3.3. Network Analysis of Plant-Pathogen Network Pairs
The pathogen gaining its resources from the host, thus inflicting damage to it, constitutes the hallmark of any host-pathogen relation. This relation is asymmetric in the sense that the host can survive better in the absence of the pathogen, while in general, the pathogen is dependent on nutrients or other components provided by the host. At the metabolic level, it is therefore plausible to assume that the presence of the metabolism of the pathogen has a negative effect on the plant metabolism, whereas the existence of the plant metabolism is positive for the pathogen. To analyze and quantify negative and positive effects of interacting metabolic networks, we calculated a ‘gain’ measuring the positive effect and an ‘impairment’ measuring the negative effect that one network has on the other. Essentially, the gain measures the number of metabolites, which can be produced more by a combined network compared to the sum of the single networks and the impairment is assessed by calculating the negative effect that removal of required nutrients of the pathogen has on the host’s capability to produce essential biomass precursors (details are given in the relevant section in Methods).
We calculated the metabolic gain for each plant-pathogen pair by assuming the photoautotrophic plant seed consisting of carbon dioxide and inorganic nutrients. As described in the relevant paragraph in the Methods, we calculate a symmetric gain, which describes the overall increase of producible metabolites, and asymmetric gains, which specify the advantages for each partner by only including metabolites occurring in the network of the respective interacting partner. This plant asymmetric gain describes how many new compounds could in principle be synthesized by the plant if all metabolic reactions in the pathogen could be used constructively. While this may seem irrelevant considering that pathogens do not help their hosts but rather exploit them, it is nevertheless an interesting theoretical exercise, because a transition from pathogenic exploitation to symbiotic mutualism can occur, and vice versa. For example, type III secretion systems that are crucial for the pathogenicity of P. syringae
, are also active in the plant growth promoting Pseudomonas fluorescens
SBW 2528 [50
], and haustoria are established by pathogenic fungi, e. g. powdery mildew, as well as by symbiotic mycorrhiza (here called arbuscules). Some symbionts may have evolved from pathogens and adapted to their hosts by providing them with valuable chemical substances [51
]. It is plausible to assume that pathogens, at least to some degree, may also be physiologically beneficial to their host’s metabolism.
The plant asymmetric gain varies considerably for the five pairs. By far the highest value is observed for Glycine max with Sclerotinia sclerotiorum. Therefore, the metabolism of the parasite S. sclerotiorum appears to have a high potential to be of use for its host. It can be speculated that in evolutionary terms this parasitism has some chance to evolve into a symbiotic relationship, because both partners can – at least at the metabolic level – in principle benefit considerably from each other. Except for one pair (Gm/Ss) where it is relatively even, the asymmetric gain is considerably higher for all pathogens relative to the plant hosts in all pairs. This is not surprising considering that we performed our calculation under the assumption that only inorganic nutrients are available. Clearly, the parasites cannot utilize this combination without presence of the plants. Interestingly, the symmetric metabolic gain and the plant asymmetric gain do not seem to be correlated with network distance. Intuitively, one would expect that the larger the network overlap (small Jaccard distance), the smaller the gain, because the networks have essentially the same metabolic capacity, and conversely, the smaller the overlap (large Jaccard distance), the larger the gain. However, no significant correlation, even slightly negative rather than the expected positive correlation, was found between the gain or asymmetric gain and the Jaccard distance (with the Pearson correlation coefficients and associated p-values of r=-0.25, p=0.68, and r=-0.10, p=0.87, respectively). For example, Glycine max and Sclerotinia sclerotiorum exhibit the largest gain amongst the five pairs (191 (symmetric), 238 (asymmetric)), whereas the network distance of 0.145 is rather moderate. By contrast, Arabidopsis thaliana and Pseudomonas syringae exhibit a rather large Jaccard distance of 0.216, but the metabolic gain is only 1 (symmetric) and 34 (asymmetric).
Overview of the metabolic gain and Jaccard distance for all five plant-pathogen pairs.
| ||gain||asymmetric gain plant||asymmetric gain pathogen||Jaccard distance|
|At - Ps||1||34||146||0.216|
To assess the negative impact of pathogens on the hosts, we calculated how the presence of the pathogens impairs the production of essential biomass precursors in the host networks (see Methods). To allow for a systematic analysis, we determined the impairment not only for the specific interactions of pathogens on their natural hosts, but extended the analysis to all combinations of host-pathogen pairs. Effectively, the non-natural plant-pathogen pairs produced in silico
serve as a null-model to which any specific effects of the actual plant-pathogen combinations can be contrasted. The result is depicted in Figure 4
in which the shadings of the squares indicate the impairment scores in a logarithmic scale. A white square represents a score of less than 0.01, a black square of a score close to 1.
Relative impairment scores for essential biomass precursors for all investigated host-pathogen pairs. Impairment scores are indicated by grey-scale in a logarithmic scale. White squares indicate an impairment score of less than 0.01, black squares a score of 1. The network pairs are grouped by pathogens so that each panel displays the effect of one particular pathogen on each of the five plant networks and the complete MetaCyc network (MC). Native plant-pathogen pairs are highlighted in bold face.
Interestingly, only few biomass precursors show a high score while most precursors are only marginally impaired. Only for histidine, lysine, methionine and thymidine triphosphate (TTP) scores over 0.05 are observed. Histidine is strongly impaired in all investigated plant-pathogen pairs with scores between 0.25 and 0.85. Methionine and cysteine are also rather uniformly impaired in all pairs, but with considerably lower scores (between 0.05 and 0.08 for methionine and between 0.03 and 0.04 for cysteine). By contrast, the impairment of lysine and TTP is very heterogeneous throughout the investigated plant-pathogen pairs. While the production of thymidine triphosphate is strongly impaired by the pathogens X. oryzae (0.40-0.65), U. maydis (0.95-0.96) and S. sclerotiorum (0.99), it is only weakly impaired by the other two pathogens P. syringae (0.03) and M. larici-populina (0.03-0.06). This means that for U. maydis and S. sclerotiorum for almost all predicted nutrient combinations, removal of the nutrients leads to a disability of the host network to produce the essential nucleotide phosphate TTP. Interestingly, their metabolic networks exhibit a low pairwise distance (0.144) so it can be speculated that the mechanism is similar, despite the fact that S. sclerotiorum is a nectrotrophic parasite, while U. maydis is biotrophic. On the other hand, the network of M. larici-populina, the third fungal pathogen which is also biotrophic, also exhibits a low distance to S. sclerotiorum and U. maydis, but the impairment on host networks is considerably different. Similarly, lysine is considerably impaired by the pathogens U. maydis (0.18-0.21) and M. larici-populina (0.25-0.29), but only marginally by the other three pathogens (scores below 0.02).
The observation that only a subset of biomass precursors is susceptible to impairment may result from a general, non-plant-specific, vulnerability of the respective synthesis pathways. To investigate the general fragility of these synthesis pathways, we have included the network comprising all reactions from MetaCyc [52
] as a hypothetical host network. The impairment of the pathogens on the MetaCyc network (marked MC) is displayed in the top row of each panel in Figure 4
. Histidine, for example, is as strongly impaired in the MetaCyc network as it is in the plant networks, suggesting that the full set of reactions does not provide a higher robustness for histidine synthesis when compared to the plant-specific synthesis pathways. Lysine production is impaired in the MetaCyc network only by the two pathogens U. maydis
and M. larici-populina
, which also impair lysine production in the plant networks. However, the considerably lower impairment scores (0.12 and 0.09, respectively) indicate that the full set of MetaCyc reactions provides more alternative synthesis routes and thus displays an increased robustness against competition by the pathogens. An interesting pattern is observed for the impairment of TTP. The pathogens U. maydis
and S. sclerotiorum
, which strongly impair TTP production in plant networks, also exhibit the strongest effect on the full network, albeit with a lower impairment score (0.64 and 0.27, respectively). By contrast, X. oryzae
, which also strongly impairs TTP in plant networks, has only a negligible effect on the full network (<0.01). This indicates that in the case of X. oryzae
other metabolic routes exist in MetaCyc, which could circumvent the removal of the required nutrients, while this is not the case for U. maydis
and S. sclerotiorum.
As a general tendency, the impairment patterns appear to be largely determined by the pathogens and rather independent of the host species. For a systematic investigation of the similarities in the impairment patterns, we quantify the overall effect of a pathogen on a host by the vector containing as elements the 28 impairment scores for the biomass precursors and calculated the pairwise Manhattan (1-norm) distances. These distances were used to perform a multi-dimensional scaling [53
] to visualize similarities and differences. The resulting plot (Figure 5
) highlights that pairs containing the same pathogens (same colors) are always grouped together, confirming that the impairment pattern is largely determined by the pathogen. Furthermore, it can be observed that in most cases the impairment on the full MetaCyc network (squares) is clearly distinguished from the impairment on the plant networks. The only exception is P. syringae
, which displays a similar effect on the full network as on the plant-specific networks. This exception can be explained by the fact that P. syringae
mainly impairs the production of histidine. However, as discussed above, histidine production is almost equally impaired in the full MetaCyc network as in the plant-specific networks.
Multi-dimensional scaling (MDS) of all pairwise impairment patterns. Each colored symbol represents one host-pathogen pair, where hosts are characterized by different symbols and pathogens by different colors. Similar impairment patterns are located near each other in the plot. Axes denote the two-dimensional space in which the respective data points were placed by the MDS procedure.