1
Supplementary Text
2
1. Model
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(a) Description of resource-consumer model
4
Agrobacterium tumefaciens, the facultative pathogen and soil microbe, can sustain growth in
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what we define as three distinct environments: bulk soil (B), rhizosphere (R), and tumor (T). The
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bulk soil environment (B) only contains general resources (G), available to all plasmid
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genotypes. The rhizosphere environment (R) contains general resources (G) in addition to the
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plant-produced resources deoxy-fructosyl-glucosamine, DFG and γ-butyrolactones, GBLs (D),
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which can only be utilized by pAt+ cells. The tumor environment (T), contains general
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resources (G), plant-produced DFG and GBLs (D), as well as the infected-plant-produced
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resources, opines (O) which can only be catabolized by pTi+ cells.
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Based on the principles of a basic Monod model for bacterial population growth, the change in
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population size of each competing genotype is determined as a function of the initial population
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size (𝑁𝑝−, 𝑁𝑝𝑇𝑖+, 𝑁𝑝𝐴𝑡+, or 𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ ), birth (
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environment-specific resources into new cells; 𝑋𝑦 representing the concentration of each
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resource ‘𝑋‘ (𝐺; general, 𝐷; DFG and GBLs, or 𝑂; opines) in the pertinent environment ‘𝑦’ (𝐵;
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bulk, 𝑅; rhizosphere, or 𝑇; tumor)), and death (𝑚𝑝 ; a constant per capita rate that is equal for all
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genotypes). For a between genotype comparison of growth and death rates in the conditions of
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competition see Tables S5 and S6. Although the general, rhizosphere, and tumor resources
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include multiple forms of carbon that are utilized by cells with varying efficiencies, we find that
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the qualitative outcomes predicted by the model are robust to changes in resource levels and
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substitutability. Thus, for the purpose of simplicity, we assume that all resources are
𝑟𝑚𝑎𝑥 ∗𝑋𝑦
𝑘+𝑋𝑦
; determined by the conversion of available
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substitutable. Growth of each plasmid-bearing genotype is limited by the predicted associated
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plasmid costs (𝐶𝑝 ).
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For a description of all terms, see Table S4.
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𝑑𝑁𝑝 (𝑦)
𝑟𝑚𝑎𝑥 ∗ 𝑋𝑦
= 𝑁𝑝 (1 − 𝐶𝑝 ) (
) − 𝑁𝑝 𝑚𝑝
𝑑𝑡
𝑘 + 𝑋𝑦
29
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We have established that the cost of the Ti plasmid is very low outside of the disease
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environment, but when actively engaged in pathogenesis, the costs associated with virulence are
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significant [1]. Thus, the primary benefits and costs of this plasmid are coupled with, and
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restricted to, this environment and the presence of opines. This tradeoff is incorporated into the
34
model so that Ti plasmid costs are set to correlate with the relative abundance of opines (i.e. the
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product of disease) as a Type II functional response.
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𝑑𝑁𝑝𝑇𝑖+ (𝑦)
𝑂𝑦
𝑟𝑚𝑎𝑥 ∗ 𝑋𝑦
= 𝑁𝑝𝑇𝑖+ (1 − 𝐶𝑝𝑇𝑖+𝑜 − 𝐶𝑝𝑇𝑖𝑚𝑎𝑥
)(
) − 𝑁𝑝𝑇𝑖+ 𝑚𝑝𝑇𝑖+
𝑑𝑡
𝑘𝐶𝑝𝑇𝑖 + 𝑂𝑦
𝑘 + 𝑋𝑦
38
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Whereas the cost associated with the Ti plasmid are minimized when it is not receiving a
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resource benefit, we found that when in isolation, the At plasmid has a very high cost under
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carbon-limiting conditions. We know that cells harboring the At plasmid carry specific genes
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that are likely to provide environment-specific benefits such as catabolism of GBLs and DFG,
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which are abundant in the rhizosphere [2]. In spite of the high cost of the At plasmid, its large
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size and prevalence in nature suggest the benefits it confers to A. tumefaciens cells are
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substantial. As the only known benefits of the At plasmid are associated with carriage of
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catabolism genes targeted to plant produces resources, we assume that the cost of the At plasmid
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is the same in all environments and the benefits are restricted to the rhizosphere and tumor,
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where D resources (GBLs and DFG), are expected to be present in equal abundance.
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𝑑𝑁𝑝𝐴𝑡+ (𝑦)
𝑟𝑚𝑎𝑥 ∗ 𝑋𝑦
= 𝑁𝑝𝐴𝑡+ (1 − 𝐶𝑝𝐴𝑡 ) (
) − 𝑁𝑝𝐴𝑡+ 𝑚𝑝𝐴𝑡+
𝑑𝑡
𝑘 + 𝑋𝑦
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Competitions performed in the study also demonstrate that the costs of co-inhabitant plasmids
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are lower than pAt alone. This non-additivity was observed under carbon-limiting conditions
54
when neither rhizosphere or tumor-specific opines resources (D and O, respectively) were
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present. Further substantiating this non-additivity is that the costly Ti plasmid virulence genes
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are expressed at lower levels in pAt+ cells (figure 2), suggesting a lower cost for pTi+ cells in
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the disease environment as well. This non-additivity was incorporated into the model as the
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modifier 𝑍, allowing us to establish the potential ecological significance of plasmid epistasis on
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the outcome of the predicted resource-consumer interactions.
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𝑑𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (𝑦)
𝑑𝑡
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= 𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (1 − 𝑍(𝐶𝑝𝐴𝑡 + 𝐶𝑝𝑇𝑖𝑜 + 𝐶𝑝𝑇𝑖𝑚𝑎𝑥
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− 𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ 𝑚𝑝𝐴𝑡+𝑝𝑇𝑖+
𝑂𝑦
𝑟𝑚𝑎𝑥 ∗ 𝑋𝑦
)) (
)
𝑘𝐶𝑝𝑇𝑖 + 𝑂𝑦
𝑘 + 𝑋𝑦
64
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The concentrations of available resources (G, D, and O) in each environment (B, R, and T) are
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calculated by the flow rates into the environment, the conversion of resources into cell biomass,
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and the rate of diffusion out of the environment. The supply rate (𝑋𝑦𝑜 ) and diffusion rate (𝐹𝑥 ) are
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held constant for all resources, where 𝐹𝑥 is always assumed to be less than 𝑟𝑚𝑎𝑥 and the rate of
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resource conversion into cell biomass is genotype dependent.
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Concentration of resources (𝑋) in the environment (𝑦):
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𝑑𝑋(𝑦)
= (𝑋𝑦𝑜 − 𝑋𝑦∗ )𝐹𝑥
𝑑𝑡
−
(𝑟𝑚𝑎𝑥 ∗ 𝑋𝑦 )
𝑁𝑝−
𝑁𝑝𝐴𝑡+ (1 − 𝐶𝑝𝐴𝑡 )
+
𝑒
𝑘 + 𝑋𝑦
𝑘 + 𝑋𝑦
(
𝑁𝑝𝑇𝑖+ (1 − 𝐶𝑝𝑇𝑖𝑜 − 𝐶𝑝𝑇𝑖𝑚𝑎𝑥
+
𝑂𝑦
)
𝑘𝐶𝑝𝑇𝑖 + 𝑂𝑦
𝑘 + 𝑋𝑦
𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (1 − 𝑍(𝐶𝑝𝐴𝑡 + 𝐶𝑝𝑇𝑖𝑜 + 𝐶𝑝𝑇𝑖𝑚𝑎𝑥
+
𝑂𝑦
))
𝑘𝐶𝑝𝑇𝑖 + 𝑂𝑦
𝑘 + 𝑋𝑦
)
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(b) Resource-dependent population growth
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Population growth of four plasmid genotypes: p- (plasmidless), pAt+, pTi+ and pAt+pTi+,
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depends on the relative availability of environment-specific resources.
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The change in population size of the plasmidless genotype in all three environments depends on
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the initial population size (𝑁𝑝 ), its growth rate (
𝑟𝑚𝑎𝑥 +𝐺𝑦
𝑘+𝐺𝑦
; determined by the conversion of
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available general resources (𝐺) into new cells) and the death rate (𝑚𝑝− ; a constant per capita rate
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that is equal for all genotypes). Equations 1.1, 1.2, and 1.3 describe the change in population
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size for the plasmidless genotype in all three environments: 𝐵, 𝑅, and 𝑇.
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Equation 1.1
𝑑𝑁𝑝− (𝐵)
𝑟𝑚𝑎𝑥 ∗ 𝐺𝐵
= 𝑁𝑝− (
) − 𝑁𝑝− 𝑚𝑝−
𝑑𝑡
𝑘 + 𝐺𝐵
90
91
92
Equation 1.2
𝑑𝑁𝑝− (𝑅)
𝑟𝑚𝑎𝑥 ∗ 𝐺𝑅
= 𝑁𝑝− (
) − 𝑁𝑝− 𝑚𝑝−
𝑑𝑡
𝑘 + 𝐺𝑅
93
94
95
96
Equation 1.3
𝑑𝑁𝑝− (𝑇)
𝑟𝑚𝑎𝑥 ∗ 𝐺𝑇
= 𝑁𝑝− (
) − 𝑁𝑝− 𝑚𝑝−
𝑑𝑡
𝑘 + 𝐺𝑇
97
Change in population size of the singly infected pAt+ genotype depends on the availability of
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general resources (𝐺) and plant-produced resources (𝐷), which are present in both the
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rhizosphere and the tumor environments. Growth of this genotype is also affected by the cost
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associated with harboring the plasmid (𝐶𝑝𝐴𝑡 ). Thus, in a mixed population of p- and pAt+ cells,
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plasmidless cells can take advantage of general resources more quickly due to the absence of any
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plasmid cost. But, because both genotypes are able to use 𝐺 resources, and all resources are
103
equally substitutable, as 𝐺 resources are depleted where 𝐷 resources are present, the pAt+
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genotype will increase in population size as the p- genotype declines. Equations 2.1, 2.2, and 2.3
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describe the change in population size of the pAt+ genotype in all three environments.
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107
Equation 2.1
𝑑𝑁𝑝𝐴𝑡+ (𝐵)
𝑟𝑚𝑎𝑥 ∗ 𝐺𝐵
= 𝑁𝑝𝐴𝑡+ (1 − 𝐶𝑝𝐴𝑡 ) (
) − 𝑁𝑝𝐴𝑡+ 𝑚𝑝𝐴𝑡+
𝑑𝑡
𝑘 + 𝐺𝐵
108
109
110
Equation 2.2
𝑑𝑁𝑝𝐴𝑡+ (𝑅)
𝑟𝑚𝑎𝑥 ∗ 𝐺𝑅 ∗ 𝐷𝑅
= 𝑁𝑝𝐴𝑡+ (1 − 𝐶𝑝𝐴𝑡+ ) (
) − 𝑁𝑝𝐴𝑡+ 𝑚𝑝𝐴𝑡+
𝑑𝑡
𝑘 + 𝐺𝑅 + 𝐷𝑅
111
112
113
Equation 2.3
𝑑𝑁𝑝𝐴𝑡+ (𝑇)
𝑟𝑚𝑎𝑥 ∗ 𝐺𝑇 ∗ 𝐷𝑇
= 𝑁𝑝𝐴𝑡+ (1 − 𝐶𝑝𝐴𝑡+ ) (
) − 𝑁𝑝𝐴𝑡+ 𝑚𝑝𝐴𝑡+
𝑑𝑡
𝑘 + 𝐺𝑇 + 𝐷𝑇
114
115
116
The change in population size for the singly infected pTi+ genotype is distinct in that growth
117
depends on the availability of both general resources (𝐺) and opines (𝑂). Additionally, growth
118
on opines is directly coupled with tumor-associated costs (1 − 𝐶𝑝𝑇𝑖+𝑜 − 𝐶𝑝𝑇𝑖𝑚𝑎𝑥 𝑘
119
Equations 3.1, 3.2, and 3.3 describe the change in population growth for the pTi+ genotype in the
120
𝐵, 𝑅, and 𝑇 environments.
121
122
Equation 3.1
𝑑𝑁𝑝𝑇𝑖+ (𝐵)
𝑟𝑚𝑎𝑥 ∗ 𝐺𝐵
= 𝑁𝑝𝑇𝑖+ (1 − 𝐶𝑝𝑇𝑖+𝑜 ) (
) − 𝑁𝑝𝑇𝑖+ 𝑚𝑝𝑇𝑖+
𝑑𝑡
𝑘 + 𝐺𝐵
123
124
125
Equation 3.2
𝑂𝐵
𝐶𝑝𝑇𝑖 +𝑂𝐵
).
𝑑𝑁𝑝𝑇𝑖+ (𝑅)
𝑟𝑚𝑎𝑥 ∗ 𝐺𝑅
= 𝑁𝑝𝑇𝑖+ (1 − 𝐶𝑝𝑇𝑖+𝑜 ) (
) − 𝑁𝑝𝑇𝑖+ 𝑚𝑝𝑇𝑖+
𝑑𝑡
𝑘 + 𝐺𝑅
126
127
128
129
Equation 3.3
𝑑𝑁𝑝𝑇𝑖+ (𝑇)
𝑂𝑇
𝑟𝑚𝑎𝑥 ∗ 𝐺𝑇 ∗ 𝑂𝑇
= 𝑁𝑝𝑇𝑖+ (1 − 𝐶𝑝𝑇𝑖+𝑜 − 𝐶𝑝𝑇𝑖𝑚𝑎𝑥
)(
) − 𝑁𝑝𝑇𝑖+ 𝑚𝑝𝑇𝑖+
𝑑𝑡
𝑘𝐶𝑝𝑇𝑖 + 𝑂𝑇
𝑘 + 𝐺𝑇 + 𝑂𝑇
130
131
The change in population size of the pAt+pTi+ genotype depends on the availability of all
132
resource types: general (𝐺), DFG and GBLs (𝐷), and opines (𝑂). Here, the modifier 𝑍, is
133
incorporated to account for the non-additive costs of the two plasmids. Equations 4.1, 4.2, and
134
4.3 describe the change in population growth for the pAt+pTi+ genotype in each relative
135
environment.
136
137
138
Equation 4.1
𝑑𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (𝐵)
𝑟𝑚𝑎𝑥 ∗ 𝐺𝐵
= 𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (1 − 𝑍(𝐶𝑝𝐴𝑡 + 𝐶𝑝𝑇𝑖𝑜 )) (
) − 𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ 𝑚𝑝𝐴𝑡+𝑝𝑇𝑖+
𝑑𝑡
𝑘 + 𝐺𝐵
139
140
141
Equations 4.2
𝑑𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (𝑅)
𝑟𝑚𝑎𝑥 ∗ 𝐺𝑅 ∗ 𝐷𝑅
= 𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (1 − 𝑍(𝐶𝑝𝐴𝑡 + 𝐶𝑝𝑇𝑖𝑜 )) (
) − 𝑁𝑝𝐴𝑡+𝑚𝑇𝑖+ 𝑚𝑝𝐴𝑡+𝑝𝑇𝑖+
𝑑𝑡
𝑘 + 𝐺𝑅 + 𝐷𝑅
142
143
Equation 4.3
144
𝑑𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (𝑇)
𝑑𝑡
145
= 𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (1 − 𝑍(𝐶𝑝𝐴𝑡 + 𝐶𝑝𝑇𝑖𝑜
146
+ 𝐶𝑝𝑇𝑖𝑚𝑎𝑥
𝑂𝑇
𝑟𝑚𝑎𝑥 ∗ 𝐺𝑇 ∗ 𝐷𝑇 ∗ 𝑂𝑇
)) (
) − 𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ 𝑚𝑝𝐴𝑡+𝑝𝑇𝑖+
𝑘𝐶𝑝𝑇𝑖 + 𝑂𝑇
𝑘 + 𝐺𝑇 + 𝐷𝑇 + 𝑂𝑇
147
148
(c) Genotype-dependent resource availability
149
The concentrations of available resources are a function of the supply, diffusion rate, and use
150
(conversion into microbial biomass). In this model, the supply and diffusion rates for each
151
resource are constant, and it is the composition of the microbial population (i.e. relative
152
genotypes) that varies as a function of available resources.
153
Thus, the change in concentration of each type of resources depends on 1) the relative abundance
154
of each genotype, and 2) the availability of other resources in that specific environment that are
155
contributing to increases in population size.
156
157
Equation 5.1 describes the change in 𝐺 resources in the bulk soil environment.
158
𝐷 and 𝑂 =0.
159
160
161
Equation 5.1
𝑑𝐺(𝐵)
= (𝐺𝐵𝑜 − 𝐺𝐵∗ )𝐹𝐺
𝑑𝑡
162
−
(𝑟𝑚𝑎𝑥 ∗ 𝐺𝐵 ) 𝑁𝑝−
𝑁𝑝𝐴𝑡+ (1 − 𝐶𝑝𝐴𝑡 ) 𝑁𝑝𝑇𝑖+ (1 − 𝐶𝑝𝑇𝑖𝑜 )
(
+
+
𝑒
𝑘 + 𝐺𝐵
𝑘 + 𝐺𝐵
𝑘 + 𝐺𝐵
163
+
𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (1 − 𝑍(𝐶𝑝𝐴𝑡 + 𝐶𝑝𝑇𝑖𝑜 ))
)
𝑘 + 𝐺𝐵
164
165
Equations 6.1 and 6.2 describe the change in 𝐺 and 𝐷 resources in the rhizosphere. 𝑂 = 0.
166
167
168
Equation 6.1
𝑑𝐺(𝑅)
= (𝐺𝑅𝑜 − 𝐺𝑅∗ )𝐹𝐺
𝑑𝑡
169
−
𝑁𝑝𝐴𝑡+ (1 − 𝐶𝑝𝐴𝑡 ) 𝑁𝑝𝑇𝑖+ (1 − 𝐶𝑝𝑇𝑖𝑜 )
𝑟𝑚𝑎𝑥 ∗ 𝐺𝑅 𝑁𝑝−
(
+
+
𝑒
𝑘 + 𝐺𝑅
𝑘 + 𝐺𝑅 +𝐷𝑅
𝑘 + 𝐺𝑅
170
+
𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (1 − 𝑍(𝐶𝑝𝐴𝑡 + 𝐶𝑝𝑇𝑖𝑜 ))
)
𝑘 + 𝐺𝑅 +𝐷𝑅
171
172
173
Equation 6.2
𝑑𝐷(𝑅)
𝑟𝑚𝑎𝑥 ∗ 𝑅𝑅 𝑁𝑝𝐴𝑡+ (1 − 𝐶𝑝𝐴𝑡 ) 𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (1 − 𝑍(𝐶𝑝𝐴𝑡 − 𝐶𝑝𝑇𝑖𝑜 )
= (𝑅𝑅𝑜 − 𝑅𝑅∗ )𝐹𝑅 −
(
+
)
𝑑𝑡
𝑒
𝑘 + 𝐺𝑅 +𝐷𝑅
𝑘 + 𝐺𝑅 +𝐷𝑅
174
175
Equations 7.1, 7.2, and 7.3 describe the change in 𝐺, 𝐷, and 𝑂 resources in the tumor
176
environment.
177
178
179
180
181
182
Equation 7.1
𝑑𝐺(𝑇)
= (𝐺𝑇𝑜 − 𝐺𝑇∗ )𝐹𝐺
𝑑𝑡
−
𝑁𝑝−
𝑁𝑝𝐴𝑡+ (1 − 𝐶𝑝𝐴𝑡 )
𝑟𝑚𝑎𝑥 ∗ 𝐺𝑇
(
+
𝑒
𝑘 + 𝐺𝑇
𝑘 + 𝐺𝑇 +𝐷𝑇
𝑁𝑝𝑇𝑖+ (1 − 𝐶𝑝𝑇𝑖𝑜 − 𝐶𝑝𝑇𝑖𝑚𝑎𝑥
+
+
𝑂𝑇
)
𝑘𝐶𝑝𝑇𝑖 + 𝑂𝑇
𝑘 + 𝐺𝑇 +𝑂𝑇
𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (1 − 𝑍(𝑚𝑝𝐴𝑡 + 𝐶𝑝𝑇𝑖𝑜 + 𝐶𝑝𝑇𝑖𝑚𝑎𝑥
𝑘 + 𝐺𝑇 +𝐷𝑇 + 𝑂𝑇
𝑂𝑇
))
𝑘𝐶𝑝𝑇𝑖 + 𝑂𝑇
)
183
184
185
Equation 7.2
𝑑𝐷(𝑇)
= (𝐷𝑇𝑜 − 𝐷𝑇∗ )𝐹𝐷
𝑑𝑡
186
−
𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (1 − 𝑍(𝐶𝑝𝐴𝑡 + 𝐶𝑝𝑇𝑖𝑜 + 𝐶𝑝𝑇𝑖𝑚𝑎𝑥
187
188
189
190
191
𝑟𝑚𝑎𝑥 ∗ 𝐷𝑇 𝑁𝑝𝐴𝑡+ (1 − 𝐶𝑝𝐴𝑡 )
(
𝑒
𝑘 + 𝐺𝑇 +𝐷𝑇
+
𝑘 + 𝐺𝑇 +𝐷𝑇 + 𝑂𝑇
𝑂𝑇
)
𝑘𝐶𝑝𝑇𝑖 + 𝑂𝑇
)
Equation 7.3
𝑑𝑂(𝑇)
= (𝑂𝑇𝑜 − 𝑂𝑇∗ )𝐹𝑂
𝑑𝑡
𝑟𝑚𝑎𝑥 ∗ 𝑂𝑇
−
(
𝑒
𝑁𝑝𝑇𝑖+ (1 − 𝐶𝑝𝑇𝑖𝑜 − 𝐶𝑝𝑇𝑖𝑚𝑎𝑥
𝑂𝑇
)
𝑘𝐶𝑝𝑇𝑖 + 𝑂𝑇
𝑘 + 𝐺𝑇 +𝑇𝑇
𝑁𝑝𝐴𝑡+𝑝𝑇𝑖+ (1 − 𝑍(𝐶𝑝𝐴𝑡 + 𝐶𝑝𝑇𝑖𝑜 + 𝐶𝑝𝑇𝑖𝑚𝑎𝑥
+
𝑘 + 𝐺𝑇 +𝐷𝑇 + 𝑂𝑇
𝑂𝑇
)
𝑘𝐶𝑝𝑇𝑖 + 𝑂𝑇
)
192
193
2. Supplementary Methods
194
(a) Curing the Ti plasmid
195
The Ti plasmid was cured from A. tumefaciens C58 using the approach developed by Uraji et al.
196
2002, and explained in detail in Morton and Fuqua 2012. The curing vector, pEM112, was
197
constructed by cloning the entire replication region of pTiC58 (repABC) into pNPTS138, which
198
contains a counterselectable sacB gene conferring sucrose sensitivity. We used a stock of
199
Agrobacterium tumefaciens C58 (designated C58-CU) provided by Professor Steven Beer at
200
Cornell University, that is reported to be as close to the original isolate (Cherry gall, Geneva,
201
NY, 1958) as is available. The curing vector was introduced into C58-CU by conjugation from
202
an Escherichia coli S17-1 λpir donor. Plating on ATGN supplemented with kanamycin selects
203
for A. tumefaciens pEM112 transconjugants. The two plasmids, pEM112 and pTiC58, use the
204
same replication and partitioning machinery, and are therefore incompatible [3, 4]. As a
205
consequence, transconjugants that contain both plasmids give rise to segregants that lack one or
206
the other plasmid. Growth in the presence of kanamycin selects for only those cells that carry
207
pEM112 and have lost pTiC58. These cells were subsequently screened for loss of the Ti
208
plasmid by a lack of AHL production and diagnostic PCR screening for loss of plasmid genes,
209
targeting multiple regions distributed across the Ti plasmid. Subsequently, these pTiC58-
210
derivatives were grown in the presence of 5% sucrose and in the absence of antibiotic to select
211
for cells that had lost the curing vector. These sucrose-resistant colonies were patched onto
212
ATGN supplemented with kanamycin to screen for KmS clones. Sucrose-resistant and
213
kanamycin-sensitive clones were screened for the absence of repABC by PCR.
214
215
(b) Curing the At plasmid
216
Curing of the At plasmid was first attempted using the same method first described by Uragi et
217
al. 2002. The curing vector that was generated, pEM123 contained the repABC region of
218
pAtC58. However, this method alone was ineffective in curing the At plasmid. Exposure to
219
kanamycin, resistance to which was conferred by the expression of the resistance gene carried on
220
the curing vector, instead selected for the co-integration of the At and curing plasmids. The
221
repABC region provided plenty of genetic material for recombination to occur and the result was
222
that these chimeric plasmids would be stably maintained throughout the entire selective process.
223
224
In an attempt to destabilize the At plasmid, a deletion in the core sequence of the plasmid
225
stability gene, repA, was generated. Interestingly, the result of the allelic replacement process
226
yielded clones that harbored both the wild-type and mutant forms of the plasmid. Due to
227
plasmid incompatibility and the inherently less stable quality of the repA- plasmid, the mutant
228
plasmids were quickly lost from the population. The difficulty in curing the At plasmid
229
suggested that either some genes on the At plasmid were essential, or that a toxin-antitoxin (TA)
230
system was preventing survival of any pAt- variants that arose. The At plasmid carries several
231
putative toxin-antitoxin systems that were identified by RASTA-Bacteria (Rapid Automated
232
Scan for Toxins and Antitoxins in Bacteria). One pair of adjacent genes, Atu5112 and Atu5113,
233
exhibit homology to the established TA system, HipA and HipB, respectively. HipA is a protein
234
kinase that, in the absence of neutralization by HipB, phosphorylates EF-Tu to inhibit protein
235
synthesis and induce cell dormancy [5]. A genome-wide screen for essential genes in A.
236
tumefaciens C58 recovered very few isolates with transposon insertions in Atu5113, the putative
237
antitoxin, providing support for this being a functional TA system (Curtis and Brun,
238
unpublished). In an attempt to remove this selection for maintenance of the At plasmid
239
(dormancy of any pAt-cured derivative), the predicted toxin, Atu5112 was deleted. The curing
240
plasmid was subsequently introduced into the ΔAtu5112 mutant, and positive transformants were
241
subjected to heat shock (42°C for 60 seconds) before passaging for two more days in the
242
presence of kanamycin (to select for preferential maintenance of the curing vector). This
243
combinatorial approach proved to be effective and approximately 5 out of 500 screened colonies
244
had lost the At plasmid: a curing rate quite low relative to the Ti plasmid (~1% versus 85%). For
245
further confirmation of plasmid loss, all five clones were screened by PCR using primers specific
246
to regions distributed around the At plasmid. They were also tested for pAt-conferred bcl
247
activity by spotting putative pAt- clones on a petri plate containing growth media, an AHL-
248
producing strain, an AHL-reporter strain, Xgal, and salicylic acid (inducer of bcl expression).
249
Strains that exhibit bcl activity are able to catabolize the AHLs produced by the AHL-producing
250
strain that is growing in the media. The presence of the AHL-reporter strain in combination with
251
Xgal results in the development of a blue color in the media wherever AHLs are present.
252
Catabolism of these AHLs results in a loss of blue color, or a clear zone, around any bcl-
253
expressing pAt+ strain.
254
255
(c) Testing isogenicity of derived strains
256
To determine if plasmid curing, conjugation, and additional genetic manipulations had resulted
257
in unintended genetic changes impacting the fitness of the derived wild-type (doubly-infected
258
strain), we competed the derived ERM89 (pAt+pTi+) strain with the original C58-CU strain
259
(same methods and analyses as all other competitions) and found no significant difference in
260
relative fitness (WC58-CU = 1.0025, SE= 0.005, p = 0.6020).
261
262
(d) Generating isogenic strains
263
To control for the possibility of mutations that could have arisen during the curing process,
264
isogenic strains were generated by reintroduction of the At and Ti plasmids into the plasmid-free
265
derivative, ERM52. Markers conferring resistance to gentamycin and ampicillin were
266
introduced by allelic replacement into the Ti and At plasmids of C58-CU, respectively. For the
267
Ti plasmid, aacC1 was cloned into the intergenic region between the two convergent genes,
268
Atu6112 and Atu6113. For the At plasmid, bla (conferring resistance to ampicillin) was inserted
269
between Atu5196 and Atu5197 (also convergent). These plasmids were then conjugatively
270
transferred to ERM52 to generate pAt+, pTi+, and pAt+pTi+ derivatives (ERM73, ERM76, and
271
ERM77). To limit marker effects during competition, the aacC1 and bla genes were
272
subsequently removed by the same allelic replacement strategy.
273
274
(e) Measurement of virulence gene induction
275
Strains ERM89 (pAt+pTi+) and ERM66 (pTi+) were transformed with the reporter plasmid,
276
pSW209Ω (S. C. Winans, Cornell University) which carries a PvirB::lacZ fusion (PvirB from
277
pTiA6). Cells were grown approximately 24 h in Induction Broth (pH 5.6, 50 μM phosphate, and
278
200 μM acetosyringone) [50]. When cultures reached mid-log phase of growth, they were
279
assayed for β-galactosidase activity as described [46]. Activity is presented in terms of Miller
280
Units; a quantitative measure of specific activity that accounts for gene-expression mediated β-
281
galactosidase activity, normalized to growth. The equation for calculating Miller Units (𝑀𝑈) is
282
as follows:
283
𝑀𝑈 = 1000 ∗ (𝑂𝐷
284
optical density of the culture, 𝑡 is the time of the reaction, and 𝑉 is the culture volume. Each
285
treatment was carried out in triplicate.
𝐴420
600 ∗𝑡∗𝑉)
, where 𝐴420 represents absorbance of o-nitrophenol, 𝑂𝐷600 represents
286
287
Supplementary References
288
289
290
291
292
293
294
295
296
1. Platt T.G., Bever J.D., Fuqua C. 2012 A cooperative virulence plasmid imposes a high fitness
cost under conditions that induce pathogenesis. Proc R Soc Lond Ser B-Biol Sci 279(1734),
1691-1699. (doi:10.1098/rspb.2011.2002).
2. Baek C.-H., Farrand S.K., Park D.K., Lee K.E., Hwang W., Kim K.S. 2005 Genes for
utilization of deoxyfructosyl glutamine (DFG), an amadori compound, are widely dispersed in
the family Rhizobiaceae. FEMS Microbiol Ecol 53(2), 221-233.
3. Uraji M., Suzuki K., Yoshida K. 2002 A novel plasmid curing method using incompatibility
of plant pathogenic Ti plasmids in Agrobacterium tumefaciens. Genes & Genetic Systems
77(1), 1-9. (doi:10.1266/ggs.77.1).
297
298
299
300
301
302
303
4. Cevallos M.A., Cervantes-Rivera R., Gutierrez-Rios R.M. 2008 The repABC plasmid family.
Plasmid 60(1), 19-37. (doi:10.1016/j.plasmid.2008.03.001).
5. Hansen S., Vulic M., Min J.K., Yen T.J., Schumacher M.A., Brennan R.G., Lewis K. 2012
Regulation of the Escherichia coli HipBA Toxin-Antitoxin System by Proteolysis. PLoS One
7(6). (doi:10.1371/journal.pone.0039185).