bacterial genes (Mushegian and Koonin, 1996); a subsequent comparative study, including genomes from several free-living and endosymbiotic bacteria, proposed a minimal set of 206 genes (Gil et al., 2004). A limitation of this approach lies in the possibility of the same function being fulfilled by non-orthologous proteins in different organisms, in which case it would not be captured by searching for orthologous genes. Transposon mutagenesis studies (Hutchison et al., 1999) avoid this drawback by directly probing the dispensability of individual genes in a single organism via random gene disruption, and testing for viability. In M. genitalium, such studies have suggested 382 out of the 482 protein-coding genes to be essential (Glass et al., 2006).

An important limitation of deriving a minimal gene set from essentiality information on individual genes lies in the fact that more than one gene can fulfill the same function, and while neither gene is essential individually, at least one of them has to be present in a functional minimal genome. Thus, while removal of either gene would be nonlethal, removing both would create a synthetic lethality. This problem can, in principle, be circumvented by sequential gene deletion starting from a given wild-type organism (as partially done for Escherichia coli and Bacillus subtilis (Juhas et al., 2014; Pósfai et al., 2006)), with testing for viability and growth rate after each deletion. In principle, this would not only yield the information on a minimal genome, but also would produce a living organism controlled by such a genome. However, the time and resource costs of minimizing a genome by serial deletion of dispensable genes are prohibitive.

In 2016, we developed a successful bottom-up approach to design a minimal genome and create a living cell controlled by it (Hutchison et al., 2016a). Starting with the gene sequence from the 1079 kbp genome of the ruminant pathogen Mycoplasma mycoides capri serovar LC GM12, a minimal genome of 531 kbp was designed and constructed containing 473 genes (438 protein-coding genes and 35 genes for RNAs) (Hutchison et al., 2016a). The resulting strain, JCVI-syn3.0 (NCBI GenBank: https://www.ncbi.nlm.nih.gov/nuccore/CP014940.1 (Hutchison et al., 2016b)), has a genome smaller than that of any independently-replicating cell found in nature and is considered to be our ‘working approximation to a minimal cell’. This achievement was the culmination of a series of breakthroughs in synthetic biology. In 2007, the successful transplantation of an M. mycoides capri LC GM12 genome into a Mycoplasma capricolum recipient cell was reported (Lartigue et al., 2007), transforming the recipient cell to the species of the implanted DNA. In 2008, the complete genome of M. genitalium was synthesized from scratch, starting with chemically synthesized oligonucleotides and stepwise recombination in vitro and subsequently in Saccharomyces cerevisiae (yeast), using the available genetic manipulation tools to assemble the genome as a plasmid inside the yeast cell (Gibson et al., 2008). These methods enabled the construction of JCVI-syn1.0, the first cell controlled by a synthetic genome (NCBI GenBank: https://www.ncbi.nlm.nih.gov/nuccore/CP002027.1) (Gibson et al., 2010a; Gibson et al., 2010b). This was accomplished by synthesizing of a copy of the M. mycoides capri LC GM12 genome along with vector sequences that allowed cloning in E. coli and yeast, and its subsequent transplantation into M. capricolum recipient cells to yield JCVI-syn1.0. These milestones enabled the synthesis of reduced versions of the JCVI-syn1.0 genome with subsequent transplantation into M. capricolum to test for viability. The genome reduction process was guided by transposon mutagenesis studies on the original JCVI-syn1.0 genome, as well as on intermediate reduced genome versions. Successful genome minimization depended on identifying both essential genes, whose disruption is immediately lethal, and quasi-essential genes, whose disruption causes an observable growth disadvantage. Quasi-essential genes were identified by observing if cells with potentially growth-reducing gene disruptions were outgrown during sufficiently long competition experiments, so that cells sampled from much later generations no longer contained the disrupted gene. Three cycles of genome design, assembly and growth testing yielded JCVI-syn3.0 (Hutchison et al., 2016a).

JCVI-syn3.0 contains all the genes of JCVI-syn1.0 that are required for growth. This includes both essential and quasi-essential genes. Individually non-essential genes were removed in the design for JCVI-syn3.0 to the greatest extent possible without causing synthetic lethality or a major growth disadvantage from simultaneous knockouts. However, in a few cases, genes that appear to be non-essential were retained for ease of genome design and construction. Intriguingly, the role of a considerable fraction of the minimal genome of JCVI-syn3.0 could not be elucidated in spite of extensive bioinformatic analyses. At the time of publication of the minimal cell, 149 of the genes (∼31% of the genome) could not be assigned a completely specific biological function. Assignment to a broad functional category could not even be made for a subset of 79 genes. These genes of unknown or poorly defined function potentially point toward required features of cellular life yet to be discovered.

The original minimal cell JCVI-syn3.0 genome was assembled by combining individually minimized 1/8 chromosome segments (Hutchison et al., 2016a). Phenotypic traits of JCVI-syn3.0 included extensive filamentation and vesicle formation during growth, and a doubling time of 2–3 hr (compared to the spheroidal morphology and 1 hr doubling time conferred by the JCVI-syn1.0 genome). To address these phenotypic disadvantages, an alternative design of segment six was found to restore consistent morphologic features and increase the growth rate, while retaining a near-minimal genome. This new design incorporated 19 additional genes from JCVI-syn1.0 segment six that were not present in JCVI-syn3.0, including those encoding the cell partitioning proteins FtsZ and SepF along with others of unknown function; in addition, two other genes retained in JCVI-syn3.0 segment six were removed. The complete genome sequence of this strain, termed JCVI-syn3A, is available through NCBI under the accession number https://www.ncbi.nlm.nih.gov/nuccore/CP016816.2. (Glass, 2017) This entry contains 498 genomic features, however three of those are pseudo-genes and two are genes required for cloning in yeast.

JCVI-syn3A has a doubling time of ∼2 hr and consistently forms spherical cells of approximately 400 nm in diameter. With a 543 kbp genome containing 493 genes of which 452 code for proteins and 38 for RNAs, JCVI-syn3A still has a smaller genome than any natural autonomously replicating cell while providing a robust and versatile platform to study the basics of life. In particular, this minimal cell opens up the possibility to pursue the construction of a complete in silico model including the function of all genes. The map of protein coding genes (Figure 1) clearly shows the fundamental importance of Syn3A as a platform to study the principles of life. Among the model bacteria E. coli and the related and well-studied (Güell et al., 2009; Kühner et al., 2009; Yus et al., 2009; Maier et al., 2011; Wodke et al., 2013) Mycoplasma pneumoniae, JCVI-syn3A has the smallest ratio of genes involved in metabolism to those in genetic information processing. With 91 it also has the smallest number of genes that are considered to have no known (unclear) function compared to 311 and 1780 for M. pneumoniae and E. coli, respectively (see Table 1 and Supplementary file 1C–1D for an itemized account of the functional categories for the three genomes).

Figure 1

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A model for ribosome biogenesis that includes DNA replication, transcription, translation, and ribosome assembly in slow growing E. coli has already been developed (Earnest et al., 2015; Earnest et al., 2016). As its components have on average 50% sequence identity to those genetic information processing genes in JCVI-syn3A, this model is assumed to be applicable to JCVI-syn3A as well. Hence, the next important step in modeling JCVI-syn3A is the reconstruction of its metabolic network.

The metabolic reconstruction presented here is based on the curated genome annotation, extensive experimental information from the literature on M. mycoides capri and other mycoplasma species, and accompanying transposon insertion and proteomics data. Our model features 338 reactions organized in nine subsystems (see Supplementary file 1B), involving 304 metabolites, catalyzed by gene products of 155 genes, thus covering a third of the genes of JCVI-syn3A. The reconstruction process enabled us to suggest several annotation refinements and updates, and yielded a metabolic network that is fairly complete.

Together with the reconstructed biomass composition of JCVI-syn3A and estimates of its reaction flux constraints and energy expenses, the reconstructed metabolic network was cast into a flux-balance analysis (FBA) model (Orth et al., 2010). FBA yields the set of steady-state reaction fluxes through a metabolic network that maximize a pre-defined objective function, for example production of cellular biomass. The solution space of possible fluxes is constrained not only by the steady-state assumption, but also by specific flux limits accounting for maximal uptake/secretion rates or cellular energy expenses. If these flux limits are not known, the network stochiometry predicts the biomass yield achieved by the cell, that is gram biomass produced/gram carbon source taken up (or equivalently biomass production rate/carbon substrate uptake rate). If flux constraints, in particular substrate uptake rates are known or can be assumed, the yield as growth rate per uptake rate can be converted to an absolute growth rate. While measurements to derive such flux constraints are not available yet for JCVI-syn3A, some measurements are available from other mycoplasmas and bacteria that have the same high-affinity glucose transporter (PtsG) found in JCVI-syn3A. Using the glucose uptake rate measured in M. pneumoniae (Wodke et al., 2013) (which is similar to the one measured in slow-growing E. coli (Fuhrer et al., 2005)) and other constraint estimates allows us to provisionally predict a growth rate for JCVI-syn3A; this model growth rate is however sensitive to the assumed uptake rate (see Section 'Sensitivity analysis' in Appendix 1). In this article, the growth rate predicted by the model is presented with the understanding that for the aforementioned reasons, the prediction is provisional and comes with a degree of uncertainty. This uncertainty has no bearing on the prediction of in silico gene essentialities (see below), which can be obtained by removing certain genes in the model and their associated reactions, and testing whether FBA still predicts a nonzero growth rate for the resulting in silico knockout.

This FBA model for JCVI-syn3A allows for the analysis of the properties of minimized metabolism in JCVI-syn3A. In particular, gene essentiality can be compared between the model and experimental transposon mutagenesis data. Random gene disruption by bombardment with transposon insertions (Hutchison et al., 1999; Glass et al., 2006) was used to identify non-essential genes in JCVI-syn1.0 that to the most part were removed during the construction of JCVI-syn3.0 (Hutchison et al., 2016a); here, genome-scale transposon mutagenesis studies were carried out on JCVI-syn3A to survey the individual essentiality of all its remaining genes. We find that transposon- and model-derived gene essentiality agree well, with every in silico essential gene being at least quasi-essential in vivo (i.e. removal might not be immediately detrimental, but give a growth disadvantage). The metabolic reconstruction allows us to rationalize the non-essentiality of some genes, and to propose possible further gene removals in JCVI-syn3A. These suggestions from the model are of particular interest as transposon mutagenesis experiments only probe the individual essentiality of genes and do not yield information on which genes could be removed simultaneously. The metabolic construction, on the other hand, allows us to suggest which genes might be simultaneously removed. At the same time, in silico and in vivo essentiality also show some discrepancies, which lead us to postulate new hypotheses about specific gene functions or metabolic capabilities. Protein expression profiles of essential and non-essential genes, classified by either transposon mutagenesis studies or FBA in silico gene knockouts, were not found to differ significantly–possibly indicating by and large the absence of expression regulation that would discriminate gene products based on their essentiality. Finally, the reconstruction process as well as the gene essentiality comparison have yielded a number of informed hypotheses and suggestions for specific experiments that will guide the ongoing experimental investigation of gene functions in the minimal cell.

While the minimal cell JCVI-syn3A is a new organism with little experimental data yet available, its natural precursor M. mycoides capri has been studied in depth, which informed all aspects of the metabolic model. To refer to genes in the JCVI-syn3A genome, we use the locus names of the form MMSYN1_xxxx as used in the annotation of JCVI-syn1.0 (Hutchison et al., 2016a) to allow us to discuss genes deleted in JCVI-syn3A more clearly. The MMSYN1_ prefix is omitted for brevity. Understanding the in vivo essentiality of genes in JCVI-syn3A is an important first step to the development of the metabolic reconstruction: this is presented first in Section 'Transposon mutagenesis experiments probe in vivo gene essentiality'. Using the protein expression profiles measured for JCVI-syn3A, the biomass composition of JCVI-syn3A is then derived in Section 'Biomass composition and reaction', as well as the biomass reaction used in the model. The construction and justification of the metabolic model is presented in Section 'Metabolic reconstruction'. The steady-state fluxes obtained from the model are then compared in Section 'Steady state fluxes' to experimental fluxes, as well as to protein abundance-based flux limits. The metabolic energy usage of JCVI-syn3A is analyzed in Section 'Energy usage'. Gene essentiality obtained from in silico gene knockouts is presented in Section 'In silico gene knockouts and mapping to in vivo essentiality'. Finally, we compare protein expression profiles between essential and non-essential proteins as identified in the model or in vivo in Section 'Abundances of essential and non-essential proteins'.

Transposon mutagenesis experiments probe in vivo gene essentiality

Transposon insertion mutagenesis studies were performed in order to probe the dispensability of individual genes in JCVI-syn3A (see Section 'Materials and methods'). In this experiment, transposons are randomly inserted into the chromosomes of a population of cells that is then plated under selection for a drug resistance gene carried by the transposon (Hutchison et al., 1999; Glass et al., 2006). After transferring to a liquid culture (‘passage zero’, ${\displaystyle {\mathrm{P}}_0}$), four serial passages are performed. DNA from the pooled colonies is isolated and sequenced to determine the location of transposon insertions within the genome at the beginning or at the end of the experiment. When determining transposon locations at the beginning of the experiment, ${\displaystyle {\mathrm{P}}_1}$ is used over ${\displaystyle {\mathrm{P}}_0}$ to limit any contamination from the DNA of non-viable cells. The number of insertions observed in a coding region can then be used to infer the importance of that gene. We note that not every insertion will necessarily obliterate a gene’s function. A graphical presentation of the essentiality classification along with the distribution of transposon insertions over a portion of the genome is presented in Figure 2. It shows that secA/0095 is heavily hit with insertions in the 3' 25% of the gene (but practically nowhere else); however, SecA is certainly essential because it is a necessary component of the protein translocase, which inserts proteins such as transporters into the membrane. While the absence of gene products for genes carrying transposon inserts has not been confirmed experimentally, genes with relatively high insertion counts are more likely to be functionally disrupted and thus non-essential. Genes that are not required by the organism to grow in rich media will contain many transposon insertions (‘non-essential’ genes), whereas genes required for cell viability will be sparsely hit by transposon insertions. To identify genes whose disruption might not be immediately detrimental but might cause a growth defect apparent later on, sequencing of the transposon mutagenesis library was performed on ${\displaystyle {\mathrm{P}}_4}$ cells as well. Cells with a gene disruption that is not immediately lethal but causes a growth disadvantage will be outgrown after ${\displaystyle {\mathrm{P}}_4}$, and the number of insertions for that gene will then significantly decrease from ${\displaystyle {\mathrm{P}}_1}$ to ${\displaystyle {\mathrm{P}}_4}$. These genes are denoted ‘quasi-essential’.

Figure 2 with 3 supplements see all

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A Poisson mixture model was used to partition the coding regions into two sets of genes based on the transposon insertion density. By comparing the assignment of genes into classes of sparse and dense transposon insertions between ${\displaystyle {\mathrm{P}}_1}$ and ${\displaystyle {\mathrm{P}}_4}$, essentiality can be inferred. This classification method considers a gene to be essential if it has been classified to have sparse transposon insertions in both ${\displaystyle {\mathrm{P}}_1}$ and ${\displaystyle {\mathrm{P}}_4}$, quasi-essential if it was classified to have dense transposon insertions for ${\displaystyle {\mathrm{P}}_1}$ and sparse insertions for ${\displaystyle {\mathrm{P}}_4}$, and non-essential if the gene was classified as densely hit for both ${\displaystyle {\mathrm{P}}_1}$ and ${\displaystyle {\mathrm{P}}_4}$. See Section 'Materials and methods' for a complete description of the classification method. Figure 2—figure supplement 3 shows the fit of the model to the distribution of transposon insertion counts per gene.

In six out of 452 instances, the mixture model failed to classify the gene, either due to low assignment confidence or due to increased insertions from ${\displaystyle {\mathrm{P}}_1}$ to ${\displaystyle {\mathrm{P}}_4}$. The short ribosomal proteins S9 (rpsl/0637), L27 (rpmA/0499), and L31 (rpmE/0137) were manually assigned as essential since they are necessary to construct a functional ribosome. The gene secA/0095 could not be automatically classified since the mixture model predicted the gene to be more heavily hit with insertions in ${\displaystyle {\mathrm{P}}_4}$ than in ${\displaystyle {\mathrm{P}}_1}$; it was assigned as essential as it is a major component of the translocase assembly. The insertions occurred in the C-terminus linker domain considered to be important for binding to phospholipids and preprotein translocation. A gene of unclear function (0235) was predicted by the model to be essential at a slightly higher probability than quasi-essential (0.471 vs. 0.416, respectively); however, it was manually assigned to be quasi-essential following its previous assignment in JCVI-syn2.0 (Hutchison et al., 2016a). Thioredoxin (trxA/0065) was assumed to be essential since its associated reductase (trx/0819) was predicted to be essential by the mixture model. Only one gene was misclassified: the ATP synthase subunit $ϵ$ (atpC/0789), initially classified as non-essential, was manually reassigned to essential since all other ATPase subunits (atpD/0790 through atpB/0796) were essential according to the mixture model. The majority of transposon insertions in atpC/0789 are found in the $3^{\prime }$ region, similar to the pattern seen in secA. However, it is possible that the $ϵ$ subunit may not actually be essential since in M. pneumoniae, transposon insertions into the atpC (MPN597) lead to viable cells with decreased cytadherence activity (Shimizu et al., 2014).

The set of genes classified quasi-essential could potentially include essential genes which cannot be discriminated using these transposon insertion data. For these misclassified genes, it is possible that although expression of the gene product essential for cell growth has been halted, previously translated essential proteins remain in the cell in sufficient quantities to maintain cell growth and division up to ${\displaystyle {\mathrm{P}}_1}$. A further discussion of this argument is presented in Section 'Completeness of the model' and Appendix 1.

The genes identified as non-essential by the Poisson mixture model may contain ‘weakly’ quasi-essential genes, that is disrupted genes which confer a minor growth disadvantage. This behavior would manifest as a decrease in transposon insertions between ${\displaystyle {\mathrm{P}}_1}$ and ${\displaystyle {\mathrm{P}}_4}$, but not such a steep decline that the gene is observed with little to no insertions. To identify these ‘weakly’ quasi-essential genes, the genes classified as non-essential were subjected to further classification using $k$-means clustering of the ratio of the number of transposon insertions in ${\displaystyle {\mathrm{P}}_4}$ to ${\displaystyle {\mathrm{P}}_1}$ assuming two clusters (see Figure 2—figure supplement 3). Of the 118 genes initially classified non-essential, 42 were reclassified as quasi-essential.

The assignment of essentiality classes and distribution of transposon insertions over the entire genome are presented in Figure 2—figure supplements 1,2, and Supplementary file 3. Genomic positions of transposon insertions are listed in Supplementary file 2. Figure 3 summarizes the breakdown of the essential, quasi-essential, and non-essential genes according to the functional classes. Of the 452 coding genes in JCVI-syn3A, 60% are essential, 25% are quasi-essential, and 15% are non-essential by this analysis. The detailed breakdown of the JCVI-syn3A genome into these classes (Table 1) shows that of the 91 genes of unclear function, 30 are essential, 32 are quasi-essential, and 29 are non-essential. Those 30 essential genes could represent new biological mechanisms not yet defined and should motivate the search to discover their function (Alberts, 2011).

Figure 3

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Table 1

		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

Since on average only one transposon insertion occurs per cell and the identification of insertion locations within the genome is performed over an ensemble of cells, these transposon mutagenesis studies can only reveal individual gene essentialities. To probe the essentiality of groups of genes, one would need to perform targeted multiple knockout studies. However, for metabolic genes, flux balance analysis of the metabolic reconstruction can predict the essentiality of groups of genes. In Targeted gene removal experiments, the individual gene essentiality results are expanded to include the assignment of essentiality to combinations of genes in silico, leading to potential combinations of genes to remove to further minimize the genome. The classifications of the genes used in the metabolic reconstruction are shown in Table 2.

Table 2

Locus	Reaction	EssTn5	EssFBA	Locus	Reaction	EssTn5	EssFBA	Locus	Reaction	EssTn5	EssFBA
Amino acid metabolism				Cofactor metabolism				0798	UPPRT	■	■
0381*	AHCi	$\cdot$	$\cdot$	0823	5FTHFPGS	$\mathrm{\square }$	■	0330	dAdn kinase 1	$\cdot$	$\cdot$
0163	ALATRS	■	■	0390	FMETTRS	$\mathrm{\square }$	■	0382	dAdn kinase 2	$\cdot$	$\cdot$
0535	ARGTRS	■	■	0291	FMNAT	■	■	Transport
0076	ASNTRS	■	■	0443	FTHFCL	■	■	0822	5FTHFabc	$\mathrm{\square }$	■
0287	ASPTRS	■	■	0799	GHMT	$\mathrm{\square }$	■	0876	AA permease 1	$\cdot$	$\cdot$
0837	CYSTRS	■	■	0684	MTHFC	$\mathrm{\square }$	■	0878	AA permease 2	$\mathrm{\square }$	$\cdot$
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${}^{\mathrm{\ast }}$	MAT	■	$\cdot$	0117	APG3PAT	■	■	0836	COAabc	■	■
0012	METTRS	■	■	0139	BPNT	$\mathrm{\square }$	■	0642	EcfA	■	■
0528	PHETRS	■	■	0147	CLPNS	■	■	0643	EcfA	■	■
0529	PHETRS	■	■	0697	DAGGALT	$\mathrm{\square }$	$\cdot$	0641	EcfT	■	■
0282	PROTRS	■	■	0114	DAGPST/DAGGALT	■	■	0233	GLCpts	■	■
0133	Peptidase 1	$\cdot$	$\cdot$	0304	DASYN	■	■	0234	GLCpts	■	■
0305	Peptidase 2	$\mathrm{\square }$	$\cdot$	0420	FAKr	■	■	0694	GLCpts	■	■
0444	Peptidase 3	$\mathrm{\square }$	$\cdot$	0616	FAKr	■	■	0779	GLCpts	■	■
0479	Peptidase 4	$\mathrm{\square }$	$\cdot$	0617	FAKr	$\mathrm{\square }$	■	0886	GltP	$\mathrm{\square }$	$\cdot$
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	$\mathrm{\square }$	■	0787	MG2abc	■	■
0260	VALTRS	■	■	0875	PGSA	■	■	0314	NACabc	■	■
Central metabolism				0113	PSSYN	■	■	0165	Opp	$\mathrm{\square }$	$\cdot$
0230	ACKr	■	$\cdot$	0813	UDPG4E	■	■	0166	Opp	$\mathrm{\square }$	$\cdot$
0493	AGDC	$\mathrm{\square }$	$\cdot$	0814	UDPGALM	■	■	0167	Opp	$\mathrm{\square }$	$\cdot$
0495	AMANK	$\cdot$	$\cdot$	Macromolecules				0168	Opp	$\mathrm{\square }$	$\cdot$
0494	AMANPEr	$\cdot$	$\cdot$	0394	Lon	$\mathrm{\square }$	■	0169	Opp	$\mathrm{\square }$	$\cdot$
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	$\mathrm{\square }$	■	0009	RNS	■	■
0435	MAN6PI	$\cdot$	$\cdot$	0515	DCMPDA	$\mathrm{\square }$	$\cdot$	0010	RNS	■	■
0227	PDH_E2/_acald	$\cdot$	$\cdot$	0447	DUTPDP	■	■	0011	RNS	■	■
0228	PDH_E3	$\cdot$	$\cdot$	0203	GK	■	■	0195	SPRMabc	$\mathrm{\square }$	■
0220	PFK	■	■	0216	GUAPRT	$\mathrm{\square }$	■	0196	SPRMabc	$\mathrm{\square }$	■
0445	PGI	■	■	0747	PNP	$\mathrm{\square }$	$\cdot$	0197	SPRMabc	$\mathrm{\square }$	■
0606	PGK	■	■	0344	PPA	■	■	0706	THMPPabc	$\mathrm{\square }$	■
0729	PGM	■	■	0771	RNDR	$\mathrm{\square }$	■	0707	THMPPabc	$\mathrm{\square }$	■
0831	PRPPS	■	■	0772	RNDR	■	■	0708	THMPPabc	$\mathrm{\square }$	■
0229	PTAr	■	$\cdot$	0773	RNDR	$\mathrm{\square }$	■
0221	PYK	■	■	0140	TMDK1/DURIK1	■	■
0262	RPE	$\mathrm{\square }$	■	0045	TMPK	■	■
0800	RPI	$\mathrm{\square }$	■	0065	TRDR	■	■
0316	TKT	$\mathrm{\square }$	■	0819	TRDR	■	■
0727	TPI	■	■	0537	UMPK	■	■

Preliminary triple knockout experiments involving various sets of non-essential genes lead to cells with greatly impaired growth rates (data not shown). The fact that ∼15% of the genes in JCVI-syn3A are individually non-essential is not inconsistent with the near-minimality of the genome as a whole: it is not possible to remove all non-essential genes without vastly decreasing the growth rate or outright killing the cell. Furthermore, a genome comprised only of essential and quasi-essential genes is non-viable as well, since the removal of a non-essential gene can cause a previously quasi-essential gene to become non-essential in the new construct. As JCVI-syn3A grows more slowly than JCVI-syn1.0 (2 hr doubling time vs. 1 hr), a gene disruption that in JCVI-syn1.0 led to outgrowth by unaffected competitor cells might still survive through passage four in JCVI-syn3A. As a result, genes that were classified as quasi-essential in JCVI-syn1.0 can appear non-essential in JCVI-syn3A, and could in principle be removed as well—for the price of some gradual further decrease in growth rate. This lack of a clear cutoff again underscores the ‘trade-off between genome size and growth rate’ taking place during genome minimization (Hutchison et al., 2016a).

Biomass composition and reaction

The cellular components of JCVI-syn3A fall into three categories: macromolecules, lipids and capsule, and small molecules and ions. Appendix 1—table 1 lists the mass fractions for all components included in the JCVI-syn3A biomass composition. These mass fractions are used to derive the coefficients in the biomass reaction depicted in Figure 4 for each component based on its molecular weight. The different biomass components are summarized below with the full discussion and derivation in Appendix 1. The growth-associated maintenance (GAM) ATP cost shown in Figure 4 is described in Section 'GAM/NGAM'.

Figure 4

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Macromolecules

The macromolecular mass fractions are based on the experimental composition of M. mycoides capri (Razin et al., 1963), which is assumed to provide a very good approximation for JCVI-syn3A (which was derived from an M. mycoides capri substrain). The DNA fraction is slightly increased from the reported 5% to 5.5%, which corresponds to exactly one chromosome in JCVI-syn3A. Assuming almost all RNA to be present as ribosomal RNA (rRNA) and around 4600 bases per ribosome yields an upper limit of ∼670 ribosomes per average cell. This number would correspond to ∼20,000 ribosomes in E. coli when scaled by cell volume, which is within the observed growth dependent range of 8000–73,000 for E. coli (Bremer and Dennis, 2008). The RNA base composition is based upon the close relative M. mycoides capri serovar LC (M. mycoides capri LC) strain Y (Mitchell and Finch, 1977). (M. mycoides capri LC Y is referred to as ‘M. mycoides mycoides goat strain Y’ in the older literature, but has recently been included in subspecies capri (Manso-Silván et al., 2009). The DNA composition is determined by the GC content of the genome (24%). The absolute number of proteins can be estimated from the average protein molecular weight in JCVI-syn3A, which is obtained from the proteomics studies reported in Section 'Abundances of essential and non-essential proteins' (see also Section 'Mass Spectrometry Based Proteomics' within Section 'Methods and methods' for experimental details). For an average cell, this amounts to ∼77,000 proteins; the resulting protein volume density in a 400 nm spherical cell is 2.3 × 10⁶ proteins/µm³, which compares well to the estimated density of 3.5 - 4.4 × 10⁶ proteins/µm³ in E. coli (Milo, 2013). The protein amino acid composition is computed directly from the proteomics data.

In addition to a generic protein species (describing the average JCVI-syn3A protein), two specific proteins are included: acyl carrier protein (ACP, acpA/0621) and dUTPase (dut/0447). ACP carries a 4′-phosphopantetheine moiety in its holo form, and including the holoenzyme in the biomass equation enforces flux through the corresponding prosthetic group attachment reaction. dUTPase is included for technical reasons discussed in Section 'Nucleotide metabolism'. We use cellular abundances of 138 (ACP) and 10 (dUTPase), derived from the proteomics experiments. The resulting mass fractions are then subtracted from the total protein mass fraction.

Lipids and capsule

Based on the experimental lipid composition of M. mycoides capri serovar capri PG3 (Archer, 1975) and M. mycoides capri LC Y (Plackett, 1967a), the model includes the phospholipids phosphatidylglycerol and cardiolipin, the glycolipid monogalactosyl-diacylglycerol (Gal-DAG), cholesterol, diacylglycerol and free fatty acids. For fatty acids, palmitic acid (C16:0) and oleic acid (C18:1 cis-9) are considered to be the two most important representatives, and an average ‘fatty acid’ with a molecular weight averaged between palmitate and oleate is used in all lipid species. In addition, a galactan polysaccharide capsule is included in the biomass. M. mycoides capri LC GM12 (the strain from which JCVI-syn3A is derived) is known to produce a galactan polysaccharide (specifically, poly-${\displaystyle \beta }$−1${\displaystyle \to }$6-galactofuranose) (Schieck et al., 2016); while it is not yet experimentally known whether the minimal cell still produces this galactan, genetic features suggest it does. As other M. mycoides capri LC strains form a polysaccharide capsule but secrete negligible amounts of polysaccharide (Bertin et al., 2015), the JCVI-syn3A galactan is assumed to form a capsule as well and is included as polygalactosyl-diacylglycerol (lipogalactan).

Small molecules and ions

In addition to the macromolecules and lipids, we also include pools of free amino acids, nucleotides and deoxynucleotides in our biomass, as well as cofactors and ions expected to be needed in JCVI-syn3A. A minimal medium for JCVI-syn3A has yet to be obtained, so we use the minimal media reported for M. mycoides capri LC Y (Rodwell, 1969) and M. pneumoniae (Yus et al., 2009) as a guideline for required ions and cofactors: Any compound present in a minimal medium is required by the cell, and the compound or its downstream product(s) need to be included in the biomass composition. From the two media mentioned, all inorganic ions are included in the JCVI-syn3A biomass composition except for sulfate, for which there is no known need in JCVI-syn3A. The vitamin choline from the M. pneumoniae minimal medium is also excluded as M. mycoides capri does not synthesize its own phosphatidylcholine (Plackett, 1967a). Transition metal ions are also not included. While these media are used to determine which compounds to include in the biomass composition, their intracellular concentrations/mass fractions are obtained from measurements in M. mycoides capri (Leblanc and Le Grimellec, 1979; Mitchell and Finch, 1979; Neale et al., 1983a) or other mycoplasmas (Linker and Wilson, 1985) or taken from the iJW145 M. pneumoniae (Wodke et al., 2013) and iJO1366 E. coli models (Orth et al., 2011).

Metabolic reconstruction

The metabolic reconstruction of JCVI-syn3A features 338 reactions involving 304 metabolites, catalyzed by gene products of 155 genes, thus covering a third of the genome. The scope of the reconstruction includes all reactions associated with providing the components of the reconstructed biomass (see Figure 4). Not covered are metabolic functions outside the ‘core’ functions, in particular RNA modifications and damage repair reactions. While many RNA modification enzymes are known already, the prevalence of specific RNA modifications in the RNA pool is not yet known. A few RNA modification enzymes are however discussed with regard to folate metabolism in Section 'The role of folate metabolism'. The majority of damage reactions and possible repair thereof are mostly not yet known, and are hence omitted save for two genes in cofactor and nucleotide metabolism. Approximately 30 genes pertaining to RNA modification are listed in our KEGG ortholog search as ‘Genetic Information Processing’ and will be included in a future model for ribosome biogenesis and tRNA biogenesis.

The model reactions are organized in nine subsystems, which are listed in Supplementary file 1B together with their respective number of reactions and genes included. Among these subsystems, ‘Biomass production’ contains the biomass reaction discussed in Section 'Biomass composition and reaction'. ‘Exchange’ contains the model reactions that describe metabolite exchange with the media. All other subsystems are discussed in detail in the following subsections. Figure 5 shows a global view of the metabolic network (excluding biomass and exchange reactions). Individual maps for the subsystems for central, nucleotide, cofactor, lipid, macromolecule and amino acid metabolism are depicted in Figures 6–7 and Figures 9–12. Table 2 lists all genes included in the reconstruction, together with their in vivo and in silico essentiality.

Figure 5

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Figure 6 with 1 supplement see all

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Figure 7

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Experimentally, JCVI-syn3A is grown in the rich and not fully defined SP4 media (Glass et al., 2015; Tully et al., 1979), since a defined media supporting its normal growth has yet to be obtained. Consequently, a rich in silico medium that provides for all biomass precursors the cell can take up is assumed, with glucose as the only energy source.

Central metabolism

A schematic diagram of central metabolism in JCVI-syn3A is provided in Figure 6. The only annotated sugar importer in the JCVI-syn3A genome is the glucose PTS system comprising PtsI (ptsI/0233), PtsH (ptsH/0694), Crr (glucose-specific IIA component, crr/0234) and PtsG (ptsG/0779). The phosphate-transfer reaction chain from phosphoenolpyruvate (PEP) to glucose (Postma et al., 1993) is lumped together into a single reaction that imports glucose and phosphorylates it with PEP. The presence of ManA (mannosamine-6-phosphate isomerase, manA/0435) and NagB (glucosamine 6-phosphate deaminase, nagB/0726) suggests possible utilization of mannose and glucosamine as well, which is supported by mutation studies suggesting that in M. mycoides mycoides strain T1, PtsG is involved in the uptake of all three sugars (Lee et al., 1986). Thus PTS-mediated combined uptake and phosphorylation is included for glucose, glucosamine and mannose. Furthermore, the presence of a putative nagA operon (0493 through 0495) suggests possible uptake of N-acetylmannosamine or N-acetylneuraminate (sialic acid). Therefore, an uptake reaction for N-acetylmannosamine is included, noting that this is probably not imported through the glucose PTS system, as concomitant phosphorylation would render the putative N-acetylmannosamine kinase NagC (0495) redundant. Lacking more information, we assume that this import reaction is mediated by an ATP-binding cassette (ABC) transporter instead.

As uptake and secretion measurements for JCVI-syn3A are not yet available, similar uptake rates to those measured in M. pneumoniae (Wodke et al., 2013) are used, as has been done in previous mycoplasma models (Ferrarini et al., 2016). Since glucose is considered to be the only energy source, upper limits of 0.0 mmol gDW⁻¹ h^-1 on mannose, glucosamine and N-acetylmannosamine uptake are applied. A maximum glucose uptake rate of 7.4 mmol gDW⁻¹ h^-1 measured in mid-exponential phase M. pneumoniae is used. We note that since mannose and glucosamine would compete for the same PTS importer with glucose, their uptake would not increase the overall sugar uptake.

Four possible sugar sources are assumed to feed into glycolysis via fructose-6-phosphate (F6P): glucose, mannose, glucosamine and N-acetylmannosamine. Only the putative N-acetylmannosamine-6-phosphate epimerase NanE (nanE/0494) has been annotated; however, it seems likely that this gene forms part of an operon together with its two adjacent genes. The RAST annotation pipeline (Overbeek et al., 2014) suggests the putative ROK family gene 0495 codes for N-acetylmannosamine kinase (NagC). Its genomic context suggests 0493, annotated as a putative dipeptidase, codes for N-acetylglucosamine-6-phosphate deacetylase (NagA). This would complete the N-acetylmannosamine utilization pathway and would be consistent with the putative amide cleavage functionality.

Starting from fructose-6-phosphate, the annotation of JCVI-syn3A contains a complete glycolytic pathway as well as the non-oxidative branch of the pentose phosphate pathway up to the nucleotide precursor phosphoribosylpyrophosphate (PRPP), in line with experimental studies on M. mycoides capri LC Y (Cocks et al., 1985; Mitchell, 1976). As in other mycoplasmas (Pollack et al., 1997), the gene for transaldolase (TALA) has not been identified; however, transaldolase activity has been detected experimentally (Desantis et al., 1989). In particular, it has been detected in M. mycoides capri LC Y cell extracts (Cocks et al., 1985), albeit the detected specific activity was rather weak (13 μmol min^-1 g cell protein^-1) and the level of background noise in that study is not known. Thus, this reaction is included in the model but we note that the evidence seems ambiguous.

While the completion of the glycolytic pathway via lactate dehydrogenase (LDH, ldh/0475) is possible, several genes from the acetate fermentation branch have been deleted, namely the E1 subunit of pyruvate dehydrogenase (PDH_E1, MMSYN1_0225 and MMSYN1_0226) and NADH oxidase (NOX, MMSYN1_0223). However, the remaining subunits of the PDH complex (PDH_E2 and PDH_E3, pdhC/0227 and pdhD/0228), as well as the path from acetyl-CoA to acetate, are still present in the genome. NAD regeneration could possibly be carried out by one of the remaining oxidoreductases of unclear functionality. Until further information becomes available, the remaining PDH complex is assumed to act on pyruvate to yield acetyl-CoA and finally acetate.

Another possible function for the remaining PDH complex would be oxidation of acetaldehyde to acetyl-CoA, which would not require a decarboxylation in the absence of the PDH_E1 subunit (see Appendix 1). Phosphopentomutase (PPM) and deoxyribose phosphate aldolase (DRPA) activity have been experimentally observed in M. mycoides capri LC Y (Cocks et al., 1985), enabling the breakdown of deoxyribose 1-phosphate (dR1P) into glyceraldehyde 3-phosphate (G3P) and acetaldehyde (acald). A gene for deoxyribose phosphate aldolase has been annotated in JCVI-syn3A (deoC/0732). A strong candidate for phosphopentomutase activity is the putative phosphoglucomutase (PGMT) gene deoB/0733. It is preceded by the deoxyribose phosphate aldolase gene (deoC/0732) and (in the original JCVI-syn1.0 genome) succeeded by the pyrimidine nucleoside phosphorylase MMSYN1_0734, that is it is neighboring two genes responsible for dR1P production and dR5P breakdown, respectively. At the same time, it shows some similarity (21% sequence identity) to the phosphopentomutase TK1777 from the archaeon Thermococcus kodakaraensis. TK1777 showed activity mainly against d1RP, but also weaker activity against glucose 1-phosphate (G1P) (Rashid et al., 2004). Thus, deoB/0733 is assumed to be the gene responsible for both activities.

Secretion of acetate (Rodwell and Rodwell, 1954), lactate (Rodwell, 1967) and pyruvate (Rodwell, 1969) has been observed in M. mycoides; with mutational data on M. mycoides mycoides T1 (Lee et al., 1986) indicating a common transporter for pyruvate and lactate. While it is not clear how mycoplasmas secrete acetate, lactate and pyruvate, proton symporters have been suggested for lactate and acetate (Wodke et al., 2013) and such reactions are assumed in other mycoplasma models (Suthers et al., 2009; Karr et al., 2012; Ferrarini et al., 2016). The genome of JCVI-syn3A contains several annotated efflux proteins, but all these show features of ATP-coupled transporters, suggesting they are not involved in lactate or acetate export. Thus, lactate, pyruvate and acetate secretion reactions are included as proton symports, noting that for the purposes of this model, this is equivalent to assuming secretion of neutral acid species. The acetate secretion rate is constrained to a maximum of 6.9 mmol gDW⁻¹ h^-1 following (Wodke et al., 2013), and the lactate secretion rate is kept unconstrained as the optimal FBA solution will always route as much flux as possible through the acetate pathway, yielding one more ATP per pyruvate. The pyruvate secretion rate is also left unconstrained; this reaction only carries flux under certain in silico gene knockout conditions.

Nucleotide metabolism

A schematic diagram of nucleotide metabolism in JCVI-syn3A is presented in Figure 7. The JCVI-syn3A annotation contains a putative ribonucleoside (RNS) ABC import system (rnsD/0008 through rnsB/0011), which is assumed to import all nucleosides (ribo- and deoxyribo-), but no individual bases or free ribose, in accordance with the experimental characterization of the ribonucleoside ABC importer in Streptococcus mutans (Webb and Hosie, 2006). While intact nucleotides are rarely taken up as a whole, there have been reports of M. mycoides capri LC Y being capable of taking up deoxymononucleotides (Neale et al., 1984; Youil and Finch, 1988). However, no gene has been identified for this functionality in that strain, nor is there any hint that the minimized JCVI-syn3A still possesses this ability. We note that competition experiments suggest distinct uptake systems for nucleosides and nucleotides (Maniloff, 1992), that is the RNS importer should not allow for nucleotide uptake. The presence of several nucleoside kinases as well as phosphoribosyltransferases in JCVI-syn3A suggests that this nucleotide uptake ability, if present at all, cannot provide nucleotides in sufficient amounts and thus no nucleotide uptake reactions are included. The mechanism of nucleobase uptake in mycoplasmas has not been established, but a proton symport mechanism has been suggested (Maniloff, 1992). This mechanism is used in the model as well, as done in other mycoplasma models (Wodke et al., 2013; Suthers et al., 2009; Karr et al., 2012; Ferrarini et al., 2016).

The further reconstruction of nucleotide metabolism is aided by the extensive experimental studies on nucleotide salvage pathways in M. mycoides capri LC Y (Mitchell and Finch, 1977; Mitchell et al., 1978; Mitchell and Finch, 1979; Neale et al., 1983a; Neale et al., 1983b; Cocks et al., 1988) and M. mycoides mycoides SC (Wang et al., 2001; Welin et al., 2007). The reactions detected or inferred are in agreement with the existing annotations, and help to refine possible specificities and suggest additional functionalities.

The genome of JCVI-syn3A contains three deoxynucleoside kinases: tdk/0140, dak1/0330, and dak2/0382. The thymidine kinase, 0140, is assumed to phosphorylate both thymidine and deoxyuridine. As further discussed in Appendix 1, it is furthermore assumed that dak1/0330 and dak2/0382 both act on deoxyadenosine, deoxyguanosine and deoxycytidine, but not significantly on any ribonucleosides. Therefore, AMP, GMP and UMP are only formed directly from their respective bases by the corresponding phosphoribosyltransferases (hptA/0216, apt/0413, and upp/0798).

The genome of JCVI-syn3A contains several mononucleotide kinases (tmk/0045, gmk/0203, cmk/0347, pyrH/0537, and adk/0651) that can phosphorylate all (deoxy-)mononucleotides except for dUMP (Neale et al., 1983b), but in line with other mycoplasmas (Pollack et al., 2002), the genome of JCVI-syn3A contains no gene for nucleoside diphosphate kinase (ndk). Instead, the glycolytic enzymes phosphoglycerate kinase (PGK) and pyruvate kinase (PYK) have been found to phosphorylate other dinucleotides besides ADP in several mycoplasmas (Pollack et al., 2002). Specifically, PYK was found to act on all (deoxy-)dinucleotides and PGK was found to act on all purine (deoxy-)dinucleotides, but not on pyrimidines. These reactions complete the pathways from the mononucleotides to the final (deoxy-)trinucleotides. We note that the apparent absence of cytidine kinase activity implies that the only route to cytidine nucleotides goes through CTP synthase (CTPS, pyrG/0129; aminating UTP to CTP). All deoxytrinucleotides except dTTP can be obtained either from their deoxynucleosides or from the corresponding ribodinucleotide through the action of ribonucleotide diphosphate reductase (RNDR, nrdE/0771 through nrdF/0773).

In addition to these synthetic pathways, JCVI-syn3A also contains several catabolic reactions. The phosphorolysis of purine nucleosides observed in M. mycoides capri LC Y (Mitchell and Finch, 1977; Mitchell et al., 1978) can be carried out by purine nucleoside phosphorylase (PNP, punA/0747). PNP is assumed to also act on purine deoxynucleosides, as this activity has been demonstrated in M. capricolum and M. gallisepticum (McElwain and Pollack, 1987). However, no pyrimidine nucleoside phosphorylase activity is assumed to be left in JCVI-syn3A (see Appendix 1).

Hydrolase activity against several mononucleotides (GMP, dAMP, dGMP, dUMP and dTMP), a dinucleotide (dCDP) and several trinucleotides (CTP, dCTP and dUTP) has been experimentally observed (Mitchell et al., 1978; Neale et al., 1983a; Cocks et al., 1988). A putative dUTPase is annotated in JCVI-syn3A (dut/0447). We note the presence of several hydrolases of unclear function that may possibly carry out the other reactions. Thus, all these hydrolysis reactions are included in the network, without the assignment of a specific gene. Experimental studies suggest a common enzyme for all deoxymononucleotidase reactions, but separate enzymes for dUTP and dCTP hydrolysis (Cocks et al., 1988).

While the observation of dUTPase activity in the natural M. mycoides capri does not itself imply that this activity has to be present in JCVI-syn3A, possible DNA incorporation of dUTP (formed from UDP through RDNR and subsequent phosphorylation) is a problem all organisms must contend with, as exemplified by the essentiality of dUTPase in E. coli (el-Hajj et al., 1988) and S. cerevisiae (Gadsden et al., 1993). The situation is exacerbated in JCVI-syn3A after the deletion of the repair enzyme uracil-DNA glycosylase (MMSYN1_0436), such that hydrolysis of dUTP is the only apparent defense mechanism against its incorporation into DNA. Inclusion of this reaction (Figure 7; DUTPDP) is therefore warranted even if the annotation of the candidate gene dut/0447 is only tentative. To avoid RNDR being in silico essential solely by virtue of being the only source of dUTP in the network (through reactions RNDR4 and PYK9; see Figure 7), dUTPase is included directly in the biomass, rather than enforcing a minimal flux through dUTP formation.

The breakdown of dUTP to dUMP, however, raises the question of the downstream degradation. The uptake requirement of M. mycoides capri for some form of thymine (Rodwell, 1969; Neale et al., 1983a), in spite of availability of dUMP, indicates the absence of thymidylate synthase activity (an otherwise common usage for dUMP as a precursor for dTMP), in line with the lack of an annotation for a thymidylate synthase gene in JCVI-syn3A. While the aforementioned hydrolysis reaction (Figure 7; NTD1) would enable degradation of dUMP to deoxyuridine, the deletion of the pyrimidine nucleoside phosphorylase MMSYN1_0734 renders deoxyuridine a dead-end. Thus, the issue arises of how JCVI-syn3A disposes of the dUMP/deoxyuridine formed. The first possibility is through pyrimidine nucleoside phosphorylase activity either by some unidentified paralog of MMSYN1_0734, or by some side activity of the purine nucleoside phosphorylase (punA/0747). The second possibility would be the export of either dUMP or deoxyuridine. We note that the possibility to recycle deoxyuridine through pyrimidine nucleoside phosphorylase in the natural M. mycoides capri (and thereby also dUMP after its dephosporylation) renders an additional dedicated export system for either metabolite unlikely, but side activity of some other system would be possible.

While the aforementioned dependence of M. mycoides capri on external thymine/thymidine rules out any thymidylate synthase activity high enough to meet cellular dTTP needs, such activity has been reported for M. mycoides mycoides SC (Wehelie et al., 2010); however, the reported activity was extremely low (∼10 pmol/min/mg cell protein) and no responsible gene could be identified. If such activity was present in JCVI-syn3A as well at a higher level, it might provide for a way to dispose of dUMP. Furthermore, the presence of deoxycytidylate deaminase (DctD, dctD/0515) enables the conversion of dCMP to dUMP (which is used in wild-type M. mycoides capri to ultimately convert thymine to thymidine (Neale et al., 1983a). No experimental evidence is available of this enzyme running in the reverse direction to aminate dUMP with free ammonia to form dCMP. However, CTP synthase (CTPS, pyrG/0129), which catalyzes the conversion of UTP to CTP, spends ATP and uses glutamine as an amino donor, which suggests that DctD catalyzing the amination without ATP and from free ammonia is unlikely. Instead, the question arises whether CTPS in JCVI-syn3A may have a relaxed substrate specificity. The E. coli CTPS was found to not act on UMP (Lieberman, 1956; Long and Pardee, 1967) or dUTP (Scheit and Linke, 1982), but may have activity against UDP (Lieberman, 1956; Long and Pardee, 1967). The Lactococcus lactis enzyme has been found to act on dUTP (Willemoës and Sigurskjold, 2002) as does one of the isozymes in S. cerevisiae (Pappas et al., 1999). As relaxed enzymatic substrate specificity in mycoplasmas is a common phenomenon, a further broadening of the substrate range of CTPS in JCVI-syn3A seems possible. The preceding hypothetical mechanisms are summarized in Figure 8. Currently, the available data does not allow for the determination of which of these potential dUMP disposal mechanisms occurs in JCVI-syn3A; however, the increased substrate spectrum of CTP synthase currently seems the most plausible, thus it is included in the model to deal with the produced dUMP.

Figure 8

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Cofactor metabolism

The cofactor metabolism of JCVI-syn3A is shown in Figure 9. Many vitamins are known to be taken up through the energy coupling factor (ECF) system (Rodionov et al., 2009), which consists of a membrane permease EcfT, a dimer of the ATPase EcfA, and a substrate-binding subunit EcfS (Erkens et al., 2012). The gene for the latter can either occur in a cluster with the genes for EcfA and EcfT, or several ecfS genes can be spread across the genome, their protein products displaying distinct substrate specificities but interacting with a common EcfT(EcfA)${}_2$ module in the membrane. The JCVI-syn3A genome annotation lists three consecutive genes ecfT/0641, ecfA/0642 and ecfA/0643 and four ecfS genes spread throughout the genome (ecfS/0314, ecfS/0345, folT/0822, and ecfS/0836). Folate, riboflavin, coenzyme A, nicotinate, and pyridoxal are all imported through the ECF system. We note that the absence of related salvage enzymes in JCVI-syn3A necessitates the uptake of complete coenzyme A; this is in accordance with the experimental requirement of M. mycoides capri LC Y for coenzyme A rather than coenzyme A precursors (Rodwell, 1967; Rodwell, 1969), as well as the apparently incomplete coenzyme A salvage pathway already in JCVI-syn1.0. As no kinase has been identified for pyridoxal, pyridoxal phosphate is assumed to be imported directly.

Figure 9

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For the case of folate, folT/0822 is assumed to be the necessary substrate-binding unit due to sequence conservation (Danchin and Fang, 2016) and its adjacency to folC (Eudes et al., 2008). While the exact form of folate in the SP4 medium is not known, we note that generally, 5-formyl-tetrahydrofolate (5-formyl-THF, folinic acid) is the most stable folate derivative, and is known to be imported by FolT (Eudes et al., 2008). Furthermore, M. mycoides capri LC Y was found to be unable to utilize folate itself (Neale et al., 1981), in line with JCVI-syn3A lacking a gene coding for dihydrofolate reductase. It is thus assumed that folate is taken up in the form of 5-formyl-THF. Genes for the proteins driving the folate cycle consist of GlyA (glyA/0799), FolD (folD/0684) and Fmt (fmt/0390), together with the repair enzyme YgfA (ygfA/0443/FTHFCL). YgfA is not only required to utilize imported 5-formyl-THF, but also to recycle the 5-formyl-THF produced from the hydrolysis of 5,10-methenyl-THF (a side reaction of GlyA (Stover and Schirch, 1990)). The RAST annotation pipeline (Overbeek et al., 2014) suggests that the putative membrane protein gene 0877 is ribU, coding for the riboflavin-specific ECF component. As the substrate specificity of the remaining EcfS components (ecfS/0314, ecfS/0345 and ecfS/0836) is unclear, they are tentatively assigned to nicotinate, pyridoxal phosphate, and coenzyme A respectively. For downstream conversion, the genome contains pathways for NAD(P) and FMN/FAD formation from nicotinate and riboflavin.

Spermine and thiamine have their own uptake systems (potC/0195 through potA/0197 and thiB/0706 through 0708, respectively). For thiamine, the deletion of the corresponding diphosphokinase (MMSYN1_0261) suggests that thiamine diphosphate (ThDP) must be taken up directly. Sequence and structural information suggest this to be possible (see Appendix 1 and Appendix 1—figure 1). While free lipoate is a component of the minimal media for M. mycoides capri LC Y (Rodwell, 1969) and M. pneumoniae (Yus et al., 2009) and is a possible ECF system substrate (Rodionov et al., 2009), two putative lipoyl transferases have been deleted in JCVI-syn3A (MMSYN1_0224 and MMSYN1_0464), such that free lipoate cannot be used to provide the lipoyl moiety of pyruvate dehydrogenase subunit E2 (pdhC/0227). Instead, simultaneous import and transamidation of lipoate from lipoyllysine onto PdhC, catalyzed by the peptidase 0401, is tentatively assumed in the model (see Appendix 1).

Lipids and capsule

The lipid and capsule metabolism of JCVI-syn3A is presented in Figure 10. In the metabolic reconstruction, the import of four lipid components is necessary: free fatty acids, glycerol, cholesterol and triacylglycerol. These are all assumed to be imported through passive processes. Free fatty acids have been found to be incorporated into the membrane of the mollicute Spiroplasma floricola both actively and via passive diffusion (Tarshis and Salman, 1992). Glycerol is usually imported through dedicated transport systems, yet the glycerol permease GlpF (MMSYN1_0217) has been deleted in JCVI-syn3A. However, it is known that cells can take up glycerol by passive membrane permeation (McElhaney et al., 1973; Eze and McElhaney, 1981) and physicochemical data suggests this could provide sufficient glycerol uptake to fuel the lipid synthesis needs of JCVI-syn3A (see Appendix 1). Cholesterol is known to be incorporated into membranes spontaneously (McLean and Phillips, 1981; Bittman et al., 1990) and has been suggested to be incorporated by simple physical absorption in M. mycoides capri cells as well (Rigaud and Leblanc, 1980). Triacylglycerol was identified as a membrane component in M. mycoides capri, but it is not known whether it is still included in the membrane of JCVI-syn3A, so a passive uptake reaction for it is included, noting that the presence of triacylglycerol in the biomass expression then only affects the model by lowering the amounts needed of other lipid species.

Figure 10

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The existing annotation with the refinements for the two glycosyltransferases epsG/0113 and cps/0114 contains nearly complete pathways to produce all membrane components identified in the biomass, with the only gaps occurring in the fatty acid phosphorylation and diacylglycerol (DAG) production pathways. JCVI-syn3A shares the fatty acid utilization pathway from Staphylococcus aureus (Parsons et al., 2014), which starts with phosphorylation of free fatty acids in the membrane and subsequent binding to glycerol phosphate (by plsY/0117) and acyl carrier protein (ACP, by plsX/0419). Holo-ACP is formed from apo-ACP and coenzyme A by ACP synthase (acpS/0513), releasing adenosine $3^{\prime }$,$5^{\prime }$-bisphosphate (pAp). The DHH phosphoesterase family protein ytqI/0139 is 30% identical to the experimentally confirmed bifunctional oligoribonuclease/pAp phosphatase NrnA from M. pneumoniae (MPN140) (Postic et al., 2012). Thus, it is assumed that ytqI/0139 catalyzes the degradation of pAp to AMP.

Fatty acid kinase consists of a kinase FakA and a fatty acid-binding protein FakB. Both JCVI-syn3A and S. aureus contain two copies of FakB, and these have been demonstrated in Staphylococcus aureus to display distinct substrate specificities, one acting preferably on unsaturated fatty acids and the other on saturated fatty acids. Assuming similar specificities in JCVI-syn3A would be consistent with the assumed prevailing fatty acids (palmitate and oleate). No annotation for FakA exists in JCVI-syn3A, however, (Parsons et al., 2014) reports the location of the fakA gene in M. pneumoniae (MPN547), which shows 33% sequence identity to fakA/0420 and shares the same genomic context (located upstream of plsX). Thus, it is assumed that fakA/0420 is the missing FakA subunit, completing the lipid assembly pathway from free fatty acids to cardiolipin.

As discussed above, JCVI-syn3A has the pathway from glucose-6-phosphate to UDP-galactofuranose, the sugar building block for galactosyl-diacylglycerol (Gal-DAG) and the galactan lipopolysaccharide. In M. genitalium, DAG is produced from phosphatidate via phosphatidate phosphatase (PAPA). While no such enzyme is annotated in JCVI-syn3A, and a BLAST search with the M. genitalium gene (MPN455) against JCVI-syn3A scores no hits, we note that the presence of a number of unassigned phosphatases in the genome of JCVI-syn3A makes it plausible that one of them could act on phosphatidate. An alternative possibility might be phosphatidate phosphatase side activity by phosphatidylglycerophosphatase (pgpA/0214), which has been reported for phosphatidylglycerophosphatase B (PgpB) in E. coli (Dillon et al., 1996). With no gene yet for phosphatidate phosphatase but plausible candidates, this reaction is included as a gap fill.

The product DAG serves as the lipid moiety for the synthesis of lipogalactan (catalyzed by epsG/0113 and cps/0114, see Section 'Biomass composition and reaction') and Gal-DAG. Lacking further evidence, Gal-DAG is assumed to be formed by either of the two glycosyltransferases, cps/0114 and 0697.

Possibly related to lipid metabolism, the existing annotation lists a putative choline/ethanolamine kinase (0906); however, as M. mycoides capri LC is experimentally known to not produce phosphatidylcholine (Plackett, 1967a), we assume that this kinase has some yet to be determined substrate and do not include it in the model.

Macromolecules and amino acids

Schematics of the macromolecular and amino acid reaction networks are provided in Figure 11 and Figure 12. The genome of JCVI-syn3A contains a putative oligopeptide ABC importer (Opp/Ami, oppB/0165 through oppA/0169). In Lactococcus lactis, the Opp system has been found to import peptides of four to at least 35 amino acids with little dependence of uptake rates on peptide length or amino acid composition (Detmers et al., 1998; Doeven et al., 2004). For the sake of simplicity, peptides imported are assumed to be representative homotetrapeptides of all amino acids except cysteine, since cysteine contained in peptides cannot be easily utilized by mycoplasmas (Yus et al., 2009). The model assumes that any of four peptidases (ietS/0133, 0305, 0444, and 0479) can split these peptides into individual amino acids.

Figure 11

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Figure 12

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In addition, the glutamate/aspartate permease gltP/0886, as well as two amino acid permeases of unknown specificity (0876 and 0878) have been identified in JCVI-syn3A. The substrate specificities of these two amino acid permeases are not known. However, M. mycoides capri LC Y has been found capable of taking up all amino acids in their free form (Rodwell, 1969) (glutamic and aspartic acid not investigated); thus, the least constraining assumption is made that both permeases can take up all amino acids, except for glutamic and aspartic acid, whose uptake is already enabled by GltP. Proton symport reactions are assumed for each amino acid except glutamate and aspartate, which are symported by GltP while translocating two protons per substrate, as observed in E. coli (Tolner et al., 1995).

In order to distinguish between free nucleotides and amino acids and those incorporated in nucleic acids and proteins, explicit macromolecular synthesis reactions (DNA replication, RNA transcription, protein translation) are included that consume amino acids and nucleotides according to the assumed macromolecular compositions. These reactions produce representative DNA, RNA and protein species that enter the biomass reaction according to the mass fractions in Appendix 1—table 1. Similar macromolecular synthesis costs as in E. coli (Feist et al., 2007; Neidhardt et al., 1990) are assumed, that is 1.37 ATP/nucleotide in DNA synthesis, 0.41 ATP/nucleotide in RNA synthesis, 2 ATP/amino acid in tRNA charging, and 2.32 ATP/peptide bond in protein synthesis. Protein products are represented as species containing 385 amino acids (based on the average protein length obtained from the JCVI-syn3A proteomics, and accounting for the N-terminal methionine cleaved off the nascent peptide–see below). DNA and RNA are represented as species of 100 bases each to keep the nucleotide reaction coefficients small (and also since no average RNA length is known).

In addition, separate translation reactions are included for three additional proteins: ACP and PdhC, whose prosthetic group attachment is included in the model; and dUTPase, which is included explicitly in the biomass. Protein translation in the model uses charged tRNA that are produced from one synthetase for each amino acid, except for glutamine. Instead, glutamyl-tRNA(Gln) is transamidated to glutaminyl-tRNA(Gln) via an amidotransferase (gatB/0687 through gatC/0689) using glutamine or asparagine as amino donor. Translation is assumed to be initiated with formylated methionyl initiator tRNA (fMet-tRNA_fMet). While the natural M. mycoides capri has been found to be able to initiate protein synthesis with the unformylated species (Neale et al., 1981), the folate cycle (including the Met-tRNA_fMet formyltransferase fmt/0390) is quasi-essential in JCVI-syn3A and the assumption is hence made that formylation cannot be omitted in JCVI-syn3A. Translation reactions for both apo-ACP and the generic protein species include deformylation and methionine cleavage, in line with ca. 80% of proteins in a proteome assumed to have the initial methionine cleaved (Frottin et al., 2006), and based on the alanine in second position of the apo-ACP sequence acpA/0621, favorable for methionine cleavage (Frottin et al., 2006). For PdhC (pdhC/0227) and dUTPase (dut/0447), only deformylation is considered, as the phenylalanine and isoleucine in second position of their sequences are not favorable for methionine cleavage (Frottin et al., 2006). The excess formate is assumed to be secreted by passive means.

Explicit protein and RNA degradation reactions are included in the model to account for protein and mRNA turnover in the cell and to regenerate the amino acid and nucleotide pools, respectively. In line with most other mycoplasma genomes (Gur and Sauer, 2008), the JCVI-syn3A genome only contains two AAA+ proteases, Lon (lon/0394) and (putatively) FtsH (ftsH/0039). Lon has been found to degrade ssrA-tagged peptides in place of ClpXP in Mesoplasma florum (Gur and Sauer, 2008) and M. pneumoniae (Ge and Karzai, 2009). It is tentatively assumed to be the main protease for protein turnover in general in the metabolic reconstruction. An ATP expense of 225 ATP per protein of 385 residues is assumed (see Appendix 1).

Lower bounds on the protein and RNA degradation reactions of $3.5\times {10}^{-4}\text{mmol}{\text{gDW}}^{-1}{\text{h}}^{-1}$ mmol gDW⁻¹ h^-1 and $7.7\times {10}^{-3}\text{mmol}{\text{gDW}}^{-1}{\text{h}}^{-1}$ mmol gDW⁻¹ h^-1, respectively, are imposed assuming similar degradation rates as in M. pneumoniae (Wodke et al., 2013), which shares the protease Lon. Assuming that the entire proteome is subject to turnover (as observed in M. pneumoniae (Maier et al., 2011)), the protein degradation constraint corresponds to an average protein half life time of 25 hr, which describes the time an average protein persists throughout cell divisions before being actively degraded; it is based on ¹³C mass-spectrometry measurements of protein degradation in M. pneumoniae that corrected for protein dilution by cell growth (Maier et al., 2011). The protein degradation constraint thus accounts for the ATP expense arising from the slow (compared to the cell cycle time) but non-zero active protein degradation. With an ATP consumption of 225 ATP per degraded protein, protein breakdown thus requires an ATP expense of 0.079 mmol gDW⁻¹ h^-1. In addition, the imposed protein degradation flux implies an additional ATP cost due to the required additional protein synthesis (see Section 'GAM/NGAM'). The RNA degradation constraint corresponds to an mRNA half-life time of around 1 min, assuming mRNA to account for ∼5% of all cellular RNA (Westermann et al., 2012; Rosenow et al., 2001) (and assuming other RNA degradation to be negligible in the exponential growth phase). This is comparable to the mRNA half-life time of 2 min determined in Mycoplasma gallisepticum (Kirk and Morowitz, 1969). While RNA degradation itself is assumed to not consume ATP, it incurs an indirect ATP cost due to the required additional RNA synthesis, analogous to the situation for protein degradation.

Finally, we note the presence of methionine adenosyltransferase (metK/0432), which produces the methyl donor $S$-adenosylmethionine used by several nucleic acid methylation enzymes; these nucleic acid modifications are, however, not included in the model due to lack of sufficient information on kind and abundance of such modifications in JCVI-syn3A. The demethylation product $S$-adenosylhomocysteine is broken down by $S$-adenosylhomocysteine hydrolase (mtnN/0381) to homocysteine. As there is no obvious way for further breakdown of homocysteine in JCVI-syn3A, it is assumed to be secreted for the time being. As a precedent, secretion—rather than further conversion—of homocysteine has been suspected in Plasmodium falciparum (Beri et al., 2017). As the magnitude of the required efflux rate is expected to be small, secretion is furthermore assumed to occur via passive permeation.

Ion uptake

The ion transport reactions of JCVI-syn3A are shown in Figure 13. The JCVI-syn3A annotation lists importers for magnesium (mgtA/0787) and phosphate (Pst system, pstS/0425 through pstB/0427); Danchin and Fang (2016) have suggested the putative magnesium import gene corA/0879 could import calcium as well based on its similarity to the B. subtilis calcium importer YloB (Raeymaekers et al., 2002). While the sequence identity of corA/0879 to YloB (CAB31439) is lower than between YloB and the deleted calcium importer MMSYN1_0246 (22% vs. 30%), we note that the putative calcium-binding sites in YloB (Raeymaekers et al., 2002) are equally well conserved in both JCVI-syn3A genes, and include corA/0879 as the calcium importer. Active transport processes for Na⁺ and K⁺ are inferred from biochemical evidence (see Appendix 1). Based on this, an ATP-consuming K⁺/Na⁺ antiport reaction is catalyzed by KtrAB (natA/0685 and trkA/0686) in the model, and an Na⁺/H⁺ antiport reaction is included as well without a specific gene assignment. Import systems have not been identified for sodium or chloride, but chloride has been proposed to freely permeate the membrane of M. mycoides capri (Schummer and Schiefer, 1983) and sodium has been suggested to ‘leak’ through membranes of M. gallisepticum (Linker and Wilson, 1985). Alternatively, leakage through some other transporter cannot be ruled out. Thus, passive uptake reactions are assumed for sodium and chloride. Finally, a passive H⁺ influx reaction is used, which is counteracted by the proton-extruding ATPase. Lacking more information, all ion uptake rates in the model are left unconstrained.

Figure 13

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Damage reactions

On top of the enzymes involved in required metabolic reactions, we note the presence of possible damage reactions and repair enzymes to repair this damage. As discussed, 5,10-methenyl-THF is hydrolyzed to 5-formyl-THF as a side reaction of GlyA (Stover and Schirch, 1990) and YgfA catalyzes the opposite direction. 5-formyl-THF is not only the only form of THF taken up in the model, but is also a potent inhibitor of folate-related enzymes (Stover and Schirch, 1992), and as such YgfA is an important repair enzyme. To account for the known damage reaction of 5,10-methenyl-THF hydrolysis, a small lower bound (0.01 mmol gDW⁻¹ h^-1) is imposed to ensure non-zero flux through this reaction. Reconversion of 5-formyl-THF to 5,10-methenyl-THF via YgfA consumes 1 ATP per 5-formyl-THF and thus requires 0.01 mmol gDW⁻¹ h^-1 ATP. Another damage preemption/repair enzyme included in the model is dUTPase (dut/0447), which hydrolyses dUTP and prevents its erroneous incorporation into DNA. Rather than enforcing a minimal flux through this reaction, dUTPase is included directly in the biomass for technical reasons (see Section 'Nucleotide metabolism').

GAM/NGAM

In order to account for cellular energy expenses, the growth- and non-growth-associated maintenance costs (GAM and NGAM) need to be included in the model. The GAM describes the energy cost associated with cellular growth, and therefore enters the biomass reaction as a certain amount of ATP spent per unit biomass production. The NGAM describes the basic, growth rate-independent cellular energy requirements and is therefore frequently implemented as a lower constraint on a separate ATP hydrolysis reaction (Feist et al., 2006; Orth et al., 2011; Wodke et al., 2013). Both parameters can be measured experimentally (e.g. from chemostat measurements (Thiele and Palsson, 2010)); however, to our knowledge no such measurements are available for any mycoplasma, and thus the cellular energy expenses must be estimated differently.

The GAM consists of a component that can be related to macromolecular synthesis energy costs and a non-quantifiable part. The macromolecular synthesis costs are outlined in Section 'Macromolecules and amino acids'; for the macromolecular composition of JCVI-syn3A, they yield a total cost of 21.54 mmol gDW⁻¹ ATP. For the non-quantifiable part, 25 mmol gDW⁻¹ ATP is used following the previously published M. pneumoniae model (Wodke et al., 2013). Together with the ATP costs for macromolecular synthesis, this yields a total GAM of 46.54 mmol gDW⁻¹, which is comparable to the 53.95 mmol gDW⁻¹ estimated for E. coli (Orth et al., 2011). The use of these values is supported by the overall conservation of the gene expression apparatus.

The NGAM captures non-growth related energy expenses, such as macromolecular turnover and maintenance of the transmembrane pH gradient, which has been suggested to be a considerable energy expense in mycoplasmas (Wodke et al., 2013). These two energy expenses are included in the JCVI-syn3A model as an approximate NGAM. Rather than accounting for all NGAM expenses through a single ATP hydrolysis reaction, the total NGAM is distributed across several reactions (akin to the quantifiable GAM fraction). The total macromolecular turnover costs can be obtained from the assumed protein and RNA degradation fluxes (3.5 × 10^-4 mmol gDW⁻¹ h^-1 and 7.7 × 10^-3 mmol gDW⁻¹ h^-1, respectively; see Section 'Macromolecules and amino acids'); the ATP costs in protein and RNA synthesis; and the associated costs for tRNA charging and nucleotide phosphorylation, respectively. Specifically, a constant part of the protein/RNA synthesis fluxes (and, preceding these, tRNA charging and nucleotide phosphorylation) is routed through protein/RNA degradation, and only the surplus beyond this constant flux actually contributes to model growth. These turnover-associated ATP costs amount to 2.73 mmol gDW⁻¹ h^-1 in the model for JCVI-syn3A. The maintenance of a transmembrane pH gradient in M. mycoides capri PG3 has been experimentally demonstrated (Benyoucef et al., 1981a; Benyoucef et al., 1981b) and an approximate value for the H⁺ extrusion rate by the transmembrane ATPase has been obtained (Benyoucef et al., 1981b). The observed rate of ∼70 nmol/min/mg cellular protein corresponds to a proton flux of ∼2.30 mmol gDW⁻¹ h^-1 in JCVI-syn3A, which translates to an ATP consumption of 0.57 mmol gDW⁻¹ h^-1 (4H⁺/ATP). This value is hence implemented as a lower bound on the model ATPase reaction. The resulting total NGAM in the model for JCVI-syn3A is then 3.30 mmol gDW⁻¹ h^-1, which comes out similar to the iJO1366 model for E. coli (3.15 mmol gDW⁻¹ h^-1) (Orth et al., 2011).

Steady state fluxes

With the chosen parameters, the model yields an in silico growth rate of μ = 0.34 hr${}^{-1}$, corresponding to a doubling time of t_d = 2.02 hr ($t_{\mathrm{d}}=\ln (2)/\mu$); this comes close to the experimental doubling time of ca. 105 min (see Figure 14). This exact agreement is sensitive to the choices for uptake/secretion and GAM parameters however (see Section 'Sensitivity analysis' in Appendix 1 and Appendix 1—figure 2), and the in silico growth rate should thus be more understood as a provisional prediction. This does not constitute a problem for the subsequent analyses; when the impact of in silico gene knockouts on growth rate is studied in Section 'In silico gene knockouts and mapping to in vivo essentiality', nearly all knockouts either abolish the growth rate entirely (lethal knockouts) or do not affect it at all. Thus, this analysis is not affected by the rather qualitative nature of the growth rate prediction by the model.

Figure 14

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In the rest of this section, the steady-state fluxes produced by the model are compared to literature data, and to theoretical flux limits obtained from protein abundances and enzyme turnover numbers ($k_{\mathrm{cat}}$). While no fluxomics data is yet available for JCVI-syn3A, some experimental fluxes from the literature allow for a few comparisons. The purine incorporation flux into RNA has been determined for M. mycoides capri LC Y (Mitchell et al., 1978) as 0.5–1.0 nmol/min/mg cellular protein, corresponding to 0.016–0.033 mmol gDW⁻¹ h^-1. This is close to the in silico net flux of ATP and GTP into RNA of 0.047 and 0.043 mmol gDW⁻¹ h^-1, respectively. Here, the net flux is defined as the difference between NTP consumed by the RNA polymerase reaction and NMP released by the RNA degradation reaction. The in silico K⁺ uptake of 0.29 mmol gDW⁻¹ h^-1 also falls within a factor of two of the experimental uptake rate of 0.49 mmol gDW⁻¹ h^-1 (15 nmol/min/mg cellular protein) in M. mycoides capri PG3 (Benyoucef et al., 1982a). These comparisons serve as an internal consistency check on the model, demonstrating that the in silico uptake/incorporation rates as resulting from biomass composition and growth rate indeed reproduce the experimental values.

Furthermore, it has been reported that M. mycoides capri LC Y spends 10% of its glucose uptake on polysaccharide capsule production (Plackett, 1967a). The in silico fluxes through central metabolism are depicted in Figure 6—figure supplement 1. As can be seen, the in silico flux through phosphoglucomutase (PGMT, deoB/0733, leading from glucose-6-phosphate to galactan and Gal-DAG in Figures 6 and 10) is lower than the experimental value, amounting to only 1.8% (0.135 mmol gDW⁻¹ h^-1, from 7.4 mmol gDW⁻¹ h^-1 glucose taken up). However, the model does qualitatively reproduce the further splitting between galactan and Gal-DAG production, which gives a ratio of ∼22:1 (UDP-galactofuranose consumption of 0.129 mmol gDW⁻¹ h^-1 vs. 0.006 mmol gDW⁻¹ h^-1), compared to a ratio of ∼64:1 from ³H labeling (Plackett, 1967a). Thus, with the chosen parameters, the model reproduces several experimental fluxes; with the only significant difference occurring in capsule production.

FBA flux predictions were also compared to reaction flux bounds ($V_{\text{max}}$) calculated from protein abundances and enzyme turnover numbers ($k_{\mathrm{cat}}$) (Labhsetwar et al., 2013; Labhsetwar et al., 2017). The protein abundances were derived from proteomics experiments (see Section 'Abundances of essential and non-essential proteins') and the turnover numbers were extracted from the BRENDA database (Scheer et al., 2010) (see Figure 15, Figure 15—figure supplement 1 and Appendix 1). $V_{\text{max}}$ values could be obtained for 105 ‘non-pseudo’ reactions (i.e. excluding exchange, biomass and macromolecular reactions). Of these, 86 had non-zero fluxes. The zero-flux reactions include for example reactions pertaining to alternative sugars, which are unused in the assumed medium. Of the reactions with non-zero fluxes, only 19 reactions required fluxes in the FBA optimal solution higher than their proteomics-derived $V_{\text{max}}$ (see Figure 15—figure supplement 1A). The reaction with the lowest $V_{\text{max}}$/flux ratio is adenylate kinase, which is predicted to carry a flux of 2.23 mmol gDW⁻¹ h^-1, compared to a proteomics-derived $V_{\text{max}}$ of only 0.01 mmol gDW⁻¹ h^-1. However, the $k_{\text{cat}}$ for this enzyme as found for B. subtilis in BRENDA is 0.053 s^-1, which falls in the lower tenth percentile of the $k_{\text{cat}}$ data for all reactions in the model. The second-smallest $V_{\text{max}}$ to flux ratio is found for aspartate-tRNA synthetase (0.11 mmol gDW⁻¹ h^-1 in model vs. 0.005 mmol gDW⁻¹ h^-1 from proteomics/$k_{\text{cat}}$); other amino acyl-tRNA synthetases with significantly low $V_{\text{max}}$/flux ratio ($<$ 0.25) are the ones for threonine and serine. These three amino acyl-tRNA synthetases have the smallest $k_{\text{cat}}$ numbers among all amino acyl-tRNA synthetases. The third-lowest $V_{\text{max}}$ to flux ratio is found for fructose bisphosphate aldolase (7.21 mmol gDW⁻¹ h^-1 in model, 0.39 mmol gDW⁻¹ h^-1 from proteomics/$k_{\text{cat}}$). This protein has 227 copies in the cell on average, which places it among the least abundant proteins in central metabolism. Furthermore, the $k_{\text{cat}}$ value for this enzyme retrieved from BRENDA for Bacillus cereus is 2.95 s^-1, which is also one of the lowest values found among proteins in central metabolism. The only other reactions with a $V_{\text{max}}$/flux ratio less than 0.25 are: DASYN (cdsA/0304), which produces the lipid intermediate CDP-diacylglycerol; ACPS (acpS/0513), which attaches the $4^{\prime }$-phosphopantetheine to apo-ACP; and GUAPRT (hptA/0216), which produces GMP from guanine. DASYN has a $V_{\text{max}}$ to flux ratio of 0.19, that is only slightly decreased. ACPS has both the lowest $k_{\text{cat}}$ among the model enzymes (0.001 s^-1) and one of the lowest protein abundances (just one copy per cell per our proteomics data). If either value turned out to be not accurate, this could easily raise the $V_{\text{max}}$/flux ratio above the current level of 0.07. However, the discrepancy observed for GUAPRT is interesting in the light of the aforementioned mononucleotide uptake capabilities in M. mycoides capri (Neale et al., 1984; Youil and Finch, 1988) (see Section 'Nucleotide metabolism'). While there is no other evidence for the possible conservation of this capability in JCVI-syn3A, this flux bound might suggest that this uptake capability still at least partially exists in JCVI-syn3A, and might be worth investigating. All other reactions with $V_{\text{max}}$ lower than the FBA flux differ by less than a factor of four; even though the FBA flux exceeds the estimated $V_{\text{max}}$, the disagreement is rather modest. For the reactions that do show higher disagreement, we note that the $k_{\text{cat}}$ values obtained tend to be on the lower end either within the respective group of reactions, or across the model. This suggests that these $k_{\text{cat}}$ values might merit closer investigation. Overall, the proteomics-derived bounds are consistent with the FBA fluxes, with only a handful of reactions showing significant discrepancies.

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It should be noted that the rates of enzyme-catalyzed reactions in vivo are typically less than $V_{\max }$ to allow the cell to respond to increases in substrate concentration. Accordingly, $V_{\text{max}}$ is expected to be greater than the metabolic flux necessary to sustain the cell, such that the flux required under typical growth conditions can be achieved without enzyme saturation. In line with this argument, a histogram of $V_{\text{max}}$ values for reactions in the model shows the bulk of reactions to have a $V_{\text{max}}$1–3 orders of magnitudes higher than the flux required in the FBA solution (see Figure 15—figure supplement 1B).

In silico gene knockouts and mapping to in vivo essentiality

In addition to studying fluxes of the unperturbed model, the FBA framework also allows to study the impact of in silico gene disruptions by simulating knockouts in COBRApy (Ebrahim et al., 2013), that is by removing all reactions associated with a gene of interest from the model and calculating the growth rate from the resulting model. A knockout is defined to be lethal if the resulting growth rate is zero or the FBA problem becomes infeasible. By this definition, 123 of the 155 genes included in the model are essential (79%). In this analysis, two genes are currently non-essential in silico for ‘technical’ reasons: metK/0432 (methionine adenosyltransferase, MAT) and mtnN/0381 ($S$-adenosylhomocysteine (SAH) hydrolase, reaction ID: AHCi). These genes are part of the $S$-adenosylmethionine (SAM) pathway and would be connected through nucleic acid methylation reactions (consuming SAM and producing SAH), which were not included in the model due to missing experimental information. As these two reactions currently cannot carry flux, it does not make sense to consider their in silico essentiality in the comparison to experiment.

An individual breakdown of in silico gene essentialities is provided in Table 2, which lists all genes modeled in silico, together with their catalyzed reaction (or general description for genes with several reactions, like the peptidases), and their essentiality in silico and in vivo. Genes non-essential in silico are found in amino acid, central and nucleotide metabolism as well as transport reactions, and only one gene in lipid metabolism. Some non-essentialities are functionally connected. For example, as the peptide importer Opp (oppB/0165 through oppA/0169) is non-essential in silico, the downstream peptidases have to be non-essential as well. A further analysis of in silico essentialities is featured in Sections 'Interpretation of individual gene essentialities', 'The role of folate metabolism', and 'A partial bypass to the pentose phosphate pathway'.

These in silico essentialities can be compared to the experimental transposon data (see Section 'Transposon mutagenesis experiments probe in vivo gene essentiality'). Figure 16 shows an overall mapping between in silico and in vivo essentiality (including all genes in JCVI-syn3A, including RNA genes, pseudogenes and the two ‘technical non-essentials’). A more detailed analysis for the genes included in the model is presented in Table 4, which displays the confusion matrix for the in silico to in vivo comparison, that is the distribution of model genes among the in silico and in vivo classifications. The left table represents the breakdown for all model genes except the two technical non-essentials, while the right table shows the breakdown if genes related to amino acid utilization are also excluded (see below). Whilst the model only distinguishes essential and non-essential genes, the experimental classification includes quasi-essentiality, which falls somewhere in between essentiality and non-essentiality. Thus, for any evaluation of predictive performance of the model, some assumption has to be made with regard to the in vivo quasi-essential genes. Table 5 summarizes several statistics obtained for specific cases, discussed in the following.

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Table 4

	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	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

Two limiting cases of interest are treating all quasi-essential genes as either in vivo essential (1) or in vivo non-essential (2). Given that the identification of quasi-essential genes was crucial for the successful genome minimization in JCVI-syn3.0 (Hutchison et al., 2016a), treating these genes as essential might be the biologically more relevant assumption. If all quasi-essential genes are considered essential (i.e. adding the numbers in the second row in Table 4, left matrix to the first row), the model displays an accuracy of 88%. (Accuracy = (TP+TN)/total; we opt to define essential genes as ‘positive’ and non-essential genes as ‘negative’, so that a true positive gene (TP) is essential in model and experiment; a false positive (FP) is essential in the model but non-essential in experiment; a true negative (TN) is non-essential in model and experiment; and a false negative (FN) is non-essential in the model but essential in experiment. ‘Total’ is the sum of all genes included in the analysis.) The resulting sensitivity (TP/(TP+FN)) is 87%, while the specificity (TN/(TN+FP)) is 100%: All in silico essential genes are at least quasi-essential in vivo, so there are no ‘strong’ false positive predictions (of genes to be essential that are actually non-essential in vivo). If, alternatively, all quasi-essential genes are considered non-essential in vivo (adding the numbers in the second row to the third row in the left confusion matrix in Table 4), the accuracy comes out a bit lower at 83%; the sensitivity increases to 96% while the specificity drops to 54%. This low specificity can be explained by considering the comparatively low number of in vivo non-essentials among the genes included in the model (12): Considering all quasi-essentials (two thirds of which are essential in the model) to be non-essential as well then leads to a large relative fraction of ‘non-essentials’ not detected by the model, even though the overall accuracy does not change much compared to case (1). As a more balanced measure of model prediction performance, Table 5 also features the Matthews correlation coefficient (MCC) in the last column, which can range from −1.0 (perfect disagreement) via 0.0 (same agreement as a random model) to 1.0 (perfect agreement). For both cases described above (treating quasi-essentials as either all essential or all non-essential), the MCC comes out to ∼0.59.

While this does not amount to perfect agreement, we note that the quasi-essentials in the middle row in Table 4 (upper confusion matrix) actually encompass the vast majority of false model predictions. Thus, in addition to the two limiting cases presented above, it is also instructive to consider the prediction performance when including only those genes that can be classified as essential or non-essential in vivo, that is those genes that can be compared to the model classification without further assumptions. In this case, the specificity reaches 100% as in case (1) above, as there are again no false positives; the sensitivity reaches the same value as in case (2) above (96%) as there are only four false negatives; and the accuracy increases to 97% in this case. The MCC also comes out higher at 0.85. This demonstrates that the lower MCC and other metrics obtained before really arise from the large number of quasi-essential genes included in the model, that are inherently difficult to describe in an FBA model: For example, nucleic acid stabilization by polyamines is a known essential process, and the minimal media for both M. mycoides capri LC Y (Rodwell, 1969) and M. pneumoniae (Yus et al., 2009) hence include spermine, which is thus a biomass component in the model for JCVI-syn3A. While this renders the corresponding uptake genes (potC/0195 through potA/0197) essential in the model, they are only quasi-essential in vivo (see Table 2).

Similarly, it is of interest to consider one set of genes whose functionality is difficult to capture precisely based on the currently available information, namely the genes pertaining to uptake and utilization of amino acids (in free or peptide form): As can be seen in Table 2, from the overall 14 ‘weak’ false negative predictions (in silico non-essential genes that are quasi-essential in vivo), 10 comprise the peptide importer Opp (oppB/0165 through oppA/0169), two amino acid permeases (0878 and gltP/0886) and three of the four peptidases (0305, 0444 and 0479). As further discussed in Section 'Interpretation of individual gene essentialities', the in vivo essentiality of these genes is likely affected by their exact substrate profiles and maximal uptake rates. If all 12 genes related to amino acid utilization (i.e. the genes above plus ietS/0133 and 0876) are excluded from the prediction comparison, the right confusion matrix in Table 4 is obtained. The resulting metrics are listed in the last two rows of Table 5, where the remaining quasi-essentials are again included in the in vivo essentials (upper row) or in the non-essentials (lower row). As can be seen, in the first case, the accuracy and sensitivity both rise to 94% compared to the full set of genes (88% and 87%, row one in Table 5); the MCC rises to 0.72. In the second case, the specificity drops from 54% to 39% compared to the full set of genes (row two in Table 5), and the MCC decreases to 0.46. However, this must be seen in the light of the excluded genes comprising mainly weak false negatives, that is quasi-essential genes that are non-essential in silico, and no weak false positives (quasi-essentials that are essential in silico). Thus, even though genes are excluded that show disagreement between model and experiment, the agreement worsens because these genes happened to be classified as ‘true negatives’ in case (2). The improved model metrics in case (1) for excluding amino acid genes thus seem more relevant.

In summary, this analysis demonstrates an overall good agreement between model and experiment, which is mainly impacted by the in vivo quasi-essential genes, whose essentiality is inherently difficult to capture in an FBA model. The disagreements observed (quasi-essential genes, and a few strong false negatives) are discussed in detail in Sections 'Interpretation of individual gene essentialities', 'The role of folate metabolism', and 'A partial bypass to the pentose phosphate pathway'. Some of them can be rationalized, while others lead to new hypotheses.

Finally, performing in silico double knockouts (Figure 16—figure supplement 1) yields just one synthetic lethality (i.e. lethality of a two-gene knockout where the individual knockouts are non-lethal)–namely, a double knockout of the two amino acid permeases 0876 and 0878, which prevents the cell from acquiring cysteine.

Abundances of essential and non-essential proteins

Absolute cellular abundances (molecules per average cell) of JCVI-syn3A proteins were obtained from mass spectrometry based proteomics and the assumed protein dry mass fraction. Relative and absolute protein abundances were used in the reconstruction of the JCVI-syn3A biomass composition (see Section 'Biomass composition and reaction') and estimates of the $V_{\max }$ for reactions in the metabolic model. They also served for the further study of the JCVI-syn3A proteome, both with respect to the fraction of proteins with known functions, and in regard to expression of essential vs. non-essential proteins.

Comparing the overall JCVI-syn3A proteomics breakdown in Figure 17a to the genome breakdown in Figure 1 shows that the ‘Unclear’ fraction is even smaller in the proteome than in the genome, suggesting that at least a generic function can be immediately assigned to >90% of the proteome. Furthermore, proteins classified as ‘Metabolism’ alone account for ∼25% of the proteome. Considering all proteins included in the FBA model (i.e. also the synthetases classified as ‘Genetic Information Processing’) covers a subset of 40% of the proteome. Thus, studying expression features for genes in the model should yield relevant insights into the proteome as a whole.

Figure 17

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Figure 17b compares distributions of absolute protein abundances between in silico essential, in silico non-essential and all proteins. Figure 17c shows the same comparison based on the transposon mutagenesis classification of essentiality (also including quasi-essential genes). As can be seen, the expression profiles for essential and non-essential proteins are qualitatively similar both to each other and to the expression profile of all proteins in JCVI-syn3A. This holds for both the genome-wide transposon data-based comparison (Figure 17c) and the comparison for the subset of (mostly) metabolic FBA genes (Figure 17b). While this does not yet allow for strong conclusions, it does suggest the presence of little regulation, if at all, that would discriminate gene products based on their essentiality. This conclusion would be in line with the small number of identified regulatory proteins left in the genome of JCVI-syn3A.

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.

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