Philippa Kaur

Detailed population structure of L1–4 and a hierarchical sub-lineage naming systemWe assembled a high-quality data set of whole genomes, antibiotic resistance phenotypes, and geographic sites of isolation for 9584 clinical Mtb samples (“Methods” section and Supplementary Data 1). Of the total, 4939 (52%) were pan-susceptible, i.e., susceptible to at least isoniazid and rifampicin (and all other antibiotics when additional phenotypic data were available), and 4645 (48%) were resistant to one or more antibiotics (Supplementary Fig. 1a). Using the 62 SNS lineage barcode6, 738 isolates were classified as L1 (8%), 2193 as L2 (22%), 1104 as L3 (12%) and 5549 as L4 (58%, Supplementary Fig. 1b). Among the 4939 pan-susceptible isolates, we identified high-quality genome-wide SNSs (83,735 for L1, 56,736 for L2, 76,817 for L3, and 185,622 for L4) that we used in building maximum-likelihood phylogenies for each major lineage (L1–4, “Methods” section). We computed an index of genetic divergence (FST) between groups defined by each bifurcation in each phylogeny. Sub-lineages were defined as monophyletic groups that had high FST ( >0.33) and were also clearly separated from other groups in principal component analysis (PCA, see “Methods” section). We also defined internal groups to sub-lineages (see “Methods” section): an internal group is a monophyletic group genetically divergent (by FST and PCA) from its neighboring groups, but has one or more ancestral branches that show a low degree of divergence or low support (bootstrap values). Internal groups do not represent true sub-lineages in a hierarchical fashion, but defining them allows us to further characterize the Mtb population structure. We provide code to automate all the steps described above. Our approach is scalable and can be used on other organisms (see “Methods” section).To better classify Mtb isolates in the context of the global Mtb population structure, we developed a hierarchical sub-lineage naming scheme (Supplementary Data 2) where each subdivision in the classification corresponds to a split in the phylogenetic tree of each major Mtb lineage. Starting with the global Mtb lineage numbers (e.g., L1), we recursively introduced a subdivision (e.g., from 1.2 to 1.2.1 and 1.2.2) at each bifurcation of the phylogenetic tree whenever both subclades sufficiently diverged. Formally, we defined these splits using bootstrap criteria, and independent validations by FST and PCA (see “Methods” section). Internal groups were denoted with the letter “i” (e.g., 4.1.i1). This proposed system overcomes two major shortcomings of the existing schemas: same-level sub-lineages are never overlapping (unlike the system of Stucki et al.8 sub-lineage 4.10 includes sub-lineages 4.7–4.9), and the names reflect both phylogenetic relationships and genetic similarity (unlike semantic naming such as the “Asia ancestral” lineage in the system of Shitikov et al.7). Further, this naming system can be standardized to automate the process of lineage definition. These advantages come at the price of long sub-lineage names in the case of complex phylogenies (e.g., for L4, sub-lineage 4.10 gets the lineage designation 4.2.1.1.1.1.1.1). For compatibility with naming conventions already in use and to keep names as short as possible, we designed a second, shorthand, naming system that expands the Coll et al. lineage schema by adding new subdivisions and differentiating between sub-lineages and internal groups. For instance, sub-lineage 4.3.1 is designated as 4.3.i1, informing the user that this is an internal group of sub-lineage 4.3. To simplify the use of the hierarchical naming schema and the updated shorthand schema, we provide a table that compares them side by side along with naming systems currently in use (Supplementary Data 2).Using the sub-lineage definition rules and the sub-lineage naming scheme described above, we characterized six previously undescribed sub-lineages of L1 (Fig. 1 and Supplementary Fig. 2); five of which expand the current description of 1.2. We also detected an internal group of 91 isolates (1.1.3.i1) characterized by a long defining branch in the phylogeny (corresponding to 82 SNSs), a high FST (0.48), and geographically restricted to Malawi (85/91, 93% isolates, Fig. 1 and Supplementary Fig. 3). We estimated the date of the emergence of the MRCA of such a group (see “Methods” section) and we found it to be between 1497 and 1754. We found four previously undescribed sub-lineages of L3 (Fig. 2 and Supplementary Fig. 4), revising L3 into four main groups, whereas previously only two partitions of one sub-lineage were characterized (3.1). We found that the latter two partitions are in fact internal groups of the largest sub-lineage (3.1.1) in our revised classification.Fig. 1: Phylogenetic tree reconstruction of lineage 1 (binary tree).Gray circles define splits where the FST (fixation index) calculated using the descendants of the two children nodes is greater than 0.33. The sub-lineages are defined by colored areas (blue: sub-lineages already described in the literature; green: sub-lineages described here; purple: internal sub-lineages). Source data are provided as a Source Data file.Full size imageFig. 2: Phylogenetic tree reconstruction of lineage 3 (binary tree).Gray circles define splits where the FST (fixation index) calculated using the descendants of the two children nodes is greater than 0.33. The sub-lineages are defined by colored areas (green: sub-lineages described here; purple: internal sub-lineages). Source data are provided as a Source Data file.Full size imageL2 is divided into two groups: proto-Beijing and Beijing with the latter in turn partitioned into two groups: ancient- and modern-Beijing7. Each one of these groups is characterized by further subdivisions (three for the ancient-Beijing group and seven for the modern-Beijing group; see Supplementary Fig. 4). We found a new sub-lineage (2.2.1.2, Fig. 3, and Supplementary Fig. 5) within the previously characterized ancient-Beijing group. However, genetic diversity within the modern-Beijing group (2.2.1.1.1) was lower than in the other L2 sub-lineages and the tree topology and FST calculations did not support further hierarchical subdivisions. Although we did find three internal groups of modern-Beijing: two undescribed and one that corresponds to the Central Asia group7. For L4, our results support a complex population structure with 21 sub-lineages and 15 internal groups. In particular, we found 11 previously undescribed sub-lineages and 5 internal groups that expand our understanding of previously characterized sub-lineages (e.g., 4.2.2; 4.2 in the Coll et al. classification) or that were not characterized since these isolates were simply classified as L4 (e.g., 4.11, Fig. 4, and Supplementary Fig. 6) using the other barcodes.Fig. 3: Phylogenetic tree reconstruction of lineage 2 (binary tree).Gray circles define splits where the FST (fixation index) calculated using the descendants of the two children nodes is greater than 0.33. The sub-lineages are defined by colored areas (blue: sub-lineages already described in the literature; green: sub-lineages described here; purple: internal sub-lineages). Source data are provided as a Source Data file.Full size imageFig. 4: Phylogenetic tree reconstruction of lineage 4 (binary tree).Gray circles define splits where the FST (fixation index) calculated using the descendants of the two children nodes is greater than 0.33. The sub-lineages are defined by colored areas (blue: sub-lineages already described in the literature; green: sub-lineages described here; purple: internal sub-lineages). Source data are provided as a Source Data file.Full size imageA new barcode to define L1–4 Mtb sub-lineages and a software package to type Mtb strains from WGS dataWe defined a SNS barcode for distinguishing the obtained sub-lineages (Supplementary Data 3). We characterized new synonymous SNSs found in 100% of isolates from a given sub-lineage, but not in other isolates from the same major lineage, compiling 95 SNSs into an expanded barcode (Supplementary Data 3). We validated the barcode by using it to call sub-lineages in the hold-out set of 4645 resistant isolates and comparing the resulting sub-lineage designations with maximum-likelihood phylogenies inferred from the full SNS data (Supplementary Figs. 7–10). A sub-lineage was validated if it was found in the hold-out data and formed a monophyletic group in the phylogeny. Considering the “recent” sub-lineages, i.e., the most detailed level of classification in our system, we were able to validate eight out of nine L1 sub-lineages including five out of six of the new sub-lineages described here, with the exception of 1.1.1.2. We validated all four new L3 sub-lineages, all five L2 sub-lineages including the one previously undescribed, and 16 of the 21 L4 sub-lineages including two described here. The sub-lineages we could not confirm were not represented by any isolate in the validation phylogenies. We did not observe any paraphyletic sub-lineages in the revised classification system.We developed fast-lineage-caller, a software tool that classifies Mtb genomes using the SNS barcode proposed above. For a given genome, it returns the corresponding sub-lineage as output using our hierarchical naming system in addition to four other existing numerical/semantic naming systems, when applicable (see “Methods” section). The tool also informs the user on how many SNSs support a given lineage call and allows for filtering of low-quality variants. The tool is generalizable and can manage additional barcodes defined by the user to type the core genome of potentially any bacterial species.Geographic distribution of the Mtb sub-lineagesNext, we examined whether certain sub-lineages were geographically restricted, which would support the Mtb-human co-evolution hypothesis, or whether they constituted prevalent circulating sub-lineages in several different countries (i.e., geographically unrestricted)8. We used our SNS barcode to determine the sub-lineages of 17,432 isolates (see “Methods” section) sampled from 74 countries (Supplementary Fig. 11 and Supplementary Data 4, 5). We computed the Simpson diversity index (Sdi) as a measure of geographic diversity that controls for variable sub-lineage frequency (see “Methods” section) for each well-represented sub-lineage or internal group (n  > 20). We hypothesized that geographically unrestricted lineages would have a higher Sdi. We found Sdi to correlate highly (⍴ = 0.68; p-value = 5.7 × 10−7) with the number of continents from which a given sub-lineage was isolated (Supplementary Fig. 12). The Sdi ranged between a minimum of 0.05 and a maximum of 0.72, with a median value of 0.46 (Fig. 5). The known geographically restricted sub-lineages8 had an Sdi between 0.28 and 0.5 (Fig. 5 and Supplementary Table 1), while the known geographically unrestricted sub-lineages8,9 had an Sdi between 0.55 and 0.61 (Fig. 5 and Supplementary Table 2). We found 11 sub-lineages/internal groups with Sdi 0.61 (Supplementary Table 4), i.e., more extreme than previously reported geographically restricted or unrestricted sub-lineages, respectively.Fig. 5: Histogram of the Simpson diversity index calculated for sub-lineages of lineages 1–4.A data set of 17,432 isolates from 74 countries was used to perform this analysis. Yellow triangles designate the Simpson diversity index values of sub-lineages designated as geographically restricted by Stucki et al. Light gray circles designate the Simpson diversity index values of sub-lineages designated as geographically unrestricted by Stucki et al. Source data are provided as a Source Data file.Full size imageWhile the currently known geographically restricted sub-lineages are all in L4, we found evidence of geographic restriction for two sub-lineages/internal groups of L1. The first, the L1 internal group 1.1.3.i1, showed a very low Sdi (0.06) and was only found at high frequency among the circulating L1 isolates in Malawi (Fig. 6). This finding is also in agreement with the L1 phylogeny (Fig. 1) that shows a relatively long (82 SNS) branch defining this group. The second geographically restricted L1 sub-lineage is 1.1.1.1 (Sdi = 0.12) that was only found at high frequency among circulating L1 isolates in South-East Asia (Vietnam and Thailand, Fig. 7). To exclude the possibility that these two groups appeared geographically restricted as a result of oversampling transmission outbreaks, we calculated the distribution of the pairwise SNS distance for each of these two sub-lineages. We measured a median SNS distance of 204 and 401, respectively, refuting this kind of sampling error for these groups (typical pairwise SNS distance in outbreaks 0.67 and results on L4 transmissibility below.Differences in transmissibility between the Mtb global lineagesThe observation that some lineages/sub-lineages are more geographically widespread than others raises the question of whether this results from differences in marginal transmissibility across human populations. On a topological level, we observed L2 and L3 phylogenies to be qualitatively different from those of L1 and L4 (Figs. 1–4): displaying a star-like pattern with shorter internal branches and longer branches near the termini. We confirmed this quantitatively by generating a single phylogenetic tree for all 9584 L1–4 isolates and plotting cumulative branch lengths from root to tip for each main lineage (Supplementary Fig. 20). Star-like topologies have been postulated to associate with rapid or effective viral or bacterial transmission e.g., a “super-spreading” event in outbreak contexts25. To compare transmissibility between the four lineages, we compared the distributions of terminal branch lengths expecting a skew toward shorter terminal branch lengths supporting the idea of higher transmissibility. We found L4 to have the shortest median terminal branch length, followed in order by L2, L3, and L1 (medians: 6.2 × 10−5, 8.2 × 10−5, 10.2 × 10−5, 17.5 × 10−5, respectively; all pairwise two-sided Wilcoxon rank-sum tests significant p-value < 0.001; Fig. 9). Shorter internal node-to-tip distance is a second phylogenetic correlate of transmissibility; the distribution of this measure across the four lineages revealed a similar hierarchy to the terminal branch length distribution (Supplementary Fig. 21). We also computed the cumulative distribution of isolates separated by increasing total pairwise SNS distance (Supplementary Fig. 22). The proportion of L4 isolates separated by More

Review of C–R stability theoryTo set the context for how the R–N module will be used to understand the dynamics of nutrient-limited ecosystem models, we first briefly review stability results from modular food web theory. We do this by laying out a set of examples that serve to illustrate that in general, strong C–R interactions promote oscillatory dynamics while carefully placed weak C–R interactions dampen them5. We begin with the Rosenzweig–MacArthur C–R system as our base C–R module (Fig. 1a). It is biologically supported and produces a range of biologically plausible dynamics5, making it an appropriate system for this analysis. It exhibits three different dynamical phases over a gradient of interaction strengths (energetically defined sensu Nilsson et al. 2018) such that increasing the attack rate (({a}_{{CR}})) increases interaction strength15 (Fig. 2). We use the return time after a small perturbation (i.e., eigenvalues) to highlight the natural stability trade-off that occurs as interaction strength is changed, (i.e., the “checkmark” stability pattern)5,6. Equations and parameters can be found in Supplementary Results 1A. We draw your attention to three notable dynamical phases of the C–R module. At low interaction strengths the dominant eigenvalue (({lambda }_{{max }})) is negative and real and the C–R module follows a monotonic return to a stable equilibrium (Fig. 2a). During this phase ({lambda }_{{max }}) decreases from 0 (i.e., where ({a}_{{{CR}}}) allows the consumer to persist) to more negative values and thus stronger interactions tend to increase stability (Fig. 2d, i). At moderate interaction strengths, there is a sudden shift to eigenvalues with a non-zero complex part and population dynamics overshoot the equilibrium (Fig. 2b). Increases in interaction strength then further excite population dynamics and we observe less stable dynamics across this phase (Fig. 2d, ii). Last, the system reaches a Hopf bifurcation where the dominant eigenvalue becomes positive, yielding sustained cycles or oscillations (Fig. 2c, d, iii). As interaction strength increases across this phase, it is difficult to determine stability from the magnitude of a positive dominant eigenvalue; however, destabilization with increased interaction strength is readily observed in that the cycles become increasingly larger oscillations with a high coefficient of variation (CV)5. Note that while the Rosenzweig–MacArthur C–R system is shown here under a single set of parameters, analysis of the Jacobian shows the qualitative results to be general5. Moreover, the qualitative stability pattern remains for a type I and type III functional response5.Fig. 1: C–R and R–N base modules.a Rosenzweig–MacArthur C–R module modelled with Holling type II functional response and logistic resource growth, where (R) is resource biomass and (C) is consumer biomass. Parameters: (r) is the intrinsic growth rate of (R), (K) is the carrying capacity of (R), ({a}_{{mathrm {CR}}}) is the attack rate of (C) on (R), (e) is the assimilation rate of (C), ({R}_{0}) is the half-saturation density of (C), ({m}_{R}) and ({m}_{C}) are the mortality rates of (R) and (C), respectively. b R–N module modelled with a Monod nutrient uptake equation and external nutrient input, where (N) is a limiting-nutrient pool and (R) is the resource biomass. Parameters: ({I}_{N}) is external nutrient input to (N), ({a}_{{RN}}) is nutrient uptake rate by (R), (k) is the half-saturation density of (R), ({l}_{N}) and ({l}_{R}) are nutrient loss rates from (N) and (R), respectively.Full size imageFig. 2: C–R checkmark stability response.d Local stability (real and complex parts of the dominant eigenvalue; ({lambda }_{{max }})) as a function of interaction strength (({a}_{{{mathrm {CR}}}})) for the Rosenzweig–MacArthur C–R module. Time series reflect dynamics associated with region i, ii, and iii, respectively, following a perturbation that removes 50% of consumer biomass: a Stable equilibrium; monotonic dynamics. b Stable equilibrium; overshoot dynamics. c Unstable equilibrium; limit cycle. Boldness of arrows indicates the strength of interaction (({a}_{{CR}})).Full size imageWe now couple C–R modules into higher order food web modules to demonstrate how the addition of weak and/or strong interactions to a system can be used to predict dynamics at steady state (Fig. 3), constituting the “algebra” of C–R modules. Equations and parameters can be found in Supplementary Results 1B–D. We start with the three trophic level food chain (Fig. 3a), consisting of two coupled C–R modules (i.e., C1-R and P–C1). Theory has tended to find two weakly interacting C–R modules to generally produce locally stable equilibria16 (Fig. 3a). Increasing the strength of the C1–R interaction causes it to act like an oscillator (see Fig. 2c, above), and with enough increase this underlying oscillation is reflected in the limit cycles of the entire food chain (Fig. 3b). If the P–C1 interaction is strengthened as well, we end up with two coupled oscillators—the recipe for chaos17,18 (Fig. 3c). As such, coupled strong interactions are not surprisingly the recipe for complex and highly unstable dynamics.Fig. 3: Algebra of C–R modules.Time series showing the general dynamical outcomes for the food chain and diamond module at steady state with varied combinations of C–R interaction strengths. a Weak–weak interaction; point attractor. b Strong–weak interaction; limit cycle. c Strong–strong interaction; chaos. d Strong–strong, weak interaction; limit cycle. e Strong–strong, weak–weak interaction; point attractor.Full size imageFollowing McCann et al.19, we now add a weakly coupled consumer C2 to the food chain system of Fig. 3c. This weak consumer essentially draws energy away from the strong P–C1–R pathway and in doing so partially mutes the coupled oscillators, bringing the dynamics back to a more even limit cycle (Fig. 3d) and under certain conditions can drive equilibrium dynamics19. Last, the predator is weakly coupled to C2, creating a strong and weak pathway. The second weak interaction further draws energy away from the strong pathway, muting the oscillators entirely and bringing the system in this example to a point attractor (Fig. 3e). These examples show that well placed weak interactions (i.e., non-oscillatory phases, Fig. 2a, b) can be used to draw energy away from strong pathways and act as potent stabilizers of potentially oscillatory pathways. Note that weak interactions play a similarly stabilizing role in the omnivory module20 and further, weak interactions have been shown to stabilize large food web networks4,6 suggesting the principles derived from modular theory scale up to whole systems. Taken altogether, the oscillatory nature of strong C–R interactions generally promotes oscillatory dynamics in higher order systems, while the careful placement of weak C–R interactions—which are monotonic in nature—act to dampen oscillations. Although not discussed to our knowledge, we conjecture that if a subsystem exists such that strong interactions lead to monotonic dynamics (i.e., without oscillatory decay), strong interactions in this case would serve as a potent stabilizer. Below, we show the R–N module appears to be such a case.R–N module and stabilityTowards understanding how the R–N subsystem may interact in a higher order system, we first briefly consider the stability of the R–N module alone (akin to what we discussed for the C–R module above). The R–N module consists of a resource that takes up nutrients according to a Monod-like growth term, is open to flows from the external environment as a result of geochemical processes, and nutrients are lost to the external environment according to a linear term11 (Fig. 1b). Performing a local stability analysis about the interior equilibrium reveals the R–N module to be locally stable for all biologically feasible parameterizations, as determined by the signs of the trace and determinant of the Jacobian matrix (see Supplementary Results 2B). We now perform further numerical and analytical analyses to understand how stability is influenced by interaction strength.As the maximum rate of nutrient uptake (({a}_{{RN}})) is increased (i.e., R–N interaction strength), stability is generally increased (Fig. 4d), with the real part of the dominant eigenvalue (({lambda }_{{max }})) tending from 0 (i.e., where ({a}_{{RN}}) allows the resource to persist) towards an asymptote of ({-l}_{R}) (see Supplementary Results 2C). Numerical analysis reveals that the asymptote at ({-l}_{R}) can be approached from above or below depending on the relative leakiness of the R and N compartments (i.e., the rate at which nutrients are lost to the external environment from compartment R (({l}_{R})) and N (({l}_{N}))). For ({l}_{N} , > , {l}_{R}) (Fig. 4d), the R–N module only follows a monotonic return to equilibrium as interaction strength is increased, with increased interaction strength only tending to increased stability (i.e., reduce return time). For ({l}_{N} < {l}_{R}) (Fig. 4d), the R–N module follows a monotonic return to equilibrium for weak (Fig. 4a) and strong (Fig. 4c) interaction strength, but modest overshoot dynamics are observed for intermediate interaction strength (Fig. 4b). Stability tended to increase with interaction strength for weak to intermediate interaction strength (i.e., dominant eigenvalue becomes more negative), then slightly decrease as interaction strength became strong. A special case exists when ({l}_{R}={l}_{N}) (Fig. 4d), where stability increases with interaction strength until ({lambda }_{{max }}) becomes locked in at ({-l}_{R}), indicating stability does not change regardless of any further increase in interaction strength. Overall, the R–N interaction tends to generally stabilize in all cases (dominant eigenvalue goes from zero to a more negative saturating value with monotonic dynamics), although there are some intermediate cases that produce complex eigenvalues that suggest population dynamic overshoot potential (Fig. 4b). Note that we obtain qualitatively similar results when implicitly strengthening the R–N interaction by increasing nutrient loading (see Supplementary Results 2D and Supplementary Fig. 1). Now, given the above framework for coupled C–R modules—where weak C–R interactions with underlying monotonic dynamics dampen the oscillatory potential of strong C–R interactions—the underlying monotonic dynamics of the R–N module suggest that R–N interactions ought to be stabilizing when coupled to strong C–R interactions. Further, the underlying increase in stability (i.e., more rapid return to equilibrium) as R–N interaction strength is increased suggests the stabilizing potential of the R–N module ought to increase as the interaction becomes stronger.Fig. 4: R–N stability response to increasing interaction strength.Time series showing R density following a perturbation that lowered R density to 50% of equilibrium density for a low (({a}_{{RN}}=0.8)), b intermediate (({a}_{{RN}}=1)), and c high maximum rate of nutrient uptake (({a}_{{RN}}=2.8)). d Local stability (dominant eigenvalue; ({lambda }_{{max }})) of the R–N subsystem as ({a}_{{RN}}) is increased for ({l}_{N} , > , {l}_{R}), ({l}_{N}={l}_{R}), and ({l}_{N} < {l}_{R}), where ({l}_{R}) and ({l}_{N}) are the rate at which nutrients are lost to the external environment from compartment R and N, respectively. Solid lines are real parts and dashed lines are complex parts of ({lambda }_{{max }}).Full size imageTo look into this conjecture, we first coupled R–N to multiple configurations of strong and expectantly oscillatory C–R interactions and increased R–N interaction strength (({a}_{{RN}})). Following this, we added nutrient cycling and repeated the experiment to demonstrate that our results can be generalized to nutrient-limited ecosystem models. The full equations and parameter values for each model are listed in Supplementary Results 3A–D and 4A, B. We begin with the C–R–N system, where C–R and R–N are coupled through R (Fig. 5a). The initial increase in ({a}_{{RN}}) implicitly strengthens the C–R interaction and fuels the oscillatory potential of C–R and cycles emerge almost immediately after C is able to persist. As ({a}_{{RN}}) is increased further the cycles disappear and we obverse a steep stabilization phase, followed by a modest period of destabilization. Adding a weakly coupled predator gives a similar outcome, although the system continually stabilizes as ({a}_{{RN}}) is increased (Fig. 5b). If the P–C interaction is strengthened (i.e., both C1–R and P–C1 are strong, the recipe for chaos), R–N is unable to dampen oscillations even with a strong interaction strength, although a strong interaction gives tighter bound cycles than a weak interaction (Fig. 5c). We next add a weakly coupled consumer to the nutrient-limited food chain with strong P–C1 and C1–R interactions (Fig. 5d). As seen previously, this interaction draws energy out of the strong pathway, partially muting oscillatory potential. Thus, the ability for a strong R–N interaction to once again return the system to a stable equilibrium is not surprising. Finally, we add a detrital compartment to show that strong R–N interactions remain potent stabilizers in the context of nutrient cycling (Fig. 6b) when compared to a nutrient-limited food chain without nutrient cycling (Fig. 6a).Fig. 5: Nutrient-limited food chain stability.a–d Non-equilibrium dynamics (log10(C1,max/C1,min)) and equilibrium stability (real part of the dominant eigenvalue; ({lambda }_{{max }})) of the C–R–N, P–C–R–N with a single oscillator, P–C–R–N with coupled oscillators, and P–C1–C2–R–N modules, respectively, as ({a}_{{RN}}) is varied.Full size imageFig. 6: Nutrient-limited ecosystem module stability.a, b Non-equilibrium dynamics (log10(Cmax/Cmin)) and equilibrium stability (real part of the dominant eigenvalue; ({lambda }_{{max }})) of the C–R–N nutrient-limited food chain model and the C–R–N–D nutrient-limited ecosystem model, respectively, as ({a}_{{RN}}) is varied.Full size imageNote that we repeat our analysis of higher order modules by implicitly increasing R–N interaction strength through nutrient loading (see Supplementary Results 3E and 4C and Supplementary Figs. 2 and 3). In all cases, increased nutrient loading led to less stable dynamics, consistent with DeAngelis’ (1992) paradox of enrichment finding where increased nutrient loading lead to destabilizing autotroph–herbivore oscillations. More

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COMMENT
20 October 2021

Congo Basin rainforest — invest US$150 million in science

The world’s second-largest rainforest is key to limiting climate change — it needs urgent study and protection.

Lee J. T. White

0
,

Eve Bazaiba Masudi

1
,

Jules Doret Ndongo

2
,

Rosalie Matondo

3
,

Arlette Soudan-Nonault

4
,

Alfred Ngomanda

5
,

Ifo Suspense Averti

6
,

Corneille E. N. Ewango

7
,

Bonaventure Sonké

8
&

Simon L. Lewis

9

Lee J. T. White

Lee J. T. White is Minister of Water, Forests, Oceans, Environment, Climate Change and Land-use Planning, Gabonese Republic.

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Eve Bazaiba Masudi

Eve Bazaiba Masudi is Deputy Prime Minister and Minister of Environment and Sustainable Development, Democratic Republic of the Congo.

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Jules Doret Ndongo

Jules Doret Ndongo is Minister of Forestry and Wildlife, Republic of Cameroon.

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Rosalie Matondo

Rosalie Matondo is Minister of Forest Economy, Republic of the Congo.

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Arlette Soudan-Nonault

Arlette Soudan-Nonault is Minister of Environment, Sustainable Development and the Congo Basin, Republic of the Congo.

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Alfred Ngomanda

Alfred Ngomanda is director of the National Centre for Scientific Research and Technology (CENAREST), Gabonese Republic.

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Ifo Suspense Averti

Ifo Suspense Averti is an associate professor in tropical forest ecology at Marian Ngouabi University, Republic of the Congo.

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Corneille E. N. Ewango

Corneille E. N. Ewango is a professor of tropical forest ecology and management at the University of Kisangani, Democratic Republic of the Congo.

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Bonaventure Sonké

Bonaventure Sonké is a professor of plant systematics and ecology at the University of Yaounde I, Republic of Cameroon.

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Simon L. Lewis

Simon L. Lewis is professor of global change science at University College London and the University of Leeds, UK.

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A warden with an orphaned mountain gorilla in the Virunga National Park sanctuary in the Democratic Republic of the Congo.Credit: Phil Moore/AFP/Getty

Earth’s second-largest expanse of tropical forest lies in central Africa, in the Congo Basin. The region supports the livelihoods of 80 million people. The rainfall that the forest generates as far away as the Sahel and the Ethiopian highlands supports a further 300 million rural Africans. These forests are crucial to regulating Earth’s climate, and are home to forest elephants, gorillas and humans’ closest relatives, chimpanzees and bonobos.Such services to people and the planet are not guaranteed, given rapid climate change and ongoing development in the region. The forest’s ability to absorb carbon dioxide is slowing as temperatures rise1. Deforestation, although lower than elsewhere in the tropics over recent decades, has led to the loss of more than 500,000 hectares of forest in 2019 alone (see go.nature.com/3dnxm9e). Without new policies, this is expected to increase.Yet, too often, central Africa’s rainforests are ignored or downplayed. The Congo Basin forests receive much less academic and public attention than do those in the Amazon and southeast Asia. Between 2008 and 2017, the Congo Basin received just 11.5% of international financial flows for forest protection and sustainable management in tropical areas, compared with 55% for southeast Asia and 34% for the Amazon region2.The area is neglected even by comparison with the rest of Africa. For example, a key UK-funded programme of climate research, called Future Climate for Africa, invested £20 million (US$27 million) in modelling and four projects focused on eastern, western and southern Africa. None focused on the Congo Basin or central Africa.The result of this neglect is clear in high-level climate assessments. Central Africa was one of only two regions worldwide without enough data for the Intergovernmental Panel on Climate Change to assess past trends in extreme heat in its 2021 Working Group I report (the other was the southern tip of South America).
A collaborative look at the Congo Basin
We are a group of ministers who have responsibility for forests in the region, and scientists who work on the ground and advise governments. Together we call for a Congo Basin Climate Science Initiative. This should comprise a $100-million, decade-long programme of research, tied to a separate $50-million fund to train Congo Basin nationals to become PhD-level scientists. Such funding would transform our understanding of these majestic forests, providing crucial input for policymakers to help them enact policies to avoid the region’s looming environmental crises.There is precedent for such a transformation. In the mid-1990s, rainforest science in the Amazon region was limited and was largely conducted by overseas scientists. Formally beginning in 1998 and led by Brazilians, the Large-Scale Biosphere-Atmosphere Experiment in Amazonia programme, known as the LBA, was a 10-year, $100-million effort. It revolutionized understanding of the Amazon rainforest and its role in the Earth system.The LBA involved 6 years of intensive measurements and covered climatology, hydrology, ecology and biogeochemistry across an area of 550 million hectares. It comprised 120 projects and 1,700 participants, 990 of whom were Brazilians3. One of its greatest legacies was the creation of a new cadre of Brazilian researchers. Two decades on, Brazil is now widely acknowledged as the world’s leading nation for tropical forest monitoring, and is at the forefront of rainforest science.We should — we must — do the same for central Africa.Known unknownsThe Greater Congo Basin covers some 240 million hectares of contiguous forests, straddling 8 nations (see ‘Earth’s second green lung’). Merely sampling this vast area is daunting. Access often requires days of travel in dugout canoes and long treks through the humid jungle, punctuated by wading through swamps. There is also a pervasive prejudice: too many people think working in the Congo Basin region is perilous, whether the hazards are political instability, unfamiliar diseases or dangerous animals. In reality, for the vast majority of central Africa, the risks are similar to working in the Amazon rainforest or east African savannah ecosystems.

Source: Ref. 1

These various challenges can be surmounted. Papers from the past few years, co-authored by many of us, highlight how important and understudied the region is. In 2017, the world’s largest tropical peatland complex was mapped for the first time — an area spanning 14.6 million hectares in the heart of the Congo basin4. This work radically shifted our understanding of carbon stores in the region. In March 2020, an international consortium showed that Africa’s rainforests annually absorb the same amount of carbon1 as was emitted each year by fossil-fuel use across the entire African continent in the 2010s5.In December 2020, a striking 81% decline in fruit production over 3 decades in an area of forest in Gabon was shown to coincide with climate warming and an 11% decline in the body condition of forest elephants (they rely on fruit for part of their diet)6. And in April, the first region-wide assessment of tree community composition in central Africa was published7, mapping areas that are vulnerable to climate change and human pressures.
Biodiversity needs every tool in the box: use OECMs
Overall, the strikingly recent (although somewhat limited) data suggest that the tropical forests of the Congo Basin are more carbon-dense8, more efficient at slowing climate change1 and more resistant to our changing climate9 than are Amazon tropical forests. But we do not know how increasing droughts, higher temperatures, selective logging and deforestation might interact — including the possibility of reduced rainfall in the Sahel10 and Ethiopian highlands11. Some 2,500 years ago, vast swathes of the Congo Basin forests were lost during a period of climate stress, but researchers do not understand the historic context of that event, nor the likelihood of a repeat12.Little is known about the region because not enough science is done in central Africa. Remarkably, researchers still do not understand the basic principles of why different types of forest occur where they do in the Congo Basin. Climate models for this region are poor, both because of the complex interplay of Atlantic, Indian and Southern ocean influences and because of a lack of local climate data. Without more data and more specialists, it is impossible to make reliable predictions of these forests’ responses to changes in climate and land use.Next stepsInvestment in basic science is urgently needed to fill these gaps. A Congo Basin Climate Science Initiative should focus on three important overarching questions: how does the Congo Basin currently operate as an integrated system? How will changes in land use and climate affect its function? And how sustainable are different options for development?Within these broad topics are more specific questions that politicians will need answers to if nations are to achieve net-zero CO2 emissions by 2050. One such question is how much carbon is stored in vegetation and soils. These and other quantities must be reported as part of countries’ commitments to the 2015 Paris climate agreement. At present, most central African countries rely on default values, which could be way off the mark. A recent paper13 on African montane forests largely near the edges of the basin, for example, showed that measured carbon storage values were 67% higher than the default values.

A child on the Mongala River in the dense forest of the Democratic Republic of the Congo.Credit: Pascal Maitre/Panos

A science initiative will work only if there is enthusiasm and leadership from researchers and active support from key Congo Basin countries, alongside buy-in from funders. We envision three steps to achieve these aims.First, scientists from the Congo region should hold a workshop with the LBA architects and participants to assess lessons from the Amazon region. This south–south cooperation would build a scientist-led framework to address the crucial research questions.Second, a meeting of politicians and advisers from the region would facilitate discussions of the policy-relevant questions that scientists should investigate. This would be led by Cameroon, the Democratic Republic of the Congo, Gabon and the Republic of the Congo — the four nations conducting the most research in the region. The meeting will help to lock in political support across ministries responsible for forests, environment, water, climate, science and universities.
Nature-based solutions can help cool the planet — if we act now
Third, partners will need to develop an overarching science programme that is acceptable to funders. Such a programme will probably include scaling up many efforts that are already under way, but which are currently insufficient in scope or unreliably funded. This would speed up scientific progress.For example, a handful of established, long-term field sites already exist in the Greater Congo Basin, including in Lopé National Park in Gabon and in the Yangambi Biosphere Reserve in the Democratic Republic of the Congo. These ‘supersites’ are sophisticated field stations with full-time staff who collect reliable, long-term data sets on vegetation, animals and the physical environment, including greenhouse-gas fluxes at Yangambi. But the sites are too few in number, and they rely on the heroic efforts of local champions. There should be a dozen or so locations across the region, with consistent funding to support complex research projects.Similarly, the African Tropical Rainforest Observation Network (AfriTRON), established in 2009, tracks every tree in permanent sample plots to estimate the carbon balance of undisturbed forests. Although this observatory has ramped up from its original 40 sites in central Africa to more than 200 today, these cover just 250 hectares of the roughly 240-million-hectare total. That is very sparse sampling from which to draw regional conclusions.Meanwhile, the Forest Global Earth Observatory (ForestGEO), established in 1990 to understand how tropical forests maintain such a diverse number of tree species, has established just 4 sites in central Africa in 30 years, with none in the centre of the basin. There is an obvious need for expansion.

African forest elephants in Ivindo National Park, Gabon.Credit: Amaury Hauchard/AFP/Getty

Finally, the 2016 AfriSAR airborne field campaign, a collaboration between NASA, the European Space Agency and the Gabonese Agency for Space Studies and Observation, showed how to combine different data sets to carefully map forest types and their carbon stocks in Lopé National Park in Gabon. This model could be replicated elsewhere in the basin.All of this work will require linking theory, observations, experiments and modelling. It should attract a diversity of leading international experts to focus on Africa and provide training to Congo Basin nationals. A $100-million research programme would provide new opportunities and much-needed career options for African scientists. The tied investment of $50 million, focused on building talent, could produce approximately 200 PhDs awarded by leading universities worldwide. This would create a new generation of scientists, including future leaders, from central Africa. The training programme would ensure the necessary step-change in science capacity, and provide opportunities for young African researchers who currently find it hard to compete for international scholarships, which are often won by students from Asia or South America.Agreeing on open access for all the data collected, as in the LBA programme, will significantly boost the initiative’s science impact.Money well spentThis $150-million science programme over 10 years needs investors. One option would be to combine funds from governments that have made large forest- and science-related investments in the Congo Basin in the past, notably Belgium, France, Germany, Norway, the United Kingdom, the United States and the European Union. Alternatives include United Nations agencies, international climate funds and private philanthropy organizations. Such a programme should be high on funders’ agendas, given the UN Sustainable Development Goals (SDGs). These include raising capacity for effective climate-change-related planning and management (SDG13), increasing financial resources to conserve and sustainably use biodiversity and ecosystems (SDG15), boosting the number of researchers in lower-income countries, and increasing research and development (R&D) funding (SDG9), all before 2030.Global R&D funding was $2.2 trillion in 201914. Thus, investing $150 million over a decade to better understand and protect the world’s second-largest extent of tropical forest is modest. To put this sum in context, the US government’s total projected cost for the Human Genome Project was $2.7 billion, and the European Space Agency spends approximately $500 million on its larger, long-lasting scientific satellites. The $100 million that the LBA brought to the Amazon in the 1990s is equivalent to about $160 million in today’s terms.
Ethiopia, Somalia and Kenya face devastating drought
The investment in science will pay for itself many times over. Consider just the role of forests as reservoirs of zoonotic diseases. Better forest management lowers the risk of disease outbreaks, let alone a pandemic15.Critics might argue that direct interventions in development aid are more urgent than investing in climate and ecological science. However, these funds are usually independent and do not compete. Furthermore, the old division between ending poverty and protecting the environment no longer applies: Africans will suffer disproportionately if temperatures are not limited as per the Paris agreement. That must include protection of the forests of the Congo Basin.Further efforts could help to support the goals of the Congo Basin science programme. For example, there is a lack of economic models that show how standing forests can become more valuable than converted landscapes. Developing these would support policy decisions to maintain forest cover.There are also several efforts under way to improve forest management that aim to empower local people, increase income and protect the environment. These include the transfer of land-management decisions to local populations, such as through community forestry, and creating high-value end products from selective logging rather than relying on the export of raw, unprocessed timber. A new science initiative could assess various approaches to understand what works best.We know so little about the majestic forests of central Africa. A Congo Basin Climate Science Initiative would curb our collective ignorance. A lack of investment is the barrier to safeguarding these precious ecosystems. Surmount this, and the future of Earth’s second ‘great green lung’ will be brighter.

Nature 598, 411-414 (2021)
doi: https://doi.org/10.1038/d41586-021-02818-7

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Competing Interests
L.J.T.W. (Gabon), E.B.M. (Democratic Republic of the Congo), J.D.N. (Cameroon), R.M. (Republic of the Congo) and A.S-N. (Republic of the Congo) are ministers of forests and/or the environment. Their countries stand to benefit if international donors take on board the recommendations in this Comment article.

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Rhizosphere soil samplingA total of 20 rhizosphere soil samples (20 tomato plants) were collected at the flowering stage from a tomato field located in Qilin town, Jiangsu province, China, 118°57’ E, 32°03’ N, which had been infested by the pathogen Ralstonia solanacearum for more than 15 years [8]. After uprooting plants, excess soil was first gently shaken from the roots, and the remaining soil attached to roots was considered as rhizosphere soil. Each rhizosphere soil sample was then used for bacterial strain isolation.Isolation and identification of rhizobacteriaIsolationA total of 640 bacterial strains were isolated from the fresh rhizosphere soil samples, according to a previously established protocol [11]. Briefly, 1 g of each rhizosphere sample was mixed with 9 mL MS buffer solution (50 mM Tris-HCl [pH 7.5], 100 mM NaCl, 10 mM MgSO4, 0.01% gelatin) in a rotary shaker at 170 rpm min−1 for 30 min at 30 °C. After serial dilution in MS buffer solution, 100-μl volumes of the diluted soil suspensions were plated on 1/10 tryptone soy agar (1/10 TSA, 1.5 g L−1 tryptone, 0.5 g L−1 soytone, 0.5 g L−1 sodium chloride, and 15 g L−1 agar, pH 7.0). After a 48-h incubation at 30 °C in the dark, 32 isolates were randomly picked per rhizosphere soil sample. To avoid potential fungal contamination, only highly diluted samples were used for isolation. The isolates were then re-streaked on TSA plates for colony purification. Approximately 5.5% (35 isolates) of the bacterial isolates failed to grow on the TSA plates for unknown reasons when we re-streaked them and were therefore omitted from the dataset. The final collection thus consisted of 605 bacterial isolates derived from 20 rhizosphere soil samples. All purified isolates were cultured in 100 μl tryptone soy broth (TSB, liquid TSA) in 96-well microtiter plates at 30 °C with shaking (rotary shaker at 170 rpm) for 18 h before freezing and storing at −80 °C in 15% glycerol.Strain identificationWe sequenced the full 16 S rRNA gene to taxonomically identify all 605 rhizobacterial isolates. The 16 S rRNA gene was sequenced via Sanger sequencing of PCR products from glycerol stocks by Shaihai Songon Biotechnology Co., Ltd, Shaihai Station. The PCR system (25 µl) was composed of 1 µl of bacterial cells (overnight culture), 12.5 µl mixture, 1 µl of forward (27 F: 5-AGA GTT TGA TCA TGG CTC AG-3) and reverse primer (1492 R: 5-TAC GGT TAC CTT GTT ACG ACT T-3) each [17] and 9.5 µl of sterilized water. PCR was performed by initially denaturizing at 95 °C for 5 min, cycling 30 times with a 30-s denaturizing step at 94 °C, annealing at 58 °C for 30 s, extension at 72 °C for 1 min 30 s, and a final extension at 72 °C for 10 min. The 16 S rRNA gene sequences were identified using NCBI databases and homologous sequence similarity. A total of 90 bacterial isolates that were identified as Ralstonia solanacearum were removed from further analyses, resulting in 515 remaining isolates.Direct effect of rhizobacteria on pathogen growth in vitroWe used R. solanacearum strain QL-Rs1115 tagged with the pYC12-mCherry plasmid as a model bacterial pathogen [8, 18]. We first tested the direct effects of the 515 non-R. solanacearum bacterial strains on the growth of R. solanacearum in vitro by using supernatant assays. Briefly, after 48 h of growth in NB (nutrient broth) medium (glucose 10.0 g l−1, tryptone 5.0 g l−1, yeast extract 0.5 g l−1, beef extract 3.0 g l−1, pH 7.0) on a shaker at 170 rpm, 30 °C, all bacterial cultures were filter sterilized to remove living cells (0.22 µm filter). Subsequently, 20 µl of sterile supernatant from each strain’s culture and 2 µl overnight culture of the pathogen (adjusted to OD600 = 0.5 after 12 h growth at 30 °C with shaking) were added into 180 µl of fresh NB medium (5-times diluted, in order to better reflect the effect of the supernatant). Control treatments were inoculated with 20 µl of 5 X diluted NB media instead of the bacterial supernatant. Each treatment was conducted in triplicate. All bacterial cultures were grown for 48 h at 30 °C with shaking (170 rpm) before measuring pathogen density as red mCherry protein fluorescence intensity (excitation: 587 nm, emission: 610 nm) [9, 11] which was linearly related to the CFU of pathogen R. solanacearum (Fig. S1). To test for significance of growth promotion or inhibition, R. solanacearum densities were log10-transformed prior to analyses of variance (ANOVA) and Bonferroni t test to compare mean differences between each rhizobacterial supernatant treatment and the control treatment, with p values less than 0.05 considered statistically significant. The effect on pathogen growth was defined as the percentage of improvement or reduction in pathogen growth by the supernatant compared to the control treatment. When the effect on pathogen growth was positive, i.e., when the supernatants from strains significantly promoted the growth of the pathogen, they were considered as helpers of the pathogen. If the effect on pathogen growth was negative, i.e., when the supernatants from strains significantly inhibited the growth of the pathogen, they were considered as inhibitors of the pathogen.Assessing strain redundancy among the 515 non-Ralstonia solanacearum bacteriaWe assessed possible redundancy among the 515 strains of the non-Ralstonia solanacearum rhizobacteria. To encompass both taxonomic and functional redundancies, we considered the 16 S rRNA gene sequences as well as the direct effect of their supernatant on Ralstonia solanacearum. Self BLAST searches were performed on the full 515 sequence dataset using the makeblastdb and blastn commands from the BLAST command line tool [19]. Sequences showing >99% identity over >95% of the full length of the 16 S rRNA gene were considered as taxonomically redundant. We then compared the direct effects on pathogen growth of the taxonomically redundant strains, and removed those showing the same patterns of interactions (positive, negative or neutral). Accordingly, (see the dataset “Library of rhizobacterial strains” in the supplementary information), 355 of the 515 strains (68.9%) were removed from the original dataset for further analyses.Phylogenetic tree constructionThe 16 S rRNA gene sequences of the 160 non-redundant bacteria were aligned using MUSCLE [20]. Sequences in the alignment were trimmed at both ends to obtain maximum overlap using the MEGA X software, which was also used to construct taxonomic cladograms [21]. We constructed a maximum-likelihood (ML) tree, using a General Time Reversible (GTR) + G + I model, which yielded the best fit to our data set. Bootstrapping was carried out with 100 replicates retaining gaps. A taxonomic cladogram was created using the EVOLVIEW web tool (https://evolgenius.info//evolview-v2/). To show the relationship between phylogeny and the effects of rhizobacteria on pathogen growth, we added taxonomic status (phylum) of each rhizobacterial strain and its effect on pathogen growth as heatmap rings to the outer circle of the tree separately (Fig. 2B).Fig. 2: Taxonomic characterization of rhizobacterial isolates that inhibited or helped the growth of Ralstonia solanacearum.A Distribution of in vitro effects of 160 rhizobacterial supernatants on R. solanacearum growth. The red vertical line represents no effect on R. solanacearum growth. B Cladogram depicting the phylogenetic relationship among the 160 isolates based on their full-length 16 S rRNA gene sequences. The inner ring depicts the different effect of isolates supernatant on R. solanacearum growth: positive effect (blue), negative effect (red) and no significant effect (gray). The outer ring shows the four phyla to which the isolates belong. C The proportion of rhizobacterial isolates per phylum whose supernatant showed inhibitory, stimulatory or no effect on R. solanacearum growth. The size of the circles represents the number of rhizobacterial isolates in the given phylum. The thickness of lines represents the percentage of rhizobacterial isolates that have the indicated effect on R. solanacearum growth in each phylum.Full size imageEffects of rhizobacteria on pathogen helper strains growth in vitroWe then assessed the potential of different rhizosphere isolates to inhibit helper strains. We first selected two model helper strains (Phyllobacterium ifriqiyense LM1 (Pi) and Microbacterium paraoxydans LM2 (Mp)), which showed strong positive effects on pathogen growth both in co-culture and in supernatant assays (Fig. S2). We defined the effect of rhizobacterial strains on the growth of helpers as the indirect effect on R. solanacearum growth. To study these indirect effects, we first chose a subset of 46 rhizobacterial strains representing a gradient of positive, neutral or negative effect on pathogen growth based on supernatant assays (results in x axis of Figs. 3C and 4A, B, C). We then tested the effects of these 46 rhizobacterial strains on the growth of each of the two helper strains using supernatant assays. Briefly, after 48 h growth in NB media, each of the 46 bacterial monocultures was passed through a 0.22 µm filter to remove living cells. Then 20 µl of sterile supernatant from each strain’s culture and 2 µl overnight culture of Pi or Mp (adjusted to OD600 = 0.5 after 12 h growth at 30 °C with shaking) were added into 180 µl of fresh NB medium (5-times diluted, in order to better reflect the effect of the supernatant). Control treatments were inoculated with 20 µl of 5× diluted NB media instead of a bacterial supernatant. Each treatment was replicated four times. All bacterial cultures were grown for 24 h at 30 °C with shaking (170 rpm) before measuring helper density as optical density (OD600). To test for significance of growth promotion or inhibition, we used analyses of variance (ANOVA) and Bonferroni t test to compare mean differences of helper density between each rhizobacterial supernatant treatment and the control treatment, with p values lower than 0.05 being considered statistically significant. The effect of rhizobacteria on the helpers’ growth (results in y axis of Fig. 3C and x axis of Fig. 4D, E, F) was defined as the percentage of increase or reduction in helper growth by the supernatant compared to the control treatment.Fig. 3: Effect of helper strains on Ralstonia solanacearum growth and plant disease severity.Effects of the two helper strains Phyllobacterium ifriqiyense (Pi) and Microbacterium paraoxydans (Mp) on Ralstonia solanacearum (Rs) growth in vitro (A) and in vivo (B) and on plant disease severity (C). Different letters indicate significant differences based on Tukey post hoc test. Error bars show ±1 SE (n = 3 for in vitro, n = 4 for in vivo). D Effects of 46 rhizobacterial strains on the growth of R. solanacearum and the two model helper strains in vitro. The x-axis shows the direct effect of each rhizobacterial strain on R. solanacearum growth (data from the experiment in which R. solanacearum was grown in the presence of supernatant from each of the 46 rhizobacterial strains—the same data is presented on the x axis of Fig. 4A). The y-axis shows the effect of each rhizobacterial strain on each of the two helper strains (data from the experiment in which each helper was grown in the presence of supernatant from each of the 46 rhizobacterial strains—the same data is presented on the x axis of Fig. 4C). In (C), “−1”, “0” and “1” on the x-axis denote that R. solanacearum growth is completely inhibited, not influenced or increased 2× by supernatant from the rhizobacteria, respectively. Similarly, “−1”, “0” and “1” on the y-axis denote the same growth effects with reference to growth of the helper strains. Black dots indicate results involving interactions with Pi, and red dots indicate results involving interactions with Mp.Full size imageFig. 4: The importance of direct versus indirect effects on Ralstonia solanacearum density and disease severity in the presence of helper strains.In the presence of helper Phyllobacterium ifriqiyense (Pi) or Microbacterium paraoxydans (Mp), respectively, the importance of direct effects on the density of R. solanacearum both (A) in vitro and (B) in vivo, and (C) disease severity (the data on the x axis of (A) are the same data which was presented on the x axis of Fig. 3C, the data on x axis of (B) and (C) are part of the data on x axis of (A)); the importance of indirect effects on the density of R. solanacearum both (D) in vitro and (E) in vivo, and (F) disease severity (the data on the x axis of (D) are the same data which was presented on the y axis of Fig. 3C, the data on x axis of (E) and (F) are part of the data on x axis of (D)). In all panels, “−1”, “0” and “1” on the x-axis denote that R. solanacearum growth (A, B, and C) or helper growth (D, E, and F) is completely inhibited, not influenced or increased 2× by supernatant from the rhizobacteria, respectively.Full size imageIn vitro pathogen growth in the presence of a helper strain and supernatant from rhizobacterial isolatesTo disentangle the direct effects from the indirect effects of rhizobacteria on R. solanacearum growth, we compared their relative effects using in vitro triculture assays comprised of R. solanacearum, one of the two helper strains and supernatant of one of the 46 chosen rhizobacterial strains. Briefly, after 48 h of growth in NB media, each of the 46 bacterial monocultures was passed through a 0.22 µm filter to remove living cells. Then, 20 µl of sterile supernatant from each strain’s culture and 2 µl overnight culture of Pi or Mp (densities were adjusted to ~107 cells per ml) were added to 180 µl of fresh NB medium (5-times diluted). Each treatment was replicated four times. At the same time, 2 µl overnight culture of mCherry-tagged R. solanacearum (density was adjusted to ~106 cells per ml) was added to each treatment in 96-well plates at 30 °C with shaking (170 rpm). After 24-h growth, R. solanacearum density (results in y axis of Fig. 4A, D) was measured as the red mCherry protein fluorescence intensity (excitation: 587 nm, emission: 610 nm) with a SpectraMax M5 plate reader.In vivo pathogen growth and plant disease development in the presence of a helper strain and a rhizobacterial strainTo validate in vitro results, we set up greenhouse experiments where plants were inoculated with a bacterial consortium consisting of R. solanacearum, one of the two helper strains and a test rhizobacterial strain. Tomato seeds (Lycopersicon esculentum, cultivar “Ai hong sheng”) were surface-sterilized by soaking them in 3% NaClO for 5 min and in 70% ethyl alcohol for 1 min before being germinated on water-agar plates for 2 days. Seeds were then sown into seedling trays containing gamma irradiation-sterilized (to avoid potential effects of the resident community) seedling substrate (Huainong, Huaian Soil and Fertilizer Institute). At the three-leaf stage, tomato plants were transplanted to seedling trays containing 200 g of the same seedling substrate as describe above.To relate our results to practical application conditions, we selected a subset of 12 strains that displayed a range of inhibitions effects on pathogen and helpers (Table S1) out of the 46 rhizobacterial isolates used for the in vitro assays. Each rhizobacterial strain was used in combination with each of the two helper strains and R. solanacearum, resulting in a total of 28 treatments (Table S2), including a water control, R. solanacearum alone, and R. solanacearum with just each of the two helper strains (results in Fig. 3B, C). For each treatment, four replicate seedling trays were used, with each replicate seedling tray containing 4 tomato plants. Three days after transplantation, plants of each treatment were inoculated with one of the two helper strains, alone or in combination with one of the rhizobacterial strains, using the root drenching method at a final concentration of 108 CFU g−1 soil for each bacterial strain [22]. Seven days after inoculation of helper alone or together with rhizobacteria, R. solanacearum was introduced to the roots of all plants at a final concentration of 107 CFU g−1 soil. The positive control treatment with R. solanacearum alone was inoculated only with the pathogen, and the negative control treatment was not inoculated with any bacteria. Tomato plants were maintained under standard greenhouse conditions (i.e., at natural temperature variation ranging from 28 °C to 32 °C, 15/9 h day/night conditions) and watered regularly with sterile water. Seedling trays were rearranged randomly every two days. Forty days after transplantation, plants were destructively harvested. The disease index for each plant was recorded based on a scale ranging from 0 to 4 [23]. Disease severity for each replicate seedling plate was calculated as described by: Disease severity = [∑ (The number of diseased plants in the disease index category × disease index category)/ (Total number of plants used in the experiment × highest disease index category)] ×100% [23, 24]. Simultaneously, we collected rhizosphere soil samples following an established protocol [4]. Briefly, two plants were randomly chosen from each replicate seedling tray to collect rhizosphere soils and further combined to yield one sample, resulting in a total of 112 rhizosphere soil samples for which R. solanacearum population densities were determined.Quantification of R. solanacearum at the end of the in vivo experimentWe determined R. solanacearum densities using quantitative PCR (qPCR). DNA was extracted from rhizosphere soils using a Power Soil DNA isolation kit (Mo Bio Laboratories) following the manufacturer’s protocol. DNA concentrations were determined by using a NanoDrop 1000 spectrophotometer (Thermo Scientific) and extracted DNA was used for R. solanacearum density measurements using specific primers (forward, 5ʹ-GAA CGC CAA CGG TGC GAA CT-3ʹ; reverse, 5ʹ-GGC GGC CTT CAG GGA GGT C-3ʹ) targeting the fliC gene, which encodes the R. solanacearum flagellum subunit [25]. The qPCR analyses were carried out with a StepOnePlus Real-Time RCR Instrument using SYBR green fluorescent dye detection and three technical replicates as described previously [4].Statistical analysesTo meet assumptions of normality and homogeneity of variance, R. solanacearum densities measured in vitro and in vivo were log10-transformed. When comparing mean differences between treatments, we used analyses of variance (ANOVA) and the Tukey Test, where p values lower than 0.05 were considered statistically significant. R. solanacearum densities were explained by two quantitative indices, the direct effect of rhizobacteria on R. solanacearum growth (the effect of rhizobacteria on R. solanacearum growth) and the indirect effect of rhizobacteria on R. solanacearum growth (the effect of rhizobacteria on helper strains’ growth). Nonlinear regression analyses (Sigmoidal, Sigmoid, 3 Parameter) were used to analyze the relationship between the direct effect and pathogen density, as well as the relationship between indirect effects and pathogen density in the presence of helper strains in vitro. The relationships between them, and between direct/indirect effects and disease severity in the presence of helper strains in vivo, were analyzed using linear regressions. These analyses were carried out using the R 3.6.3 program (www.r-project.org) and Sigma Plot (V.12.5).To further consider the growth inhibition of R. solanacearum, and disease suppression, we fitted a linear model to estimate the relative importance of direct effects versus indirect effects on the density of R. solanacearum both in vitro and in vivo, and on disease severity. This model considered the interaction scenario where rhizobacterial strains inhibited both the pathogen and its helpers (see the R script “Model” in the supplementary information). These analyses were performed in R version 3.6.3 [26] in conjunction with the package car, readxl and dplyr, and tidyverse 1.2.1 [27]. Briefly, proportional effects were normalized using a folded cube root transformation as suggested in J.W. Tukey [28] and fitted using a linear model with direct effects, indirect effects, and an interaction between helper strains and indirect effects as fixed factors. Normality of residuals was tested using the Shapiro-Wilk normality test and visual inspection of QQ-plots with standardized residuals. Type-II sum of squares were calculated using the ANOVA function from car 3.0-2 [29]. Subsequent visualization of the model outcome (results in Fig. 5) showed the predicted R. solanacearum densities and disease severity for different values of the inhibition via pathogen (Direct) or helper (Indirect) as estimated from the statistical model. For the Direct effect line, the indirect effect is set to be zero, while for the Indirect effect line, the direct effect is set to be zero.Fig. 5: The relative importance of direct versus indirect effects on Ralstonia solanacearum density and disease severity in the presence of helper strains.Relative importance of direct versus indirect effects on Ralstonia solanacearum density both in vitro (A) and in vivo (B), and disease severity (C) in presence of helper strains on the interaction scenario where rhizobacterial strains inhibited both the pathogen and its helpers (quadrant “H−P−” in Fig. 3C). This shows the predicted R. solanacearum densities and disease incidence for different values of the inhibition via pathogen (Direct) or helper (Indirect) as estimated from the statistical model (Table 1) which with direct effects, indirect effects, and an interaction between helper strains and indirect effects as fixed factors. For the Direct line, the indirect effect was set to zero, while for the indirect line, the direct effect was set to zero.Full size image More

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