Model-driven design allows growth of Mycoplasma pneumoniae on serum-free media

Abstract

Mycoplasma pneumoniae is a slow-growing, human pathogen that causes atypical pneumonia. Because it lacks a cell wall, many antibiotics are ineffective. Due to its reduced genome and dearth of many biosynthetic pathways, this fastidious bacterium depends on rich, undefined medium for growth, which makes large-scale cultivation challenging and expensive. To understand factors limiting growth, we developed a genome-scale, constraint-based model of M. pneumoniae called iEG158_mpn to describe the metabolic potential of this bacterium. We have put special emphasis on cell membrane formation to identify key lipid components to maximize bacterial growth. We have used this knowledge to predict essential components validated with in vitro serum-free media able to sustain growth. Our findings also show that glycolysis and lipid metabolism are much less efficient under hypoxia; these findings suggest that factors other than metabolism and membrane formation alone affect the growth of M. pneumoniae. Altogether, our modelling approach allowed us to optimize medium composition, enabled growth in defined media and streamlined operational requirements, thereby providing the basis for stable, reproducible and less expensive production.

Fig. 1. Representation of maintenance energy exploitation in two GEMs of M. pneumoniae.

a Maintenance energy in model iJW145 and (b) Maintenance energy in model iEG158_mpn. In iEG158_mpn simulations, the ATPase is observed to run in reverse to allow efflux of protons for cytosol de-acidification. Maintenance reaction in iEG158_mpn was therefore updated to explicitly include a proton leakage component (67%).

Fig. 2. Reconstructed average composition of M. pneumoniae membrane lipids and their respective fatty acid chains when the bacterium is grown on a serum-rich medium.

The inner circle indicates lipid groups: neutrolipids (NL), glycolipids (GL), sphingolipids (SL) and phospholipids (PL). Lipids are represented in the intermediate circle as belonging to the different groups of the inner one; therefore, PL are constituted by phosphatidylcholine, cardiolipin, phosphatidic acid and phosphatidylglycerol, SL by sphingomyelin, GL by glycolipids and NL by cholesterol and diacyl-glycerol. Each lipid is then represented in the outer circle with their average fatty acid chains composition: the different proportions are made of palmitic acyl chains (C16:0), palmitoleic acyl chains (C16:1), stearic acyl chains (C18:0), oleic acyl chains (C18:1) and linoleic acyl chains (C18:2).

Fig. 3. Implementation scheme of the lipid pathways and membrane formation in iEG158_mpn, when M. pneumoniae is grown on serum-free medium.

The wide variety of lipid species was simplified by considering all carry a representative acyl chain distribution instead of a mix with different acyl chain configurations. This simplification considerably reduces the complexity of the model while at the same time maximizing the amount of quantitative information. a Cholesterol is directly incorporated in the membrane, as well as sphingomyelin (SM), for which all the different fatty acid chains proportions have been considered. Phosphatidylcholine is imported using ATP and either goes to build the membrane or it is degraded into its fatty acid chains and glycerol-phosphocholine (G3PC). All the fatty acids introduced in the medium (C16:0, C16:1, C18:0, C18:1, C18:2) will be linked to an acyl-carrier protein (ACP) at the expense of ATP. The ACP then releases the fatty acid chain to glycerol 3-phosphate (glyc3p) and the product, reacting with the ACP carrying fatty acids, leads to the production of phosphatidic acid (PA). b PA then undergoes synthesis of phosphatidylglycerol (PG) or glycolipids.

Fig. 4. Interrelation between cholesterol, sphingomyelin and phosphatidylcholine proportions in the membrane of M. pneumoniae as simulated by iEG158_mpn when grown in silico on the predicted optimal serum-free medium.

a If cholesterol constitutes 35% of the total membrane lipids, phosphatidylcholine (PC) proportion is up to 10%, while sphingomyelin/sphingolipids is reduced at its minimum to 12%. b If cholesterol constitutes 50% of the membrane lipids, phosphatidylcholine (PC) proportion decreases to 6%, while sphingomyelin increases to 15%. Outer cycle represents the average proportions of acyl chains carried by each lipid, as also showed in Fig. 3: the gray colors represent, in order of shading from the lightest to the darkest, C16:0, C16:1, C18:0, C18:1, C18:2.

Fig. 5. Growth of M. pneumoniae strain M129 on serum-free semi-defined medium MCMyco measured by genomic DNA quantification after three culture passages.

Growth is detected when M. pneumoniae is grown on full MCMyco medium (red) and in the same medium after removing both sphingomyelin (SPM) and phosphatidylcholine (PC) (green). In absence of the two phospholipids, M. pneumoniae growth is undetectable. Measurements represent an average of 6 replicates and error bars represent the standard deviations of the 6 replicates.

Fig. 6. Impact of phosphatidylcholine and sphingomyelin on M.

pneumoniae strain M129 cell growth using the serum-free vB13 medium. a Growth curve analysis determined by metabolic growth index (430/560 absorbance rate) comparing cell growth in a Hayflick rich medium (HF) (black) with cell growth in vB13 (red), vB13 without phosphatidylcholine (PC) (purple), vB13 without sphingomyelin (SPM) (blue) and vB13 without both phospholipids (green). b Protein biomass measurement at 96 h, corresponding to the end of the growth curve shown in panel A. Data represent the mean of two replicates and error bars the standard deviation.

Fig. 7. Visualization of flux differences in M. pneumoniae glycolysis pathway.

Visualization of flux differences in glycolysis reaction when M. pneumoniae is growing under hypoxia (oxygen uptake 6 mmol.g_DW⁻¹.h⁻¹) respect to baseline oxygen availability (7.54 mmol.g_DW⁻¹.h⁻¹). Purple is indicative of a small difference (absolute value of flux change <±1) and red of a more considerable one (absolute value of flux change ≥±1). Gray arrows represent reactions that are unused or whose flux does not change when oxygen availability is reduced to 6 mmol.g_DW⁻¹.h⁻¹.

Fig. 8. In silico strategy for predicting lipid composition of growth medium.

All the possible lipids and precursors are provided in the medium for uptake. Each possible membrane composition that is found in the literature can be incorporated in the model as part of the biomass synthesis reaction. In our case, we considered three membrane compositions whose lipid proportions change according to the cholesterol percentage. Fatty acid profile can be found in literature (as in our model) or be experimentally determined. Lipid1 is known to be directly incorporated into the membrane, Lipid2 is metabolized in the cytosol and, whether a lipase acts, degraded into component head group and fatty acids. Lipid3 is synthetized in the cytosol from lipid precursors. Each lipid is integrated into the membrane according to the compositions previously reported in the literature. According to the reported membrane composition and fatty acid profile, a consensus minimal lipid composition of the medium is predicted.