Rapid network setup
A first example of the murine phospholipid network involved in phagosomal signaling illustrates the setup of a complex metabolic network of interest using YANAsquare. By querying the KEGG pathways "Glycerolipid metabolism (map 00561)", "Glycerophospholipid metabolism (map 00564)" and "Inositol phosphate metabolism (00562)" the complete set of 56 reactions and 64 metabolites available in the mouse metabolism is set up. After sensible setting of the system boundaries this leads directly to 57 elementary modes [see Tab. 4–6, Additional File 1]. Next, typical network extensions and modifications can be analyzed and illustrated. For instance, a reduced network with just 35 metabolites and 24 reactions is more accurate to identify key phospholipid conversions in the phagosome [see Tab. 1–3, Additional File 1]. In particular this reduced network includes as external components a number of phospholipid components which in the meantime have been shown to stimulate or inhibit the phagosome [23].
In this well connected network different conditions can be tested and interesting modes be rapidly visualized applying YANAsquare (as in Fig. 2), e.g. those modes yielding a specific phospholipid or those yielding ATP. Thus, setting ATP as an internal metabolite results in 14 ATP producing actin nucleation modes (from a total of 128 in this network) involving different phospholipids [for network definition and results, see Additional File 1]. Certainly this is a simplistic picture of the much more complex processes in phagosomal signaling. However the prediction of ATP generating actin nucleating modes identified and motivated experiments for different phospholipids, which are in fact in line with the model predictions. In particular, the modes suggest that the phospholipids phosphatidyl-inositol-phosphate and -bisphosphate (PIP and PIP2) as well as sphingosine-1-phosphate (S1P) stimulate the network whereas diacylglycerol (DAG) and phosphatidyl-choline (PC) are not in any of the modes and predicted to be inhibitory. This is in line with experimental data (Kühnel et al., unpublished, [23]) regarding exactly these phospholipids which have been obtained from phagosome actin nucleation assays. In these assays [23], murine phagosomes nucleate GFP labeled actin after phospholipid stimulation and the polymerization is monitored by confocal light microscopy.
Applying the KEGG browser additional phospholipid conversions involving e.g. phosphatidylethanolamine and phosphatidylsphingosine can easily be added. Furthermore, the network can be easily extended to prostaglandines and their conversions [KEGG map No. 00600, 00590, for network definition and results, see Additional File 1].
Genome-scale pathway analysis in Staphylococci
The capabilities of YANAsquare further allow rapid and comparative genome-scale metabolic analyses. The tutorial and examples included in the supplementary material contain helpful hints for the user to achieve this, including network simplification rules to avoid combinatorial explosion of elementary modes. In our illustration example, different Staphylococci strains are compared for their main metabolic capabilities: S. aureus COL, S. aureus N315, S. saprophyticus and S. epidermidis. Note that the comparisons serve illustration purposes and more detailed studies are required for an accurate modeling of these strains. However the examples show how the tool YANAsquare allows a rapid first overview and points out metabolic differences.
The KEGG browser allows easy establishment of the complete network of interest. Regarding the growth equation of S. aureus [24], it is evident that we require the metabolism of central sugars, amino acids, and fatty acids as well as the nucleotide metabolism. This is easily compiled using the KEGG browser. Two different strains are readily accessed using KEGG: S. aureus COL and S. aureus N315. The central metabolism of S. aureus COL including carbohydrates, nucleotides, amino acids and lipid metabolism is rapidly established applying YANAsquare. Using the KEGG maps 00010, 00020, 00030, 00251, 00252, 00260, 00271, 00272, 00290, 00300, 00330, 00340, 00400, 00620, 00720, a system with 134 metabolites and 3957 modes is obtained [see Additional File 1, Tab. 10–15; for rules for abbreviation and network simplification see tutorial]. YANAsquare shows also quickly that regarding central metabolism, strain N315 is rather similar to S. aureus COL (same number of modes and metabolites). Clear differences in central metabolism become readily apparent changing to S. epidermidis and S. saprophyticus instead. Reading in the KEGG maps (the serial numbers are the same as in S. aureus) we now obtain a system with 134 metabolites and 11910 modes (S. epidermidis) as well as 132 metabolites and 11844 modes (S. saprophyticus), respectively [for example modes see Additional File 1; the complete list of modes is available from the authors on request].
A first comparison of the network models obtained predicts that S. aureus has lower ATP production than S. epidermidis, since its tricarboxylic acid cycle (TCA) is not complete: one involved enzyme – malate dehydrogenase (MDH; EC: 1.1.1.37) – is absent in S. aureus strains. It is a well-known NAD-dependent enzyme, which synthesizes oxaloacetate and NADH, the latter metabolite will contribute 3 moles of ATP through oxidative phosphorylation. A second enzyme, membrane-associated malate: quinone-oxidoreductase (EC: 1.1.99.16) can be expressed at certain stages. As this is then highly expressed in S. aureus, this partially compensates for the lack of MDH. However, the corresponding reaction requires external supply of Vitamin K (the S. aureus strain lacks the capability of producing it). The model for this can be readily established, e.g. by switching the menaquinol and menaquinone metabolites from internal to external in the software to simulate the synthesis of metabolites if Vitamin K is present in the medium. Comparing the resulting pathways between the different strains further, we find that the S. aureus strain can bypass the above TCA gap via malic enzyme (EC: 1.1.1.38), phosphoenolpyruvate-protein phosphotransferase (EC: 2.7.3.9) and phosphoenolpyruvate carboxylase (EC: 4.1.1.49) to produce oxaloacetate which can take part in the next round of the TCA cycle or be used to synthesize amino acids. However, this alternative way consumes ATP and requires the energy-consuming PTS system (EC: 2.7.1.69, EC: 2.7.3.9).
S. aureus has more functional enzymes and modes regarding amino acid production, whereas S. saprophyticus has fewer modes for certain amino acids. For instance, the absence of threonine aldolase (EC 4.1.2.5) disrupts the connection between threonine and glycine pathways. Even its urea cycle is not complete (e.g., S. saprophyticus modes no: 4 - 8 etc.), as for instance carbamoyl phosphokinase (EC: 2.7.2.2) is not present. S. saprophyticus lacks the capability of producing carbamoyl phosphate directly. Instead, this is supplied from amino acids (e.g., S. saprophyticus modes no: 4 - 8 etc.). All missing metabolic capabilities (e.g., compared to the modes no. 3, 19, 21, 81, 91, 100 in S. aureus) should potentially reduce the robustness, even though the system can still synthesize all the essential amino acids with an appropriate medium. S. epidermidis lacks arginase (EC: 3.5.3.1) and its urea cycle is incomplete. Arginine has to be converted into citrulline by arginine deiminase (EC: 3.5.3.6), forming a shunt (e.g. modes no. 22). An advantage shared by S. saprophyticus and S. epidermidis is that their native malate dehydrogenase (EC: 1.1.1.37) is present (e.g. modes no: 21, 22, 24 in S. saprophyticus and modes no. 16, 17, 18 in S. epidermidis), so that their citric acid cycle is more efficient and produces more ATP. These are only some strain-specific differences for illustration and exploiting the new software to show how the full scale metabolic models may be easily and quickly compared. In practice, after this initial analysis in most cases a more detailed study of individual sub-networks has to follow, often in combination with further genome re-annotation as well as experimental tests (e.g. [17]).
Robustness analysis
Furthermore, our software provides a systematic test routine to get an estimate of the robustness of a metabolic network. The principle is to validate the flux preservation when a certain involved enzyme is knocked out from the system. Our program simulates this evolutionary or genetic event by removing enzymes one at a time and re-inspecting all generated flux modes. These survival modes after a single enzyme gene knock-out are used to predict the number of metabolites which can be still produced in the restricted system. Consequently all the enzymes are examined and an average metabolite production is calculated. By dividing the number through the maximum number of external metabolites which can be produced by the complete system, we achieve a percentage, referred to as the robustness score, which is an important feature of the system (see implementation). Table 1 illustrates robustness differences among these three systems. From the results (the third row), S. saprophyticus achieves the highest score (92.84), it is definitely the most robust organism among the three Staphylococci compared. This is potentially caused by its flexible living environments, not only in vivo as a pathogen but also outside any host. In contrast, S. aureus has strictly adapted to the host environment so that it requires more complex medium in order to acquire a recognizable growth rate.