# Chodkowski & Shade: Exometabolite-driven maintenance competition in bacteria

John L Chodkowski, Ashley Shade

► To cite this version:

John L Chodkowski, Ashley Shade. Chodkowski & Shade: Exometabolite-driven maintenance competition in bacteria. mSystems, 2024, 9 (4), pp.e00064-24. 10.1128/msystems.00064-24. hal-04590727

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1 Bioactive exometabolites drive maintenance competition in

2 simple bacterial communities

3 John L. Chodkowski¹ and Ashley Shade²*

4 ¹Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824,

5 USA

6 ²Universite Claude Bernard Lyon 1, Laboratoire d'Ecologie Microbienne, UMR CNRS 5557, UMR INRAE

7 1418, VetAgro Sup, 69622 Villeurbanne, France

8 * Corresponding author and material requests. Email: ashley.shade@cnrs.fr; ORCiD: 0000-0002-7189-

9 3067

10

11 Competing Interests

12 The authors declare no competing financial interests.

13

14 Abstract

15 During prolonged resource limitation, bacterial cells can persist in metabolically
16 active states of non-growth. These maintenance periods, such as those experienced in
17 stationary phase, can include upregulation of secondary metabolism and release of
18 exometabolites into the local environment. As resource limitation is common of many
19 environmental microbial habitats, we hypothesized that neighboring bacterial
20 populations employ exometabolites to compete or cooperate during maintenance, and
Bioactive exometabolites drive maintenance competition in
simple bacterial communities

John L. Chodkowski¹ and Ashley Shade²*

¹Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824,
USA

²Universite Claude Bernard Lyon 1, Laboratoire d'Ecologie Microbienne, UMR CNRS 5557, UMR INRAE
1418, VetAgro Sup, 69622 Villeurbanne, France

* Corresponding author and material requests. Email: ashley.shade@cnrs.fr; ORCiD: 0000-0002-7189-3067

10

Competing Interests

The authors declare no competing financial interests.

13

Abstract

During prolonged resource limitation, bacterial cells can persist in metabolically
active states of non-growth. These maintenance periods, such as those experienced in
stationary phase, can include upregulation of secondary metabolism and release of
exometabolites into the local environment. As resource limitation is common of many
environmental microbial habitats, we hypothesized that neighboring bacterial
populations employ exometabolites to compete or cooperate during maintenance, and
that these exometabolite-facilitated interactions can drive community outcomes. Here, we evaluated the consequences of exometabolite interactions over stationary phase among three environmental strains: *Burkholderia thailandensis* E264, *Chromobacterium subtsugae* ATCC 31532, and *Pseudomonas syringae* pv. *tomato* DC3000. We assembled them into synthetic communities that only permitted chemical interactions. We compared the responses (transcripts) and outputs (exometabolites) of each member with and without neighbors. We found that transcriptional dynamics were changed with different neighbors, and that some of these changes were coordinated between members. The dominant competitor *B. thailandensis* consistently upregulated biosynthetic gene clusters to produce bioactive exometabolites for both exploitative and interference competition. These results demonstrate that competition strategies during maintenance can contribute to community-level outcomes. It also suggests that the traditional concept of defining competitiveness by growth outcomes may be narrow, and that maintenance competition could be an additional or alternative measure.

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## Importance

Free-living microbial populations often persist and engage in environments that offer few or inconsistently available resources. Thus, it is important to investigate microbial interactions in this common and ecologically relevant condition of non-growth. This work investigates the consequences of resource limitation for community metabolic output and for population interactions in simple synthetic bacterial communities. Despite non-growth, we observed active, exometabolite-mediated competition among the bacterial

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that these exometabolite-facilitated interactions can drive community outcomes. Here, we evaluated the consequences of exometabolite interactions over stationary phase among three environmental strains: *Burkholderia thailandensis* E264, *Chromobacterium subtsugae* ATCC 31532, and *Pseudomonas syringae* pv. *tomato* DC3000. We assembled them into synthetic communities that only permitted chemical interactions.

We compared the responses (transcripts) and outputs (exometabolites) of each member with and without neighbors. We found that transcriptional dynamics were changed with different neighbors, and that some of these changes were coordinated between members. The dominant competitor *B. thailandensis* consistently upregulated biosynthetic gene clusters to produce bioactive exometabolites for both exploitative and interference competition. These results demonstrate that competition strategies during maintenance can contribute to community-level outcomes. It also suggests that the traditional concept of defining competitiveness by growth outcomes may be narrow, and that maintenance competition could be an additional or alternative measure.

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## Importance

Free-living microbial populations often persist and engage in environments that offer few or inconsistently available resources. Thus, it is important to investigate microbial interactions in this common and ecologically relevant condition of non-growth. This work investigates the consequences of resource limitation for community metabolic output and for population interactions in simple synthetic bacterial communities. Despite non-growth, we observed active, exometabolite-mediated competition among the bacterial

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populations. Many of these interactions and produced exometabolites were dependent
on the community composition, but we also observed that one dominant competitor
consistently produced interfering exometabolites regardless. These results are
important for predicting and understanding microbial interactions in resource-limited
environments.

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49

Introduction

Bacteria interact with other bacteria and their environment within complex, multi-
species communities. Bacterial interactions rely on the ability to sense and respond to
both biotic and abiotic stimuli [1, 2]. These stimuli include physical, chemical or
molecular cues, and can alter bacterial behaviors [3, 4], and ultimately, can also alter
community functioning [5, 6]. It is expected that interspecies interactions play an
important role in shaping microbial community dynamics [7]. However, multiple stimuli in
the environment make it difficult to disentangle the separate influences of abiotic versus
biotic stimuli on microbial community dynamics [8]. Therefore, efforts to characterize
and distinguish community responses to biotic stimuli, such as those that facilitate
interspecies interactions, will provide insights into the specific roles that microbial
interactions play in shaping their communities [9].

Interspecies interactions can be facilitated through small molecules [10].
Extracellular small molecules are collectively referred to as exometabolites [11, 12, 13].
Depending on the exometabolite produced, these molecules can mediate interspecies
interactions that range from competitive to cooperative [14]. Of these interaction types,
populations. Many of these interactions and produced exometabolites were dependent
on the community composition, but we also observed that one dominant competitor
consistently produced interfering exometabolites regardless. These results are
important for predicting and understanding microbial interactions in resource-limited
environments.

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49

Introduction
---

Bacteria interact with other bacteria and their environment within complex, multi-
species communities. Bacterial interactions rely on the ability to sense and respond to
both biotic and abiotic stimuli [1, 2]. These stimuli include physical, chemical or
molecular cues, and can alter bacterial behaviors [3, 4], and ultimately, can also alter
community functioning [5, 6]. It is expected that interspecies interactions play an
important role in shaping microbial community dynamics [7]. However, multiple stimuli in
the environment make it difficult to disentangle the separate influences of abiotic versus
biotic stimuli on microbial community dynamics [8]. Therefore, efforts to characterize
and distinguish community responses to biotic stimuli, such as those that facilitate
interspecies interactions, will provide insights into the specific roles that microbial
interactions play in shaping their communities [9].

Interspecies interactions can be facilitated through small molecules [10].
Extracellular small molecules are collectively referred to as exometabolites [11, 12, 13].
Depending on the exometabolite produced, these molecules can mediate interspecies
interactions that range from competitive to cooperative [14]. Of these interaction types,
65 competition has been shown to have a major influence in structuring microbial
66 communities [15, 16, 17]. Thus, competitive interactions that are mediated by
67 exometabolites are also expected to influence microbial community dynamics. In
68 addition, different types of exometabolites can be employed by bacteria to gain
69 advantage in both exploitative (e.g. nutrient scavenging) and interference (direct cell
70 damage) categories of competition.

71 Traditionally, competition has been viewed through the lens of resource
72 acquisition [18]. In previous studies, competitiveness is modeled with respect to yield
73 given resource consumption and growth [19, 20]. However, competition for survival or
74 maintenance may be just as important as competition for yield, especially during periods
75 of resource limitation [21, 22]. Competition during maintenance is likely common in
76 environments that experience relatively long periods of nutrient famine punctuated by
77 short periods of nutrient influx, for example such as in soils, sequencing batch reactors,
78 and the gut [23, 24, 25, 26]. The stationary phase of a bacterial growth curve falls within
79 this context of growth cessation, and pulses of nutrients may be transiently available as
80 cells die and lyse (necromass), while the total population size remains stagnant.
81 Stationary phase is often coordinated with a metabolic shift to secondary metabolism
82 [27, 28]. Therefore, an effective "maintenance" competitor may produce bioactive
83 exometabolites, like antibiotics, which are often produced because of secondary
84 metabolism. Bacteria can activate biosynthetic gene clusters (BSGCs) to produce
85 bioactive exometabolites [29]. The activation of BSGCs is closely tied to stress
86 responses, suggesting that bacteria can sense the stress of competition [30, 31]. While
87 it is known that certain exometabolites can trigger BSGC upregulation and, more
65 competition has been shown to have a major influence in structuring microbial
66 communities [15, 16, 17]. Thus, competitive interactions that are mediated by
67 exometabolites are also expected to influence microbial community dynamics. In
68 addition, different types of exometabolites can be employed by bacteria to gain
69 advantage in both exploitative (e.g. nutrient scavenging) and interference (direct cell
70 damage) categories of competition.

71 Traditionally, competition has been viewed through the lens of resource
72 acquisition [18]. In previous studies, competitiveness is modeled with respect to yield
73 given resource consumption and growth [19, 20]. However, competition for survival or
74 maintenance may be just as important as competition for yield, especially during periods
75 of resource limitation [21, 22]. Competition during maintenance is likely common in
76 environments that experience relatively long periods of nutrient famine punctuated by
77 short periods of nutrient influx, for example such as in soils, sequencing batch reactors,
78 and the gut [23, 24, 25, 26]. The stationary phase of a bacterial growth curve falls within
79 this context of growth cessation, and pulses of nutrients may be transiently available as
80 cells die and lyse (necromass), while the total population size remains stagnant.
81 Stationary phase is often coordinated with a metabolic shift to secondary metabolism
82 [27, 28]. Therefore, an effective "maintenance" competitor may produce bioactive
83 exometabolites, like antibiotics, which are often produced because of secondary
84 metabolism. Bacteria can activate biosynthetic gene clusters (BSGCs) to produce
85 bioactive exometabolites [29]. The activation of BSGCs is closely tied to stress
86 responses, suggesting that bacteria can sense the stress of competition [30, 31]. While
87 it is known that certain exometabolites can trigger BSGC upregulation and, more
generally alter transcription [32], there is much to understand about the outcomes of interspecies interactions for BSGCs in multi-member microbial communities.

Here, we build on our previous research to understand how exometabolite-mediated interactions among bacterial neighbors contribute to community outcomes in a simple, three-member community (Table 1). These three members are commonly associated with terrestrial environments (soils or plants) and were chosen because of reported [33] and observed interspecies exometabolite interactions in the laboratory.

We used a synthetic community (SynCom) approach [34] by applying our previously described transwell system [35], which allowed for evaluation of “community goods” within a media reservoir that was shared among members. The members’ populations were physically isolated by membrane filters at the bottom of each transwell, but could interact chemically via the reservoir. In our prior work, we investigated each member’s exometabolites and transcription over stationary phase, and the objective was to understand monoculture responses (in minimal glucose media) before assembling the more complex 2- and 3-member communities. We previously found that each member in monoculture produced a variety of exometabolites in stationary phase, including bioactive molecules involved in competition [36]. In this work, we build to 2- and 3-member communities to ask: How do members interact via exometabolites in simple communities during maintenance (stationary phase), and what are the competitive strategies and outcomes of those interactions? What genetic pathways, molecules, and members drive the responses?

We found that *B. thailandensis* had a major influence on the transcriptional responses of both *C. subtsugae* and *P. syringae*, and that this influence could be
generally alter transcription [32], there is much to understand about the outcomes of interspecies interactions for BSGCs in multi-member microbial communities.

Here, we build on our previous research to understand how exometabolite-mediated interactions among bacterial neighbors contribute to community outcomes in a simple, three-member community (Table 1). These three members are commonly associated with terrestrial environments (soils or plants) and were chosen because of reported [33] and observed interspecies exometabolite interactions in the laboratory.

We used a synthetic community (SynCom) approach [34] by applying our previously described transwell system [35], which allowed for evaluation of “community goods” within a media reservoir that was shared among members. The members’ populations were physically isolated by membrane filters at the bottom of each transwell, but could interact chemically via the reservoir. In our prior work, we investigated each member’s exometabolites and transcription over stationary phase, and the objective was to understand monoculture responses (in minimal glucose media) before assembling the more complex 2- and 3-member communities. We previously found that each member in monoculture produced a variety of exometabolites in stationary phase, including bioactive molecules involved in competition [36]. In this work, we build to 2- and 3-member communities to ask: How do members interact via exometabolites in simple communities during maintenance (stationary phase), and what are the competitive strategies and outcomes of those interactions? What genetic pathways, molecules, and members drive the responses?

We found that *B. thailandensis* had a major influence on the transcriptional responses of both *C. subtsugae* and *P. syringae*, and that this influence could be
attributed to an increase in both interference and exploitative competition strategies.
These findings show that diverse competitive strategies can be deployed even when
bacterial neighbors are surviving rather than exponentially growing. Therefore, we
suggest that contact-independent, exometabolite-mediated interference and exploitation
are important competitive strategies in resource-limited environments and support the
non-yield outcome of maintenance.

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118 **Materials and Methods**

119 **Bacterial strains and culture conditions**

120 We selected three environmental bacterial strains for the SynCom experiments
121 that were originally isolated from various plant/soil habitats and that had prior evidence
122 of exometabolite interactions among them in the laboratory [Table 1; 33, 37-40]. Freezer
123 stocks of *B. thailandensis*, *C. subtsugae*, and *P. syringae* were plated on half-
124 concentration Trypticase soy agar (TSA50) at 27°C for at least 24 h. Members were
125 inoculated in 7 ml of M9-0.2% glucose medium and grown for 16 h at 27°C, 200 rpm.
126 Cultures were then diluted into 50 ml M9-0.2% glucose medium such that exponential
127 growth phase was achieved after 10 h of incubation at 27°C, 200 rpm. Members were
128 diluted in 50 ml M9 glucose medium to target ODs (*B. thailandensis* 0.3 OD, *C.
129 subtsugae*: 0.035 OD, *P. syringae* 0.035 OD). The high initial OD for *B. thailandensis*
130 was necessary such that stationary phase would be achieved by all members within a 2
131 h window after 24 h incubation in the transwell plate. The glucose concentration in the
132 final dilution varied upon community membership- 0.067% for monocultures, 0.13% for
attributed to an increase in both interference and exploitative competition strategies.
These findings show that diverse competitive strategies can be deployed even when
bacterial neighbors are surviving rather than exponentially growing. Therefore, we
suggest that contact-independent, exometabolite-mediated interference and exploitation
are important competitive strategies in resource-limited environments and support the
non-yield outcome of maintenance.

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## 118 Materials and Methods

### 119 Bacterial strains and culture conditions

We selected three environmental bacterial strains for the SynCom experiments that were originally isolated from various plant/soil habitats and that had prior evidence of exometabolite interactions among them in the laboratory [Table 1; 33, 37-40]. Freezer stocks of *B. thailandensis*, *C. subtsugae*, and *P. syringae* were plated on half-concentration Trypticase soy agar (TSA50) at 27°C for at least 24 h. Members were inoculated in 7 ml of M9-0.2% glucose medium and grown for 16 h at 27°C, 200 rpm. Cultures were then diluted into 50 ml M9-0.2% glucose medium such that exponential growth phase was achieved after 10 h of incubation at 27°C, 200 rpm. Members were diluted in 50 ml M9 glucose medium to target ODs (*B. thailandensis* 0.3 OD, *C. subtsugae*: 0.035 OD, *P. syringae* 0.035 OD). The high initial OD for *B. thailandensis* was necessary such that stationary phase would be achieved by all members within a 2 h window after 24 h incubation in the transwell plate. The glucose concentration in the final dilution varied upon community membership- 0.067% for monocultures, 0.13% for
133 pairwise cocultures, and 0.2% for the 3-member community. For each member, 48 ml of
134 diluted culture was transferred as 4 mL aliquots in 12, 5 mL Falcon tubes to more
135 efficiently prepare replicate transwell plates.

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137 Synthetic community experiments

138 Transwell plate preparation was performed as previously described [35]. Briefly,
139 we used sterile filter plates with 0.22-µm-pore polyvinylidene difluoride (PVDF) filter
140 bottoms (Millipore MAGVS2210). Prior to use, filter plates were washed three times with
141 sterile water using a vacuum apparatus (NucleoVac 96 vacuum manifold; Clontech
142 Laboratories). The filter of well H12 was removed with a sterile pipette tip and tweezer,
143 and 31 ml of M9 glucose medium was added to the reservoir through well H12. The
144 glucose concentration in the reservoir varied upon community membership- 0.067% for
145 monocultures, 0.13% for pairwise cocultures, and 0.2% for the 3-member community.
146 Glucose concentration was adjusted to plate occupancy (e.g., 3-member communities
147 had higher number of wells occupied than 2- or 1-member). Our aim was for each
148 member to achieve stationary phase at similar times across all conditions to compare
149 transcripts and exometabolites under similar growth trajectories. In other words,
150 available resources were standardized while keeping the well occupancy for each
151 member constant. With this design, transcripts and exometabolites in cocultures that
152 deviated from those in monocultures could be attributed to interspecies interactions and
153 not complicated by offset in member growth trajectories across the experimental
154 conditions.
133 pairwise cocultures, and 0.2% for the 3-member community. For each member, 48 ml of
134 diluted culture was transferred as 4 mL aliquots in 12, 5 mL Falcon tubes to more
135 efficiently prepare replicate transwell plates.

136

137 Synthetic community experiments

138 Transwell plate preparation was performed as previously described [35]. Briefly,
139 we used sterile filter plates with 0.22-µm-pore polyvinylidene difluoride (PVDF) filter
140 bottoms (Millipore MAGVS2210). Prior to use, filter plates were washed three times with
141 sterile water using a vacuum apparatus (NucleoVac 96 vacuum manifold; Clontech
142 Laboratories). The filter of well H12 was removed with a sterile pipette tip and tweezer,
143 and 31 ml of M9 glucose medium was added to the reservoir through well H12. The
144 glucose concentration in the reservoir varied upon community membership- 0.067% for
145 monocultures, 0.13% for pairwise cocultures, and 0.2% for the 3-member community.
146 Glucose concentration was adjusted to plate occupancy (e.g., 3-member communities
147 had higher number of wells occupied than 2- or 1-member). Our aim was for each
148 member to achieve stationary phase at similar times across all conditions to compare
149 transcripts and exometabolites under similar growth trajectories. In other words,
150 available resources were standardized while keeping the well occupancy for each
151 member constant. With this design, transcripts and exometabolites in cocultures that
152 deviated from those in monocultures could be attributed to interspecies interactions and
153 not complicated by offset in member growth trajectories across the experimental
154 conditions.
Each well was filled with 130 µL of culture or medium (prepared as described, above; see methods section: Bacterial strains and culture conditions). For each plate, a custom R script (RandomArray.R [see script at https://github.com/ShadeLab/PAPER_Chodkowski_mSystems_2017/blob/master/R-analysis/RandomArray.R]) was used to randomize community member placement in the wells so that each member occupied a total of 31 wells per plate. In total, there were 7 community conditions- 3 monocultures, 3 pairwise cocultures, and the 3-member community. Each member occupied 31 wells per plate regardless of experimental condition. Thus, "baseline" exometabolites could be determined in the monocultures, and then deviations in exometabolite abundance or detection in the cocultures could be attributed to interspecies interactions. A time course was performed for each replicate. The time course included an exponential phase time point (12.5 h) and 5 time points assessed every 5 h over stationary phase (25 h – 45 h). Four biological replicates were performed for each community condition for a total of 28 experiments. For each experiment, 6 replicate filter plates were prepared for destructive sampling for a total of 168 transwell plates.

Filter plates were incubated at 27°C with gentle shaking (~0.32 rcf). For each plate, a custom R script (RandomArray.R [see script at https://github.com/ShadeLab/PAPER_Chodkowski_mSystems_2017/blob/master/R-analysis/RandomArray.R]) was used to randomize wells for each organism assigned to RNA extraction (16 wells) and flow cytometry (5 wells). The following procedure was performed for each organism when a transwell plate was destructively sampled: i) wells containing spent culture assigned to RNA extraction were pooled (~100 µL/well) into a
Each well was filled with 130 µL of culture or medium (prepared as described, above; see methods section: Bacterial strains and culture conditions). For each plate, a custom R script (RandomArray.R [see script at https://github.com/ShadeLab/PAPER_Chodkowski_mSystems_2017/blob/master/R-analysis/RandomArray.R]) was used to randomize community member placement in the wells so that each member occupied a total of 31 wells per plate. In total, there were 7 community conditions- 3 monocultures, 3 pairwise cocultures, and the 3-member community. Each member occupied 31 wells per plate regardless of experimental condition. Thus, "baseline" exometabolites could be determined in the monocultures, and then deviations in exometabolite abundance or detection in the cocultures could be attributed to interspecies interactions. A time course was performed for each replicate. The time course included an exponential phase time point (12.5 h) and 5 time points assessed every 5 h over stationary phase (25 h – 45 h). Four biological replicates were performed for each community condition for a total of 28 experiments. For each experiment, 6 replicate filter plates were prepared for destructive sampling for a total of 168 transwell plates.

Filter plates were incubated at 27°C with gentle shaking (~0.32 rcf). For each plate, a custom R script (RandomArray.R [see script at https://github.com/ShadeLab/PAPER_Chodkowski_mSystems_2017/blob/master/R-analysis/RandomArray.R]) was used to randomize wells for each organism assigned to RNA extraction (16 wells) and flow cytometry (5 wells). The following procedure was performed for each organism when a transwell plate was destructively sampled: i) wells containing spent culture assigned to RNA extraction were pooled (~100 µL/well) into a
1.7 mL microcentrifuge tube and flash frozen in liquid nitrogen and stored at -80 until further processing. ii) 20 µL from wells assigned for flow cytometry were diluted into 180 µL Tris-buffered saline (TBS; 20 mM Tris, 0.8% NaCl [pH 7.4]). In community memberships where *P. syringae* was arrayed with *B. thailandensis*, *P. syringae* had a final dilution of 70-fold in TBS. In community memberships where *P. syringae* was arrayed in monoculture or in coculture with *C. subtsugae*, *P. syringae* had a final dilution of 900-fold in TBS. Final dilutions for *B. thailandensis* and *C. subtsugae* were 1 300-fold and 1 540-fold, respectively. Each member was diluted differently to achieve a suitable events/second range on the flow cytometer for accurate cell counting. Populations were then stained and analyzed on the flow cytometer for live/dead counting (see Supplementary Methods). iii) Spent medium (~31 ml) from the shared reservoir was transferred to 50 mL conical tubes, flash-frozen in liquid nitrogen and stored at -80 °C prior to metabolite extraction.

RNA-seq

RNA extraction, sequencing, quality control, and count matrix generation was performed as previously published [36, see Supplementary Methods].

Transcriptomics

Quality filtering and differential gene expression analysis

Count matrices for each member were quality filtered in two steps: genes containing 0 counts in all samples were removed, and genes with a transcript count of ≤10 in more than 90% of samples were removed. DESeq2 [41] was used to extract size
1.7 mL microcentrifuge tube and flash frozen in liquid nitrogen and stored at -80 until further processing. ii) 20 µL from wells assigned for flow cytometry were diluted into 180 µL Tris-buffered saline (TBS; 20 mM Tris, 0.8% NaCl [pH 7.4]). In community memberships where *P. syringae* was arrayed with *B. thailandensis*, *P. syringae* had a final dilution of 70-fold in TBS. In community memberships where *P. syringae* was arrayed in monoculture or in coculture with *C. subtsugae*, *P. syringae* had a final dilution of 900-fold in TBS. Final dilutions for *B. thailandensis* and *C. subtsugae* were 1 300-fold and 1 540-fold, respectively. Each member was diluted differently to achieve a suitable events/second range on the flow cytometer for accurate cell counting. Populations were then stained and analyzed on the flow cytometer for live/dead counting (see Supplementary Methods). iii) Spent medium (~31 ml) from the shared reservoir was transferred to 50 mL conical tubes, flash-frozen in liquid nitrogen and stored at -80 °C prior to metabolite extraction.

RNA-seq

RNA extraction, sequencing, quality control, and count matrix generation was performed as previously published [36, see Supplementary Methods].

Transcriptomics

Quality filtering and differential gene expression analysis

Count matrices for each member were quality filtered in two steps: genes containing 0 counts in all samples were removed, and genes with a transcript count of ≤10 in more than 90% of samples were removed. DESeq2 [41] was used to extract size
factor and dispersion estimates. These estimates were used as external input into ImpulseDE2 for the analysis of differentially regulated genes [42]. ImpulseDE2 determines differential expression by comparing longitudinal count datasets. Case-control (Cocultures-monoculture control) analyses were analyzed to identify genes with differences in temporal regulation at an FDR-corrected threshold of 0.01. Genes that passed the FDR threshold were further filtered for genes that had at least one time point with a log2 fold-change (LFC) >= 1 or <= -1. Thus, we defined differentially expressed genes (DEGs) as genes that met both the FDR-corrected and LFC thresholds. For each member, differences in gene regulation between the three coculture conditions was visualized with Venn diagrams using the VennDiagram package [43].

Differentially expressed genes were first determined by comparing each coculture condition to the monoculture control and applying a LFC threshold (see above). We then determined a second set of DEGs by comparing pairwise cocultures to each other. ImpulseDE2 case-control analyses were performed as follows: *B. thailandensis* coculture with *C. subtsugae* (case) compared to *B. thailandensis* coculture with *P. syringae* (control), *C. subtsugae* coculture with *B. thailandensis* (case) compared to *C. subtsugae* coculture with *P. syringae* (control), and *P. syringae* coculture with *B. thailandensis* (case) compared to *P. syringae* coculture with *C. subtsugae* (control).

Genes that passed the FDR-corrected threshold of 0.01 based on ImpulseDE2 analysis and had at least one time point with a LFC of >= 1 or <= -1 represented coculture specific DEGs. The DEGs determined from monoculture comparisons and coculture comparisons were then categorically grouped using Clusters of Orthologous Groups (COG).
factor and dispersion estimates. These estimates were used as external input into ImpulseDE2 for the analysis of differentially regulated genes [42]. ImpulseDE2 determines differential expression by comparing longitudinal count datasets. Case-control (Cocultures-monoculture control) analyses were analyzed to identify genes with differences in temporal regulation at an FDR-corrected threshold of 0.01. Genes that passed the FDR threshold were further filtered for genes that had at least one time point with a log2 fold-change (LFC) >= 1 or <= -1. Thus, we defined differentially expressed genes (DEGs) as genes that met both the FDR-corrected and LFC thresholds. For each member, differences in gene regulation between the three coculture conditions was visualized with Venn diagrams using the VennDiagram package [43].

Differentially expressed genes were first determined by comparing each coculture condition to the monoculture control and applying a LFC threshold (see above). We then determined a second set of DEGs by comparing pairwise cocultures to each other. ImpulseDE2 case-control analyses were performed as follows: *B. thailandensis* coculture with *C. subtsugae* (case) compared to *B. thailandensis* coculture with *P. syringae* (control), *C. subtsugae* coculture with *B. thailandensis* (case) compared to *C. subtsugae* coculture with *P. syringae* (control), and *P. syringae* coculture with *B. thailandensis* (case) compared to *P. syringae* coculture with *C. subtsugae* (control).

Genes that passed the FDR-corrected threshold of 0.01 based on ImpulseDE2 analysis and had at least one time point with a LFC of >= 1 or <= -1 represented coculture specific DEGs. The DEGs determined from monoculture comparisons and coculture comparisons were then categorically grouped using Clusters of Orthologous Groups (COG).
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COG analysis

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Protein fasta files were downloaded from NCBI and uploaded to eggNOG-mapper v2 (http://eggnog-mapper.embl.de/) to obtain COGs. The DEGs determined from ImpulseDE2 and LFC thresholds were categorized as upregulated or downregulated based on temporal expression patterns. DEGs with consistent positive LFC throughout all stationary phase time points were categorized as upregulated. DEGs with consistent negative LFC throughout all stationary phase time points were categorized as downregulated. These DEGs were then assigned to COGs, grouped based on temporal up/downregulation patterns, and plotted using ggplot2 [44].

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246

Principal coordinates analysis and statistics

Normalized gene matrices were extracted from DESeq2 and filtered to only contain DEGs (coculture to monoculture comparisons) based on our previously described definition. A variance-stabilizing transformation was performed on normalized gene matrices using the rlog function in DESeq2. A distance matrix based on the Bray-Curtis dissimilarity metric was then calculated on the variance-stabilized gene matrices and principal coordinates analysis was performed using the R package vegan [45]. Principal coordinates were plotted using ggplot2. Coordinates of the first two PCoA axes were used to perform PROTEST analysis using the PROTEST function in vegan. Dissimilarity matrices were used to perform PERMANOVA and variation partitioning using the adonis and varpart functions in vegan, respectively. The RVAideMemoire package [46] was used to perform a post-hoc pairwise PERMANOVAs. Lastly,
224

COG analysis
225

Protein fasta files were downloaded from NCBI and uploaded to eggNOG-mapper v2 (http://eggnog-mapper.embl.de/) to obtain COGs. The DEGs determined from ImpulseDE2 and LFC thresholds were categorized as upregulated or downregulated based on temporal expression patterns. DEGs with consistent positive LFC throughout all stationary phase time points were categorized as upregulated. DEGs with consistent negative LFC throughout all stationary phase time points were categorized as downregulated. These DEGs were then assigned to COGs, grouped based on temporal up/downregulation patterns, and plotted using ggplot2 [44].
226
227
228
229
230
231
232
233

234

Principal coordinates analysis and statistics
235

Normalized gene matrices were extracted from DESeq2 and filtered to only contain DEGs (coculture to monoculture comparisons) based on our previously described definition. A variance-stabilizing transformation was performed on normalized gene matrices using the rlog function in DESeq2. A distance matrix based on the Bray-Curtis dissimilarity metric was then calculated on the variance-stabilized gene matrices and principal coordinates analysis was performed using the R package vegan [45]. Principal coordinates were plotted using ggplot2. Coordinates of the first two PCoA axes were used to perform PROTEST analysis using the PROTEST function in vegan. Dissimilarity matrices were used to perform PERMANOVA and variation partitioning using the adonis and varpart functions in vegan, respectively. The RVAideMemoire package [46] was used to perform a post-hoc pairwise PERMANOVAs. Lastly,
236
237
238
239
240
241
242
243
244
245
246
distances were extracted from the Bray-Curtis dissimilarity matrix that compared each
247 coculture condition to the monoculture condition at each time point within each member.
248 These distances were used to produce time series distance plots.
249

250

## Biosynthetic gene cluster (BSGC) analysis

NCBI accession numbers were uploaded to antiSMASH 6 beta bacterial version
252 [47] to identify genes involved in BSGCs using default parameters. Where possible,
253 literature-based evidence and BSGCs uploaded to MIBiG [48] were used to better
254 inform antiSMASH predictions. Log2 fold-changes (LFCs) were calculated for all
255 predicted biosynthetic genes within each predicted cluster by comparing coculture
256 expression to monoculture expression at each time point. Average LFCs were
257 calculated from all predicted biosynthetic genes within a predicted BSGC at each time
258 point. Temporal LFC trends were plotted using ggplot2. An upregulated BSGC was
259 defined as a BSGC that had at least two consecutive time points in stationary phase
260 with a LFC > 1.
261

262

## Network analysis

Unweighted co-expression networks were created from quality filtered and
264 normalized expression data. Networks were generated for pairwise cocultures
265 containing *B. thailandensis*. First, data were quality filtered as previously described
266 (see methods section: *Quality filtering and differential gene expression analysis*). Then,
267 normalized expression data was extracted from DESeq2. Twenty-three and twenty-
268 four RNA-seq samples from each member were used for network analysis in the *B*.
269
distances were extracted from the Bray-Curtis dissimilarity matrix that compared each
247
coculture condition to the monoculture condition at each time point within each member.
248
These distances were used to produce time series distance plots.
249

250

## Biosynthetic gene cluster (BSGC) analysis

NCBI accession numbers were uploaded to antiSMASH 6 beta bacterial version
252
[47] to identify genes involved in BSGCs using default parameters. Where possible,
253
literature-based evidence and BSGCs uploaded to MIBiG [48] were used to better
254
inform antiSMASH predictions. Log2 fold-changes (LFCs) were calculated for all
255
predicted biosynthetic genes within each predicted cluster by comparing coculture
256
expression to monoculture expression at each time point. Average LFCs were
257
calculated from all predicted biosynthetic genes within a predicted BSGC at each time
258
point. Temporal LFC trends were plotted using ggplot2. An upregulated BSGC was
259
defined as a BSGC that had at least two consecutive time points in stationary phase
260
with a LFC > 1.
261

262

## Network analysis

Unweighted co-expression networks were created from quality filtered and
264
normalized expression data. Networks were generated for pairwise cocultures
265
containing *B. thailandensis*. First, data were quality filtered as previously described
266
(see methods section: *Quality filtering and differential gene expression analysis*). Then,
267
normalized expression data was extracted from DESeq2. Twenty-three and twenty-
268
four RNA-seq samples from each member were used for network analysis in the *B*.
269
thailandensis-C. subtsugae and B. thailandensis-P. syringae cocultures, respectively (23/24 samples/member; 6 time points, 4 biological replicates). Only 23 samples were used in the B. thailandensis-C. subtsugae network analysis because RNA-seq failed for C. subtsugae at 45 h, biological replicate 2. Interspecies networks were then inferred from the expression data using the context likelihood of relatedness [49] algorithm within the R package Minet [50]. Gene matrices for each coculture pair were concatenated to perform the following analysis. Briefly, the mutual information coefficient was determined for each gene-pair. To ensure robust detection of co-expressed genes, a resampling approach was used as previously described [51]. Then, a Z-score was computed on the mutual information matrix. A Z-score threshold of 4.5 was used to determine an edge in the interspecies network. Interspecies networks were uploaded into Cytoscape version 3.7.1. for visualization, topological analysis, and enrichment analysis [52].

Gene annotation and gene ontology (GO) files were obtained for B. thailandensis, P. syringae, and C. subtsugae for enrichment analyses. For B. thailandensis, annotation and ontology files were downloaded from the Burkholderia Genome Database (https://www.burkholderia.com). For P. syringae, annotation and ontology files were downloaded from the Pseudomonas Genome Database (http://www.pseudomonas.com/strain/download). Annotation and ontology files for C. subtsugae were generated using Blast2GO version 5.2.5 [53]. InterProScan [54] with default parameters were used to complement gene annotations from C. subtsugae. GO terms were assigned using Blast2GO with default parameters. In addition, genes involved in secondary metabolism were manually curated and added to these files as
thailandensis-C. subtsugae and B. thailandensis-P. syringae cocultures, respectively (23/24 samples/member; 6 time points, 4 biological replicates). Only 23 samples were used in the B. thailandensis-C. subtsugae network analysis because RNA-seq failed for C. subtsugae at 45 h, biological replicate 2. Interspecies networks were then inferred from the expression data using the context likelihood of relatedness [49] algorithm within the R package Minet [50]. Gene matrices for each coculture pair were concatenated to perform the following analysis. Briefly, the mutual information coefficient was determined for each gene-pair. To ensure robust detection of co-expressed genes, a resampling approach was used as previously described [51]. Then, a Z-score was computed on the mutual information matrix. A Z-score threshold of 4.5 was used to determine an edge in the interspecies network. Interspecies networks were uploaded into Cytoscape version 3.7.1. for visualization, topological analysis, and enrichment analysis [52].

Gene annotation and gene ontology (GO) files were obtained for B. thailandensis, P. syringae, and C. subtsugae for enrichment analyses. For B. thailandensis, annotation and ontology files were downloaded from the Burkholderia Genome Database (https://www.burkholderia.com). For P. syringae, annotation and ontology files were downloaded from the Pseudomonas Genome Database (http://www.pseudomonas.com/strain/download). Annotation and ontology files for C. subtsugae were generated using Blast2GO version 5.2.5 [53]. InterProScan [54] with default parameters were used to complement gene annotations from C. subtsugae. GO terms were assigned using Blast2GO with default parameters. In addition, genes involved in secondary metabolism were manually curated and added to these files as
individual GO terms. These genes were also used to update the GO term GO:0017000 (antibiotic biosynthetic process), composed of a collection of all the biosynthetic genes. (see methods section: Biosynthetic gene cluster (BSGC) analysis).

Topological analysis was performed as follows: Nodes were filtered from each coculture network to only select genes from one member at a time. The GLay community cluster function in Cytoscape was used to determine intra-member modules. Functional enrichment analysis was then performed on the modules using the BiNGO package [55] in Cytoscape.

To determine interspecies co-regulation patterns, we filtered network nodes that contained an interspecies edge. Functional enrichment analysis was performed on the collection of genes containing interspecies edges for each member using the BiNGO package in Cytoscape. Then, we selected all genes contained within modules of interest (e.g. *B. thailandensis* modules containing either thailandamide or malleilactone genes in the *B. thailandensis-C. subtsugae* coculture network or *B. thailandensis-P. syringae* coculture network, respectively) in Cytoscape. Node selection was extended by selecting the first neighbors of the selected nodes. This resulted in interspecies edges. The resultant nodes were transformed into a circular layout and exported for manual edits in InkScape. The biosynthetic gene cluster organization of thailandamide and malleilactone were obtained from MIBig and drawn in InkScape.

Protein sequences from an interspecies gene of interest (CLV_2968) within a network module that also contained thailandamide genes from the *B. thailandensis-C. subtsugae* network and an interspecies gene of interest (PSPTO_1206) within a network module that also contained malleilactone genes from the *B. thailandensis-P*.
individual GO terms. These genes were also used to update the GO term GO:0017000 (antibiotic biosynthetic process), composed of a collection of all the biosynthetic genes. (see methods section: Biosynthetic gene cluster (BSGC) analysis).

Topological analysis was performed as follows: Nodes were filtered from each coculture network to only select genes from one member at a time. The GLay community cluster function in Cytoscape was used to determine intra-member modules. Functional enrichment analysis was then performed on the modules using the BiNGO package [55] in Cytoscape.

To determine interspecies co-regulation patterns, we filtered network nodes that contained an interspecies edge. Functional enrichment analysis was performed on the collection of genes containing interspecies edges for each member using the BiNGO package in Cytoscape. Then, we selected all genes contained within modules of interest (e.g. *B. thailandensis* modules containing either thailandamide or malleilactone genes in the *B. thailandensis-C. subtsugae* coculture network or *B. thailandensis-P. syringae* coculture network, respectively) in Cytoscape. Node selection was extended by selecting the first neighbors of the selected nodes. This resulted in interspecies edges. The resultant nodes were transformed into a circular layout and exported for manual edits in InkScape. The biosynthetic gene cluster organization of thailandamide and malleilactone were obtained from MIBig and drawn in InkScape.

Protein sequences from an interspecies gene of interest (CLV_2968) within a network module that also contained thailandamide genes from the *B. thailandensis-C. subtsugae* network and an interspecies gene of interest (PSPTO_1206) within a network module that also contained malleilactone genes from the *B. thailandensis-P*.
syringae network were obtained. A protein blast for each protein was run against B.

thailandensis protein sequences. B. thailandensis locus tags were extracted from the

top blast hit from each run. Normalized transcript counts for these 4 genes of interest

were plotted in R. Time course gene trajectories were determined using a loess

smoothing function.

321

322

## 323 Metabolomics

LCMS, feature detection, and quality control

Standard operating protocols were performed at the Department of Energy Joint

Genome Institute as previously described [36]. MZmine 2 [56] was used for feature

detection and peak area integration as previously described [36]. Select exometabolites

were identified in MZmine 2 by manual observation of both MS and MS/MS data. We

extracted quantities of these identified exometabolites for ANOVA and Tukey HSD post-

hoc analysis in R. We filtered features in three steps to identify coculture-accumulated

exometabolites. The feature-filtering steps were performed as follows on a per-member

basis: (i) retain features where the maximum peak area abundance occurred in any of

the coculture communities; (ii) a noise filter, the minimum peak area of a feature from a

replicate at any time point needed to be 3 times the maximum peak area of the same

feature in one of the external control replicates, was applied; (iii) coefficient of variation

(CV) values for each feature calculated between replicates at each time point needed to

be less than 20% across the time series.
syringae network were obtained. A protein blast for each protein was run against B.

thailandensis protein sequences. B. thailandensis locus tags were extracted from the

top blast hit from each run. Normalized transcript counts for these 4 genes of interest

were plotted in R. Time course gene trajectories were determined using a loess

smoothing function.

321

322

## 323 Metabolomics

LCMS, feature detection, and quality control

Standard operating protocols were performed at the Department of Energy Joint

Genome Institute as previously described [36]. MZmine 2 [56] was used for feature

detection and peak area integration as previously described [36]. Select exometabolites

were identified in MZmine 2 by manual observation of both MS and MS/MS data. We

extracted quantities of these identified exometabolites for ANOVA and Tukey HSD post-

hoc analysis in R. We filtered features in three steps to identify coculture-accumulated

exometabolites. The feature-filtering steps were performed as follows on a per-member

basis: (i) retain features where the maximum peak area abundance occurred in any of

the coculture communities; (ii) a noise filter, the minimum peak area of a feature from a

replicate at any time point needed to be 3 times the maximum peak area of the same

feature in one of the external control replicates, was applied; (iii) coefficient of variation

(CV) values for each feature calculated between replicates at each time point needed to

be less than 20% across the time series.
338 Four final feature data sets from polar and nonpolar analyses in both ionization modes
339 were analyzed in MetaboAnalyst 5.0 [57], as reported in our prior work [36, see
340 Supplementary Methods]. In addition, exometabolites categorized as primary
341 metabolites were identified according to Metabolomics Standards Initiative (MSI) level 1
342 criteria [58], as reported in our prior work [36, see Methods].

343

344 *Principal coordinates analysis and statistics*

345 A distance matrix based on the Bray-Curtis dissimilarity metric was used to
346 calculate dissimilarities between exometabolite profiles. Principal coordinates analysis
347 was performed using the R package vegan. Principal coordinates were plotted using
348 ggplot2. Coordinates of the first two PCoA axes were used to perform Protest analysis
349 using the protest function in vegan. Dissimilarity matrices were used to perform
350 PERMANOVA and variation partitioning using the adonis and varpart functions in
351 vegan, respectively. The RVAideMemoire package was used to perform a post-hoc
352 pairwise PERMANOVAs. Monoculture controls were removed to focus on coculture
353 trends.

354

355

356

357

358 **Results**

359 *Overview*
338 Four final feature data sets from polar and nonpolar analyses in both ionization modes
339 were analyzed in MetaboAnalyst 5.0 [57], as reported in our prior work [36, see
340 Supplementary Methods]. In addition, exometabolites categorized as primary
341 metabolites were identified according to Metabolomics Standards Initiative (MSI) level 1
342 criteria [58], as reported in our prior work [36, see Methods].

343

344 *Principal coordinates analysis and statistics*

345 A distance matrix based on the Bray-Curtis dissimilarity metric was used to
346 calculate dissimilarities between exometabolite profiles. Principal coordinates analysis
347 was performed using the R package vegan. Principal coordinates were plotted using
348 ggplot2. Coordinates of the first two PCoA axes were used to perform Protest analysis
349 using the protest function in vegan. Dissimilarity matrices were used to perform
350 PERMANOVA and variation partitioning using the adonis and varpart functions in
351 vegan, respectively. The RVAideMemoire package was used to perform a post-hoc
352 pairwise PERMANOVAs. Monoculture controls were removed to focus on coculture
353 trends.

354

355

356

357

358 **Results**

359 *Overview*
Our major data types included both transcriptomics and metabolomics, and we integrate these to interpret SynCom dynamics and interactions. Our longitudinal design resulted in 288 RNAseq samples across the three members, and 168 community metabolomics samples analyzed in each of four mass spectral modes (polar/nonpolar, positive/negative modes = 672 total mass spectral profiles). After quality control, we were left with 281 RNAseq and 605 total mass spectral profiles for the integrated analyses

[https://github.com/ShadeLab/Paper_Chodkowski_3member_SynCom_2021/tree/master/SummaryOfSamples]. First, we present a summary of experiments and cell viability (section 1). Then, we present results of general responses of transcription (section 2) and exometabolomics (section 3), separately. Then, we integrate transcriptomic and metabolomic efforts to determine the upregulation of biosynthetic gene clusters (BSGCs) and identify exometabolites of interest from mass spectrometry (section 4).

Lastly, we then present a transcriptomics co-expression network to ask how the upregulation of BSGCs influenced interspecies interactions through coordinated longitudinal gene expression (section 5).

## 1. SynCom design/sampling scheme and membership cell viability

We had four replicate, independent timeseries for each of seven community memberships (three of each monoculture, three of each pair in coculture, and the 3-member community). We define membership as the specific strains present in a given condition. Here, we focus on the multi-member analyses (2 and 3-member combinations) to gain insights into community outcomes (Fig. 1A). The SynCom transwell system isolated member populations among separate transwells but permitted
Our major data types included both transcriptomics and metabolomics, and we integrate these to interpret SynCom dynamics and interactions. Our longitudinal design resulted in 288 RNAseq samples across the three members, and 168 community metabolomics samples analyzed in each of four mass spectral modes (polar/nonpolar, positive/negative modes = 672 total mass spectral profiles). After quality control, we were left with 281 RNAseq and 605 total mass spectral profiles for the integrated analyses

[https://github.com/ShadeLab/Paper_Chodkowski_3member_SynCom_2021/tree/master/SummaryOfSamples]. First, we present a summary of experiments and cell viability (section 1). Then, we present results of general responses of transcription (section 2) and exometabolomics (section 3), separately. Then, we integrate transcriptomic and metabolomic efforts to determine the upregulation of biosynthetic gene clusters (BSGCs) and identify exometabolites of interest from mass spectrometry (section 4).

Lastly, we then present a transcriptomics co-expression network to ask how the upregulation of BSGCs influenced interspecies interactions through coordinated longitudinal gene expression (section 5).

## 1. SynCom design/sampling scheme and membership cell viability

We had four replicate, independent timeseries for each of seven community memberships (three of each monoculture, three of each pair in coculture, and the 3-member community). We define membership as the specific strains present in a given condition. Here, we focus on the multi-member analyses (2 and 3-member combinations) to gain insights into community outcomes (Fig. 1A). The SynCom transwell system isolated member populations among separate transwells but permitted
exchange of their collective exometabolites via the plate's shared media reservoir (Fig. 1B). We collected data (transcripts, metabolites, etc) over a timeseries that included one exponential phase time point (12.5 h) followed by 5 stationary phase time points (25-45 h sampled at 5-hour intervals; Fig. 1C).

We observed relatively unchanged viability in *B. thailandensis* across all conditions (Fig. S1; panels A-D). On the contrary, we observed a slight reduction (~2.1 log2 fold change) in *C. subtsugae* live cell counts, and a drastic reduction (~4.7 log2 fold change) in *P. syringae* live cell counts, when either member was cocultured with *B. thailandensis* (Fig. 2; panels A vs C and panels D vs F, respectively). Reductions in cell viability of *C. subtsugae* and *P. syringae* were also present in the 3-member community (Fig. S1; panels E and F). *C. subtsugae* and *P. syringae* had minimal effects on each other (Fig. 2; panels B and E). Dead cell accumulation of *P. syringae* plateaued in coculture conditions compared to monoculture, suggesting cell lysis (Fig. 2, panels D-F). We note that one doubling occurred in *B. thailandensis* and *P. syringae* monocultures, and in *C. subtsugae* in pairwise coculture with *P. syringae*. We elaborated on this finding as the possibility of a reductive cell division in our previous manuscript [36].

## 2. Stationary phase transcript dynamics of microbial community members

Differentially expressed genes were determined by comparing time series transcript trajectories applying an FDR and LFC threshold (see methods: *Quality filtering and differential gene expression analysis*). First, we compared each coculture to the monoculture control. A range of 153 to 276 genes were differentially expressed by each member in coculture, irrespective of the identity of neighbors (Fig. S2). In addition,
exchange of their collective exometabolites via the plate's shared media reservoir (Fig. 1B). We collected data (transcripts, metabolites, etc) over a timeseries that included one exponential phase time point (12.5 h) followed by 5 stationary phase time points (25-45 h sampled at 5-hour intervals; Fig. 1C).

We observed relatively unchanged viability in *B. thailandensis* across all conditions (Fig. S1; panels A-D). On the contrary, we observed a slight reduction (~2.1 log2 fold change) in *C. subtsugae* live cell counts, and a drastic reduction (~4.7 log2 fold change) in *P. syringae* live cell counts, when either member was cocultured with *B. thailandensis* (Fig. 2; panels A vs C and panels D vs F, respectively). Reductions in cell viability of *C. subtsugae* and *P. syringae* were also present in the 3-member community (Fig. S1; panels E and F). *C. subtsugae* and *P. syringae* had minimal effects on each other (Fig. 2; panels B and E). Dead cell accumulation of *P. syringae* plateaued in coculture conditions compared to monoculture, suggesting cell lysis (Fig. 2, panels D-F). We note that one doubling occurred in *B. thailandensis* and *P. syringae* monocultures, and in *C. subtsugae* in pairwise coculture with *P. syringae*. We elaborated on this finding as the possibility of a reductive cell division in our previous manuscript [36].

## 2. Stationary phase transcript dynamics of microbial community members

Differentially expressed genes were determined by comparing time series transcript trajectories applying an FDR and LFC threshold (see methods: *Quality filtering and differential gene expression analysis*). First, we compared each coculture to the monoculture control. A range of 153 to 276 genes were differentially expressed by each member in coculture, irrespective of the identity of neighbors (Fig. S2). In addition,
each member also had differential gene expression that was unique to a particular neighbor(s). Summarizing across all cocultures, 1089/5639 (19.3%) coding sequences (CDSs), 1991/4393 CDSs (45.3%), and 3274/5576 CDSs (58.7%) DEGs were determined for *B. thailandensis*, *C. subtsugae*, and *P. syringae*, respectively. Primary drivers of transcriptional response patterns for each member were community membership (PCoA axis 1) and time (PCoA axis 2) (Fig. 3, Table S1). Together, these data suggest that there are both general and specific consequences of neighbors for the transcriptional responses of these bacterial community members.

Temporal trajectories in member transcript profiles were generally reproducible across replicates (PROTEST analyses, Table S2). Each member had a distinct transcript profile ($0.480 \le r2 \le 0.778$ by Adonis; P value, 0.001; all pairwise false discovery rate [FDR]-adjusted P values, $\le 0.01$ except for two community memberships, Table S3). For all ordinations, community membership had the most explanatory value (Axis 1), followed by time (Axis 2), with the most variation explained by the interaction between time and membership (Table S1). Membership alone accounted for 60.6% and 77.0% of the variation explained in *C. subtsugae* and *P. syringae* analyses, respectively and 46.3% in the *B. thailandensis* analysis (Table S1).

When included in the community, *B. thailandensis* strongly determined the transcript profiles of the other two members. For example, the inclusion of *B. thailandensis* in a coculture differentiated transcript profiles for both *C. subtsugae* and *P. syringae* (Fig. 3B & 3C, Figs. S3-S5). The transcript profile differences between monoculture and coculture conditions are largest for *C. subtsugae* (Fig. S4) and *P. syringae* (Fig. S5) when *B. thailandensis* is included in the coculture. Thus, *B*.
each member also had differential gene expression that was unique to a particular neighbor(s). Summarizing across all cocultures, 1089/5639 (19.3%) coding sequences (CDSs), 1991/4393 CDSs (45.3%), and 3274/5576 CDSs (58.7%) DEGs were determined for *B. thailandensis*, *C. subtsugae*, and *P. syringae*, respectively. Primary drivers of transcriptional response patterns for each member were community membership (PCoA axis 1) and time (PCoA axis 2) (Fig. 3, Table S1). Together, these data suggest that there are both general and specific consequences of neighbors for the transcriptional responses of these bacterial community members.

Temporal trajectories in member transcript profiles were generally reproducible across replicates (PROTEST analyses, Table S2). Each member had a distinct transcript profile ($0.480 \le r2 \le 0.778$ by Adonis; P value, 0.001; all pairwise false discovery rate [FDR]-adjusted P values, $\le 0.01$ except for two community memberships, Table S3). For all ordinations, community membership had the most explanatory value (Axis 1), followed by time (Axis 2), with the most variation explained by the interaction between time and membership (Table S1). Membership alone accounted for 60.6% and 77.0% of the variation explained in *C. subtsugae* and *P. syringae* analyses, respectively and 46.3% in the *B. thailandensis* analysis (Table S1).

When included in the community, *B. thailandensis* strongly determined the transcript profiles of the other two members. For example, the inclusion of *B. thailandensis* in a coculture differentiated transcript profiles for both *C. subtsugae* and *P. syringae* (Fig. 3B & 3C, Figs. S3-S5). The transcript profile differences between monoculture and coculture conditions are largest for *C. subtsugae* (Fig. S4) and *P. syringae* (Fig. S5) when *B. thailandensis* is included in the coculture. Thus, *B*.
thailandensis appears to have had a dominating influence on the transcriptional
response of neighbors, and these responses were dynamic with respect to time.

We analyzed clusters of orthologous groups of proteins (COGs) to infer the
responses of members to their neighbors. Differentially expressed genes (DEGs) were
categorized as upregulated or downregulated based on temporal patterns and
representation in COGs (Fig. S6). We focused on the largest differences between total
DEGs upregulated and total DEGs downregulated within a COG, which provides
insights into broad biological processes affected by community membership. COGs with
large differences toward upregulation in B. thailandensis included cell motility [N],
secondary metabolites biosynthesis, transport, and catabolism [Q], and signal
transduction mechanisms [T] while COGs with large differences toward downregulation
included defense mechanisms [V], energy production and conversion [C], translation,
and ribosomal structure and biogenesis [J]. These results suggest that B. thailandensis
responds to neighbors via downregulation of growth and reproduction and upregulation
of secondary metabolism. We therefore hypothesized that B. thailandensis was
producing bioactive exometabolites against C. subtsugae and P. syringae to
competitively inhibit their growth.

Because of the strong transcript response of C. subtsugae and P. syringae when
neighbored with B. thailandensis (Fig. 3B & 3C), we focused on COGs within
community memberships with B. thailandensis (Fig. S6B & S6C, rows 2 & 3). The COG
with large differences toward upregulation in both C. subtsugae and P. syringae were
translation, ribosomal structure and biogenesis [J]. COG groups tending toward
downregulation in C. subtsugae and P. syringae were signal transduction mechanisms
thailandensis appears to have had a dominating influence on the transcriptional
response of neighbors, and these responses were dynamic with respect to time.

We analyzed clusters of orthologous groups of proteins (COGs) to infer the
responses of members to their neighbors. Differentially expressed genes (DEGs) were
categorized as upregulated or downregulated based on temporal patterns and
representation in COGs (Fig. S6). We focused on the largest differences between total
DEGs upregulated and total DEGs downregulated within a COG, which provides
insights into broad biological processes affected by community membership. COGs with
large differences toward upregulation in B. thailandensis included cell motility [N],
secondary metabolites biosynthesis, transport, and catabolism [Q], and signal
transduction mechanisms [T] while COGs with large differences toward downregulation
included defense mechanisms [V], energy production and conversion [C], translation,
and ribosomal structure and biogenesis [J]. These results suggest that B. thailandensis
responds to neighbors via downregulation of growth and reproduction and upregulation
of secondary metabolism. We therefore hypothesized that B. thailandensis was
producing bioactive exometabolites against C. subtsugae and P. syringae to
competitively inhibit their growth.

Because of the strong transcript response of C. subtsugae and P. syringae when
neighbored with B. thailandensis (Fig. 3B & 3C), we focused on COGs within
community memberships with B. thailandensis (Fig. S6B & S6C, rows 2 & 3). The COG
with large differences toward upregulation in both C. subtsugae and P. syringae were
translation, ribosomal structure and biogenesis [J]. COG groups tending toward
downregulation in C. subtsugae and P. syringae were signal transduction mechanisms
[T] and secondary metabolites biosynthesis, transport, and catabolism [Q], respectively.
These results suggest that the presence of *B. thailandensis* alters its neighbor's ability
to respond to the environment and inhibits secondary metabolism. The effects of *B.
thailandensis* on *C. subtsugae* and *P. syringae* were also evident by mapping timeseries
LFCs onto KEGG pathways. Various enzymes involved in central metabolism, fatty acid
degradation, growth, transport, and response systems were upregulated when *B.
thailandensis* was grown with either or both members
(https://figshare.com/s/b7f5e559a32cc5c8a61f).

The above analyses focused on DEGs determined by comparing each coculture
to the monoculture control. However, we also wanted to understand differences
between pairs to determine if the alterations in transcripts were attributed to specific
memberships (aka interspecies interactions). A total of 436, 1762, and 2962 DEGs
were determined when comparing the pairs including *B. thailandensis*, the pairs
including *C. subtsugae*, and the pairs including *P. syringae*, respectively. We detected
member-specific effects on the COGs that were differentially expressed (Fig. S7).
These data suggest that there were transcriptional changes driven by particular
members and given their partner. Due to the physical separation of members in our
SynCom plate system, these member-specific interspecies interactions were very likely
exometabolite-mediated.

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3. Stationary phase exometabolite dynamics of microbial communities

Because member populations are physically separated in the SynCom transwell
system but allowed to interact chemically, observed transcript responses in different
[T] and secondary metabolites biosynthesis, transport, and catabolism [Q], respectively.
These results suggest that the presence of *B. thailandensis* alters its neighbor's ability
to respond to the environment and inhibits secondary metabolism. The effects of *B.
thailandensis* on *C. subtsugae* and *P. syringae* were also evident by mapping timeseries
LFCs onto KEGG pathways. Various enzymes involved in central metabolism, fatty acid
degradation, growth, transport, and response systems were upregulated when *B.
thailandensis* was grown with either or both members
(https://figshare.com/s/b7f5e559a32cc5c8a61f).

The above analyses focused on DEGs determined by comparing each coculture
to the monoculture control. However, we also wanted to understand differences
between pairs to determine if the alterations in transcripts were attributed to specific
memberships (aka interspecies interactions). A total of 436, 1 762, and 2 962 DEGs
were determined when comparing the pairs including *B. thailandensis*, the pairs
including *C. subtsugae*, and the pairs including *P. syringae*, respectively. We detected
member-specific effects on the COGs that were differentially expressed (Fig. S7).
These data suggest that there were transcriptional changes driven by particular
members and given their partner. Due to the physical separation of members in our
SynCom plate system, these member-specific interspecies interactions were very likely
exometabolite-mediated.

471

3. Stationary phase exometabolite dynamics of microbial communities

Because member populations are physically separated in the SynCom transwell
system but allowed to interact chemically, observed transcript responses in different
community memberships are inferred to result from exometabolite interactions. Spent medium from the shared medium reservoir was collected from each transwell plate and analyzed using mass spectrometry to detect exometabolites. Our previous manuscript focused on exometabolite dynamics in monocultures [36]. Here, we focused our analysis on those exometabolites that had maximum accumulation in a coculture (either in pairs or in 3-member community). Consistent with the transcript analysis, we found that both community membership and time explained the exometabolite dynamics, and that the explanatory value of membership and time was maintained across all polarities and ionization modes (Fig. 4, Table S4).

Temporal trajectories in exometabolite profiles were generally reproducible across replicates with some exceptions (PROTEST analyses, Table S5, Supplementary File 1). Exometabolite profiles were distinct by community membership ($0.475 \le r2 \le 0.662$ by Adonis; P value, 0.001; all pairwise false discovery rate [FDR]-adjusted P values, $\le 0.01$ except for two comparisons, Table S6), and also dynamic over time. As observed for the member transcript profiles, the interaction between membership and time had the highest explanatory value for the exometabolite data (Table S4).

We found that the *C. subtsugae-P. syringae* coculture exometabolite profiles were consistently the most distinct from the other coculture memberships (Fig. 4), supporting, again, that the inclusion of *B. thailandensis* was a major driver of exometabolite dynamics, possibly because it provided the largest or most distinctive contributions to the community exometabolite pool. Indeed, we observed that a majority of the most abundant exometabolites were either detected uniquely in the *B. thailandensis* monoculture or accumulated substantially in its included community
community memberships are inferred to result from exometabolite interactions. Spent medium from the shared medium reservoir was collected from each transwell plate and analyzed using mass spectrometry to detect exometabolites. Our previous manuscript focused on exometabolite dynamics in monocultures [36]. Here, we focused our analysis on those exometabolites that had maximum accumulation in a coculture (either in pairs or in 3-member community). Consistent with the transcript analysis, we found that both community membership and time explained the exometabolite dynamics, and that the explanatory value of membership and time was maintained across all polarities and ionization modes (Fig. 4, Table S4).

Temporal trajectories in exometabolite profiles were generally reproducible across replicates with some exceptions (PROTEST analyses, Table S5, Supplementary File 1). Exometabolite profiles were distinct by community membership ($0.475 \le r2 \le 0.662$ by Adonis; P value, 0.001; all pairwise false discovery rate [FDR]-adjusted P values, $\le 0.01$ except for two comparisons, Table S6), and also dynamic over time. As observed for the member transcript profiles, the interaction between membership and time had the highest explanatory value for the exometabolite data (Table S4).

We found that the *C. subtsugae-P. syringae* coculture exometabolite profiles were consistently the most distinct from the other coculture memberships (Fig. 4), supporting, again, that the inclusion of *B. thailandensis* was a major driver of exometabolite dynamics, possibly because it provided the largest or most distinctive contributions to the community exometabolite pool. Indeed, we observed that a majority of the most abundant exometabolites were either detected uniquely in the *B. thailandensis* monoculture or accumulated substantially in its included community
memberships (Fig. S8). Some exometabolites detected in *B. thailandensis*-inclusive communities were not detected in its monocultures (Fig. S8D), suggesting that the inclusion of neighbors contributed to the accumulation of these particular exometabolites (e.g. upregulation of biosynthetic gene clusters or lysis products). *C. subtsugae* and *P. syringae* contributed less to the 3-member community exometabolite profile, as exometabolites detected in the *C. subtsugae*- *P. syringae* coculture were less abundant and had lower accumulation over time in the 3-member community (Fig. S8A). Together, these results suggest that *B. thailandensis* can suppress or overwhelm expected outputs from neighbors.

Exometabolites categorized as primary metabolites were identified according to Metabolomics Standards Initiative (MSI) level 1 criteria [58]. We identified primary metabolites accumulated in the shared medium reservoir over time in each monoculture (Fig. 5; [36]) to compare their dynamics in cocultures. These primary metabolites were detected to decrease in concentration across coculture conditions, suggesting metabolic inhibition or interspecies uptake. In addition, we also found a subset of primary metabolites that accumulate substantially in exponential phase in monocultures (Fig. S9). Taken together, each member contributed a unique set of primary metabolites to the community exometabolite pool. The uptake and metabolism of these primary metabolites by the non-producing members may directly affect the available pool of exometabolites in cocultures, particularly with respect to exometabolites contributed from secondary metabolism.

In summary, we observed both increased accumulation and unique production of exometabolites in pairs and in the 3-member community, with *B. thailandensis*
memberships (Fig. S8). Some exometabolites detected in *B. thailandensis*-inclusive communities were not detected in its monocultures (Fig. S8D), suggesting that the inclusion of neighbors contributed to the accumulation of these particular exometabolites (e.g. upregulation of biosynthetic gene clusters or lysis products). *C. subtsugae* and *P. syringae* contributed less to the 3-member community exometabolite profile, as exometabolites detected in the *C. subtsugae-P. syringae* coculture were less abundant and had lower accumulation over time in the 3-member community (Fig. S8A). Together, these results suggest that *B. thailandensis* can suppress or overwhelm expected outputs from neighbors.

Exometabolites categorized as primary metabolites were identified according to Metabolomics Standards Initiative (MSI) level 1 criteria [58]. We identified primary metabolites accumulated in the shared medium reservoir over time in each monoculture (Fig. 5; [36]) to compare their dynamics in cocultures. These primary metabolites were detected to decrease in concentration across coculture conditions, suggesting metabolic inhibition or interspecies uptake. In addition, we also found a subset of primary metabolites that accumulate substantially in exponential phase in monocultures (Fig. S9). Taken together, each member contributed a unique set of primary metabolites to the community exometabolite pool. The uptake and metabolism of these primary metabolites by the non-producing members may directly affect the available pool of exometabolites in cocultures, particularly with respect to exometabolites contributed from secondary metabolism.

In summary, we observed both increased accumulation and unique production of exometabolites in pairs and in the 3-member community, with *B. thailandensis*
contributing the most to the shared exometabolite pool as determined by comparisons
with its monoculture exometabolite profile. Related, the transcriptional responses of C.
*subtsugae* and *P. syringae* in the 3-member community is most like their respective
transcriptional response when neighbors with *B. thailandensis* alone, despite the
presence of the third neighbor.

4. *B. thailandensis* increases competition strategies in the presence of neighbors

Given the observed reduction in cell viability (Fig. 2) and that there have been
competitive interactions between *B. thailandensis* and *C. subtsugae* previously reported
[33], we hypothesized that *B. thailandensis* was using competition strategies to influence
its neighbors via production of bioactive exometabolites. If true, we would expect
transcriptional upregulation in *B. thailandensis* biosynthetic gene clusters (BSGC) that
encode bioactive exometabolites. Indeed, when compared to the monoculture control,
we found evidence of upregulated BSGCs across various time points in stationary
phase in *B. thailandensis* cocultures (Fig. 6, Table S7). Some of these upregulation
patterns were associated to particular pairs of members and some upregulation patterns
were strongest in the full community (e.g. thailandamide). For example, *B. thailandensis*
upregulated an unidentified non-ribosomal peptide synthetase (NRPS) when paired with
*P. syringae*, but when paired with *C. subtsugae*, upregulated a different BSGC encoding
an unidentified beta-lactone. This suggests that *B. thailandensis* responded to
neighbors by upregulating genes involved in the production of bioactive compounds,
likely to gain a competitive advantage. However, not all BSGCs in *B. thailandensis* were
upregulated. Some BSGCs were unaltered or downregulated (Fig. S10). *C. subtsugae*
contributing the most to the shared exometabolite pool as determined by comparisons
with its monoculture exometabolite profile. Related, the transcriptional responses of C.
*subtsugae* and *P. syringae* in the 3-member community is most like their respective
transcriptional response when neighbors with *B. thailandensis* alone, despite the
presence of the third neighbor.

4. *B. thailandensis* increases competition strategies in the presence of neighbors

Given the observed reduction in cell viability (Fig. 2) and that there have been
competitive interactions between *B. thailandensis* and *C. subtsugae* previously reported
[33], we hypothesized that *B. thailandensis* was using competition strategies to influence
its neighbors via production of bioactive exometabolites. If true, we would expect
transcriptional upregulation in *B. thailandensis* biosynthetic gene clusters (BSGC) that
encode bioactive exometabolites. Indeed, when compared to the monoculture control,
we found evidence of upregulated BSGCs across various time points in stationary
phase in *B. thailandensis* cocultures (Fig. 6, Table S7). Some of these upregulation
patterns were associated to particular pairs of members and some upregulation patterns
were strongest in the full community (e.g. thailandamide). For example, *B. thailandensis*
upregulated an unidentified non-ribosomal peptide synthetase (NRPS) when paired with
*P. syringae*, but when paired with *C. subtsugae*, upregulated a different BSGC encoding
an unidentified beta-lactone. This suggests that *B. thailandensis* responded to
neighbors by upregulating genes involved in the production of bioactive compounds,
likely to gain a competitive advantage. However, not all BSGCs in *B. thailandensis* were
upregulated. Some BSGCs were unaltered or downregulated (Fig. S10). *C. subtsugae*
upregulated only 1 BSGC, an uncharacterized hybrid nonribosomal peptide synthetase-type I polyketide synthase, in coculture with *B. thailandensis*, while *P. syringae* did not upregulate any BSGC in any coculture (Figs. S11 & S12). Interspecies interactions led to the upregulation of BSGCs in both *B. thailandensis* and *C. subtsugae* and three of these BSGCs encode potentially novel bioactive exometabolites.

Because *B. thailandensis* upregulated the transcription of various BSGCs when grown in cocultures, we asked if this led to the unique production of or increased accumulation of secondary metabolites as compared to when it was grown in monoculture. We identified 6 of the 11 exometabolites from the BSGCs in *B. thailandensis* that were upregulated and quantified their abundances from mass spectrometry data (Fig. 7, Supplementary File 2). We found that each identified exometabolite differentially accumulated between community memberships containing *B. thailandensis* (Table S8), particularly when comparing the *B. thailandensis* monoculture to each coculture (Table S9). As expected, these identified exometabolites were not detected in communities that did not include *B. thailandensis* (data not shown). Bactobolin was the only identified exometabolite that accumulated in monoculture to equivalent levels of accumulation in all coculture conditions. The other identified secondary metabolites were not detected or did not accumulate in monoculture, suggesting interspecies induction of secondary metabolism. Thus, in response to an exometabolite from either *C. subtsugae* or *P. syringae*, *B. thailandensis* increased its competitive strategies through the upregulation and production of many bioactive exometabolites. Of these bioactive exometabolites, three are documented antimicrobials [59, 60, 61], two are siderophores [62, 63], and one is a biosurfactant
upregulated only 1 BSGC, an uncharacterized hybrid nonribosomal peptide synthetase-type I polyketide synthase, in coculture with *B. thailandensis*, while *P. syringae* did not upregulate any BSGC in any coculture (Figs. S11 & S12). Interspecies interactions led to the upregulation of BSGCs in both *B. thailandensis* and *C. subtsugae* and three of these BSGCs encode potentially novel bioactive exometabolites.

Because *B. thailandensis* upregulated the transcription of various BSGCs when grown in cocultures, we asked if this led to the unique production of or increased accumulation of secondary metabolites as compared to when it was grown in monoculture. We identified 6 of the 11 exometabolites from the BSGCs in *B. thailandensis* that were upregulated and quantified their abundances from mass spectrometry data (Fig. 7, Supplementary File 2). We found that each identified exometabolite differentially accumulated between community memberships containing *B. thailandensis* (Table S8), particularly when comparing the *B. thailandensis* monoculture to each coculture (Table S9). As expected, these identified exometabolites were not detected in communities that did not include *B. thailandensis* (data not shown). Bactobolin was the only identified exometabolite that accumulated in monoculture to equivalent levels of accumulation in all coculture conditions. The other identified secondary metabolites were not detected or did not accumulate in monoculture, suggesting interspecies induction of secondary metabolism. Thus, in response to an exometabolite from either *C. subtsugae* or *P. syringae*, *B. thailandensis* increased its competitive strategies through the upregulation and production of many bioactive exometabolites. Of these bioactive exometabolites, three are documented antimicrobials [59, 60, 61], two are siderophores [62, 63], and one is a biosurfactant
[64]. We conclude that *B. thailandensis* produced bioactive exometabolites to competitively interact using both interference and exploitative competition strategies

Given that *B. thailandensis* upregulated competitive strategies, and responded more broadly in producing competition-supportive exometabolites when grown with neighbors, we hypothesized that these bioactive exometabolites are responsible for the altered transcriptional responses in *C. subtsugae* and *P. syringae*.

In our experimental design, we adjusted glucose concentration depending on plate occupancy. Glucose concentration increased as plate occupancy increased (31 wells vs 62 wells vs 93 wells), but a member consistently occupied 31 wells across all experimental conditions. One complication of this design is that population density and resource concentration could contribute to differences in transcripts and exometabolites in a member-agnostic manner. To address this, we performed additional SynCom experiments to affirm confidence that some changes in transcripts and exometabolites are attributable to exometabolite-mediated interspecies interactions. In these experiments, we increased the plate occupancy of *B. thailandensis* in monoculture while subsequently increasing resource concentration. Pairwise cocultures and the 3-member community SynCom experiments were repeated as well (see Supplementary methods).

We calculated the relative gene expression of three genes in the thailandamide operon (*thaF*, *thaK*, and *thaQ*) through RT-qPCR by comparing each experimental condition to the monoculture control (*B. thailandensis*, 31 wells in M9-0.067% glucose). Decreased gene expression was observed across all three genes as both plate occupancy and resource concentration increased in *B. thailandensis* monocultures. In fact, *thaF*, *thaK*, and *thaQ* gene expression was further reduced in the 93 well *B. thailandensis*
[64]. We conclude that *B. thailandensis* produced bioactive exometabolites to competitively interact using both interference and exploitative competition strategies

Given that *B. thailandensis* upregulated competitive strategies, and responded more broadly in producing competition-supportive exometabolites when grown with neighbors, we hypothesized that these bioactive exometabolites are responsible for the altered transcriptional responses in *C. subtsugae* and *P. syringae*.

In our experimental design, we adjusted glucose concentration depending on plate occupancy. Glucose concentration increased as plate occupancy increased (31 wells vs 62 wells vs 93 wells), but a member consistently occupied 31 wells across all experimental conditions. One complication of this design is that population density and resource concentration could contribute to differences in transcripts and exometabolites in a member-agnostic manner. To address this, we performed additional SynCom experiments to affirm confidence that some changes in transcripts and exometabolites are attributable to exometabolite-mediated interspecies interactions. In these experiments, we increased the plate occupancy of *B. thailandensis* in monoculture while subsequently increasing resource concentration. Pairwise cocultures and the 3-member community SynCom experiments were repeated as well (see Supplementary methods).

We calculated the relative gene expression of three genes in the thailandamide operon (*thaF*, *thaK*, and *thaQ*) through RT-qPCR by comparing each experimental condition to the monoculture control (*B. thailandensis*, 31 wells in M9-0.067% glucose). Decreased gene expression was observed across all three genes as both plate occupancy and resource concentration increased in *B. thailandensis* monocultures. In fact, *thaF*, *thaK*, and *thaQ* gene expression was further reduced in the 93 well *B. thailandensis*
monoculture compared to the 62 well *B. thailandensis* monoculture, suggesting that the
thailandamide operon trended towards reduced expression as a function of *B.
thailandensis* plate occupancy in monoculture conditions. On the contrary, *thaF*, *thaK*,
and *thaQ* had increased expression in all coculture memberships, suggesting that
exometabolite interspecies interactions were responsible for the increased expression
of a BSGC in *B. thailandensis* (Table S10).

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We performed interspecies co-expression network analysis to infer interspecies
interactions. We used temporal profiles from transcriptional responses to generate co-
expression networks for *B. thailandensis*-C. *subtsugae* and *B. thailandensis*-P. *syringae*
cocultures, respectively (Table S11). As expected, the majority of nodes in the network
had intraspecies edges only, with interspecies edges comprising 1.85% and 1.90% of
the total edges in the *B. thailandensis*-C. *subtsugae* and *B. thailandensis*-P. *syringae*
networks, respectively. We explored interspecies edges for evidence of interspecies
transcriptional co-regulation.

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We performed two analyses, module analysis and Gene Ontology (GO)
enrichment, to validate networks and infer interspecies interactions (Fig. S13). Module
analysis validated networks as intraspecies modules enriched for biological processes
(Supplementary File 3). To infer interspecies interactions, we filtered genes with
interspecies edges and performed enrichment analysis (Supplementary File 4). The top
enriched GO term for *B. thailandensis* when paired with *C. subtsugae* was antibiotic

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monoculture compared to the 62 well *B. thailandensis* monoculture, suggesting that the
thailandamide operon trended towards reduced expression as a function of *B.
thailandensis* plate occupancy in monoculture conditions. On the contrary, *thaF*, *thaK*,
and *thaQ* had increased expression in all coculture memberships, suggesting that
exometabolite interspecies interactions were responsible for the increased expression
of a BSGC in *B. thailandensis* (Table S10).

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We performed interspecies co-expression network analysis to infer interspecies
interactions. We used temporal profiles from transcriptional responses to generate co-
expression networks for *B. thailandensis*-C. *subtsugae* and *B. thailandensis*-P. *syringae*
cocultures, respectively (Table S11). As expected, the majority of nodes in the network
had intraspecies edges only, with interspecies edges comprising 1.85% and 1.90% of
the total edges in the *B. thailandensis*-C. *subtsugae* and *B. thailandensis*-P. *syringae*
networks, respectively. We explored interspecies edges for evidence of interspecies
transcriptional co-regulation.

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We performed two analyses, module analysis and Gene Ontology (GO)
enrichment, to validate networks and infer interspecies interactions (Fig. S13). Module
analysis validated networks as intraspecies modules enriched for biological processes
(Supplementary File 3). To infer interspecies interactions, we filtered genes with
interspecies edges and performed enrichment analysis (Supplementary File 4). The top
enriched GO term for *B. thailandensis* when paired with *C. subtsugae* was antibiotic

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613 synthesis of thailandamide, supporting the interpretation of interference competition.
614 Though the top enriched GO term in *B. thailandensis* when paired with *P. syringae* was
615 bacterial-type flagellum-dependent cell motility, antibiotic synthesis of malleilactone was
616 also enriched. Both thailandamide genes from the *B. thailandensis-C. subtsugae*
617 network (Fig. 8) and malleilactone genes from the *B. thailandensis-P. syringae* network
618 (Fig. S14) formed near-complete modules within their respective BSGCs. In addition,
619 genes that were part of the BSGC modules contained interspecies edges with both *C.*
620 *subtsugae* and *P. syringae*.

621 At least one gene from each of *B. thailandensis*'s upregulated BSGCs (Fig. 6)
622 had an interspecies edge, except for rhamnolipid. Our interpretation of this result is that,
623 generally, *B. thailandensis*'s upregulated BSGCs had co-expression patterns with genes
624 from the other members. In the thailandamide and malleilactone modules, some of
625 these interspecies genes were related to stress, transport, and iron-scavenging
626 (Supplementary File 5). The top GO term for both *C. subtsugae* and *P. syringae* genes
627 that had edges shared with *B. thailandensis* was bacterial-type flagellum-dependent
628 motility. Other notable enriched GO processes were efflux activity for *C. subtsugae* and
629 signal transduction for *P. syringae*. Specifically, a DNA starvation/stationary phase gene
630 (CLV04_2968, Fig. 8), *dspA*, was within the network module that also contained
631 thailandamide genes from the *B. thailandensis-C. subtsugae* network and a TonB-
632 dependent siderophore receptor gene (PSPTO_1206, Fig. S14) was within the network
633 module that also contained malleilactone genes from the *B. thailandensis-P. syringae*
634 network. Interestingly, both CLV04_2968 and PSPTO_1206 were DEGs and
635 downregulated when cocultured with *B. thailandensis* (Figs. S15A & S16A,
613 synthesis of thailandamide, supporting the interpretation of interference competition.
614 Though the top enriched GO term in *B. thailandensis* when paired with *P. syringae* was
615 bacterial-type flagellum-dependent cell motility, antibiotic synthesis of malleilactone was
616 also enriched. Both thailandamide genes from the *B. thailandensis-C. subtsugae*
617 network (Fig. 8) and malleilactone genes from the *B. thailandensis-P. syringae* network
618 (Fig. S14) formed near-complete modules within their respective BSGCs. In addition,
619 genes that were part of the BSGC modules contained interspecies edges with both *C.*
620 *subtsugae* and *P. syringae*.

621 At least one gene from each of *B. thailandensis*'s upregulated BSGCs (Fig. 6)
622 had an interspecies edge, except for rhamnolipid. Our interpretation of this result is that,
623 generally, *B. thailandensis*'s upregulated BSGCs had co-expression patterns with genes
624 from the other members. In the thailandamide and malleilactone modules, some of
625 these interspecies genes were related to stress, transport, and iron-scavenging
626 (Supplementary File 5). The top GO term for both *C. subtsugae* and *P. syringae* genes
627 that had edges shared with *B. thailandensis* was bacterial-type flagellum-dependent
628 motility. Other notable enriched GO processes were efflux activity for *C. subtsugae* and
629 signal transduction for *P. syringae*. Specifically, a DNA starvation/stationary phase gene
630 (CLV04_2968, Fig. 8), *dspA*, was within the network module that also contained
631 thailandamide genes from the *B. thailandensis-C. subtsugae* network and a TonB-
632 dependent siderophore receptor gene (PSPTO_1206, Fig. S14) was within the network
633 module that also contained malleilactone genes from the *B. thailandensis-P. syringae*
634 network. Interestingly, both CLV04_2968 and PSPTO_1206 were DEGs and
635 downregulated when cocultured with *B. thailandensis* (Figs. S15A & S16A,
respectively). Additionally, the closest homolog for *dspA* in *B. thailandensis* was unaltered (BTH_I1284, Supplementary File 6) when cocultured with *C. subtsugae* (Fig. S15B) and the closest homolog to the TonB-dependent receptor in *B. thailandensis* (BTH_I2415, Supplementary File 7) was a DEG and upregulated when cocultured with *P. syrinage* (Fig. S16B). Taken together, these co-expression networks revealed interspecies coordinated expression patterns. Specifically, we detected interspecies co-expression patterns related to antibiotic upregulation in *B. thailandensis*, suggesting *C. subtsugae* and *P. syringae* were sensing and responding directly to these competition strategies of *B. thailandensis*.

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## Discussion

Here, we used a synthetic community system to understand how exometabolomic interactions determine members transcriptional responses and exometabolite outputs. Our experiment used a systems approach to compare the seven possible community memberships of three members, and their dynamics in member transcripts and community exometabolites over stationary phase. Differential gene expression across community memberships and over time show that the exometabolites released by a member were sensed and responded to by its neighbors. Furthermore, members' ouputs in monocultures changed because of coculturing, as evidenced by differential exometabolite production. The largest transcriptional alterations in *C. subtsugae* and *P. syringae* occurred when cocultured with *B. thailandensis*. Global expression patterns in *C. subtsugae* and *P. syringae* when in the
respectively). Additionally, the closest homolog for *dspA* in *B. thailandensis* was unaltered (BTH_I1284, Supplementary File 6) when cocultured with *C. subtsugae* (Fig. S15B) and the closest homolog to the TonB-dependent receptor in *B. thailandensis* (BTH_I2415, Supplementary File 7) was a DEG and upregulated when cocultured with *P. syrinage* (Fig. S16B). Taken together, these co-expression networks revealed interspecies coordinated expression patterns. Specifically, we detected interspecies co-expression patterns related to antibiotic upregulation in *B. thailandensis*, suggesting *C. subtsugae* and *P. syringae* were sensing and responding directly to these competition strategies of *B. thailandensis*.

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## Discussion

Here, we used a synthetic community system to understand how exometabolomic interactions determine members transcriptional responses and exometabolite outputs. Our experiment used a systems approach to compare the seven possible community memberships of three members, and their dynamics in member transcripts and community exometabolites over stationary phase. Differential gene expression across community memberships and over time show that the exometabolites released by a member were sensed and responded to by its neighbors. Furthermore, members' ouputs in monocultures changed because of coculturing, as evidenced by differential exometabolite production. The largest transcriptional alterations in *C. subtsugae* and *P. syringae* occurred when cocultured with *B. thailandensis*. Global expression patterns in *C. subtsugae* and *P. syringae* when in the

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3-member community still resembled expression patterns in pairwise cocultures with *B. thailandensis*. These transcriptional alterations in *C. subtsugae* and *P. syringae* were coordinated with increases in *B. thailandensis* competitive strategies (evaluated by BSGC transcript upregulation and exometabolite abundance). That interactions within a relatively simple community altered the transcriptional responses and exometabolite outputs of each member is important because these kinds of alterations could, in turn, drive changes in community structure and/or function in an environmental setting. For example, it was shown that interspecies interactions more strongly influenced the assembly of *C. elegans* gut communities than host-associated factors [66]. Therefore, mechanistic and ecological characterization of interspecies interactions will inform as to the principles that govern emergent properties of microbial communities.

Overall, competitive interactions predominated in this synthetic community. This was first evidenced by reductions in viable cell counts in both *C. subtsugae* and *P. syringae* when cocultured with *B. thailandensis*. Interestingly, *P. syringae* was the only member to have an exponential increase in dead cell counts in monoculture. *P. syringae* dead cell count accumulation ceased in coculture conditions. We attribute this finding to the overall reduction of cell viability and/or lysis of dead cells when cocultured.

Our previous study found that, over stationary phase in monocultures, each member released and accumulated at least one exometabolite documented to be involved in either interference or exploitative competition [36]. This suggests that entry into stationary phase primed members for competitive interactions, regardless of heterospecific neighbors present. We interpret this strategy of preemptive aggression to be especially advantageous to *B. thailandensis*, as it successfully used competitive
3-member community still resembled expression patterns in pairwise cocultures with *B. thailandensis*. These transcriptional alterations in *C. subtsugae* and *P. syringae* were coordinated with increases in *B. thailandensis* competitive strategies (evaluated by BSGC transcript upregulation and exometabolite abundance). That interactions within a relatively simple community altered the transcriptional responses and exometabolite outputs of each member is important because these kinds of alterations could, in turn, drive changes in community structure and/or function in an environmental setting. For example, it was shown that interspecies interactions more strongly influenced the assembly of *C. elegans* gut communities than host-associated factors [66]. Therefore, mechanistic and ecological characterization of interspecies interactions will inform as to the principles that govern emergent properties of microbial communities.

Overall, competitive interactions predominated in this synthetic community. This was first evidenced by reductions in viable cell counts in both *C. subtsugae* and *P. syringae* when cocultured with *B. thailandensis*. Interestingly, *P. syringae* was the only member to have an exponential increase in dead cell counts in monoculture. *P. syringae* dead cell count accumulation ceased in coculture conditions. We attribute this finding to the overall reduction of cell viability and/or lysis of dead cells when cocultured.

Our previous study found that, over stationary phase in monocultures, each member released and accumulated at least one exometabolite documented to be involved in either interference or exploitative competition [36]. This suggests that entry into stationary phase primed members for competitive interactions, regardless of heterospecific neighbors present. We interpret this strategy of preemptive aggression to be especially advantageous to *B. thailandensis*, as it successfully used competitive
681 strategies against both *C. subtsugae* and *P. syringae*. *B. thailandensis*'s success was
682 supported by decreased viable *P. syringae* cells when cocultured with *B. thailandensis*.
683 Though *C. subtsugae* viable cell counts were not as affected directly by the coculture
684 with *B. thailandensis*, *B. thailandensis*-produced bactobolin [67] was detected in the
685 shared medium reservoir. Bactobolin is a bacteriostatic antibiotic previously shown to be
686 bioactive against *C. subtsugae* [33] through ribosome binding [59]. But, *C. subtsugae*
687 can resist bactobolin through upregulation of an RND-type efflux pump [68]. This finding
688 also is supported by our data, as all genes coding for the CdeAB-OprM RND-type efflux
689 system were DEGs and upregulated in *C. subtsugae* cocultures with *B. thailandensis*
690 (CLV04_2413-CLV04_2415).

691 When cocultured with *B. thailandensis*, we observed COG groups such as
692 translation, ribosomal structure and biogenesis [J] had large differences toward
693 upregulation in both *C. subtsugae* and *P. syringae*. At first glance, this seems at odds
694 with our interpretation of *B. thailandensis* competitiveness toward *C. subtsugae* and *P.
695 syringae*. In other words, how is *B. thailandensis* effectively competing via interference
696 competition if both *C. subtsugae* and *P. syringae* are upregulating machinery for
697 growth? There is both theoretical [69] and experimental [70] evidence that show how
698 cells treated with antibiotics stimulate ribosomal production to maintain a sufficient
699 number of active ribosomes. As previously mentioned, *B. thailandensis*-produced
700 bactobolin binds to the ribosome and can inhibit *C. subtsugae* [33, 59]. We also have
701 evidence that bactobolin inhibits *P. syringae* (data not shown). It could be that
702 bactobolin is stimulating ribosomal production in *C. subtsugae* and *P. syringae* as a
703 survival mechanism to maintain protein production by maintaining enough active
681 strategies against both *C. subtsugae* and *P. syringae*. *B. thailandensis*'s success was 682 supported by decreased viable *P. syringae* cells when cocultured with *B. thailandensis*. 683 Though *C. subtsugae* viable cell counts were not as affected directly by the coculture 684 with *B. thailandensis*, *B. thailandensis*-produced bactobolin [67] was detected in the 685 shared medium reservoir. Bactobolin is a bacteriostatic antibiotic previously shown to be 686 bioactive against *C. subtsugae* [33] through ribosome binding [59]. But, *C. subtsugae* 687 can resist bactobolin through upregulation of an RND-type efflux pump [68]. This finding 688 also is supported by our data, as all genes coding for the CdeAB-OprM RND-type efflux 689 system were DEGs and upregulated in *C. subtsugae* cocultures with *B. thailandensis* 690 (CLV04_2413-CLV04_2415).

691 When cocultured with *B. thailandensis*, we observed COG groups such as 692 translation, ribosomal structure and biogenesis [J] had large differences toward 693 upregulation in both *C. subtsugae* and *P. syringae*. At first glance, this seems at odds 694 with our interpretation of *B. thailandensis* competitiveness toward *C. subtsugae* and *P. 695 syringae*. In other words, how is *B. thailandensis* effectively competing via interference 696 competition if both *C. subtsugae* and *P. syringae* are upregulating machinery for 697 growth? There is both theoretical [69] and experimental [70] evidence that show how 698 cells treated with antibiotics stimulate ribosomal production to maintain a sufficient 699 number of active ribosomes. As previously mentioned, *B. thailandensis*-produced 700 bactobolin binds to the ribosome and can inhibit *C. subtsugae* [33, 59]. We also have 701 evidence that bactobolin inhibits *P. syringae* (data not shown). It could be that 702 bactobolin is stimulating ribosomal production in *C. subtsugae* and *P. syringae* as a 703 survival mechanism to maintain protein production by maintaining enough active
ribosomes. There also was evidence of *B. thailandensis* antibiotic efficacy against *C. subtsugae* and *P. syringae*, including general loss of cell viability and upregulation of various enzymes involved in central metabolism by both members when they were cocultured with *B. thailandensis* (https://figshare.com/s/b7f5e559a32cc5c8a61f). These patterns are consistent with antibiotic treatments in *Escherichia coli* and *Staphylococcus aureus* where the upregulation of oxidative phosphorylation due to drug treatment contributes to antibiotic efficacy [71, 72]. A barrage of *B. thailandensis*-produced antibiotics (Figs. 6 & 7) likely drove the transcriptional patterns in *C. subtsugae* and *P. syringae*.

Coculturing can induce secondary metabolism [73, 74, 75] because an exometabolite produced by one microbe can prompt secondary metabolism in a neighbor [31]. We found that coculturing led to the upregulation of numerous BSGCs in *B. thailandensis*. These exometabolites included bactobolin, malleilactone [62, 76]; siderophore and cytotoxin], malleobactin [77, 78]; siderophore], capistruin [79; lasso peptide], thailandamide [80; polyketide], pyochelin [63; siderophore], rhamnolipids [64; biosurfactants], and two uncharacterized BSGCs encoding nonribosomal peptide synthetases. Of these exometabolites, bactobolin, capistruin, and thailandamide have documented antimicrobial activities through translation inhibition [59], transcription inhibition [60], and inhibition of fatty acid synthesis [61], respectively. For those exometabolites we were able to identify with mass spectrometry, their accumulation in cocultures was correlated with the upregulation of their BSGCs. Furthermore, up/downregulated patterns across all *B. thailandensis* BSGCs was consistent with ScmR global regulatory patterns of secondary metabolism [81].
ribosomes. There also was evidence of *B. thailandensis* antibiotic efficacy against *C. subtsugae* and *P. syringae*, including general loss of cell viability and upregulation of various enzymes involved in central metabolism by both members when they were cocultured with *B. thailandensis* (https://figshare.com/s/b7f5e559a32cc5c8a61f). These patterns are consistent with antibiotic treatments in *Escherichia coli* and *Staphylococcus aureus* where the upregulation of oxidative phosphorylation due to drug treatment contributes to antibiotic efficacy [71, 72]. A barrage of *B. thailandensis*-produced antibiotics (Figs. 6 & 7) likely drove the transcriptional patterns in *C. subtsugae* and *P. syringae*.

Coculturing can induce secondary metabolism [73, 74, 75] because an exometabolite produced by one microbe can prompt secondary metabolism in a neighbor [31]. We found that coculturing led to the upregulation of numerous BSGCs in *B. thailandensis*. These exometabolites included bactobolin, malleilactone [62, 76]; siderophore and cytotoxin], malleobactin [77, 78]; siderophore], capistruin [79; lasso peptide], thailandamide [80; polyketide], pyochelin [63; siderophore], rhamnolipids [64; biosurfactants], and two uncharacterized BSGCs encoding nonribosomal peptide synthetases. Of these exometabolites, bactobolin, capistruin, and thailandamide have documented antimicrobial activities through translation inhibition [59], transcription inhibition [60], and inhibition of fatty acid synthesis [61], respectively. For those exometabolites we were able to identify with mass spectrometry, their accumulation in cocultures was correlated with the upregulation of their BSGCs. Furthermore, up/downregulated patterns across all *B. thailandensis* BSGCs was consistent with *ScmR* global regulatory patterns of secondary metabolism [81].
We acknowledge that this study is limited in its ability to pinpoint the underlying mechanisms driving the activation of secondary metabolism, particularly in *B. thailandensis*. Aside from self-activating mechanisms documented in *B. thailandensis* (e.g. quorum-sensing driven bactobolin production) and/or sensing antibiotics and competitively responding [82], we note two major patterns in exometabolite production in the monocultures that may have contributed to activation of secondary metabolism in the cocultures. First, each member released and accumulated a unique set of primary metabolites over their time series. These exometabolites had relatively reduced concentrations in their coculture conditions. Second, because our experimental design included a comparative time point taken during exponential growth, we also identified a unique set of primary metabolites that had substantially accumulated by 12.5 h. Indeed, primary metabolites [83] have been documented to induce secondary metabolism in *B. thailandensis*. Thus, it is possible that the dynamics observed over stationary phase could be attributed also to the uptake of exometabolites that were produced earlier in exponential phase, or to the uptake of accumulated primary metabolites. Instead of pinpointing single molecule elicitors of secondary metabolism, our systems-level approach is better used to improve understanding of the environmental and ecological factors that contribute to member or community success.

*C. subtsugae* can inhibit *B. thailandensis* [33] but we did not observe *B. thailandensis* inhibition based on cell counts. However, we did find that in stationary phase *C. subtsugae*-*B. thailandensis* cocultures, *C. subtsugae* upregulated an uncharacterized hybrid nonribosomal peptide synthetase-type I polyketide synthase. *P. syringae* was the least competitive of the three neighbors, as evidenced by a reduction
We acknowledge that this study is limited in its ability to pinpoint the underlying mechanisms driving the activation of secondary metabolism, particularly in *B. thailandensis*. Aside from self-activating mechanisms documented in *B. thailandensis* (e.g. quorum-sensing driven bactobolin production) and/or sensing antibiotics and competitively responding [82], we note two major patterns in exometabolite production in the monocultures that may have contributed to activation of secondary metabolism in the cocultures. First, each member released and accumulated a unique set of primary metabolites over their time series. These exometabolites had relatively reduced concentrations in their coculture conditions. Second, because our experimental design included a comparative time point taken during exponential growth, we also identified a unique set of primary metabolites that had substantially accumulated by 12.5 h. Indeed, primary metabolites [83] have been documented to induce secondary metabolism in *B. thailandensis*. Thus, it is possible that the dynamics observed over stationary phase could be attributed also to the uptake of exometabolites that were produced earlier in exponential phase, or to the uptake of accumulated primary metabolites. Instead of pinpointing single molecule elicitors of secondary metabolism, our systems-level approach is better used to improve understanding of the environmental and ecological factors that contribute to member or community success.

*C. subtsugae* can inhibit *B. thailandensis* [33] but we did not observe *B. thailandensis* inhibition based on cell counts. However, we did find that in stationary phase *C. subtsugae*-*B. thailandensis* cocultures, *C. subtsugae* upregulated an uncharacterized hybrid nonribosomal peptide synthetase-type I polyketide synthase. *P. syringae* was the least competitive of the three neighbors, as evidenced by a reduction
in live cell counts when cocultured with *B. thailandensis*. Also, *P. syringae* did not increase competitive strategies when cocultured, as no BSGCs were upregulated across all coculture conditions. In summary, though all three neighbors had potential to use competitive strategies and maintained competitive strategies in monoculture [36], *B. thailandensis* was most successful in cocultures over stationary phase through increased production of exometabolites involved in interference and exploitative competition strategies.

Given the upregulation of BSGCs in *B. thailandensis* and the strong transcriptional responses of *C. subtsugae* and *P. syringae* to the presence of *B. thailandensis*, we hypothesized that competitive exometabolites were contributing to their community dynamics. Thus, we used a co-expression network analysis with our longitudinal transcriptome series to infer interspecies interactions [84]. The use of this approach was first demonstrated to infer coregulation between a phototroph-heterotroph commensal pair [85]. Our network confirmed that *B. thailandensis* BSGCs had coordinated gene expression patterns with both *C. subtsugae* and *P. syringae*. Interspecies nodes in both networks contained various genes involved in the upregulated *B. thailandensis* BSGCs. We focused on interspecies edges within thailandamide nodes for the *B. thailandensis-C. subtsugae* network and interspecies edges within malleilactone nodes for the *B. thailandensis-P. syringae* network because these were significantly enriched as interspecies nodes. A *C. subtsugae* gene of interest, CLV04_2968, was contained within the thailandamide cluster of interspecies nodes. This gene codes for a DNA starvation/stationary phase protection protein and had the highest homology to the Dps protein in *Escherichia coli* across all *C. subtsugae*
in live cell counts when cocultured with *B. thailandensis*. Also, *P. syringae* did not increase competitive strategies when cocultured, as no BSGCs were upregulated across all coculture conditions. In summary, though all three neighbors had potential to use competitive strategies and maintained competitive strategies in monoculture [36], *B. thailandensis* was most successful in cocultures over stationary phase through increased production of exometabolites involved in interference and exploitative competition strategies.

Given the upregulation of BSGCs in *B. thailandensis* and the strong transcriptional responses of *C. subtsugae* and *P. syringae* to the presence of *B. thailandensis*, we hypothesized that competitive exometabolites were contributing to their community dynamics. Thus, we used a co-expression network analysis with our longitudinal transcriptome series to infer interspecies interactions [84]. The use of this approach was first demonstrated to infer coregulation between a phototroph-heterotroph commensal pair [85]. Our network confirmed that *B. thailandensis* BSGCs had coordinated gene expression patterns with both *C. subtsugae* and *P. syringae*. Interspecies nodes in both networks contained various genes involved in the upregulated *B. thailandensis* BSGCs. We focused on interspecies edges within thailandamide nodes for the *B. thailandensis*-C. subtsugae network and interspecies edges within malleilactone nodes for the *B. thailandensis*-P. syringae network because these were significantly enriched as interspecies nodes. A *C. subtsugae* gene of interest, CLV04_2968, was contained within the thailandamide cluster of interspecies nodes. This gene codes for a DNA starvation/stationary phase protection protein and had the highest homology to the Dps protein in *Escherichia coli* across all *C. subtsugae*
773 protein coding genes. Dps mediates tolerance to multiple stressors and *dps* knockouts
774 are more susceptible to thermal, oxidative, antibiotic, iron toxicity, osmotic, and
775 starvation stressors [86]. Interestingly, CLV04_2968 was downregulated when
776 cocultured with *B. thailandensis*, suggesting that *B. thailandensis* attenuates *C*.

777 *subtsugae* stress tolerance over stationary phase. While we observed a slight decrease
778 in viable *C. subtsugae* cells when cocultured with *B. thailandensis*, one may expect *C*.

779 *subtsugae* to have increased sensitivity to a subsequent stress [e.g. pH stress; 87]
780 resulting from CLV04_2968 downregulation in the presence of *B. thailandensis*.

781 In the *B. thailandensis-P. syringae* co-expression network, a *P. syringae* gene of
782 interest, PSPTO_1206, was contained within the malleilactone cluster of interspecies
783 nodes. PSPTO_1206 is annotated as a TonB-dependent siderophore receptor. A *P*.
784 *syringae* iron-acquistion receptor had coordinated expression with malleilactone, which
785 has been characterized as a siderophore with antimicrobial properties [62]. Interestingly,
786 this gene was downregulated when in coculture with *B. thailandensis*. In contrast, the
787 closest TonB-dependent siderophore receptor homolog to PSPTO_1206 in *B*.

788 *thailandensis*, BTH_I2415, was upregulated in coculture conditions with *P. syringae*. To
789 summarize, co-expression network analysis revealed interspecies coordinated gene
790 expression patterns. Though determining directionality was beyond the scope of this
791 analysis, we observed *B. thailandensis*-increased competition strategies were
792 coordinated with a potential decrease in competition strategies in *C. subtsugae* via
793 reduced stress tolerance and in *P. syringae* with reduced iron acquisition ability.

794 One feature of our study is that we adjusted glucose concentration depending on
795 plate occupancy. Glucose concentration increased as membership increased, but a
773 protein coding genes. Dps mediates tolerance to multiple stressors and *dps* knockouts
774 are more susceptible to thermal, oxidative, antibiotic, iron toxicity, osmotic, and
775 starvation stressors [86]. Interestingly, CLV04_2968 was downregulated when
776 cocultured with *B. thailandensis*, suggesting that *B. thailandensis* attenuates *C*.

777 *subtsugae* stress tolerance over stationary phase. While we observed a slight decrease
778 in viable *C. subtsugae* cells when cocultured with *B. thailandensis*, one may expect *C*.

779 *subtsugae* to have increased sensitivity to a subsequent stress [e.g. pH stress; 87]
780 resulting from CLV04_2968 downregulation in the presence of *B. thailandensis*.

781 In the *B. thailandensis-P. syringae* co-expression network, a *P. syringae* gene of
782 interest, PSPTO_1206, was contained within the malleilactone cluster of interspecies
783 nodes. PSPTO_1206 is annotated as a TonB-dependent siderophore receptor. A *P*.
784 *syringae* iron-acquistion receptor had coordinated expression with malleilactone, which
785 has been characterized as a siderophore with antimicrobial properties [62]. Interestingly,
786 this gene was downregulated when in coculture with *B. thailandensis*. In contrast, the
787 closest TonB-dependent siderophore receptor homolog to PSPTO_1206 in *B*.

788 *thailandensis*, BTH_I2415, was upregulated in coculture conditions with *P. syringae*. To
789 summarize, co-expression network analysis revealed interspecies coordinated gene
790 expression patterns. Though determining directionality was beyond the scope of this
791 analysis, we observed *B. thailandensis*-increased competition strategies were
792 coordinated with a potential decrease in competition strategies in *C. subtsugae* via
793 reduced stress tolerance and in *P. syringae* with reduced iron acquisition ability.

794 One feature of our study is that we adjusted glucose concentration depending on
795 plate occupancy. Glucose concentration increased as membership increased, but a
member consistently occupied 31 wells across all experimental conditions. One could argue that resource concentration contributed to differences in transcripts and exometabolites and not interspecies interactions. However, DEGs were present when comparing pairwise coculture conditions and these were attributed to differences in temporal regulation of COG categories (Fig. S7). More specifically, regarding BSGCs, an unidentified NRPS was upregulated in *B. thailandensis* when cocultured with *P. syringae* but not when cocultured with *C. subtsugae* (Fig. 6) and, an unidentified NRPS-Type I polyketide synthase was upregulated in *C. subtsugae* when cocultured with *B. thailandensis* but not when cocultured with *P. syringae* (Fig. S11). These differences occurred in experimental conditions where the glucose concentration was the same. Furthermore, we performed additional SynCom experiments where we increased the plate occupancy of *B. thailandensis* in monoculture while subsequently increasing resource concentration. Decreased gene expression was observed across all three RT-qPCR tested thailandamide genes as both plate occupancy and resource concentration increased in *B. thailandensis* monocultures. These same three genes had increased gene expression across all cocultures. These findings show that some undefined exometabolite interspecies interactions were responsible for the increased expression of a BSGC in *B. thailandensis*. Overall, we acknowledge that resource concentration and exometabolite output are intertwined, and subsequent work could test how initial resource availability determines SynCom outcomes.

A major goal in microbial ecology is to predict community dynamics for purposes of modulating and/or maintaining ecosystem function [88, 89]. At its core, microbial functional properties emerge, in part, from the concerted interactions of multi-species
member consistently occupied 31 wells across all experimental conditions. One could argue that resource concentration contributed to differences in transcripts and exometabolites and not interspecies interactions. However, DEGs were present when comparing pairwise coculture conditions and these were attributed to differences in temporal regulation of COG categories (Fig. S7). More specifically, regarding BSGCs, an unidentified NRPS was upregulated in *B. thailandensis* when cocultured with *P. syringae* but not when cocultured with *C. subtsugae* (Fig. 6) and, an unidentified NRPS-Type I polyketide synthase was upregulated in *C. subtsugae* when cocultured with *B. thailandensis* but not when cocultured with *P. syringae* (Fig. S11). These differences occurred in experimental conditions where the glucose concentration was the same. Furthermore, we performed additional SynCom experiments where we increased the plate occupancy of *B. thailandensis* in monoculture while subsequently increasing resource concentration. Decreased gene expression was observed across all three RT-qPCR tested thailandamide genes as both plate occupancy and resource concentration increased in *B. thailandensis* monocultures. These same three genes had increased gene expression across all cocultures. These findings show that some undefined exometabolite interspecies interactions were responsible for the increased expression of a BSGC in *B. thailandensis*. Overall, we acknowledge that resource concentration and exometabolite output are intertwined, and subsequent work could test how initial resource availability determines SynCom outcomes.

A major goal in microbial ecology is to predict community dynamics for purposes of modulating and/or maintaining ecosystem function [88, 89]. At its core, microbial functional properties emerge, in part, from the concerted interactions of multi-species
assemblages. The SynCom system provides a tractable experimental system to understand the relationships between exometabolite interactions and environmental stimuli to inform higher-order community interactions. Higher-order interactions are those that are unexpected based on interactions observed in simpler situations (e.g., of member pairs) [90, 91, 92]. Therefore, integrating different system variables, like transcriptome and metabolome dynamics, within controlled microbial communities will inform how unexpected phenomena arise and how they contribute to deviations in predictive models of community outcomes.

Our results indicated that competition strategies were maintained despite stagnant population growth. *B. thailandensis* upregulated various bioactive exometabolites involved in both interference and exploitative competition when with neighbors. An effective competitor is often defined as by its ability to outcompete neighbors via growth advantage that stems from efficient nutrient uptake and/or biomass conversion rates [93, 94]. We add to this that a competitor can also have a fitness advantage through effective maintenance, which can similarly employ interference or exploitative competitive strategies despite no net growth. Maintenance may ensure survival in some environments that impose a stationary phase lifestyle, where long periods of nutrient depletion are punctuated with short periods of nutrient flux. In these scenarios, it warrants to understand how competitive strategies are deployed in the interim of growth and the extent to which these interactions contribute to long-term community outcomes. Though population levels remain constant, sub-populations of growing cells have been observed in stationary phase [95], and continued production of competitive exometabolites may serve as an advantageous
assemblages. The SynCom system provides a tractable experimental system to understand the relationships between exometabolite interactions and environmental stimuli to inform higher-order community interactions. Higher-order interactions are those that are unexpected based on interactions observed in simpler situations (e.g., of member pairs) [90, 91, 92]. Therefore, integrating different system variables, like transcriptome and metabolome dynamics, within controlled microbial communities will inform how unexpected phenomena arise and how they contribute to deviations in predictive models of community outcomes.

Our results indicated that competition strategies were maintained despite stagnant population growth. *B. thailandensis* upregulated various bioactive exometabolites involved in both interference and exploitative competition when with neighbors. An effective competitor is often defined as by its ability to outcompete neighbors via growth advantage that stems from efficient nutrient uptake and/or biomass conversion rates [93, 94]. We add to this that a competitor can also have a fitness advantage through effective maintenance, which can similarly employ interference or exploitative competitive strategies despite no net growth. Maintenance may ensure survival in some environments that impose a stationary phase lifestyle, where long periods of nutrient depletion are punctuated with short periods of nutrient flux. In these scenarios, it warrants to understand how competitive strategies are deployed in the interim of growth and the extent to which these interactions contribute to long-term community outcomes. Though population levels remain constant, sub-populations of growing cells have been observed in stationary phase [95], and continued production of competitive exometabolites may serve as an advantageous
strategy to hinder growth of competitors. In addition, some antibiotics remain effective in
non-replicating bacteria [96]. The ability for continued maintenance via effective
competition strategies during stationary phase may provide spatiotemporal maintenance
of population levels before growth resumption [97]. Alternatively, both growth and non-
growth strategies may be occurring simultaneously (e.g. as can occur in biofilms). The
heterogeneity of biofilms may provide an environment where a bacterial population
contains both stationary cells in the center of the colony with growing cells at the
periphery of the colony that compete and alter developmental patterns of neighboring
populations [98, 99]. Thus, we expect that insights into the long-term consequences of
competition for microbial community outcomes will be gained by considering
competition in both active growth and maintenance scenarios.

853

854 Code availability

Computing code, workflows, and data sets are available at
[https://github.com/ShadeLab/Paper_Chodkowski_3member_SynCom_2021]. R
packages used during computing analyses included DEseq2 [41], ImpulseDE2 [42],
VennDiagram [43], ggplot2 [44], vegan 2.5-4 [45], RVAideMemoire [46], Minet [50],
rtracklayer [100], viridis [101], and helper functions [102, 103, 104, 105].

860

861 Data availability

Genomes for *B. thailandensis*, *C. subtsugae*, and *P. syringae* are available at the
National Center for Biotechnology Information (NCBI) under accession
numbers [NC_007651](https://www.ncbi.nlm.nih.gov/gene/NC_007651) (Chromosome I)/[NC_007650](https://www.ncbi.nlm.nih.gov/gene/NC_007650) (Chromosome
strategy to hinder growth of competitors. In addition, some antibiotics remain effective in non-replicating bacteria [96]. The ability for continued maintenance via effective competition strategies during stationary phase may provide spatiotemporal maintenance of population levels before growth resumption [97]. Alternatively, both growth and non-growth strategies may be occurring simultaneously (e.g. as can occur in biofilms). The heterogeneity of biofilms may provide an environment where a bacterial population contains both stationary cells in the center of the colony with growing cells at the periphery of the colony that compete and alter developmental patterns of neighboring populations [98, 99]. Thus, we expect that insights into the long-term consequences of competition for microbial community outcomes will be gained by considering competition in both active growth and maintenance scenarios.

853

## Code availability

Computing code, workflows, and data sets are available at [https://github.com/ShadeLab/Paper_Chodkowski_3member_SynCom_2021]. R packages used during computing analyses included DEseq2 [41], ImpulseDE2 [42], VennDiagram [43], ggplot2 [44], vegan 2.5-4 [45], RVAideMemoire [46], Minet [50], rtracklayer [100], viridis [101], and helper functions [102, 103, 104, 105].

860

## Data availability

Genomes for *B. thailandensis*, *C. subtsugae*, and *P. syringae* are available at the National Center for Biotechnology Information (NCBI) under accession numbers [NC_007651](https://www.ncbi.nlm.nih.gov/gene/NC_007651) (Chromosome I)/[NC_007650](https://www.ncbi.nlm.nih.gov/gene/NC_007650) (Chromosome
II), NZ_PKBZ01000001, and NC_004578 (Chromosome)/NC_004633 (Plasmid A)/NC_004632 (Plasmid B), respectively. An improved annotated draft genome of C. subtsugae is available under NCBI BioProject accession number PRJNA402426 (GenBank accession number PKBZ00000000). Data for resequencing efforts for B. thailandensis and P. syringae are under NCBI BioProject accession numbers PRJNA402425 and PRJNA402424, respectively. Metabolomics data and transcriptomics data are also available at the JGI Genome Portal [106] under JGI proposal identifier 502921. MZmine XML parameter files for all analyses can be viewed at and downloaded from GitHub (see Dataset 7 at https://github.com/ShadeLab/Paper_Chodkowski_MonocultureExometabolites_2020/tree/master/Datasets). Large data files (e.g., MZmine project files) are available upon request. Supplementary files are also available on GitHub (https://github.com/ShadeLab/Paper_Chodkowski_3member_SynCom_2021/tree/master/Supplemental_Files).

879

880 Acknowledgements

This material is based upon work supported by the National Science Foundation under grant DEB 1749544 and by Michigan State University. In addition, metabolite analysis and transcript sequencing were provided by a DOE-JGI Community Science Program award (proposal identifier 502921). The work conducted by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, is supported under contract number DE-AC02-05CH11231. J.L.C. was supported by the Eleanor L. Gilmore Fellowship from the Department of Microbiology and Molecular Genetics.
II), NZ_PKBZ01000001, and NC_004578 (Chromosome)/NC_004633 (Plasmid A)/NC_004632 (Plasmid B), respectively. An improved annotated draft genome of C. subtsugae is available under NCBI BioProject accession number PRJNA402426 (GenBank accession number PKBZ00000000). Data for resequencing efforts for B. thailandensis and P. syringae are under NCBI BioProject accession numbers PRJNA402425 and PRJNA402424, respectively. Metabolomics data and transcriptomics data are also available at the JGI Genome Portal [106] under JGI proposal identifier 502921. MZmine XML parameter files for all analyses can be viewed at and downloaded from GitHub (see Dataset 7 at https://github.com/ShadeLab/Paper_Chodkowski_MonocultureExometabolites_2020/tree/master/Datasets). Large data files (e.g., MZmine project files) are available upon request. Supplementary files are also available on GitHub (https://github.com/ShadeLab/Paper_Chodkowski_3member_SynCom_2021/tree/master/Supplemental_Files).

879

880 Acknowledgements

This material is based upon work supported by the National Science Foundation under grant DEB 1749544 and by Michigan State University. In addition, metabolite analysis and transcript sequencing were provided by a DOE-JGI Community Science Program award (proposal identifier 502921). The work conducted by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, is supported under contract number DE-AC02-05CH11231. J.L.C. was supported by the Eleanor L. Gilmore Fellowship from the Department of Microbiology and Molecular Genetics.
888

We thank Katherine B. Louie and Benjamin P. Bowen for support in mass spectral
analysis.

889

890

891

**Competing Interests**

892

The authors declare no competing financial interests.

893

894

**Author contributions**

895

J.L.C. and A.S. conceived of and designed the study. J.L.C. performed the research
and analyses. J.L.C. and A.S. wrote the manuscript.

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The authors declare no competing financial interests.

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J.L.C. and A.S. conceived of and designed the study. J.L.C. performed the research
and analyses. J.L.C. and A.S. wrote the manuscript.

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932 11. Kell, D. B., Brown, M., Davey, H. M., Dunn, W. B., Spasic, I., & Oliver, S. G. (2005). Metabolic footprinting and systems biology: The medium is the message. *Nature Reviews Microbiology*, 3(7), 557-565.
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933 12. Pinu, F. R., & Villas-Boas, S. G. (2017). Extracellular microbial metabolomics: The state of the art. *Metabolites*, 7(3), 43.
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934 13. Silva, L. P., & Northen, T. R. (2015). Exometabolomics and MSI: Deconstructing how cells interact to transform their small molecule environment. *Current Opinion in Biotechnology*, 34, 209-216.
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935 14. Großkopf, T., & Soyer, O. S. (2014). Synthetic microbial communities. *Current Opinion in Microbiology*, 18(1), 72-77.
https://doi.org/10.1016/j.mib.2014.02.002

951 15. Foster, K. R., & Bell, T. (2012). Competition, not cooperation, dominates interactions among culturable microbial species. *Current Biology*, 22(19), 1845-1850.
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953 16. Coyte, K. Z., Schluter, J., & Foster, K. R. (2015). The ecology of the microbiome: Networks, competition, and stability. *Science*, 350(6261), 663-666.
https://doi.org/10.1126/science.aad2602

954 17. Hibbing, M. E., Fuqua, C., Parsek, M. R., & Peterson, S. B. (2010). Bacterial competition: Surviving and thriving in the microbial jungle. *Nature Reviews Microbiology*, 8(1), 15-25.
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965 18. Tilman, D. (1982). Resource Competition and Community Structure. Princeton, NJ, U.S.A.: Princeton University Press.

968 19. Stewart, F. M., & Levin, B. R. (1973). Partitioning of Resources and the Outcome of Interspecific Competition: A Model and Some General Considerations. *The American Naturalist*, 107(954), 171-198.
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919 6. Megan Steinweg, J., Dukes, J. S., Paul, E. A., & Wallenstein, M. D. (2013). Microbial responses to multi-factor climate change: Effects on soil enzymes. *Frontiers in Microbiology*, 4, 146.
https://doi.org/10.3389/fmicb.2013.00146

921 7. Aziz, F. A. A., Suzuki, K., Ohtaki, A., Sagegami, K., Hirai, H., Seno, J., ... Futamata, H. (2015). Interspecies interactions are an integral determinant of microbial community dynamics. *Frontiers in Microbiology*, 6, 1148. https://doi.org/10.3389/fmicb.2015.01148

922 8. Orr, J. A., Vinebrooke, R. D., Jackson, M. C., Kroeker, K. J., Kordas, R. L., Mantyka-Pringle, C., ... Piggott, J. J. (2020). Towards a unified study of multiple stressors: Divisions and common goals across research disciplines. *Proceedings of the Royal Society B: Biological Sciences*, 287(1926), 20200421. https://doi.org/10.1098/rspb.2020.0421

923 9. Little, A. E. F., Robinson, C. J., Peterson, S. B., Raffa, K. F., & Handelsman, J. (2008). Rules of engagement: Interspecies interactions that regulate microbial communities. *Annual Review of Microbiology*, 62, 375-401. https://doi.org/10.1146/annurev.micro.030608.101423

931 10. Phelan, V. V., Liu, W.-T., Pogliano, K., & Dorrestein, P. C. (2011). Microbial metabolic exchange-the chemotype-to-phenotype link. *Nature Chemical Biology*, 8(1), 26-35.
https://doi.org/10.1038/nchembio.739

932 11. Kell, D. B., Brown, M., Davey, H. M., Dunn, W. B., Spasic, I., & Oliver, S. G. (2005). Metabolic footprinting and systems biology: The medium is the message. *Nature Reviews Microbiology*, 3(7), 557-565. https://doi.org/10.1038/nrmicro1177

933 12. Pinu, F. R., & Villas-Boas, S. G. (2017). Extracellular microbial metabolomics: The state of the art. *Metabolites*, 7(3), 43. https://doi.org/10.3390/metabo7030043

934 13. Silva, L. P., & Northen, T. R. (2015). Exometabolomics and MSI: Deconstructing how cells interact to transform their small molecule environment. *Current Opinion in Biotechnology*, 34, 209-216. https://doi.org/10.1016/j.copbio.2015.03.015

935 14. Großkopf, T., & Soyer, O. S. (2014). Synthetic microbial communities. *Current Opinion in Microbiology*, 18(1), 72-77. https://doi.org/10.1016/j.mib.2014.02.002

951 15. Foster, K. R., & Bell, T. (2012). Competition, not cooperation, dominates interactions among culturable microbial species. *Current Biology*, 22(19), 1845-1850.
https://doi.org/10.1016/j.cub.2012.08.005

953 16. Coyte, K. Z., Schluter, J., & Foster, K. R. (2015). The ecology of the microbiome: Networks, competition, and stability. *Science*, 350(6261), 663-666.
https://doi.org/10.1126/science.aad2602

954 17. Hibbing, M. E., Fuqua, C., Parsek, M. R., & Peterson, S. B. (2010). Bacterial competition: Surviving and thriving in the microbial jungle. *Nature Reviews Microbiology*, 8(1), 15-25.
https://doi.org/10.1038/nrmicro2259

965 18. Tilman, D. (1982). Resource Competition and Community Structure. Princeton, NJ, U.S.A.: Princeton University Press.

968 19. Stewart, F. M., & Levin, B. R. (1973). Partitioning of Resources and the Outcome of Interspecific Competition: A Model and Some General Considerations. *The American Naturalist*, 107(954), 171-198. https://doi.org/10.1086/282825
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25. Fetissov, S. O. (2017). Role of the gut microbiota in host appetite control: Bacterial growth to animal feeding behaviour. Nature Reviews Endocrinology, 13(1), 11–25. https://doi.org/10.1038/nrendo.2016.150

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27. Navarro Llorens, J. M., Tormo, A., & Martínez-García, E. (2010). Stationary phase in gram-negative bacteria. FEMS Microbiology Reviews, 34(4), 476–495. https://doi.org/10.1111/j.1574-6976.2010.00213.x

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29. Medema, M. H., Kottmann, R., Yilmaz, P., Cummings, M., Biggins, J. B., Blin, K., ... Glöckner, F. O. (2015). Minimum Information about a Biosynthetic Gene cluster. Nature Chemical Biology, 11(9), 625–631. https://doi.org/10.1038/nchembio.1890

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23. Schimel, J. P. (2018). Life in dry soils: Effects of drought on soil microbial communities and processes. Annual Review of Ecology, Evolution, and Systematics, 49, 409–432. https://doi.org/10.1146/annurev-ecolsys-110617-062614

24. Chiesa, S. C., Irvine, R. L., & Manning, J. F. (1985). Feast/famine growth environments and activated sludge population selection. Biotechnology and Bioengineering, 27(5), 562–568. https://doi.org/10.1002/bit.260270503

25. Fetissov, S. O. (2017). Role of the gut microbiota in host appetite control: Bacterial growth to animal feeding behaviour. Nature Reviews Endocrinology, 13(1), 11–25. https://doi.org/10.1038/nrendo.2016.150

26. Hiltunen, T., Laakso, J., Kaitala, V., Suomalainen, L. R., & Pekkonen, M. (2008). Temporal variability in detritus resource maintains diversity of bacterial communities. Acta Oecologica, 33(3), 291–299. https://doi.org/10.1016/j.actao.2007.12.002

27. Navarro Llorens, J. M., Tormo, A., & Martínez-García, E. (2010). Stationary phase in gram-negative bacteria. FEMS Microbiology Reviews, 34(4), 476–495. https://doi.org/10.1111/j.1574-6976.2010.00213.x

28. Čihák, M., Kameník, Z., Šmídová, K., Bergman, N., Benada, O., Kofronová, O., ... Bobek, J. (2017). Secondary metabolites produced during the germination of Streptomyces coelicolor. Frontiers in Microbiology, 8, 2495. https://doi.org/10.3389/fmicb.2017.02495

29. Medema, M. H., Kottmann, R., Yilmaz, P., Cummings, M., Biggins, J. B., Blin, K., ... Glöckner, F. O. (2015). Minimum Information about a Biosynthetic Gene cluster. Nature Chemical Biology, 11(9), 625–631. https://doi.org/10.1038/nchembio.1890

30. Cornforth, D. M., & Foster, K. R. (2013). Competition sensing: The social side of bacterial stress responses. Nature Reviews Microbiology, 11(4), 285–293. https://doi.org/10.1038/nrmicro2977

31. Okada, B. K., & Seyedsayamdost, M. R. (2017). Antibiotic dialogues: Induction of silent biosynthetic gene clusters by exogenous small molecules. FEMS Microbiology Reviews, 41(1), 19–33. https://doi.org/10.1093/femsre/fuw035

32. Goh, E. B., Yim, G., Tsui, W., McClure, J. A., Surette, M. G., & Davies, J. (2002). Transcriptional modulation of bacterial gene expression by subinhibitory concentrations of antibiotics. Proceedings of the National Academy of Sciences of the United States of America, 99(26), 17025–17030. https://doi.org/10.1073/pnas.252607699
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34. De Roy, K., Marzorati, M., Van den Abbeele, P., Van de Wiele, T., & Boon, N. (2014). Synthetic microbial ecosystems: an exciting tool to understand and apply microbial communities. Environmental Microbiology, 16(6), 1472-1481. https://doi.org/10.1111/1462-2920.12343

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1259 90. Sanchez-Gorostiaga, A., Bajić, D., Osborne, M. L., Poyatos, J. F., & Sanchez, A. (2019). High-order interactions distort the functional landscape of microbial consortia. PLoS Biology, 17(12), e3000550.
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1263 91. Mickalide, H., & Kuehn, S. (2019). Higher-Order Interaction between Species Inhibits Bacterial Invasion of a Phototroph-Predator Microbial Community. Cell Systems, 9(6), 521-533.e10.
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1267 92. D'hoe, K., Vet, S., Faust, K., Moens, F., Falony, G., Gonze, D., ... Raes, J. (2018). Integrated culturing, modeling and transcriptomics uncovers complex interactions and emergent behavior in a three-species synthetic gut community. ELife, 7, e37090.
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1272 93. Miller, T. E., Burns, J. H., Munguia, P., Walters, E. L., Kneitel, J. M., Richards, P. M., ... Buckley, H. L. (2005). A critical review of twenty years' use of the resource-ratio theory. American Naturalist, 165(4), 439-448.
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1277 94. De Jong, H., Casagranda, S., Giordano, N., Cinquemani, E., Ropers, D., Geiselmann, J., & Gouzé, J. L. (2017). Mathematical modelling of microbes: Metabolism, gene expression and growth. Journal of the Royal Society Interface, 14(136), 20170502.
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1282 95. Jõers, A., Liske, E., & Tenson, T. (2020). Dividing subpopulation of Escherichia coli in stationary phase. Research in Microbiology, 171(3-4), 153-157.
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1233 83. Li, A., Mao, D., Yoshimura, A., Rosen, P. C., Martin, W. L., Gallant, É., ... Seyedsayamdost, M. R. (2020). Multi-omic analyses provide links between low-dose antibiotic treatment and induction of secondary metabolism in Burkholderia thailandensis. mBio, 11(1), e03210-19.
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1237 84. McClure, R. S. (2019). Toward a Better Understanding of Species Interactions through Network Biology. MSystems, 4(3), e00114-19. https://doi.org/10.1128/msystems.00114-19

1239 85. McClure, R. S., Overall, C. C., Hill, E. A., Song, H.-S., Charania, M., Bernstein, H. C., ... Beliaev, A. S. (2018). Species-specific transcriptomic network inference of interspecies interactions. ISME J, 12(8), 2011-2023. https://doi.org/10.1038/s41396-018-0145-6

1244 86. Karas, V. O., Westerlaken, I., & Meyer, A. S. (2015). The DNA-binding protein from starved cells (Dps) utilizes dual functions to defend cells against multiple stresses. Journal of Bacteriology, 197(19), 3206-3215. https://doi.org/10.1128/JB.00475-15

1248 87. Nair, S., & Finkel, S. E. (2004). Dps protects cells against multiple stresses during stationary phase. Journal of Bacteriology, 186(13), 4192-4198. https://doi.org/10.1128/JB.186.13.4192-4198.2004

1252 88. Antwis, R. E., Griffiths, S. M., Harrison, X. A., Aranega-Bou, P., Arce, A., Bettridge, A. S., ... Sutherland, W. J. (2017). Fifty important research questions in microbial ecology. FEMS Microbiology Ecology, 93(5), fix044. https://doi.org/10.1093/femsec/fix044

1256 89. Konopka, A. (2009). What is microbial community ecology. ISME J, 3(11), 1223-1230.
https://doi.org/10.1038/ismej.2009.88

1259 90. Sanchez-Gorostiaga, A., Bajić, D., Osborne, M. L., Poyatos, J. F., & Sanchez, A. (2019). High-order interactions distort the functional landscape of microbial consortia. PLoS Biology, 17(12), e3000550. https://doi.org/10.1371/journal.pbio.3000550

1263 91. Mickalide, H., & Kuehn, S. (2019). Higher-Order Interaction between Species Inhibits Bacterial Invasion of a Phototroph-Predator Microbial Community. Cell Systems, 9(6), 521-533.e10.
https://doi.org/10.1016/j.cels.2019.11.004

1267 92. D'hoe, K., Vet, S., Faust, K., Moens, F., Falony, G., Gonze, D., ... Raes, J. (2018). Integrated culturing, modeling and transcriptomics uncovers complex interactions and emergent behavior in a three-species synthetic gut community. ELife, 7, e37090.
https://doi.org/10.7554/eLife.37090

1272 93. Miller, T. E., Burns, J. H., Munguia, P., Walters, E. L., Kneitel, J. M., Richards, P. M., ... Buckley, H. L. (2005). A critical review of twenty years' use of the resource-ratio theory. American Naturalist, 165(4), 439-448. https://doi.org/10.1086/428681

1277 94. De Jong, H., Casagranda, S., Giordano, N., Cinquemani, E., Ropers, D., Geiselmann, J., & Gouzé, J. L. (2017). Mathematical modelling of microbes: Metabolism, gene expression and growth. Journal of the Royal Society Interface, 14(136), 20170502.
https://doi.org/10.1098/rsif.2017.0502

1282 95. Jõers, A., Liske, E., & Tenson, T. (2020). Dividing subpopulation of Escherichia coli in stationary phase. Research in Microbiology, 171(3-4), 153-157.
https://doi.org/10.1016/j.resmic.2020.02.002
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96. McCall, I. C., Shah, N., Govindan, A., Baquero, F., & Levin, B. R. (2019). Antibiotic killing of diversely generated populations of nonreplicating bacteria. Antimicrobial Agents and Chemotherapy, 63(7), e02360-18. https://doi.org/10.1128/AAC.02360-18

97. Chesson, P. L. (1983). Coexistence of Competitors in a Stochastic Environment: The Storage Effect. Lecture Notes in Biomathematics, 52, 188–198. https://doi.org/10.1007/978-3-642-87893-0_25

98. Stewart, P. S., & Franklin, M. J. (2008). Physiological heterogeneity in biofilms. Nature Reviews Microbiology 2008 6:3, 6(3), 199–210. https://doi.org/10.1038/nrmicro1838

99. Traxler, M. F., Seyedsayamdost, M. R., Clardy, J., & Kolter, R. (2012). Interspecies modulation of bacterial development through iron competition and siderophore piracy. Molecular Microbiology, 86(3), 628. https://doi.org/10.1111/MMI.12008

100. Lawrence, M., Gentleman, R., & Carey, V. (2009). rtracklayer: An R package for interfacing with genome browsers. Bioinformatics, 25(14), 1841–1842. https://doi.org/10.1093/bioinformatics/btp328

101. Garnier, S., Ross, N., Rudis, R., Camargo, P. A., Sciaini, M., & Scherer, C. (2021). viridis - Colorblind-Friendly Color Maps for R, R package, https://sjmgarnier.github.io/viridis/. https://doi.org/10.5281/zenodo.4679424

102. Wickham, H. (2007). Reshaping data with the reshape package. Journal of Statistical Software, 21(12), 1–20. https://doi.org/10.18637/jss.v021.i12

103. Wickham, H. (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0.

104. Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., ... Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686

105. Wickham, H., François, R., Henry, L., & Müller, K. (2019). dplyr: A Grammar of Data Manipulation. R package version 0.8.3.

106. Nordberg, H., Cantor, M., Dusheyko, S., Hua, S., Poliakov, A., Shabalov, I., ... Dubchak, I. (2014). The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Research, 42, D26-31. https://doi.org/10.1093/nar/gkt1069
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96. McCall, I. C., Shah, N., Govindan, A., Baquero, F., & Levin, B. R. (2019). Antibiotic killing of diversely generated populations of nonreplicating bacteria. Antimicrobial Agents and Chemotherapy, 63(7), e02360-18. https://doi.org/10.1128/AAC.02360-18

97. Chesson, P. L. (1983). Coexistence of Competitors in a Stochastic Environment: The Storage Effect. Lecture Notes in Biomathematics, 52, 188–198. https://doi.org/10.1007/978-3-642-87893-0_25

98. Stewart, P. S., & Franklin, M. J. (2008). Physiological heterogeneity in biofilms. Nature Reviews Microbiology 2008 6:3, 6(3), 199–210. https://doi.org/10.1038/nrmicro1838

99. Traxler, M. F., Seyedsayamdost, M. R., Clardy, J., & Kolter, R. (2012). Interspecies modulation of bacterial development through iron competition and siderophore piracy. Molecular Microbiology, 86(3), 628. https://doi.org/10.1111/MMI.12008

100. Lawrence, M., Gentleman, R., & Carey, V. (2009). rtracklayer: An R package for interfacing with genome browsers. Bioinformatics, 25(14), 1841–1842. https://doi.org/10.1093/bioinformatics/btp328

101. Garnier, S., Ross, N., Rudis, R., Camargo, P. A., Sciaini, M., & Scherer, C. (2021). viridis - Colorblind-Friendly Color Maps for R, R package, https://sjmgarnier.github.io/viridis/. https://doi.org/10.5281/zenodo.4679424

102. Wickham, H. (2007). Reshaping data with the reshape package. Journal of Statistical Software, 21(12), 1–20. https://doi.org/10.18637/jss.v021.i12

103. Wickham, H. (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0.

104. Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., ... Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686

105. Wickham, H., François, R., Henry, L., & Müller, K. (2019). dplyr: A Grammar of Data Manipulation. R package version 0.8.3.

106. Nordberg, H., Cantor, M., Dusheyko, S., Hua, S., Poliakov, A., Shabalov, I., ... Dubchak, I. (2014). The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Research, 42, D26-31. https://doi.org/10.1093/nar/gkt1069
**Figure 1.** Experimental design and destructive sampling procedure of transwell plates. There were seven conditions, six time points/condition, and four independent replicates/condition (168 total transwell plates). Each member occupied 31 wells/condition to maintain member-specific population density across all conditions (A). The SynCom transwell plate maintains physical separation of members in individual wells while permitting exometabolite exchange through a 0.22-µm-pore filter bottom. Exometabolite exchange occurs via a bottom-fitted shared medium reservoir (B; [35]). Six replicate transwell plates were prepared for a time course experiment. The time course experiment included one exponential phase time point and five stationary phase time points. At specified time points, a transwell plate was destructively sampled (C). Note that all members were diluted to different starting ODs to allow for all members to achieve stationary phase within a two-hour window of each other. This figure was created with BioRender.com.
**Figure 1.** Experimental design and destructive sampling procedure of transwell plates. There were seven conditions, six time points/condition, and four independent replicates/condition (168 total transwell plates). Each member occupied 31 wells/condition to maintain member-specific population density across all conditions (A). The SynCom transwell plate maintains physical separation of members in individual wells while permitting exometabolite exchange through a 0.22-µm-pore filter bottom. Exometabolite exchange occurs via a bottom-fitted shared medium reservoir (B; [35]). Six replicate transwell plates were prepared for a time course experiment. The time course experiment included one exponential phase time point and five stationary phase time points. At specified time points, a transwell plate was destructively sampled (C). Note that all members were diluted to different starting ODs to allow for all members to achieve stationary phase within a two-hour window of each other. This figure was created with BioRender.com.
A

B. thailandensis
E264

C. subtsugae
ATCC 31532

P. syringae
DC3000

<figure><img src="image_1.png" /></figure>

Monocultures

<figure><img src="image_2.png" /></figure>

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<figure><img src="image_3.png" /></figure>

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<figure><img src="image_5.png" /></figure>

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<figure><img src="image_7.png" /></figure>

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B

<figure><img src="image_9.png" /></figure>

C

Destructive time series example

<figure><img src="image_10.png" /></figure>
A

B. thailandensis
E264

C. subtsugae
ATCC 31532

P. syringae
DC3000

<figure><img src="image_2.png" /></figure>

Monocultures

<figure><img src="image_3.png" /></figure>

Cocultures

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

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B

<figure><img src="image_10.png" /></figure>

C

Destructive time series example

<figure><img src="image_11.png" /></figure>
**Figure 2.** Loss of cell viability in *B. thailandensis* cocultures. Live (green) and dead (blue) flow cytometry cell counts for *C. subtsugae* (Top row, panels A-C) and *P. syringae* (Bottom row, panels D-F) from Syto9- and propidium iodide-stained cells (n = 4 to 5 technical replicates/time point/community membership/transwell plate and n = 4 independent replicates/time point/community membership). Cell counts are from monocultures (panels A & D), cocultures with *P. syringae* (panel B) or *C. subtsugae* (panel E), and cocultures with *B. thailandensis* (panels C & F). The bottom and top of the box are the first (Q1) and third (Q3) quartiles, respectively, and the line inside the box is the median. The whiskers extend from their respective hinges to the largest value (top), and smallest value (bottom) was no further away than 1.5x the interquartile range. Points represent outliers that are less than 1.5x the interquartile range of Q1 or greater than 1.5x the interquartile range of Q3.
**Figure 2.** Loss of cell viability in *B. thailandensis* cocultures. Live (green) and dead (blue) flow cytometry cell counts for *C. subtsugae* (Top row, panels A-C) and *P. syringae* (Bottom row, panels D-F) from Syto9- and propidium iodide-stained cells (n = 4 to 5 technical replicates/time point/community membership/transwell plate and n = 4 independent replicates/time point/community membership). Cell counts are from monocultures (panels A & D), cocultures with *P. syringae* (panel B) or *C. subtsugae* (panel E), and cocultures with *B. thailandensis* (panels C & F). The bottom and top of the box are the first (Q1) and third (Q3) quartiles, respectively, and the line inside the box is the median. The whiskers extend from their respective hinges to the largest value (top), and smallest value (bottom) was no further away than 1.5x the interquartile range. Points represent outliers that are less than 1.5x the interquartile range of Q1 or greater than 1.5x the interquartile range of Q3.
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**Figure 3.** Transcriptional responses are driven by community membership and time. Shown are principal coordinates analysis (PCoA) plots for *B. thailandensis* (A), *C. subtsugae* (B), and *P. syringae* (C). Each PCoA sub-panel presents the time series of transcriptional patterns of the focal member given each of its 4 growth conditions (one monoculture condition, two pairs, and one three-member). Each point represents a mean transcript profile for a community member given a particular condition (indicated by symbol color) and sampled at a given time point over exponential and stationary phases (in hours since inoculation, h, indicated by symbol size, n = 3 to 4 replicates per time point/community membership). The Bray-Curtis distance metric was used to calculate dissimilarities between transcript profiles. Error bars are 1 standard deviation around the mean axis scores. Note that transcriptional responses are driven by community membership on PCoA axis 1 and time on PCoA axis 2 across all plots.
**Figure 3.** Transcriptional responses are driven by community membership and time. Shown are principal coordinates analysis (PCoA) plots for *B. thailandensis* (A), *C. subtsugae* (B), and *P. syringae* (C). Each PCoA sub-panel presents the time series of transcriptional patterns of the focal member given each of its 4 growth conditions (one monoculture condition, two pairs, and one three-member). Each point represents a mean transcript profile for a community member given a particular condition (indicated by symbol color) and sampled at a given time point over exponential and stationary phases (in hours since inoculation, h, indicated by symbol size, n = 3 to 4 replicates per time point/community membership). The Bray-Curtis distance metric was used to calculate dissimilarities between transcript profiles. Error bars are 1 standard deviation around the mean axis scores. Note that transcriptional responses are driven by community membership on PCoA axis 1 and time on PCoA axis 2 across all plots.
<figure><img src="image_12.png" /></figure>

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<figure><img src="image_14.png" /></figure>

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**Figure 4.** Bacterial community exometabolite profiles differ by community membership and time. Shown are PCoA plots for exometabolite profiles from the following mass spectrometry modes: polar positive (A), polar negative (B), nonpolar positive (C), and nonpolar negative (D). Each point represents the mean exometabolite profile (relative contributions by peak area) given a particular community membership (indicated by symbol color) at a particular time point (indicated by symbol size). The Bray-Curtis distance metric was used to calculate dissimilarities between exometabolite profiles. Error bars are 1 standard deviation around the mean axis scores (n = 2 to 4 replicates). Bt is *B. thailandensis*, Cs is *C. subtsugae*, and Ps is *P. syringae*.
**Figure 4.** Bacterial community exometabolite profiles differ by community membership and time. Shown are PCoA plots for exometabolite profiles from the following mass spectrometry modes: polar positive (A), polar negative (B), nonpolar positive (C), and nonpolar negative (D). Each point represents the mean exometabolite profile (relative contributions by peak area) given a particular community membership (indicated by symbol color) at a particular time point (indicated by symbol size). The Bray-Curtis distance metric was used to calculate dissimilarities between exometabolite profiles. Error bars are 1 standard deviation around the mean axis scores (n = 2 to 4 replicates). Bt is *B. thailandensis*, Cs is *C. subtsugae*, and Ps is *P. syringae*.