Research Article

Microbiology and Infectious Disease

Niche exclusion of a lung pathogen in mice with designed probiotic communities

Department of Plant and Microbial Biology, University of California, Berkeley, United States
Division of Biosciences and Biotechnology, Lawrence Livermore National Laboratory, United States
Department of Biotechnology and Bioengineering, Sandia National Laboratories, United States
Department of Systems Biology, Sandia National Laboratories, United States
Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, United States
Department of Biological Engineering, University of California, Berkeley, United States

Aug 4, 2026

https://doi.org/10.7554/eLife.108304.3

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eLife Assessment

This study presents a valuable framework for the rational design of bacterial probiotics to protect against respiratory infections. The evidence supporting the central claim — that metabolic niche overlap predicts probiotic efficacy — is solid, combining innovative in vitro modeling with in vivo validation, though the model appears less effective for probiotics that rely on antimicrobial metabolite production.

https://doi.org/10.7554/eLife.108304.3.sa0

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Solid: Methods, data and analyses broadly support the claims with only minor weaknesses

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Abstract
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Results
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Abstract

For years, the airway microbiota have been theorized to be gatekeepers of respiratory health, as pathogens entering the airway make contact with resident microbes prior to or coincident with their interaction with host cells. Thus, modification of the native airway community may serve as a means of altering the local environment in favor of health. In this work, we hypothesize that synthetic bacterial communities introduced into the airway can serve as prophylactic countermeasures against infection by Burkholderia thailandensis in mice. We demonstrate that understanding of antagonistic interactions between a pathogen and airway microbiota in vitro can guide identification of probiotics with protective capabilities in vivo. Specifically, we show that niche overlap between the probiotic and pathogen is indicative of probiotic performance in vivo. This work serves as a foundation for the rational design of probiotic communities for protection against and treatment of respiratory infections.

Introduction

Every mammalian body compartment that opens to the outside world has microbial life associated with it (Costello et al., 2009). Until recently, the healthy lung was thought to be a sterile environment; however, recent work has demonstrated that the lower respiratory tract is colonized by a complex and roughly stable microbiome that impacts the health of the host (Santacroce et al., 2020; Man et al., 2017; Das et al., 2021). For years, the airway microbiota have been theorized to be gatekeepers of respiratory health, as pathogens entering the airway make contact with its microbiota prior to or coincident with their interactions with host cells (Man et al., 2017). While the latter interactions have been studied in detail, little is known about pathogen-microbiome interactions in the airway.

It has been hypothesized that pathogen establishment in the airway could be halted by its microbiota through either indirect or direct competition with the pathogen (Man et al., 2017; Bäumler and Sperandio, 2016; Kamada et al., 2013). During indirect competition, airway microbiota would modulate the host immune response to increase surveillance, promoting rapid recognition and clearance of foreign microbes, including pathogenic organisms. During direct competition, the airway microbiota would competitively exclude a pathogen through production of inhibitory specialized metabolites, and/or by occupying a nutritional niche that would otherwise be used by the pathogen. Enhancement of these defense mechanisms might be achieved through addition of health-promoting microbes (probiotics) directly to the lower airway, an approach that has been demonstrated to prevent pneumococcal and Pseudomonas aeruginosa infection previously (Fangous et al., 2019; Yildiz et al., 2020).

While these studies have shed light on the utility of lower airway probiotics for preventing two specific infections, greater understanding of the principles that dictate probiotic efficacy would allow rapid identification of probiotics with therapeutic potential against many pathogenic targets. To enable development of these therapies in the future, pipelines for probiotic nomination need to be made which can rapidly and cheaply identify effective therapies. Furthermore, systematic analysis of the mechanism of action of these probiotics would allow fine-tuned control of their activity allowing for more reliable therapeutic outcomes in the clinic.

Here, we seek to fill this gap in knowledge by systematically characterizing factors that make efficacious lower airway probiotics that can competitively exclude a model pathogen, Burkholderia thailandensis (Bt), from the lung environment. Specifically, we hypothesize that negative microbial interactions between a probiotic and pathogen better protect the host against infection. Our systematic study of the microbial ecology of these organisms enables development of a pipeline for nomination of effective single- and multi-organism probiotics that, to this point, remain underdeveloped. We show that using our pipeline, we can accurately identify pairwise combinations of organisms with enhanced activity against Bt. Finally, we test the ability of these prophylactically administered probiotics to promote survival during a challenge with Bt in vivo.

Results

Isolation and characterization of candidate probiotics

To develop a system for testing the efficacy of lower airway probiotics in preventing infection, it was first necessary to identify a suitable pathogen to investigate. Bt is a pathogen of mice that is genomically similar to several human pathogens, including Burkholderia pseudomallei, Burkholderia mallei and organisms of the Burkholderia cepacia complex, the latter of which poses a high risk of mortality for cystic fibrosis patients (Fauroux et al., 2004; Coenye et al., 2001; Yu et al., 2006; Brett et al., 1998; Kim et al., 2005). Thus, Bt is an appropriate subject of study for our purposes as it is safe, infectious in mice and a good model of several pathogens with clinical relevance.

To acquire candidate probiotics for use in the lower airway, we sought organisms which would be able to survive in the lower airway for an extended period of time and be capable of tolerating its low-nutrient environment (Günther et al., 1996). We hypothesized that organisms directly isolated from the airway would fulfil this criterion, as they are already well-adapted to the environment and may be more immunologically tolerated by the host. To isolate candidate probiotics, the trachea and lungs were collected from C57Bl/6 j mice, homogenized, and plated onto five different solid growth media, either directly or after an initial period of culture in blood bottles. Tissues that should be sterile (liver and spleen) were collected from the same mice and processed in parallel, in order to recover any contaminants [‘background’ (B) strains] inadvertently introduced through our bacterial isolation procedures. Bacterial colonies were repeatedly streaked on LB agar to recover clonal isolates for whole-genome sequencing. In total, 28 isolates recovered from the lower airway tissues were further characterized for use as airway probiotics.

Prior work on the lung microbiome has led to the widely held hypothesis that many of its constituents originate from the oral and/or nasal microbiome and are unable to actively replicate and persist in the lower airway (Dickson et al., 2014; Dickson and Huffnagle, 2015). We, therefore, sought to identify airway isolates that are capable of active growth in the lower respiratory tract, with the supposition that they should be better equipped to re-colonize the airway and compete against an invading pathogen. To do this, the airway isolates were cultured in lung simulating medium (LSM), which is based on the nutrient composition of sputum samples from cystic fibrosis patients, and therefore, is thought to approximate the nutritional environment of the lower respiratory tract (Palmer et al., 2007; Swart et al., 2024; Neve et al., 2024; Dunphy et al., 2021). We found that 10 of 28 airway isolates tested were capable of robust growth (>1 doubling within 48 hr) in LSM (Figure 1—figure supplement 1A). These 10 isolates, and model pathogen Bt, are arrayed on a phylogenomic tree (Figure 1A), and are henceforth referred to as candidate probiotics (CPs).

Figure 1 with 1 supplement see all

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Characterization of candidate probiotic (CP) activity against *Burkholderia thailandensis* in vitro.

(A) Phylogenomic tree of CPs (yellow) and model pathogen Bt (red), interspersed amongst publicly available genomes (no color). (B) Schematic of competition assay used to test in vitro inhibitory capabilities. Percent growth of Bt is calculated as the colony-forming unit (CFU) in co-culture at 24 hr divided by the CFU in the Bt monoculture at 24 hr multiplied by 100. Co-culture inhibition was tested for multiple starting densities of CP (see C x-axis). Starting density of Bt and CP at a 1:1 ratio are 3.33×10⁴ CFU/ml per organism. Bt monoculture is inoculated at 3.33×10⁴ CFU/ml. (C) Dose-response curves for CPs against Bt. Relative IC50 is denoted with a vertical dashed-dotted line, and 100% and 50% growth are denoted with horizontal dotted lines. Percent growth at each density is represented as mean ± SEM (N=5-15).

The phylogenetic assignments of the CPs were compared to those of airway microbiome constituents previously identified through 16 S rRNA profiling of mouse airway tissue (Barfod et al., 2013; Scheiermann and Klinman, 2017; Kostric et al., 2018; Dickson et al., 2018; O’Dwyer et al., 2019). This analysis revealed that the taxonomic groups to which the CPs belong are well represented in the mouse airway microbiome (Supplementary file 1). For example, we found that all nine of the species to which the CPs belong were detected in at least 1 of 4 previous airway microbiome profiling efforts, with 7 of the 9 species detected in more than 1 profiling effort. In contrast, only 1 of the 9 species to which the B strains (contaminants recovered from liver and spleen) belong was detected, in only 1 of the 4 profiling efforts. Similarly, 3 of the 4 genera to which CPs belong were detected in all 4 profiling efforts (whereas for B strains, only 3 of 9 genera were detected in all four efforts); and all four of the families to which the CPs belong were detected in all four profiling efforts (whereas for B strains, only 6 of 8 families were detected in all four efforts). These results indicate that the CPs belong to taxonomic groups that are typically well represented in mouse airway microbiome, consistent with the idea that they are airway microbiome constituents that have been recovered into culture.

Next, we aimed to identify CPs capable of inhibiting Bt during co-culture in LSM, with the hypothesis that they might also inhibit Bt in the lower respiratory tract and, therefore, serve as efficacious probiotics. Expression of antagonistic phenotypes is dependent on several factors, including the cell density relative to the available resources (Lloyd and Allen, 2015; van Gestel et al., 2014). Accordingly, we decided to assay interbacterial interactions in LSM across varying cell densities, with the hypothesis that this would enable us to capture information about CP potency. Each CP was co-cultured with Bt in LSM at different relative ratios (Figure 1B). We found that the CPs showed a range of activities, including negative interactions (Figure 1C top six plots) and positive interactions (Figure 1C lower four plots) with Bt; the potency of each CP, as indicated by IC50 value, is shown in Table 1. Additionally, co-culture with Bt impacted growth of some CPs but not others, potentially affecting their interaction dynamics (Figure 1—figure supplement 1B).

Table 1

Summary of pathogen inhibition by candidate probiotics (CPs).

Relative IC50 values and 95% confidence intervals (95% CI) are shown for each inhibitory CP. Not determined (n.d.) indicates that a value could not be measured. CPs are listed in order of decreasing Niche Index (NI) values.

CP	Species	Relative IC50	95% CI	Bt growth at a 1:1 ratio (%)	Niche Index
CP7	Pseudomonas stutzeri	0.001172	0.0006442–0.003346	1.893	0.875
CP4	Pseudomonas fluorescens	0.003086	0.001791–0.005331	3.200	0.869
CP19	Brevibacillus borstelensis	0.006604	0.006484–0.01141	3.046	0.775
CP17	Bacillus megaterium	5738	3598 n.d.	98.99	0.704
CP18	Peribacillus frigoritolerans	0.0001693	0.0001633–0.0001850	0.534	0.591
CP20	Bacillus clausii	n.d.	n.d.	115.2	0.457
CP26	Bacillus licheniformis	n.d.	n.d.	260.0	0.433
CP13	Bacillus licheniformis	n.d.	n.d.	913.0	0.421
CP9	Neisseria lactamica	n.d.	n.d.	157.1	0.357
CP8	Bacillus velenzensis	2.316	0.8280–5.073	56.97	0.351

Production of a specialized secondary metabolite plays a role in the antagonistic activities of CP8

Next, we sought to better understand the mechanism(s) of the observed negative interactions. First, we hypothesized that those CPs that strongly inhibit Bt may do so by simply growing at a faster rate than the pathogen, thus dominating the environment. However, we found no correlation between doubling time of the CPs in LSM and their Bt antagonism (r=–0.21, p=0.155, 95% CI [-0.47–0.088], N=49). It has been shown previously that bacteriophages can modulate interbacterial competition, and some that can target Bt have been identified (Hsu et al., 2019; Secor et al., 2017; Ulrich et al., 2013; Ronning et al., 2010); therefore, we next hypothesized that some of the negative interactions between CPs and Bt might be mediated through production of Bt-targeting phages. To test this, we attempted to recover phages from each CP using standard methods (Yu et al., 2015; Carim et al., 2021), and tested the resulting product against Bt in plaque assays (Figure 2—figure supplement 1). We found that none of the CPs appeared to produce a phage that could effectively lyse Bt.

Given this information, we next hypothesized that these negative interactions could be direct, through chemical attack (i.e. production of inhibitory metabolites); or indirect, through occupying a shared metabolic niche with the pathogen. First, we investigated the presence of inhibitory metabolites by growing Bt in sterilized co-culture supernatant from itself and a CP. We predicted that if Bt-inhibiting secondary metabolites were produced by a given CP during co-culture in LSM, then the conditioned medium recovered from the co-culture would inhibit growth of Bt. To test this, each negatively-interacting CP was co-cultured with Bt for 72 hr, filter sterilized, and combined with double-concentrated LSM to serve as medium for Bt growth curves (Figure 2A). We found that the co-culture supernatant from only one of the airway isolates (CP8) significantly reduced growth of Bt compared to supernatant from a Bt monoculture (negative control) (Figure 2B). This indicated that CP8 likely produced a specialized secondary metabolite and was engaging in interference competition. To confirm our findings, we performed agar diffusion assays on all inhibitory CPs and found that, aside from CP8, none produced a zone of inhibition on a lawn of Bt (data not shown).

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Production of a specialized metabolite plays a role in the antagonistic activities of CP8.

(A) Schematic representation of supernatant inhibition experiment workflow. Supernatant from a *Burkholderia thailandensis (Bt)* and candidate probiotic (CP) co-culture or from Bt monoculture (supernatant) is mixed 1:1 with 2 x lung simulating medium (LSM). This mixture is used as the growth medium for subsequent Bt growth curves. Fold-change in Bt growth is calculated by dividing the maximum growth of Bt in co-culture supernatant by the maximum growth of Bt grown in supernatant from itself. (B) Left: Heatmap of fold-change in max colony-forming units (CFU) of Bt grown in supernatant from each inhibitory CP and Bt co-culture relative to Bt grown in supernatant from itself. Co-culture supernatant was collected at 72 hr. A fold-change of 1 indicates no difference in growth in co-culture supernatant compared to Bt growth in supernatant from itself. A fold-change less than 1 indicates a reduction in pathogen growth. (N=5) Right: Boxplot of growth inhibition by CP8 supernatant. Data are represented as mean ± SD. Significance was determined by a t-test ****p<0.0001(C) Agar diffusion assays testing the CP8 wild-type strain (CP8 WT) and sfp mutant strain (CP8Δsfp) for inhibition of Bt after 48 hr. (D) Growth curves of CP8 WT (black) and CP8Δsfp (red) (N=5) (E) Dose-response curve of CP8 WT (black) and CP8Δsfp (red) against Bt after 24 hr of co-culture. Percent growth at each density is represented as mean ± SEM (N=5). Statistical signficance was calculated using a t-test with multiple comparisons *p<0.05.

To understand the degree to which CP8’s specialized metabolite is responsible for mediating the competitive phenotype observed in Figure 1C, we aimed to create a knockout of this molecule in CP8. Previously, we used antiSMASH (Blin et al., 2023) to identify biosynthetic gene clusters in the CP8 genome sequence, and found that CP8 likely produces 19 specialized metabolites (Aiosa et al., 2023). A review of the literature revealed that biosynthesis of many of these specialized metabolites is dependent on Sfp, a phosphopantetheinyl transferase which catalyzes their conversion to the active holo-form (Nakano et al., 1992; Quadri et al., 1998; Chen et al., 2009; Letarov et al., 2022). Therefore, we hypothesized that knocking out the sfp gene in CP8 would yield a mutant with an inability to produce a specialized metabolite with inhibitory capabilities against Bt. A clean deletion of the sfp gene was made in CP8, and the resulting mutant (CP8Δsfp) was found to produce a smaller zone of inhibition in an agar diffusion assay, indicating that interference competition was reduced (Figure 2C). Additionally, growth curves of wild-type CP8 and the CP8Δsfp mutant were performed, which showed no growth defect in the mutant strain (Figure 2D), indicating that its reduction in interference competition was not due to a growth defect. Finally, a competition assay was performed using the CP8Δsfp mutant. The mutant showed reduced inhibitory activity compared to wild-type CP8 and, in contrast, demonstrated a positive interaction with Bt when interference competition was disrupted (Figure 2E).

Metabolic niche overlap is indicative pathogen and CP antagonism

While production of an inhibitory metabolite appeared to be the primary mode of inhibition for CP8, five other CPs showed a negative interaction with Bt but did not appear to produce specialized metabolites. Previous work has shown that competition for a shared metabolic niche is an important mediator of interbacterial interactions in bacterial communities (Ho et al., 2024; Levy and Borenstein, 2013; Venturelli et al., 2018; Sonnenburg et al., 2010; Medlock et al., 2018). Therefore, we hypothesized that inhibition of Bt by these five CPs may be due to metabolic niche overlap. Specifically, we postulated that passive consumption of a limiting resource by the CP could reduce the supply to Bt, thus inhibiting its growth.

The Redfield Ratio describes the stoichiometric relationship between carbon, nitrogen, and phosphorus which supports life on Earth and is canonically 106:16:1 (Redfield, 1934). Recent work has shown that this ratio is variable across body sites, with areas like the mouth and gut being more carbon-rich, and the airways being relatively carbon-poor (Vecchio-Pagan et al., 2017). Similar to the upper airway, LSM is carbon limiting, with a C:N:P ratio of approximately 88:14:1. This suggests that carbon may be the limiting nutrient in the lower airway and thus may be the limiting resource in the airway niche. We predicted that if carbon was limiting, then supplementation of LSM with additional carbon would reduce the antagonism between the CP and Bt. To test this, LSM was supplemented with 43 mM glucose to increase the C:N:P ratio to 101:14:1. We observed a leftward shift of the dose-response curve for CP7 in the additional carbon condition (Figure 3A), and a significant reduction in inhibition at a 1:100 ratio (Figure 3B). A similar result was observed when the same experiment was conducted with CP19 (Figure 3—figure supplement 1). These results suggest that with increased carbon abundance there is a reduction in antagonism, consistent with the idea that, under the carbon-limiting conditions of the airway, carbon is the primary nutrient for which CPs and Bt are competing.

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Metabolic niche overlap is indicative of pathogen and candidate probiotic (CP) antagonism.

(A) Dose-response curve for CP7 grown in unsupplemented lung simulating medium (LSM) (black) or with additional glucose (red). Percent growth at each density is represented as mean ± SEM. Significance was determined using multiple t-tests (N=5) *p<0.05 (B) Activity of CP7 against Bt at a 1:100 ratio with or without glucose supplementation (red and gray, respectively). Data are summarized as mean ± SD. Significance was determined using a t-test (N=5)***p<0.001. (C) Carbon utilization heatmap for CPs and Bt. (D) Venn diagram of carbon sources utilized by CP7 and Bt. (E) Plot of rank percent Bt growth at a 1:1 ratio after 24 hr vs rank of Niche Index for each CP. Correlation is determined by Spearman rank correlation (r=–0.6367, p<0.0001, 95% CI [-0.7891–0.4261], N=49). (F) Total carbon consumed by CPs grown in combination with Bt at 36 hr (mean ± SD). Activity indicates relative amount of Bt inhibition at a 1:1 ratio from most (+++) to least (+) inhibitory. Signficance was determined by a two-way ANOVA with multiple comparisons *p=0.05; **p<0.01 (N=6). (G) Total electron flux at 24 hr. Red dashed line indicates the estimated abundance of Bt in each co-culture. (H) Summed electron flux of lactate (black), proline (white), and aspartic acid (gray) at 24 hr in different CP co-culture conditions (mean ± SD). Significance was determined by two-way ANOVA (N=5-6) *p<0.05.(I) Receiver operator characteristic curve showing the ability of growth on lactate to distinguish inhibitory CPs from non-inhibitory CPs. An AUROC of 1 indicates perfect distinction between inhibitors and non-inhibitors; an AUROC of 0.5 (dashed red line) indicates no distinction between inhibitors and non-inhibitors.

Furthermore, we sought to explore the extent to which metabolic overlap with Bt for carbon sources could predict inhibitory capabilities of each CP. To do this, we assayed the growth of each CP and Bt in each individual carbon source from LSM to determine which carbon sources could be utilized by each organism (Figure 3C). Bt showed a wide range of metabolic potential and was able to grow on every carbon source presented except for ornithine. This is perhaps unsurprising as it has been shown previously that pathogens acquire new metabolisms in order to survive in their host (Rohmer et al., 2011). Conversely, the CPs displayed diverse metabolic activity, with CP4 and CP7 appearing most similar to Bt (Figure 3C).

Each carbon source in LSM is used both for the production of biomass as well as the primary electron donor to generate energy via aerobic respiration. In the niche exclusion hypothesis, a competitive advantage can be obtained for an organism that is able to consume those resources that would otherwise fuel aerobic respiration of a competitor. We, therefore, hypothesized that those CPs that are capable of sharing a greater proportion of carbon-derived electrons with Bt would have more potent antagonism of the pathogen by covering a greater percentage of the energetic niche (Figure 3D). On this assumption, we built a simple index of niche overlap which weights each carbon source in LSM utilized by a given CP by the theoretical electron contribution of that given source, with the belief that such a metric could identify probiotics capable of achieving maximal niche coverage. We hypothesized that weighting each carbon source by its theoretical electron contribution would improve the accuracy of the niche index, as those sources which contribute more electrons represent a greater proportion of the energetic niche and, therefore, have a potentially greater impact on antagonistic phenotypes. While many excellent models exist for metricizing niche overlap (Hurlbert, 1978; Petraitis, 1979; Smith, 1982) and greatly inspired our model design, we sought to create a model which would describe the metabolic niche of the respiratory tract as a function of the energy available to the microorganisms within it.

Niche Index (NI) is calculated as follows:

First, the number of electron equivalents generated by each carbon source is calculated assuming the complete oxidation of these sources to carbon dioxide under standard conditions. The electron equivalents are then multiplied by the total number of molecules of the carbon source in the media. This yields the total electron equivalents for each carbon source per liter of media. These values are held in vector $\vec{E}$ .

\vec{E} = (\begin{matrix} g l u c o s e \\ l a c t a t e \\ a s p a r t i c a c i d \\ t h r e o n i n e \\ s e r i n e \\ g l u t a m i n e \\ p r o l i n e \\ g l y c i n e \\ a l a n i n e \\ c y s t e i n e \\ v a l i n e \\ m e t h i o n i n e \\ i s o l e u c i n e \\ l e u c i n e \\ t y r o s i n e \\ p h e n y l a l a n i n e \\ o r n i t h i n e \\ l y s i n e \\ h i s t i d i n e \\ t r y p t o p h a n \\ a r g i n i n e \end{matrix}) = (\begin{matrix} 8.02 \times 10^{22} \\ 6.72 \times 10^{22} \\ 5.98 \times 10^{21} \\ 1.03 \times 10^{22} \\ 8.71 \times 10^{21} \\ 1.68 \times 10^{22} \\ 2.20 \times 10^{22} \\ 4.35 \times 10^{21} \\ 1.29 \times 10^{22} \\ 1.54 \times 10^{21} \\ 1.61 \times 10^{22} \\ 1.07 \times 10^{22} \\ 2.02 \times 10^{22} \\ 2.91 \times 10^{22} \\ 1.84 \times 10^{22} \\ 1.28 \times 10^{22} \\ 8.96 \times 10^{21} \\ 3.59 \times 10^{22} \\ 6.25 \times 10^{21} \\ 3.60 \times 10^{20} \\ 4.05 \times 10^{21} \end{matrix})

The weighted value for each carbon source is:

\vec{W} = \frac{\vec{E}}{E_{g l u c o s e}}

For all CPs and Bt, a carbon utilization vector is generated. If a carbon source is consumed, then i=1, if it is not able to be consumed, then i=0.

\vec{C U_{C P}} = [\begin{matrix} i_{1} \\ i_{2} \\ i_{3} \\ i_{4} \\ ⋮ \\ i_{n} \end{matrix}],

To calculate the Niche Index of each CP with Bt, the Hadamard product of the carbon utilization vectors for each CP and Bt is multiplied by the weighting vector. The resulting value represents the electron equivalents utilized by both the CP and Bt. This value is divided by the electron equivalents utilized by Bt alone. Thus, the equation for Niche Index is:

{NI}_{C P} = \frac{[\vec{C U_{C P}} ⨀ \vec{C U_{B t}}] \cdot \vec{W}}{[C U_{B t} \cdot \vec{W}] - [\vec{C U_{C P, o r n}} \cdot W]}

NI is higher when a given CP shares a greater number of carbon-derived electrons with Bt. To evaluate the association between the co-culture interaction with Bt and the NI of each CP, a non-parametric Spearman rank-order correlation was performed with the null hypothesis that there is no monotonic association between the two variables. We found that NI is negatively associated with the co-culture growth of Bt with a given CP at a 1:1 ratio (r=–0.64, p<0.0001, 95% CI [-0.79–0.43], N=49) (Table 1 and Figure 3E). Since CP8’s potency is primarily due to its secondary metabolite production (Figure 2E), percent growth of the pathogen in co-culture with the CP8Δsfp mutant was used in this analysis to understand how NI relates to CP8’s interaction with Bt (Figure 3E). While here we have used Niche Index to calculate niche overlap between CPs and Bt in LSM, this model is generalizable and can be used to calculate the niche overlap between organisms in any media with a known molar carbon composition.

To confirm our hypothesis that those organisms that consume a greater number of carbon-derived electrons with Bt are more antagonistic, we performed exometabolomics on supernatants from three CPs. These three CPs represent a range of inhibition strengths and niche indexes, with CP7 being the most potent (IC50=0.001172, NI=0.875), CP19 displaying moderate potency (IC50=0.006604, NI=0.775), and CP8 being the least potent (IC50=2.316, NI=0.351). Each of these CPs was combined with Bt at their respective IC90 and co-incubated for 36 hr. Supernatants from these CPs at 36 hr show that more potent CPs consume a statistically greater amount of carbon from the media than less potent CPs (Figure 3F). At 24 hr when the degree of inhibition of Bt by each CP is the same, more potent probiotics consume fewer carbon-derived electrons than less potent competitors (Figure 3G). This led us to hypothesize that certain carbon sources were being prioritized, allowing more antagonistic CPs to have the same net effect on Bt while consuming fewer electrons. Furthermore, we speculated that if high priority carbon sources were to be identified, perhaps the consumption of a smaller set of these high priority carbon sources could predict activity with similar accuracy to the Niche Index.

From the exometabolomics data, consumption of three carbon sources, lactate, proline, and aspartic acid are prioritized more at 24 hr by the most potent candidate probiotic, CP7, than the least potent, CP8 (Figure 3H). Furthermore, we found that a CP’s ability to generate biomass on lactate (the second most abundant carbon source in LSM) was a good predictor of its ability to inhibit the pathogen (Figure 3I), suggesting that the ability to consume lactate in co-culture may be important for pathogen control. Overall, these results indicate that consumption of specific resources can have an especially strong influence on niche exclusion, with consumption of a specific carbon source (lactate) appearing to play a key role in niche exclusion of Bt by the most potent of the CPs (CP7). While these results are promising building blocks for the development of a metric of activity based upon a smaller feature set, a larger screen of candidate probiotics may be necessary to understand the generalizability of these features. Furthermore, future efforts to develop this model should take into account a CP’s uptake rate for a given resource as this may influence antagonism and reveal features of the metabolic niche that have disproportionate significance to interbacterial interactions.

Niche overlap aids in the identification of efficacious multi-organism probiotics

While our work showed that six of the CPs were potent inhibitors of Bt growth in co-culture, four were poor inhibitors or actually promoted Bt growth (Figure 1). Having found that NI is well correlated with CP activity in vitro, we hypothesized that this metric could be altered to identify pairwise combinations of poor-performing CPs with enhanced antagonism. To do this, we created a new metric, the Niche Index Fraction (NIF), which is calculated by generating the NI between two CPs and dividing this value by the NI of the two CPs and Bt (Figure 4A). We hypothesized that combinations with lower NIF values would have optimal coverage of the Bt niche space while minimizing niche overlap between two CPs. Thus, low NIF values might indicate combinations with enhanced inhibition of the pathogen compared to each CP alone. NIF is calculated as follows:

First, the overlap between two CPs is generated and is weighted by the relative electron contribution of each carbon source.

y = [\vec{C U_{C P x}} ⨀ \vec{C U_{C P y}}] \cdot \vec{W}

Total carbon-derived electron utilization is calculated for both CPs

p = \vec{X_{C P x, C P y}} \cdot \vec{W}, s u c h t h a t p i s n e v e r 0

For which

\begin{array}{ll} {\vec{X}}_{C P_{x}, C P_{y}} = {\begin{cases} {\vec{X}}_{C P_{x}, C P_{y}} = 1 & if {\vec{C U}}_{i, C P_{x}} = 1 and {\vec{C U}}_{i, C P_{y}} = 1 \\ {\vec{X}}_{C P_{x}, C P_{y}} = 0 & if {\vec{C U}}_{i, C P_{x}} = 0 and {\vec{C U}}_{i, C P_{y}} = 0 \\ {\vec{X}}_{C P_{x}, C P_{y}} = 1 & if {\vec{C U}}_{i, C P_{x}} \neq {\vec{C U}}_{i, C P_{y}} \end{cases} \end{array}

Thus the niche overlap between two CPs is

{N I}_{C P x, C P y} = \frac{y}{p}

The niche overlap between two CPs and Bt is thus

N I F_{C P x, C P y} = \frac{{N I}_{C P x, C P y}}{[\vec{C U_{B t}} \cdot \vec{W}] - [{\vec{X}}_{C P x, C P y, o r n} \cdot \vec{W}]}

We calculated NIF for every pairwise combination of the seven least antagonistic CPs from Figure 1. This generated a matrix of CP combinations and their respective NIF values (Figure 4B). To test this metric’s capability to identify inhibitory combinations of CPs, we chose to evaluate pairwise combinations with the three lowest NIF values, a moderate value and a high value using competition assays.

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Niche overlap aids in identification of efficacious candidate probiotic (CP) combinations.

(A) Venn diagram representation of the Niche Index Fraction (NIF) calculation. Yellow represents the fraction numerator and red the denominator. (B) Heatmap of NIF values for each colonizing CP. (C) Dose-response curve for the CP8/CP19 combination (mean ± SEM). (N=5) (D) Dose-response curve for the CP19/CP13 combination (mean ± SEM). (N=5) (E) Dose-response curve for the CP13/CP8 combination (mean ± SEM). (N=4-5) (F) Lactate consumed (as determined via GC-TOF) for 3 conditions, normalized to colony-forming units (CFU) of each CP inoculated (mean ± SD). Signficance was determined using a one-way ANOVA with multiple comparisons (N = 5-6) ****p<0.0001. (G) Dose-response curve for the CP8/CP19 (red) and CP8sfp/CP19Δsfp (black) combinations. Signficance was determined using multiple t-tests (N=5) *p<0.05(H) Schematic of the proposed mechanism for Bt inhibition by the CP8/CP19 combination. CP8 produces a specialized metabolite that inhibits growth of the Bt. Additionally, increased lactate consumption by CP19 in the presence of CP8 further reduces Bt growth. Overall, minimal niche overlap between the CPs, and high overlap with Bt allows for further inhibition of Bt with minimal inter-CP antagonism.

In these experiments, two CPs were combined at a 1:1 ratio such that the total CP concentration at a given ratio was the same as their individual CP counterparts. This enabled us to directly compare activity of an individual CP and the activity of a combination of two CPs at a given ratio as the total cell density was the same in each. From competition assays, we determined that NIF was able to predict combinations with enhanced and reduced Bt antagonism as reflected by their relative IC50 values (Figure 4C-E, Figure 4—figure supplement 1, Supplementary file 2).

For one combination, CP8 and CP19, we sought to understand the mechanism by which antagonism was being enhanced. First, to understand the effect of the interaction on all members of the co-culture, we performed growth curves of each strain (CP8, CP19, and Bt) in co-culture and compared their individual abundances when grown together to their growth in monoculture using qPCR (Figure 4—figure supplement 2). Reduced growth in co-culture of all three organisms in comparison to monoculture (Figure 1—figure supplement 1) classifies this antagonistic interaction as true competition. To better understand how CP8 and CP19 may enhance each other’s antagonistic impact on Bt, we performed exometabolomics analysis on supernatant from this combination. At 24 hr, significantly more lactate was consumed per cell by the CP8/CP19/Bt co-culture than by the CP8/Bt co-culture or CP19/Bt co-culture (p<0.0001) (Figure 4F). Similarly to the single isolate studies, this result once again suggests that lactate utilization may be important for antagonism of Bt in an airway-simulating environment.

To further investigate how the combination of CP8 and CP19 more effectively inhibits the pathogen, we performed a competition assay using the CP8Δsfp mutant in combination with CP19 to understand the degree to which secondary metabolite production was responsible for their combined effect (Figure 4G). When wild-type CP8 was replaced with the CP8Δsfp mutant, there was a significant reduction in Bt inhibitory activity compared to the wild-type CP8/CP19 combination. Together, these findings suggest that both consumption of specific carbon sources and secondary metabolite production are important for the activity of the CP8/CP19 combination and likely enable Bt inhibition activity (Figure 4H). While the concept of NIF suggests that coverage of the pathogen niche may be responsible for the observed activity in other combinations, further investigation needs to be done to understand these interactions in more detail.

CPs provide protection against infection

Having observed the ability of the CPs to compete with Bt in an in vitro airway-simulating environment, we were encouraged to test their ability to confer protection against respiratory Bt infection in vivo. We hypothesized that CPs that can colonize at high density over an extended period of time would provide better protection against Bt infection. To test the airway colonization capabilities of the CPs we administered each to C57Bl/6 j mice at 10⁶ CFU via oropharyngeal aspiration (OPA), and after a period of 7 days, we assessed the lungs and trachea for CP load via CFU enumeration (Figure 5A). We found that several CPs were able to colonize the airway for the duration of the 7 day period (mean airway CP load of >10 CFU) (Figure 5B).

Figure 5 with 3 supplements see all

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Candidate probiotics (CPs) colonize the mouse airway and protect against respiratory *Burkholderia thailandensis (Bt)* infection.

(A) Schematic of colonization testing for CPs. Each CP (10⁶ colony-forming units, CFU) is administered to the lower airway via oropharyngeal aspiration (OPA). After 7 days, the airway tissues (lungs and trachea) are collected, homogenized, and plated in order to enumerate viable CPs (expressed as CFU per tissue homogenate). (B) Colonization results from airway tissues collected at 7 days following CP administration. The dotted line represents the minimum CFU at which the CP is considered able to colonize in a reliably detectable manner. Data are represented as the mean ± SD (N=3-12). (C) Schematic of the method for survival testing. Each CP (10⁶ CFU) is administered to the lower airway via OPA at 3, 5, or 7 days post-CP a normally lethal dose of Bt (3×10⁴ - 5×10⁵ CFU) is administered to the lower airway via OPA. Survival is measured over the course of 10 days post-Bt. (D) Survival data from mice administered no CP (vehicle control) and challenged with Bt (E) Survival data from mice administered CPs at 3 days (blue), 5 days (orange), or 7 days (red) prior to Bt challenge. The Niche Index value for each CP is listed in the lower-right corner of its associated graph. Comparison of survival rate with versus without CP treatment was accomplished using the Mantel-Cox test (N=10-29) ****p<0.0001, ***p<0.0002, **p<0.01. A summary of the relationship between days surviving and Niche Index value for each dosing schedule is also shown (F) Pathogen loads in airway tissues of mice prophylactically treated with CPs. Each CP or phosphate-buffered saline (PBS) (‘no CP’ negative control) is administered to the lower airway via OPA, 3 days later Bt (3×10⁴ - 5×10⁵ CFU) is similarly administered, and 3 days later (i.e. 3 days post-challenge, 6 days post-treatment) the airway tissues are collected for enumeration of Bt CFU. Mean Bt CFU counts are as follows for each group: CP19 (1.6×10³), CP17 (1.7×10³), CP20 (2.2×10⁶), CP26 (8.7×10⁶), CP13 (1.3×10⁷), CP8 (3.9×10⁸), No CP (4.6×10⁸). Asterisks indicate significant differences in Bt CFU between treatment versus no treatment groups as determined using Dunn’s multiple comparisons test (N=6-17) ****p<0.0001, ***p<0.0002, **p<0.01. The relationship between the Niche Index values of CPs and the Bt CFU detected in mice treated with the CPs is indicated in the upper left corner.

We screened both colonizing and non-colonizing CPs for their ability to confer protection in a mouse model of respiratory Bt infection. In these studies, 10⁶ CFU of each of six CPs were administered to the airway of C57Bl/6 j mice via OPA. Following a period of 3, 5, or 7 days, Bt was administered to the airway via OPA and mortality was monitored for 10 days following Bt challenge (Figure 5C). Mice administered no CP prior to Bt challenge showed high mortality, only rarely surviving past day 4 (Figure 5D). In contrast, mice administered certain CPs prior to Bt challenge showed significantly reduced mortality (Figure 5E). Five of the six CPs were protective when administered 3 days prior to Bt challenge; CP17 was protective when administered at 3 or 5 days prior to Bt challenge; and CP19 was protective when administered at 3, 5, or 7 days prior to Bt challenge.

We observed that the most protective probiotics, CP19 and CP17, were predicted to have the highest niche overlap with Bt as indicated by their NI values (Table 1), showed an ability to inhibit Bt in competition assays (Figure 1C) and reduced Bt loads in mouse airway tissues during respiratory infection (Figure 5F). Furthermore, the CPs that robustly colonized the airway (Figure B) but did not inhibit Bt in competition assays (Figure 1C) (i.e. CP13, CP20, and CP26) showed no significant protection when administered at 5 or 7 days prior to Bt challenge (Figure 5E). A notable exception to this trend was CP8, which showed robust colonization as well as inhibition in competition assays, but showed no significant protection of mice challenged with Bt. This may be explained by its low niche overlap with the pathogen (Table 1) and/or its reduced colonization in Bt-infected mice (Figure 5—figure supplement 1). Taken together, these results suggest that niche overlap with the pathogen may be suggestive of a CPs protective capabilities. However, given the potential for certain CPs to inhibit Bt via the production of specialized metabolites (Figure 2), future work should be done to investigate the role of these unmodeled interactions in vivo.

We sought to estimate the strength of correlation between NI_CP and CP protection efficacy. Accordingly, we performed a non-parametric Spearman rank correlation analysis with the null hypothesis that there existed no relationship between NI_CP and the number of days surviving when CP treatment was performed 3, 5, or 7 days in advance of pathogen challenge. We found a moderately strong relationship between NI_CP and survival when CPs are dosed 3 days prior to pathogen challenge (r=0.48, p<0.0001, 95% CI [0.33–0.62], N=115), a very strong relationship when dosed 5 days prior (r=0.77, p<0.0001, 95% CI [0.60–0.88], N=40), and a strong relationship when dosed 7 days prior (r=0.66, p<0.0001, 95% CI [0.52–0.77], N=89) (Figure 5E). Additional studies revealed that CP13, CP20, and CP26, which robustly colonized the airway (Figure 5B) but did not show inhibition in competition experiments (Figure 1C) and conferred a survival benefit only when administered 3 days prior to pathogen challenge (Figure 5E), provided similar protection even after being rendered non-viable (via UV and heat treatment) (Figure 5—figure supplement 2). In contrast, we found that the protective effects of CP17 and CP19 when administered 5 or 7 days prior to pathogen challenge largely depended on CP viability. Taken together, these results suggest that niche exclusion is the dominant mechanism of protection except when CPs are administered 3 days prior to pathogen challenge, in which case other mechanisms that are not captured in our model (potentially including immune priming) must play a role.

We further sought to determine whether the survival benefits conferred by CPs were associated with reduced pathogen loads in the airway, which would be consistent with the niche exclusion mechanism of protection. In these studies, the CPs were administered to the airway 3 days prior to Bt challenge, and airway tissues were collected at 3 days post-challenge for Bt CFU analysis. We found that prophylactic treatment with the CPs significantly reduced pathogen loads in the airway, with the notable exception of CP8 (Figure 5F). Moreover, we observed a very strong inverse relationship between NI_CP and pathogen load (r=–0.84, p<0.0001, 95% CI [-0.90 to –0.76], N=77), where CPs with higher NI values more effectively reduced pathogen loads. While it appears that NI is a reasonable indicator of the protective effects of a CP in vivo, additional development of this model will need to be done to better capture the host environment, improve prediction, and determine the extent to which it can be used to nominate efficacious probiotics in the future.

From our in vitro studies, we found that lactate consumption by a CP was important for pathogen inhibition. To study the specific role of lactate in a scenario more similar to the in vivo context, CP19 (10⁶ CFU) or PBS (negative control) was introduced into airway tissue homogenates, and after 24 hr of incubation, the lactate levels were measured. We found that lactate levels were significantly lower in tissue homogenates inoculated with CP19 as compared to those receiving PBS only (Figure 5—figure supplement 3A). We also found that sterile filtrates recovered from CP19-conditioned tissue homogenates failed to support growth of Bt, in contrast to those recovered from PBS-treated tissue homogenates (Figure 5—figure supplement 3B). These results suggest a link between lactate depletion by CP19 and its ability to inhibit the pathogen in the context of airway tissues.

Discussion

The human respiratory tract microbiome composition has been linked to both respiratory health and the occurrence of respiratory infections (Yagi et al., 2021). As a result of this connection, manipulation of the lower airway microbiome is an attractive strategy for anti-infective therapies. Airway microbiome modulation via supplementation with probiotics has previously shown success in preventing infections; however, these studies lack a more broad and systematic analysis which is necessary to learn generalizable principles for the design of efficacious airway probiotics (Fangous et al., 2019; Yildiz et al., 2020).

In this work, we developed a system that probes interactions between airway-derived CPs and a model respiratory pathogen, Bt. From this exploration, we find that consumption of shared resources explains the majority of antagonistic phenotypes between CPs and Bt in vitro. Specifically, consumption of shared carbon sources seems to play an important role in these interactions. As a result, a metric of niche overlap, NI, is correlated with CP inhibitory activity in vitro in most instances. Furthermore, we use these same principles to design combinations of CPs with enhanced antagonism against Bt. We explore the mechanism by which one combination, CP19 and CP8, exploits both metabolic niche exclusion and inhibitory metabolite production to its advantage in competing with Bt. Finally, we find that several CPs are able to re-colonize the mouse airway for at least 7 days, and that two CPs (CP19 and CP17) significantly reduce mortality in mice subjected to otherwise lethal respiratory Bt challenge. While it appears that there is a relationship between the NI and the protective capabilities of a CP, further preclinical experiments will need to be done to determine if NI alone can guide formulation of airway probiotics with clinical applicability. Furthermore, consideration of potential effects from patient genetic background, environment, host immune response, and cross-feeding must also be explored.

The method described here is a step towards a more rapid, inexpensive, and effective means of identifying efficacious probiotics, all factors that have stalled probiotic development previously (de Melo Pereira et al., 2018). Selection of these probiotics based on their niche-exclusion capabilities, as demonstrated here, also has the potential to enable development of probiotics with better safety profiles. While probiotics with the ability to produce inhibitory small molecules can be identified easily, pathogens can rapidly evolve to evade this form of antagonism. In comparison, probiotics which occupy a niche may leave a pathogen with fewer strategies to evolve resistance, allowing long-term efficacy and more predictable effects on health in clinical applications.

Another great challenge for clinical development of airway probiotics is potency. In order to allow for adequate oxygen exchange in the lungs, alveoli need to remain unobstructed and, thus, colonization of the lung with a large dose of bacteria may result in undesirable outcomes. Therefore, probiotics which require extremely low doses to achieve a protective effect are desirable. Our methodology shows potential for repurposing of organisms that are typically poor performers for use in highly potent multi-organism formulations.

In this work, we found that one way that probiotics defend against a pathogen is through consumption of available carbon sources which would otherwise feed the pathogen. In particular, lactate consumption appears to be important for efficacy. Several studies have found that there is increased lactate in the lung during inflammatory exacerbations, which could feed pathogen growth (De Backer et al., 1997; Bensel et al., 2011; Kottmann et al., 2012). Interestingly, previous studies have shown that consumption of lactate is important for fitness in the host and pathogenesis for several different bacterial pathogens (Gillis et al., 2018; Wu et al., 2019; Exley et al., 2005; Gao et al., 2012; Ayala and Shafer, 2019). Theoretically, if probiotics that preferentially consume lactate can be designed, they may be useful in controlling infections in hosts with inflammatory lung conditions, such as cystic fibrosis, idiopathic pulmonary fibrosis, and chronic obstructive pulmonary disease (Bensel et al., 2011; Kottmann et al., 2012; Lozo Vukovac et al., 2019).

Broadly, we hypothesize that airway probiotics may have functionality by stopping the positive feedback loop of infection. In this model, pathogen infiltration into the lung and growth results in inflammation and cellular injury, causing a leak of nutrient-rich fluids into the alveolar compartment, which further promotes pathogen growth (Figure 6 (left)) (Dickson et al., 2014). Airway probiotics might stop this feedback loop by depleting nutrient abundance in the local lung environment, thereby limiting pathogen growth (Figure 6 (right)) (Dickson et al., 2014). Given this model, probiotics which interfere with pathogen metabolism could be forward-designed to work optimally within these environments in order to maximize potency and efficacy.

Figure 6

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Proposed mechanism of candidate probiotic (CP) protection.

Left: During lower airway infection, bacterial growth elicits inflammation from the host, with concurrent endothelial and epithelial injury. Intra-alveolar oedema introduces additional nutrients from the blood into the alveoli and lung lumen, enabling further growth of the pathogen. This positive feedback loop results in uncontrolled growth of the pathogen, ultimately facilitating its instantiation. Right: Probiotics delivered directly to the lower airway may limit infection by reducing nutrient abundance after oedema, which prevents further growth of the pathogen and thereby breaks the infection-promoting feedback loop.

The native airway microbiome, and its interactions with host cells and incoming pathogens, is only partially understood, making development of airway probiotics a challenge. As our knowledge grows, health-promoting functionalities could be introduced into the lower respiratory tract, through supplementation with microbiome constituents in an unaltered state (as demonstrated here) or with engineered properties, such as novel metabolisms. However, for utility in the clinic, numerous questions will need to be answered about the predictability, long-term safety, and reliability of airway probiotics as alternatives or additions to antibiotic therapies. To improve this nomination pipeline further, future models should incorporate elements which have been shown to influence interbacterial antagonism previously, such as the rate of consumption of shared resources and the impact of sudden alterations in resource availability as might occur in acute lung injury. To ensure safety, evolution of the CP in the airway should be monitored over time, and the potential contribution of cross-feeding between CP and pathogen should be investigated. Additionally, future efforts will need to focus on reliable probiotic delivery modalities, formulation stability, and potential for variance in colonization, protection, and immunogenic responses in different hosts and environmental contexts. Work is currently underway to understand the generalizability of the findings here to models of other respiratory diseases, and to evaluate their safety and efficacy for therapeutic applications.

Methods

Key resources table

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Strain, strain background (Burkholderia thailandensis)	Bt-GFP	Daniel J. Hassett
Strain, strain background (Pseudomonas fluorescens)	CP4	This paper	SAMN45914816	NCBI accession number for CP4 genome
Strain, strain background (Pseudomonas stutzeri)	CP7	This paper	SAMN45914812	NCBI accession number for CP7 genome
Strain, strain background (Bacillus velenzensis)	CP8	This paper	SAMN45914813	NCBI accession number for CP8 genome
Strain, strain background (Neisseria lactamica)	CP9	This paper	SAMN45914814	NCBI accession number for CP9 genome
Strain, strain background (Bacillus licheniformis)	CP13	This paper	SAMN39610770	NCBI accession number for CP13 genome
Strain, strain background (Bacillus megaterium)	CP17	This paper	SAMN45914815	NCBI accession number for CP17 genome
Strain, strain background (Peribacillus frigoritolerans)	CP18	This paper	SAMN39610774	NCBI accession number for CP18 genome
Strain, strain background (Brevibacillus borstelensis)	CP19	This paper	SAMN39610771	NCBI accession number for CP19 genome
Strain, strain background (Bacillus clausii)	CP20	This paper	SAMN39610772	NCBI accession number for CP20 genome
Strain, strain background (Bacillus licheniformis)	CP26	This paper	SAMN39610773	NCBI accession number for CP26 genome
Recombinant DNA reagent	T2(2)-ori (plasmid)	Qi et al., 2014	Addgene ID#216627	Bacillus knockout vector
Sequence-based reagent	CP8_probe	This paper	CP8 qPCR probe	/56-FAM/AAAGAGCGA/ZEN/CAGGGAAACAGTGCT/3IABkFQ/
Sequence-based reagent	CP8_F	This paper	qPCR primer	CAGATCTGCCGGATTCATCTT
Sequence-based reagent	CP8_R	This paper	qPCR primer	CCGGCATTCTCCATCTCATT
Sequence-based reagent	CP19_probe	This paper	CP19 qPCR probe	/5SUN/CCAGATTCG/ZEN/CTTTCTCGACCGGATT/3IABkFQ/
Sequence-based reagent	CP19_F	This paper	qPCR primer	GCAGACATGGGTGAAGAATTTG
Sequence-based reagent	CP19_R	This paper	qPCR primer	GGCGATTTCTACCGCTTCAT
Sequence-based reagent	Bt_probe	This paper	Bt qPCR probe	/5Cy5/TGTCCGGAAAGAAATCATCCTGGCT/3IAbRQSp/
Sequence-based reagent	Bt_F	This paper	qPCR primer	AGGCCTTCGGGTTGTAAAG
Sequence-based reagent	Bt_R	This paper	qPCR primer	GTAGTTAGCCGGTGCTTATTCT
Software, algorithm	Canu v2.2	Koren et al., 2017	RRID:SCR_015880	Long read assembly of candidate probiotic genomes was performed using Canu
Software, algorithm	speciateIT		RRID:SCR_014615	For speciation of 16 S sequences, speciateIT was used
Software, algorithm	KBase	Arkin et al., 2018	RRID:SCR_022162	The phylogenomic tree was built using KBase

Isolation and culture of bacteria

A Bt E264 strain that constitutively expresses GFP was provided by Daniel J. Hassett (University of Cincinnati College of Medicine; Cincinnati, OH) (Su et al., 2014). The CP isolates were recovered from the lower respiratory tracts of healthy mice using a culturomics approach (Lagier et al., 2012). All animal work was conducted in accordance with protocols approved by the Lawrence Livermore National Laboratory (LLNL) Institutional Animal Care and Use Committee (IACUC, protocol 304) and Institutional Biosafety Committee (IBC, protocol 2021–010). LLNL is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) International, and is Public Health Service-Assured (PHS assurance A3184-01). Briefly, airway (trachea and lung) tissues were collected from 24 mice (C57Bl/6 j, 10–20 weeks), homogenized in 500 μl of phosphate-buffered saline (PBS), and used to inoculate five different solid growth media [tryptic soy (TS); TS +5% sheep’s blood; M9 minimal salts +0.2% glucose; brain-heart infusion (BHI); and Luria broth (LB) (Teknova; Hollister, CA)] as well as three different BACTEC blood bottles [Plus Aerobic/F; Plus Anaerobic/F; and Lytic/10 Anaerobic/F (Becton Dickinson; Franklin Lakes, NJ)]. The inoculated solid growth media were incubated at 37 C for seven days. The inoculated blood bottles were incubated with shaking (250 rpm) for 1–5 days, and 100 μl aliquots were periodically withdrawn for use in inoculating the five different solid growth media, which were then incubated at 37 °C for seven days. All colonies detected on the solid growth media were individually transferred to LB agar and, after further incubation at 37 °C for 1–3 days, streaked to single colonies on fresh LB agar. A representative single colony from each Petri plate was used to inoculate LB liquid and solid media, and these cultures incubated at 37 °C for 1–3 days for use in preparing frozen glycerol stocks as well as extraction of genomic DNA for sequencing. Sterile tissues (liver and spleen) that were collected from the same mice using a second set of dissection instruments were processed in parallel, in order to recover contaminants of the bacterial isolation procedures [referred to as ‘background’ (B) strains].

Genome sequencing and analysis

Extraction of high molecular weight DNA from overnight cultures of CPs was accomplished using the Nanobind CBB kit (PacBio catalog no.102-301-900). Barcoded sequencing libraries were prepared using the SMRTbell prep kit 3.0 (PacBio catalog no.102-141-700) with SMRTbell barcoded adapter 3.0 (PacBio catalog no.102-009-200) and using the Covaris g-TUBE shearing method to generate DNA fragments of 7–12 kb in length. Quantification of DNA concentrations was accomplished using the Qubit fluorometer method. The barcoded libraries were sequenced using a PacBio Sequel IIe system with HiFi workflows; one SMRT Cell 8 M was used for run times of 15 hr.

Raw sequencing reads were assembled using canu v2.2 with its standard options (Koren et al., 2017). Resultant contigs were manually inspected, and BLAST was used to identify and remove duplicate assembled contigs. Phylogenetic placement in the GTDB release 202 system used the ANI-based script speciate.pI (Parks et al., 2022; Mageeney et al., 2022). Phylogenomic analysis (Figure 1A) was performed using Species Tree Builder in KBase (Arkin et al., 2018). Genome assemblies were submitted to NCBI; assembly IDs are listed in the key resources table.

Comparison of CP versus airway microbiome phylogenetic assignments

Sequencing reads from four independent 16 S rRNA profiling investigations of the mouse airway microbiome (Barfod et al., 2013; Scheiermann and Klinman, 2017; Kostric et al., 2018; Dickson et al., 2018; O’Dwyer et al., 2019) were aligned against refseq genome assemblies and placed into taxonomic groups using an established software pipeline called RapTOR. Sequence quality filtering was performed to remove low-quality ends as well as any partial primer sequences, as described previously (Vandernoot et al., 2012; Bent et al., 2015). The Duskmasker software tool was applied in order to remove low complexity and unidentified reads (Morgulis et al., 2006). FASTQ sequences with low quality scores were then removed, followed by removal of any host sequences as identified through alignment against the mouse genome sequence (GRCm38) using Bowtie 2 (Langmead and Salzberg, 2012). An additional precise alignment with mitochondrial and ribosomal genome sequences was performed to remove any of these host sequences. The remaining non-host (unaligned) sequences were then aligned to the Silva SSU database (Quast et al., 2013) using Bowtie 2 with local alignment settings. The generated ‘.sam’ files were converted to ‘.taxsum’ files using SAMtools (Danecek et al., 2021) in a custom Perl script for each sample, producing a table for read abundance at each taxonomy level, with reads aligning equally well to multiple Silva entries placed through application of the lowest common ancestor (LCA) algorithm. Finally, reads mapping to each taxonomic group represented in a given profiling dataset were enumerated; and each group was assigned a rank based on the relative abundance of reads mapping to it (i.e. the prominence with which it was represented in the profiling dataset). This information is summarized for the taxonomic groups to which the CPs [and, for comparison, the B strains (contaminants)] belong (Supplementary file 1).

Lung simulating medium (LSM) preparation

LSM was made according to Palmer et al., 2007. Adjustments were made to the original formulation to increase sodium and chloride concentrations to more closely match those in healthy lungs. As a result, LSM contains 92 mM NaCl, instead of 66.6 mM NaCl as in SCFM (Jayaraman et al., 2001). Similarly, glucose concentration was increased to 5.5 mM.

Competition assays

Three-milliliter monocultures of individual CPs and Bt were inoculated from frozen glycerol stocks and grown in LSM at 37° C with shaking (278 rpm) for 24 hr. Cultures were then serially diluted again in LSM at a 1:50 ratio, and grown at 37° C with shaking until late log phase, at which point the cultures were centrifuged at 3000 rcf for 10 min and resuspended in fresh LSM. CPs were then diluted according to the ratios being tested, where a 1:1 ratio represented a cell suspension of 3.33×10⁴ CFU/ml, a 0.1 ratio represented 3.33×10³ CFU/ml, etc. After dilution, 3.33×10⁴ CFU/ml of Bt was added to each 15 ml CP dilution tube as well as to a Bt-only control. The 15 ml volume was split into five 3 ml aliquots to make biological replicate assays. All samples were incubated at 37° C with shaking for 24 hr.

After 24 hr, samples were removed, diluted 1:100-1:100,000 in PBS, and plated onto LB plus carbenicillin (100 μg/ml) to select for Bt colonies. Plates were incubated at 37° C for 48 hr or until colonies became visible, and colony-forming units (CFU) were enumerated. Percent Bt growth was calculated by taking the CFU/ml of each replicate culture and dividing by the average of the CFU/ml in the Bt-only control and multiplying by 100. Dose-response curves and analyses were generated using the ECAnything algorithm in GraphPad Prism 10.

Competition assays measuring CP cell densities

As the CPs were generally sensitive to antibiotics, it was not possible to measure their abundance in competition assays using the antibiotic selection plating method outlined in ‘Competition Assays.’ Monocultures of each CP and Bt were inoculated from frozen glycerol stocks and grown in LSM at 37 °C with shaking (220 rpm) overnight. Cell density of the monocultures was measured using a Quantom Tx Microbial Cell Counter. Cultures were diluted to 3.33×10⁴ CFU/ml in LSM, and 800 μl of each CP were loaded into six chambers of the Cerillo Co-Culture Duet System (cat#NC2389319). The opposite side of three chambers was loaded with 800 μl of Bt in LSM, or of LSM alone as a negative control. Plates were incubated with shaking at 350 rpm at 37 °C for 24 hr, and then 100 μl of culture were removed from the CP side of each chamber and diluted 1:10. The diluted samples were counted using the Quantom Tx Microbial Cell Counter.

Phage isolation and plaque assays

A 3 ml culture of each CP was grown at 37 °C with shaking (278 rpm) for 16 hr, at which point 2 μl of the culture were added to a well of a 2 ml deep well plate containing 1 ml of LB. These cultures were grown at 30 °C with shaking (750 rpm) for 24 hr, at which point each culture was back-diluted 1:70 and grown at 30 °C with shaking until it reached an OD₆₀₀ of 1. To each of these 1 ml cultures, 2 μl of 3% hydrogen peroxide were added in order to induce phage production. After incubation at 30 °C with shaking for 16 hr, the cultures were centrifuged to pellet cells, and the supernatant was collected and sterilized using a 0.22 μM filter. Ammonium sulfate was added to 30% saturation, and precipitate was collected by centrifugation at 16,000 rcf for 40 min at 4 °C. The supernatant was decanted, and the phage-containing precipitate was resuspended in 1 ml of SM buffer (100 mM NaCl, 8 mM MgSO₄•7H₂O, 50 mM Tris-Cl, 0.01% gelatin) and stored at 4 °C for use in plaque assays.

To perform plaque assays, 3 ml of Bt was grown at 37 °C with shaking (278 rpm) for 16 hr, at which point it had reached stationary phase. Standard LB agar was prepared in 100×15 mm Petri dishes (BD Biosciences) and allowed to cool. To create the soft agar overlay, 200 μl of the Bt culture were mixed with 5 ml molten LB soft agar (0.5%) and spread over the standard LB agar in each plate. The plates were allowed to cool for 20 min. Phage-containing precipitate was serially diluted 1:10, and 2 μl of the solution was pipetted onto the soft agar and allowed to dry for 10 min. The plates were then incubated at 37 °C for 24 hr, and the presence or absence of plaques was observed.

Supernatant inhibition experiments

CPs and Bt were grown in LSM monocultures as previously described. The cultures were diluted to 5×10⁵ CFU/ml, and each CP was combined with Bt in co-cultures; additionally, a Bt-only control was generated. The co-cultures were grown at 37 °C with shaking (278 rpm) for ~72 hr, centrifuged to pellet cells, and the supernatants collected and sterilized using a 0.22 μM filter. The sterilized supernatant samples were pH adjusted to 7.03. Each supernatant sample was mixed 1:1 with 2x-concentrated LSM, and exponential-phase Bt was added to each mixture at 3.33×10⁴ CFU/mL. Growth curves were performed using a Tecan Spark plate reader, measuring OD₆₀₀ every 0.25 hr during growth at 37 °C with shaking (240 rpm) for 30 hr. Fold-change in maximum Bt CFU/ml reached was calculated by dividing the maximum CFU of Bt grown in supernatant from itself divided by the maximum CFU of Bt grown in supernatant from each CP co-culture.

Knockout of sfp in CP8

The CP8∆sfp mutant was generated using a double-crossover homologous recombination method described previously (Qi et al., 2014). Briefly, homologous arms were amplified up- and downstream of the sfp gene using PCR, and the amplicon was cloned into the T2(2)-ori plasmid in addition to the oriT/traJ region which facilitates conjugative transfer using Golden Gate assembly. The resulting plasmid was introduced into CP8 using an established method for conjugation in Bacillus (Heinze et al., 2018). The transconjugants were plated onto LB supplemented with kanamycin (20 μg/ml) and polymyxin B (5 μg/ml) to select for plasmid-bearing CP8. Colonies recovered from the selection plates were used to inoculate 3 ml LB plus kanamycin (20 μg/ml), and the resulting cultures were grown at 37 °C with shaking (278 rpm) for 16 hr. 0.05 ml of each culture were then spread on an LB plus kanamycin (20 μg/ml) plate that was incubated at 45 °C for 16 hr in order to induce the first crossover event. A single colony positive for the first crossover event by PCR was recovered from each plate and passaged six times in LB liquid culture. Liquid cultures were streaked onto 10 LB agar plates to generate single colonies. Replica plating of 96 colonies was performed to check for loss of kanamycin resistance, and PCR was used to screen for colonies in which sfp was absent. Resulting fragments and the 16S rRNA region were sequenced to confirm deletion and strain identity.

Growth curves of CP8 and the CP8∆sfp mutant

CP8 and the CP8∆sfp mutant were streaked on LB agar and incubated at 37 °C for 16 hr. Single colonies were used to inoculate 3 ml LB cultures in 14 ml plastic tubes, and the cultures were incubated at 37 °C with shaking (278 rpm) until they reached early stationary phase. The cultures were then back-diluted to an OD₆₀₀ of 0.1 in LB, and 100 μl of the cell suspension were added to each well of a 96-well plate, generating four replicate assays per condition. Growth curves were performed using a Tecan Spark plate reader, measuring OD₆₀₀ every 0.16 hr during growth at 37 °C with shaking (240 rpm) for 20 hr.

Agar diffusion assays

Overnight cultures of Bt and CP8 were made in LSM as previously described. Bt was diluted to OD₆₀₀ 0.5 and streaked onto LSM agar using a sterile cotton swab. Plates were briefly allowed to dry and 2 μl spots of CP8 overnight culture were added. Plates were incubated at 37 °C and checked for the appearance of a zone of inhibition after 48 hr.

Carbon source screening

Overnight cultures of CPs and Bt in LSM were generated as previously described. Cultures were centrifuged and the pelleted bacteria washed twice with 2 x LSM with carbon sources removed. The bacteria were then diluted to an OD₆₀₀ of 0.04, and 20 μl were added to each well of a 384-well plate in which individual carbon sources from LSM were deposited to produce a final concentration of 10 mM. Initial OD₆₀₀ readings were taken using a plate reader, and then the plates were incubated at 30 °C with shaking (800 rpm) for 3 days before a final OD₆₀₀ reading was taken. Each CP or Bt was considered able to utilize a given carbon source if the final OD₆₀₀ reading was greater than that of the inoculated blank plus two standard deviations.

Exometabolomics

Overnight cultures of CP7, CP8, CP19, and Bt were generated as previously described. The cultures were back-diluted 1:50 and then grown at 37 °C with shaking (278 rpm) to late log phase. Three-milliliter co-cultures were made in LSM combining Bt at 3.33×10⁴ CFU/ml with the relevant CP at its IC90. The co-cultures were incubated at 37 °C with shaking, and 1 ml samples were collected at 24 hr and 36 hr. For collection, each sample was centrifuged at 3000 rcf for 10 min, and the supernatant was removed and syringe filtered to remove residual cellular material. Samples were analyzed via GC-TOF by the West Coast Metabolomics Center (Davis, CA). Data were SERRF normalized to remove batch effects, then normalized to the amount injected. Metabolite concentrations were normalized to a media-only control. For analysis, concentrations of each metabolite were converted to metabolite-contributed electrons using the oxidation-reduction half reaction for each metabolite analyzed. For carbon subset analysis, total electrons from lactate, proline, and aspartic acid in the co-culture supernatant at 24 hr were added. Electron concentrations below 0 indicate that a given metabolite is more abundant in co-culture than the media control.

Pairwise combination competition assays

Pairwise combination competition assays were performed as described above (‘Competition Assays’) with the following adjustment. After resuspension in fresh media, the two CPs of interest were mixed together at a 1:1 ratio to generate the desired final CFU/ml (e.g. for a 1:1 ratio with Bt, the two CPs were combined to generate a final concentration of 3.33×10⁴ CFU/ml). Subsequent dilutions were made and carried out as previously described.

Quantification of CP8, CP19, and Bt growth in co-culture

qPCR primers and probes for the 16 S rRNA region for CP8, CP19, and Bt were purchased from IDT and are listed in the ‘Key resources’ table.

To generate standard curves, overnight cultures of CP8, CP19, and Bt were made as previously described in LB. The following day, cultures were backdiluted 1:250 in LB and 1 ml of each culture was collected at 11 timepoints. Optical density at each timepoint was measured (OD₆₀₀ values ranged from 0.05 to 1.033) and cells were pelleted by centrifugation. Genome preparations were done on each pellet using the QIAgen DNeasy Powersoil kit (cat#47014). Concentrations of genomic DNA were quantified using the Qubit dsDNA Quantification Assay Kit (cat#Q32854). A 1:5 dilution of each sample was made to adjust DNA concentration to 3 pg-100 ng. PrimeTime Gene Expression Master Mix (cat#1055772) was used to run the qPCR reactions in triplicate, with 2 μl of diluted genome preparation per well. Samples were analyzed using the BioRad CFX96 Touch Real-Time PCR detection system, with annealing at 60 °C. To create the standard curve, OD₆₀₀ values were plotted against Cq values, and a line was fit to the points.

To generate the growth curve, three biological replicates of CP8, CP19, and Bt were co-cultured in LSM at 37° C and 278 rpm for 36 hr, with 1 ml collections made every hour between 4–30 hr post-inoculation and an additional collection made at 36 hr. Optical density measurements, DNA extraction, and qPCR reaction preparation were performed as previously described. Samples were run in duplicate using the previously established conditions. Measured Cq values from the co-culture samples were converted to OD₆₀₀ values for each organism using the previously generated standard curve.

Animal models

These studies were carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals and the National Institutes of Health. All efforts were made to minimize suffering of animals. All animals were housed in ABSL2 conditions in an AAALAC-accredited and PHS-assured facility, and the protocol was approved by the Lawrence Livermore National Laboratory Institutional Animal Care and Use Committee (IACUC), which includes ethics in the evaluation of protocols. Barrier-housed, specific pathogen-free 6 week old C57Bl/6 j mice were acclimated prior to experiments. CP, Bt, and animal use were also approved by the Institutional Biosafety Committee (IBC).

CP colonization studies

Each CP was grown in monoculture in LB at 37 °C with shaking (250 rpm) for 16 hr, recovered through centrifugation, resuspended in PBS, and diluted to an OD₆₀₀ corresponding to 3.3×10⁷ CFU/ml (based on prior determination of OD₆₀₀-to-CFU plating efficiency measurements). Thirty microliter of this dosing material (corresponding to 10⁶ CFU) were administered to the airway (via OPA) of each mouse. After 7 days, the trachea and lungs were collected and homogenized in PBS; dilutions (in PBS) were plated on LB agar; and, after 1–5 days at 37 °C, the CFU enumerated in order to assess CP load in the airway tissues.

Survival studies

CP dosing material was generated and administered to mice as described above. After 3, 5, or 7 days, the mice were similarly administered (via OPA) 3×10⁴ - 5×10⁵ CFU of Bt. Weight, morbidity, and mortality were monitored for 10 days following Bt challenge; mice displaying ≥20% loss of body weight were euthanized.

For studies comparing viable versus nonviable CPs, the dosing material was generated as described above but then split into two aliquots, with one remaining untreated (viable CP) and the other subjected to inactivation treatments: Exposure to short-wavelength UV-C light (254 nm) for 20 min in a UV-Box decontamination chamber (Air Science; Fort Meyers, FL) followed by heating at 95 °C for 20 min in a dry block incubator (Thermo Fisher Scientific; Waltham, MA). This inactivation protocol causes 100% loss of viability (i.e. no CFU detected after plating on LB) for all CPs tested, as determined through previous experiments and specifically verified for the dosing materials prepared for the studies reported here (data not shown). The viable and nonviable CP dosing materials were then separately administered to mice; after 3, 5, or 7 days, the mice were challenged with Bt (3×10⁴ - 5×10⁵ CFU); and weight, morbidity, and mortality were monitored for 10 days post-challenge (all as described above).

Quantification of CP and pathogen loads in airway tissues

CP dosing material was generated and administered to mice as described above. After 3 days the mice were challenged with Bt (3×10⁴ - 5×10⁵ CFU) as described above. After an additional 3 days (i.e. at 3 days post-challenge) the airway tissues were collected and homogenized, and dilutions were plated on LB agar, as described above. After incubation at 37 °C for 24 hr, the CP CFU were enumerated in order to assess CP load in the airway tissues. After incubation at 37 °C for an additional 24–48 hr, the Bt CFU were enumerated in order to assess Bt load in the airway tissues. Note that CP versus Bt CFU were easily distinguished based on colony size and morphology.

Ex vivo lactate depletion and pathogen inhibition studies

A single colony of CP19 was grown in LSM at 37 °C overnight. The culture was centrifuged and the pellet washed twice with PBS followed by resuspension in PBS. Cell density in the suspension was quantified using a Quantom Tx Microbial Cell Counter. The suspension was then backdiluted to 3.3×10⁷ CFU/ml, and 30 ul of this diluted suspension, or PBS (negative control), were added to 120 μl of murine lung homogenate. A total of five replicate homogenate-based cultures were made per condition, housing them in a 2 ml deep-well block. The homogenate-based cultures were incubated at 37 °C with shaking at 150 rpm for 24 hr, and then centrifuged and sterile filtered. Quantification of lactate levels in the CP19-conditioned and PBS-treated tissue homogenate filtrates was accomplished using the Promega Lactate-Glo Assay (cat#J5021). Bt was grown in LSM, centrifuged and washed twice with PBS, and then resuspended in PBS to 5×10⁷ CFU/ml. CP19-conditioned and PBS-treated tissue homogenate filtrates were inoculated with Bt (10⁶ CFU) and incubated at 37 °C. Growth of the pathogen in the filtrates was monitored by measuring the OD₆₀₀ every 15 min using the Agilent Biotek Synergy Neo2.

Statistical analyses

Ordinary one-way ANOVA with Tukey’s multiple comparisons test, ROC curve analyses, Mantel-Cox survival analyses, Holm-Šidák multiple comparisons tests and all plotting of data to generate graphs, was completed using Graphpad Prism 10.

Data availability

Genomes for all CP strains are available on NCBI with the following accession numbers: SAMN45914816 (CP4), SAMN45914815 (CP17), SAMN45914814 (CP9), SAMN45914813 (CP8), SAMN45914812 (CP7), SAMN39610772 (CP20), SAMN39610773 (CP26), SAMN39610771 (CP19), SAMN39610770 (CP13). Knockout plasmid T2(2)-ori is deposited in Addgene ID#216627. All data generated in this study are available at https://doi.org/10.5061/dryad.ht76hdrxw.

The following data sets were generated

(2026) Dryad Digital Repository
Data files for Niche exclusion of a lung pathogen in mice with designed probiotic communities.

https://doi.org/10.5061/dryad.ht76hdrxw
1. Branda SS
(2017) NCBI BioSample
ID SAMN45914816. Microbe sample from Pseudomonas sp. CP4.

https://www.ncbi.nlm.nih.gov/biosample/?term=SAMN45914816
1. Branda SS
(2017) NCBI BioSample
ID SAMN45914815. Microbe sample from Priestia megaterium.

https://www.ncbi.nlm.nih.gov/biosample/?term=SAMN45914815
1. Branda SS
(2017) NCBI BioSample
ID SAMN45914814. Microbe sample from Neisseria sp. CP9.

https://www.ncbi.nlm.nih.gov/biosample/?term=SAMN45914814
1. Branda SS
(2017) NCBI BioSample
ID SAMN45914813. Microbe sample from Bacillus velezensis.

https://www.ncbi.nlm.nih.gov/biosample/?term=SAMN45914813
1. Branda SS
(2017) NCBI BioSample
ID SAMN45914812. Microbe sample from Stutzerimonas zhaodongensis.

https://www.ncbi.nlm.nih.gov/biosample/?term=SAMN45914812
1. Branda SS
(2017) NCBI BioSample
ID SAMN39610772. Microbe sample from Shouchella rhizosphaerae.

https://www.ncbi.nlm.nih.gov/biosample/?term=SAMN39610772
1. Branda SS
(2017) NCBI BioSample
ID SAMN39610773. Microbe sample from Bacillus licheniformis.

https://www.ncbi.nlm.nih.gov/biosample/?term=SAMN39610773
1. Branda SS
(2017) NCBI BioSample
ID SAMN39610771. Microbe sample from Brevibacillus borstelensis.

https://www.ncbi.nlm.nih.gov/biosample/?term=SAMN39610771
1. Branda SS
(2017) NCBI BioSample
ID SAMN39610770. Microbe sample from Bacillus licheniformis.

https://www.ncbi.nlm.nih.gov/biosample/?term=SAMN39610770

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Article and author information

Author details

Kelsey E Hern

Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, United States

Contribution
Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review and editing

Competing interests
Listed as an inventor on a patent application WO2024137229A1 related to the content of this work

"This ORCID iD identifies the author of this article:" 0000-0003-0256-4507
Ashlee M Phillips

Division of Biosciences and Biotechnology, Lawrence Livermore National Laboratory, Livermore, United States

Contribution
Investigation, Methodology, Data curation

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-5494-7916
Catherine M Mageeney

Department of Biotechnology and Bioengineering, Sandia National Laboratories, Livermore, United States

Contribution
Writing – review and editing, Methodology, Investigation, Data curation

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0001-7969-1622
Kelly P Williams

Department of Systems Biology, Sandia National Laboratories, Livermore, United States

Contribution
Writing – review and editing, Methodology

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-2606-9562
Anupama Sinha

Department of Biotechnology and Bioengineering, Sandia National Laboratories, Livermore, United States

Contribution
Methodology

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0001-5381-7370
Hans K Carlson

Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, United States

Contribution
Methodology, Investigation, Data curation

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-1583-5313
Kunal Poorey

Department of Systems Biology, Sandia National Laboratories, Livermore, United States

Contribution
Methodology, Conceptualization

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0003-3665-0235
Nicole M Collette

Division of Biosciences and Biotechnology, Lawrence Livermore National Laboratory, Livermore, United States

Contribution
Supervision, Investigation, Data curation

For correspondence
collette2@llnl.gov

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-9068-8712
Steven S Branda

Department of Biotechnology and Bioengineering, Sandia National Laboratories, Livermore, United States

Contribution
Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review and editing

For correspondence
sbranda@sandia.gov

Competing interests
Llisted as an inventor on a patent application WO2024137229A1 related to the content of this work

"This ORCID iD identifies the author of this article:" 0000-0001-8763-1013
Adam P Arkin
1. Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, United States
2. Department of Biological Engineering, University of California, Berkeley, Berkeley, United States
Contribution
Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – review and editing

For correspondence
aparkin@lbl.gov

Competing interests
Listed as an inventor on a patent application WO2024137229A1 related to the content of this work

"This ORCID iD identifies the author of this article:" 0000-0002-4999-2931

Funding

U.S. Department of Energy's National Nuclear Security Administration, Sandia National Laboratories (DE-NA0003525)

Kelsey E Hern
Ashlee M Phillips
Catherine M Mageeney
Kelly P Williams
Anupama Sinha
Hans K Carlson
Kunal Poorey
Nicole M Collette
Steven S Branda
Adam P Arkin

U.S. Department of Energy's National Nuclear Security Administration, Lawrence Livermore National Laboratory (DE-AC52-07NA27344)

Ashlee M Phillips
Nicole M Collette

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank K Sander (UCB) and K Vyas (CMS) for helpful discussion; D Sivanandan (UCB) for assistance with experiments; and M Hirakawa (SNL) for critical review of the manuscript. We thank the West Coast Metabolomics Center for exometabolomics services. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc.Inc, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

Ethics

All animal work was conducted in accordance with protocols approved by the Lawrence Livermore National Laboratory (LLNL) Institutional Animal Care and Use Committee (IACUC, protocol 304) and Institutional Biosafety Committee (IBC, protocol 2021-010). LLNL is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) International, and is Public Health Service-Assured (PHS assurance A3184-01).

Version history

Preprint posted: July 1, 2025
Sent for peer review: July 21, 2025
Reviewed Preprint version 1: December 16, 2025
Reviewed Preprint version 2: June 9, 2026
Version of Record published: August 4, 2026

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.108304. This DOI represents all versions, and will always resolve to the latest one.

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.