Interactions between metabolism and growth can determine the co-existence of Staphylococcus aureus and Pseudomonas aeruginosa

Infections caused by multiple types of bacteria are tough to treat. For example, co-infections with Staphylococcus aureus and Pseudomonas aeruginosa are so difficult to cure they may persist for years in humans and cause serious illness. But when these two types of bacteria are grown together in the laboratory, P. aeruginosa kills off all the S. aureus.

Learning why these two types of bacteria can coexist in people but not in the laboratory may lead to new treatments to clear infections. It may also help scientists grow beneficial bacteria mixes that break down pollution or produce biofuels.

Pajon and Fortoul et al. show that interactions between bacterial metabolism and growth rate determine whether S. aureus and P. aeruginosa coexist. In the experiments, they grew both types of bacteria in different environments with different food sources. They measured their growth and metabolism and how many bacteria of each species survived over time. Then, they used their data to develop a mathematical model and tested its predictions in the laboratory again. The type of bacteria that had more energy also grew faster and outcompeted the other species. Measuring the growth rate of the two species allowed the scientists to predict which one would win out and what the tipping point would be. Physically disrupting the mix of bacteria disrupted this relationship.

These results may help explain what allows these bacteria to coexist in some settings but not others. It may enable scientists to develop new ways to treat infections with P. aeruginosa and S. aureus that work by manipulating growth in the two species.

Bacterial growth and metabolism are known to drive antibacterial resistance. Studies in mice using drugs or other therapies to manipulate growth and metabolism may help scientists thwart these resistance mechanisms. The results may also help scientists design and grow beneficial multispecies bacteria communities.

Microbes rarely exist in isolation. It is more common to find them as part of diverse polymicrobial communities comprised of multiple microbial species (e.g. Peters et al., 2012). Interactions within these communities can be synergistic or antagonistic. While cooperation can augment the stability of the community (Cavaliere et al., 2017), competition amongst community members can stimulate the expression of gene products in an effort to drive competitors toward extinction (Burgess et al., 1999). In both cases, these interactions can produce novel behaviors that are not otherwise observed when community members are grown in isolation. On the one hand, they can have beneficial functions; they can produce useful products, such as biofuels (Mittermeier et al., 2023), and degrade harmful environmental pollutants (Brune and Bayer, 2012). On the other hand, they can produce behaviors detrimental to human welfare, including augmented virulence (Stacy et al., 2014) and increased antibiotic resistance (Perez et al., 2014). Accordingly, understanding the general principles that shape the interactions and co-existence of members within a polymicrobial community has wide-ranging implications, such as enhancing the production of beneficial products, and strategies to attenuate antibiotic resistance.

One polymicrobial community that is commonly observed in the clinic and is of increasing concern owing to the emergence of antibiotic resistant strains is composed of Pseudomonas aeruginosa and Staphylococcus aureus. These bacteria co-colonize burns (Norbury et al., 2016), individuals with cystic fibrosis (Sagel et al., 2009), and chronic infection sites (Serra et al., 2015; Pastar et al., 2013). Their co-occurrence leads to significantly worse healthcare outcomes (Hotterbeekx et al., 2017; Maliniak et al., 2016), including increased inflammation during pneumonia (Sagel et al., 2009) and increased wound healing time (Pastar et al., 2013; Dalton et al., 2011; Seth et al., 2012). The interactions between S. aureus and P. aeruginosa are well understood and, at a fundamental level, influence the growth and metabolism of both species (Figure 1A). Their co-culture induces interspecies competition, which enhances antibiotic resistance and virulence. In the presence of P. aeruginosa, S. aureus upregulates the expression of virulence factors, including staphylococcal protein A, α-hemolysis and Panton-Valentine leucocidin (Pastar et al., 2013). While S. aureus can initially aid in the establishment of the P. aeruginosa population (Alves et al., 2018), production of N-acetylglucosamine from S. aureus augments the virulence of P. aeruginosa by upregulating expression of multiple secreted virulence factors. Many of these are regulated by quorum sensing and include pyocyanin (Korgaonkar and Whiteley, 2011), pyoverdine (Orazi et al., 2019), 2-Heptyl-4-hydroxyquinoline N-oxide (Machan et al., 1992), and 4-hydroxy-2-heptylquinoline N-oxide (HQNO) (Li et al., 2015; Déziel et al., 2004; Nadal Jimenez et al., 2012; Korgaonkar et al., 2013). Some of these virulence factors reduce metabolism in S. aureus through multiple mechanisms. Pyocyanin induces the production of reactive oxygen species in S. aureus by reacting with menadione, which results in oxidative damage (Noto et al., 2017). Pyocyanin has been found to interfere with electron transport, reducing respiration (Biswas et al., 2009). The siderophores pyoverdine and pyochelin drive S. aureus into fermentation (Filkins et al., 2015). While exposure to HQNO can initially promote biofilm formation in S. aureus (Fugère et al., 2014), it eventually interferes with the activity of cytochrome b, which reduces ATP production (Nguyen and Oglesby-Sherrouse, 2016). N-3-oxo-dodecanoyl-L-homoserine lactone (3OC12HSL) produced by P. aeruginosa for the purposes of quorum sensing has been shown to reduce the growth of S. aureus (Qazi et al., 2006). Overall, multiple virulence factors interfere with growth and metabolism in S. aureus, ultimately leading to facultative respiration and increased antibiotic tolerance (DeLeon et al., 2014). Without a fast-growing competitor, and with additional nutrients released by S. aureus (Mashburn et al., 2005), P. aeruginosa can increase its growth and density (Figure 1A). Overall, co-occurrence of P. aeruginosa and S. aureus alters growth and metabolism, which ultimately results in a highly virulent and antibiotic-resistant infection.

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Previous studies have reported the co-existence dynamics of S. aureus and P. aeruginosa in human hosts. S. aureus is the predominant bacterial species isolated from pediatric patients with cystic fibrosis. However, during adolescence and into adulthood, the frequency of S. aureus decreases, while the frequency of P. aeruginosa increases (Filkins et al., 2015; Rajan and Saiman, 2002). One leading hypothesis to explain this trend is that P. aeruginosa outcompetes S. aureus through the production of HQNO and siderophores. Indeed, when co-cultured in vitro, P. aeruginosa has been shown to competitively exclude S. aureus over 24 hr (Filkins et al., 2015). However, long-term co-existence of these species has been observed in infected hosts, which has prompted investigations into mechanisms that can account for these conflicting observations. For example, overproduction of alginate by P. aeruginosa (Limoli et al., 2017; Price et al., 2020), upregulation of super-oxide dismutase (Treffon et al., 2018), decreased production of HQNO (Frydenlund Michelsen et al., 2016), and spatial segregation (DeLeon et al., 2014) are expected to facilitate co-existence. Differences in growth environments have also been suggested to play a role in facilitating co-existence. These include differences in environmental albumin concentration (Smith et al., 2017) and the production of proteins by the immune system (Wakeman et al., 2016). Interestingly, previous work has demonstrated that differences in nutrient availability in the growth environment can affect growth and metabolism of bacteria (Pereira and Berry, 2017). Indeed, the presence of different nutrients and metabolites in host microenvironment has been previously shown to impact metabolism and growth of bacteria (Maslowski, 2019; Bjarnsholt et al., 2022). Thus, it is possible that differences in nutrients that define the growth environment can affect the co-existence of P. aeruginosa and S. aureus. A growth environment that provides for a high metabolic rate in S. aureus could potentially buffer it against virulence factors produced by P. aeruginosa that reduce metabolism and growth. In turn, this could facilitate co-existence and perhaps allow S. aureus to outcompete P. aeruginosa. Alternatively, growth environments that reduce growth and metabolism of S. aureus could make that population more sensitive to these virulence factors, thus allowing dominance of P. aeruginosa and reducing co-existence. While these hypotheses are plausible, they have yet to be investigated. Thus, we ask whether growth environment-driven changes in growth rate and metabolism affect the co-existence of P. aeruginosa and S. aureus in co-culture. Addressing the role of growth environment on species interactions has implications in the bottom-up design of polymicrobial communities and may lead to novel treatment strategies in the clinic.

Through a series of empirical and analytical investigations, we have shown that differences in absolute growth, a combined metric that encompasses the relationship between growth rate and metabolism, can determine the population composition of S. aureus and P. aeruginosa. In a stationary environment, as the absolute growth ratio increases, the final density ratio of P. aeruginosa and S. aureus increases. The absolute growth ratio also determines the tipping point of the co-cultured population. Importantly, many virulence factors produced by P. aeruginosa (e.g., HQNO, pyocyanin) that impact growth and metabolism in S. aureus are regulated by quorum sensing. Quorum sensing involves the production and exchange of small diffusible molecules, called autoinducers (Waters and Bassler, 2005). Since each P. aeruginosa bacterium produces autoinducers, the concentration of autoinducer increases as a function of cell density. At a sufficiently high concentration, the autoinducer will activate expression of multiple virulence factors in P. aeruginosa, which begin to impact growth and metabolism in S. aureus. Given this density-dependent activation of virulence factors, it is likely that the tipping point in our systems is being determined by quorum sensing, although we did not explicitly investigate this hypothesis in this manuscript.

While the ability of the absolute growth ratio to predict final population composition is also true in a continually disturbed environment, this relationship falls apart in a periodically disturbed environment. Our model and experiments suggest that this is owing to changes in access to metabolism altering virulence factors produced by P. aeruginosa; in stationary or continuously disturbed environments, the spatial overlap of both bacteria is predicted to be high, which facilitates interactions between these virulence factors and S. aureus. However, in periodically disturbed environments, the overlap between bacteria is reduced; this limits interactions between S. aureus and virulence factors. S. aureus then dominates the population. Absolute growth appears to be the most accurate metric within the scope of our study to quantify the changes in the final ratio between P. aeruginosa and S. aureus. While we did find a significant relationship between the final ratio of both species and the ratio of [ATP] of both species when grown in the stationary condition and in TSB medium, the relationship was not as strong as the absolute growth ratio (R²=0.97 (absolute growth ratio) vs. 0.79 ([ATP] ratio)). Moreover, we did not find a significant relationship between the final ratio of both species and the ratio of [ATP] when grown in TSB medium in the continuously disturbed condition, and in either AMM medium or SCFM in the stationary condition (Figure 3—figure supplement 2).

Given the clinical importance of co-infection with both P. aeruginosa and S. aureus, multiple previous studies have identified mechanisms of co-existence. Indeed, long-term co-existence of both species can result in physiological changes that reduce their competitive interactions. Strains of P. aeruginosa isolated from patients that enter into a mucoid state show reduced production of siderophores, pyocyanin, rhamnolipids, and HQNO, which facilitates the survival of S. aureus (Limoli et al., 2017; Frydenlund Michelsen et al., 2016). These strains can also overproduce the polysaccharide alginate, which in itself is sufficient to decrease the production of these virulence factors. Moreover, exogenously supplied alginate can reduce the production of pyoverdine and expression from the PQS quorum sensing system, which is responsible for the production of HQNO (Price et al., 2020). Changes in the physiology of S. aureus can also facilitate co-existence. Strains of S. aureus isolated from patients with cystic fibrosis show multiple changes in the abundance of proteins including super oxide dismutase, the GroEL chaperone protein, and multiple surface-associated proteins (Treffon et al., 2018). Interestingly, the majority of proteins that show changes in abundance in S. aureus are related to central metabolism, which is consistent with our findings demonstrating that metabolism can influence the co-existence of both species. While it is unclear as to how long-term co-culture would affect the ratio of absolute growth, our findings provide an additional mechanism that can determine the co-existence of these bacterial species.

Previous work has shown that niches in the human body, which can vary in environmental conditions, can promote bacterial growth and persistence (Fung et al., 2019). Spatiotemporal differences in nutrient availability within these niches can promote the growth of different bacteria (Pereira and Berry, 2017; Hoffman et al., 2010). Even within the same niche, such as sputum isolated from individuals with cystic fibrosis, the concentration of critical nutrients, such as amino acids and carbon sources, can differ from host to host (Palmer et al., 2007). Variations in nutrient availability within or between niches may alter growth and metabolism of the resident bacteria, and may serve to perturb interactions within polymicrobial communities. In the case of P. aeruginosa and S. aureus, differences in nutrient availability in sputum (Palmer et al., 2007) or the epithelium (Chen et al., 2018) may alter growth, metabolism and the interactions between these species. This could alter their co-existence dynamics leading to host and/or niche specific colonization dynamics. We note that our findings may be relevant to infections occurring in both high and low O₂ environments. While P. aeruginosa is limited in its ability to perform fermentation (Glasser et al., 2014), we have provided evidence that the absolute growth ratio can affect community composition in both aerobic (Figures 2—5) and more anaerobic environments (Figure 2—figure supplement 1). The limited ability of P. aeruginosa to grow in anaerobic environments was apparent in SCFM as we could not obtain reliable or robustly quantifiable growth of this bacteria when succinate or α-ketoglutarate was provided as a carbon source. One drawback of our approach in using different carbon sources to manipulate absolute growth is that some carbon sources are osmotically active, whereas others are not, which could have additional physiological effects on the bacteria outside of changing growth and metabolism. Moreover, both species of bacteria have different carbon source preferences; as above S. aureus tends to prefer carbon sources such as glucose (Halsey et al., 2017), whereas P. aeruginosa prefers organic and amino acids (Rojo, 2010). Given the carbon source preferences of each species, in complex medium such as TSB, there is the potential that P. aeruginosa consumes amino acids prior to consuming the supplied carbon source. This is perhaps less of a concern in AMM medium or SCFM where the concentration of amino acids and additional nutrient components is reduced as compared to TSB medium. Along this line, it is certainly worth investigating how each nutrient component and its ordered utilization by both species contributes to changes in absolute growth. Minor or transient changes in absolute growth owing to preferential nutrient consumption may provide windows of opportunity for one species to increase its relative density to the other. Nevertheless, differences between in vitro and in vivo growth and metabolism owing to the nutritional composition of the growth environment may also help account for differences in the co-existence dynamics reported previously (Filkins et al., 2015; Rajan and Saiman, 2002).

It has been previously recognized that spatial organization facilitated by an undisturbed growth environment can facilitate the co-existence of bacteria, including P. aeruginosa and S. aureus (Barraza and Whiteley, 2021). Importantly, the production of HQNO from P. aeruginosa can impact the pattern of spatial organization of this co-cultured population (Barraza and Whiteley, 2021). While we did not explicitly quantify spatial organization experimentally owing to technical limitations of our microplate reader and microscope setups, in theory, co-culture in an undisturbed condition should facilitate the creation of spatial organization. We note that the use of microscopy would be a superior approach as compared to our ABM. We are currently working toward developing a system that would allow us to monitor the effect of disturbance on the spatial positioning of bacteria in real time. Nevertheless, we found that, in an undisturbed growth environment, the absolute growth ratio could predict the final population composition; increasing the absolute growth ratio increased the final density of P. aeruginosa relative to S. aureus. When disturbed periodically using a physical force, S. aureus dominated the final population composition. However, a clear trend between the absolute growth ratio and the final population composition was not observed when using a linear regression or WLS regression analysis. Our model suggests that these disturbances decrease the spatial overlap of both populations, and limit the amount of virulence factors that are sensed by S. aureus. Importantly, physical forces that are encountered where bacteria can infect hosts, including luminal areas (Thomas et al., 2004; Lipowsky et al., 1978), can fluctuate, which could serve to periodically alter the spatial positions, or overlap, between both populations. Thus, the degree to which a population of S. aureus and P. aeruginosa is disturbed may influence their ability to interact; this would impact the population composition. Importantly, we cannot rule out that increasing the amount of disturbances might have also impacted oxygenation of the medium, or caused unknown transcriptional responses in the bacteria. However, our model, and experiments using knockout strains, suggest that co-existence of S. aureus and P. aeruginosa in periodically disturbed environments is largely due to interactions governed by HQNO, and to a lesser extent, pyoverdine and pyochelin. While these disturbances have been previously shown to impact bacteria cooperation (Wilson et al., 2017) and the production of virulence factors (Quinn et al., 2021), to our knowledge, this is the study reporting that such disturbances can alter the population composition in a polymicrobial community. Thus, these disturbances represent a new method by which the composition, and potentially the functionality, of a polymicrobial community might be perturbed.

From an ecological perspective, our findings provide support for the intermediate disturbance hypothesis. This hypothesis suggests that the diversity of competing species is maximized at intermediate disturbance frequencies or intensities (Connell, 1978). While the intermediate disturbance hypothesis has faced criticism (Fox, 2013), evidence of its influence on biodiversity has been observed multiple times (Roxburgh et al., 2004), has helped to guide conservation programs (Olff and Ritchie, 1998), and can help explain the establishment of invasive species (Catford et al., 2012). Herein, our results provide evidence that further supports the intermediate disturbance hypothesis; disturbance at intermediate frequencies facilitates the co-existence of both P. aeruginosa and S. aureus, whereas in continually and undisturbed environments P. aeruginosa dominated the co-culture (Figure 5). This observation was found in both TSB medium and SCFM suggesting that it is robust in multiple environments. While previous work has also found that intermediate disturbance can influence the biodiversity of bacterial populations (Brockhurst et al., 2007), our work provides additional support for the intermediate disturbance hypothesis using two clinically important bacterial pathogens.

Co-culture of S. aureus and P. aeruginosa leads to changes in antibiotic susceptibility. For example, HQNO produced by P. aeruginosa interferes with ATP production in S. aureus. This causes a reduction in the metabolism of S. aureus and increases its resistance to aminoglycoside antibiotics. Importantly, a reduction in metabolism has been previously noted to decrease antibiotic efficacy across a wide range of bactericidal antibiotics (Lopatkin et al., 2019). Interestingly, HQNO can also increase membrane permeability of S. aureus, which increases the efficacy of antimicrobials such as chloroxylenol and fluoroquinolones (Orazi et al., 2019). Manipulation of absolute growth using different carbon sources may serve to rationally increase the efficacy of antibiotics. For example, carbon sources that cause S. aureus to have a significantly greater absolute growth relative to P. aeruginosa would limit the growth of P. aeruginosa, and thus reduce the total concentration of HQNO produced. This would allow the metabolism of S. aureus to remain relatively high, thus promoting the efficacy of antibiotics, such as aminoglycosides. Conversely, promoting the production of P. aeruginosa by using carbon sources that increase its absolute growth relative to S. aureus would augment the production of HQNO. This could increase the membrane permeability of S. aureus found in biofilms, thus augmenting sensitivity to certain antibiotics, such as fluoroquinolones. Thus, rational manipulation of absolute growth in polymicrobial interactions may serve to differentially alter susceptibility to existing antibiotics.

All raw experimental data is currently deposited in Dryad at https://doi.org/10.5061/dryad.fn2z34tz3.

1. 1. Smith RP

   (2023) Dryad Digital Repository

   Interactions between metabolism and growth can determine the co-existence of Staphylococcus aureus and Pseudomonas aeruginosa.

   https://doi.org/10.5061/dryad.fn2z34tz3

1. 1. Alves PM
   2. Al-Badi E
   3. Withycombe C
   4. Jones PM
   5. Purdy KJ
   6. Maddocks SE

   (2018) Interaction between Staphylococcus aureus and Pseudomonas aeruginosa is beneficial for colonisation and pathogenicity in a mixed biofilm

   Pathogens and Disease 76:fty003.

   https://doi.org/10.1093/femspd/fty003

   • PubMed
   • Google Scholar
2. 1. Barraza JP
   2. Whiteley M

   (2021) A Pseudomonas aeruginosa antimicrobial affects the biogeography but not fitness of Staphylococcus aureus during coculture

   MBio 12:e00047-21.

   https://doi.org/10.1128/mBio.00047-21

   • PubMed
   • Google Scholar
3. 1. Barraza I
   2. Pajon C
   3. Diaz-Tang G
   4. Marin Meneses E
   5. Abu-Rumman F
   6. García-Diéguez L
   7. Castro V
   8. Lopatkin AJ
   9. Smith RP
   10. Dozois CM

   (2023) Disturbing the spatial organization of biofilm communities affects expression of agr-regulated virulence factors in Staphylococcus aureus

   Applied and Environmental Microbiology 89:e0193222.

   https://doi.org/10.1128/aem.01932-22

   • PubMed
   • Google Scholar
4. 1. Benjamini Y
   2. Hochberg Y

   (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing

   Journal of the Royal Statistical Society 57:289–300.

   https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

   • Google Scholar
5. 1. Biswas L
   2. Biswas R
   3. Schlag M
   4. Bertram R
   5. Götz F

   (2009) Small-Colony variant selection as a survival strategy for Staphylococcus aureus in the presence of Pseudomonas aeruginosa

   Applied and Environmental Microbiology 75:6910–6912.

   https://doi.org/10.1128/AEM.01211-09

   • Google Scholar
6. 1. Bjarnsholt T
   2. Whiteley M
   3. Rumbaugh KP
   4. Stewart PS
   5. Jensen PØ
   6. Frimodt-Møller N

   (2022) The importance of understanding the infectious microenvironment

   The Lancet. Infectious Diseases 22:e88–e92.

   https://doi.org/10.1016/S1473-3099(21)00122-5

   • PubMed
   • Google Scholar
7. 1. Brockhurst MA
   2. Buckling A
   3. Gardner A

   (2007) Cooperation peaks at intermediate disturbance

   Current Biology 17:761–765.

   https://doi.org/10.1016/j.cub.2007.02.057

   • PubMed
   • Google Scholar
8. 1. Brune KD
   2. Bayer TS

   (2012) Engineering microbial consortia to enhance biomining and bioremediation

   Frontiers in Microbiology 3:203.

   https://doi.org/10.3389/fmicb.2012.00203

   • PubMed
   • Google Scholar
9. 1. Burgess JG
   2. Jordan EM
   3. Bregu M
   4. Mearns-Spragg A
   5. Boyd KG

   (1999) Microbial antagonism: A neglected Avenue of natural products research

   Progress in Industrial Microbiology 35:27–32.

   https://doi.org/10.1016/s0168-1656(99)00054-1

   • Google Scholar
10. 1. Catford JA
    2. Daehler CC
    3. Murphy HT
    4. Sheppard AW
    5. Hardesty BD
    6. Westcott DA
    7. Rejmánek M
    8. Bellingham PJ
    9. Pergl J
    10. Horvitz CC
    11. Hulme PE

    (2012) The intermediate disturbance hypothesis and plant invasions: implications for species richness and management

    Perspectives in Plant Ecology, Evolution and Systematics 14:231–241.

    https://doi.org/10.1016/j.ppees.2011.12.002

    • Google Scholar
11. 1. Cavaliere M
    2. Feng S
    3. Soyer OS
    4. Jiménez JI

    (2017) Cooperation in microbial communities and their biotechnological applications

    Environmental Microbiology 19:2949–2963.

    https://doi.org/10.1111/1462-2920.13767

    • PubMed
    • Google Scholar
12. 1. Chen YE
    2. Fischbach MA
    3. Belkaid Y

    (2018) Skin Microbiota–host interactions

    Nature 553:427–436.

    https://doi.org/10.1038/nature25177

    • PubMed
    • Google Scholar
13. 1. Connell JH

    (1978)

    Diversity in tropical rain forests and coral reefs: high diversity of trees and corals is maintained only in a nonequilibrium state

    Science 199:1302–1310.

    • Google Scholar
14. 1. Dalton T
    2. Dowd SE
    3. Wolcott RD
    4. Sun Y
    5. Watters C
    6. Griswold JA
    7. Rumbaugh KP

    (2011) An in vivo polymicrobial biofilm wound infection model to study interspecies interactions

    PLOS ONE 6:e27317.

    https://doi.org/10.1371/journal.pone.0027317

    • PubMed
    • Google Scholar
15. 1. Darch SE
    2. Simoska O
    3. Fitzpatrick M
    4. Barraza JP
    5. Stevenson KJ
    6. Bonnecaze RT
    7. Shear JB
    8. Whiteley M

    (2018) Spatial determinants of quorum signaling in a Pseudomonas aeruginosa infection model

    PNAS 115:4779–4784.

    https://doi.org/10.1073/pnas.1719317115

    • Google Scholar
16. 1. DeLeon S
    2. Clinton A
    3. Fowler H
    4. Everett J
    5. Horswill AR
    6. Rumbaugh KP

    (2014) Synergistic interactions of Pseudomonas aeruginosa and Staphylococcus aureus in an in vitro wound model

    Infection and Immunity 82:4718–4728.

    https://doi.org/10.1128/IAI.02198-14

    • PubMed
    • Google Scholar
17. 1. Déziel E
    2. Lépine F
    3. Milot S
    4. He J
    5. Mindrinos MN
    6. Tompkins RG
    7. Rahme LG

    (2004) Analysis of Pseudomonas aeruginosa 4-hydroxy-2-alkylquinolines (haqs) reveals a role for 4-hydroxy-2-heptylquinoline in cell-to-cell communication

    PNAS 101:1339–1344.

    https://doi.org/10.1073/pnas.0307694100

    • Google Scholar
18. 1. Diaz-Tang G
    2. Meneses EM
    3. Patel K
    4. Mirkin S
    5. García-Diéguez L
    6. Pajon C
    7. Barraza I
    8. Patel V
    9. Ghali H
    10. Tracey AP
    11. Blanar CA
    12. Lopatkin AJ
    13. Smith RP

    (2022) Growth productivity as a determinant of the inoculum effect for bactericidal antibiotics

    Science Advances 8:eadd0924.

    https://doi.org/10.1126/sciadv.add0924

    • PubMed
    • Google Scholar
19. 1. Filkins LM
    2. Graber JA
    3. Olson DG
    4. Dolben EL
    5. Lynd LR
    6. Bhuju S
    7. O’Toole GA
    8. DiRita VJ

    (2015) Coculture of Staphylococcus aureus with Pseudomonas aeruginosa drives S. aureus towards fermentative metabolism and reduced viability in a cystic fibrosis model

    Journal of Bacteriology 197:2252–2264.

    https://doi.org/10.1128/JB.00059-15

    • Google Scholar
20. 1. Fox JW

    (2013) The intermediate disturbance hypothesis should be abandoned

    Trends in Ecology & Evolution 28:86–92.

    https://doi.org/10.1016/j.tree.2012.08.014

    • PubMed
    • Google Scholar
21. 1. Friedman J
    2. Higgins LM
    3. Gore J

    (2017) Community structure follows simple assembly rules in microbial microcosms

    Nature Ecology & Evolution 1:1–7.

    https://doi.org/10.1038/s41559-017-0109

    • Google Scholar
22. 1. Frydenlund Michelsen C
    2. Hossein Khademi SM
    3. Krogh Johansen H
    4. Ingmer H
    5. Dorrestein PC
    6. Jelsbak L

    (2016) Evolution of metabolic divergence in Pseudomonas aeruginosa during long-term infection facilitates a proto-cooperative interspecies interaction

    The ISME Journal 10:1323–1336.

    https://doi.org/10.1038/ismej.2015.220

    • Google Scholar
23. 1. Fugère A
    2. Lalonde Séguin D
    3. Mitchell G
    4. Déziel E
    5. Dekimpe V
    6. Cantin AM
    7. Frost E
    8. Malouin F

    (2014) Interspecific small molecule interactions between clinical isolates of Pseudomonas aeruginosa and Staphylococcus aureus from adult cystic fibrosis patients

    PLOS ONE 9:e86705.

    https://doi.org/10.1371/journal.pone.0086705

    • PubMed
    • Google Scholar
24. 1. Fung C
    2. Tan S
    3. Nakajima M
    4. Skoog EC
    5. Camarillo-Guerrero LF
    6. Klein JA
    7. Lawley TD
    8. Solnick JV
    9. Fukami T
    10. Amieva MR

    (2019) High-Resolution mapping reveals that microniches in the gastric glands control Helicobacter pylori colonization of the stomach

    PLOS Biology 17:e3000231.

    https://doi.org/10.1371/journal.pbio.3000231

    • PubMed
    • Google Scholar
25. 1. Glasser NR
    2. Kern SE
    3. Newman DK

    (2014) Phenazine redox cycling enhances anaerobic survival in Pseudomonas aeruginosa by facilitating generation of ATP and a proton-motive force

    Molecular Microbiology 92:399–412.

    https://doi.org/10.1111/mmi.12566

    • PubMed
    • Google Scholar
26. 1. Halsey CR
    2. Lei S
    3. Wax JK
    4. Lehman MK
    5. Nuxoll AS
    6. Steinke L
    7. Sadykov M
    8. Powers R
    9. Fey PD

    (2017) Amino acid catabolism in Staphylococcus aureus and the function of carbon catabolite repression

    MBio 8:e01434-16.

    https://doi.org/10.1128/mBio.01434-16

    • PubMed
    • Google Scholar
27. 1. Hoffman LR
    2. Richardson AR
    3. Houston LS
    4. Kulasekara HD
    5. Martens-Habbena W
    6. Klausen M
    7. Burns JL
    8. Stahl DA
    9. Hassett DJ
    10. Fang FC
    11. Miller SI

    (2010) Nutrient availability as a mechanism for selection of antibiotic tolerant Pseudomonas aeruginosa within the CF airway

    PLOS Pathogens 6:e1000712.

    https://doi.org/10.1371/journal.ppat.1000712

    • PubMed
    • Google Scholar
28. 1. Hotterbeekx A
    2. Kumar-Singh S
    3. Goossens H
    4. Malhotra-Kumar S

    (2017) In vivo and in vitro interactions between Pseudomonas aeruginosa and Staphylococcus spp

    Frontiers in Cellular and Infection Microbiology 7:106.

    https://doi.org/10.3389/fcimb.2017.00106

    • Google Scholar
29. 1. Jin Z
    2. Li J
    3. Ni L
    4. Zhang R
    5. Xia A
    6. Jin F

    (2018) Conditional privatization of a public siderophore enables Pseudomonas aeruginosa to resist cheater invasion

    Nature Communications 9:1383.

    https://doi.org/10.1038/s41467-018-03791-y

    • PubMed
    • Google Scholar
30. 1. Julou T
    2. Mora T
    3. Guillon L
    4. Croquette V
    5. Schalk IJ
    6. Bensimon D
    7. Desprat N

    (2013) Cell-Cell contacts confine public goods diffusion inside Pseudomonas aeruginosa clonal microcolonies

    PNAS 110:12577–12582.

    https://doi.org/10.1073/pnas.1301428110

    • PubMed
    • Google Scholar
31. 1. Korgaonkar AK
    2. Whiteley M

    (2011) Pseudomonas aeruginosa enhances production of an antimicrobial in response to N-acetylglucosamine and peptidoglycan

    Journal of Bacteriology 193:909–917.

    https://doi.org/10.1128/JB.01175-10

    • PubMed
    • Google Scholar
32. 1. Korgaonkar A
    2. Trivedi U
    3. Rumbaugh KP
    4. Whiteley M

    (2013) Community surveillance enhances Pseudomonas aeruginosa virulence during polymicrobial infection

    PNAS 110:1059–1064.

    https://doi.org/10.1073/pnas.1214550110

    • Google Scholar
33. 1. Lahtvee PJ
    2. Adamberg K
    3. Arike L
    4. Nahku R
    5. Aller K
    6. Vilu R

    (2011) Multi-Omics approach to study the growth efficiency and amino acid metabolism in Lactococcus lactis at various specific growth rates

    Microbial Cell Factories 10:1–12.

    https://doi.org/10.1186/1475-2859-10-12

    • PubMed
    • Google Scholar
34. 1. Lépine F
    2. Déziel E
    3. Milot S
    4. Rahme LG

    (2003) A stable Isotope dilution assay for the Quantification of the Pseudomonas Quinolone signal in Pseudomonas aeruginosa cultures

    Biochimica et Biophysica Acta 1622:36–41.

    https://doi.org/10.1016/S0304-4165(03)00103-X

    • Google Scholar
35. 1. Li H
    2. Li X
    3. Wang Z
    4. Fu Y
    5. Ai Q
    6. Dong Y
    7. Yu J

    (2015) Autoinducer-2 regulates Pseudomonas aeruginosa PAO1 biofilm formation and virulence production in a dose-dependent manner

    BMC Microbiology 15:192.

    https://doi.org/10.1186/s12866-015-0529-y

    • PubMed
    • Google Scholar
36. 1. Limoli DH
    2. Whitfield GB
    3. Kitao T
    4. Ivey ML
    5. Davis MR
    6. Grahl N
    7. Hogan DA
    8. Rahme LG
    9. Howell PL
    10. O’Toole GA
    11. Goldberg JB

    (2017) Pseudomonas aeruginosa alginate overproduction promotes coexistence with Staphylococcus aureus in a model of cystic fibrosis respiratory infection

    MBio 8:e00186-17.

    https://doi.org/10.1128/mBio.00186-17

    • PubMed
    • Google Scholar
37. 1. Lipowsky HH
    2. Kovalcheck S
    3. Zweifach BW

    (1978) The distribution of blood rheological parameters in the microvasculature of cat mesentery

    Circulation Research 43:738–749.

    https://doi.org/10.1161/01.res.43.5.738

    • PubMed
    • Google Scholar
38. 1. Lopatkin AJ
    2. Stokes JM
    3. Zheng EJ
    4. Yang JH
    5. Takahashi MK
    6. You L
    7. Collins JJ

    (2019) Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate

    Nature Microbiology 4:2109–2117.

    https://doi.org/10.1038/s41564-019-0536-0

    • Google Scholar
39. 1. Machado H
    2. Weng LL
    3. Dillon N
    4. Seif Y
    5. Holland M
    6. Pekar JE
    7. Monk JM
    8. Nizet V
    9. Palsson BO
    10. Feist AM

    (2019) Strain-Specific metabolic requirements revealed by a defined minimal medium for systems analyses of Staphylococcus aureus

    Applied and Environmental Microbiology 85:e01773-19.

    https://doi.org/10.1128/AEM.01773-19

    • PubMed
    • Google Scholar
40. 1. Machan ZA
    2. Taylor GW
    3. Pitt TL
    4. Cole PJ
    5. Wilson R

    (1992) 2-Heptyl-4-Hydroxyquinoline N-oxide, an antistaphylococcal agent produced by Pseudomonas aeruginosa

    The Journal of Antimicrobial Chemotherapy 30:615–623.

    https://doi.org/10.1093/jac/30.5.615

    • PubMed
    • Google Scholar
41. 1. Maliniak ML
    2. Stecenko AA
    3. McCarty NA

    (2016) A longitudinal analysis of chronic MRSA and Pseudomonas aeruginosa co-infection in cystic fibrosis: a single-center study

    Journal of Cystic Fibrosis 15:350–356.

    https://doi.org/10.1016/j.jcf.2015.10.014

    • PubMed
    • Google Scholar
42. 1. Manzoni S
    2. Taylor P
    3. Richter A
    4. Porporato A
    5. Ågren GI

    (2012) Environmental and stoichiometric controls on microbial carbon-use efficiency in soils

    The New Phytologist 196:79–91.

    https://doi.org/10.1111/j.1469-8137.2012.04225.x

    • PubMed
    • Google Scholar
43. 1. Mashburn LM
    2. Jett AM
    3. Akins DR
    4. Whiteley M

    (2005) Staphylococcus aureus serves as an iron source for Pseudomonas aeruginosa during in vivo coculture

    Journal of Bacteriology 187:554–566.

    https://doi.org/10.1128/JB.187.2.554-566.2005

    • PubMed
    • Google Scholar
44. 1. Maslowski KM

    (2019) Metabolism at the centre of the host–microbe relationship

    Clinical and Experimental Immunology 197:193–204.

    https://doi.org/10.1111/cei.13329

    • Google Scholar
45. 1. Meroz N
    2. Tovi N
    3. Sorokin Y
    4. Friedman J

    (2021) Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales

    Nature Communications 12:2891.

    https://doi.org/10.1038/s41467-021-23247-0

    • PubMed
    • Google Scholar
46. 1. Mittermeier F
    2. Bäumler M
    3. Arulrajah P
    4. de J García Lima J
    5. Hauke S
    6. Stock A
    7. Weuster-Botz D

    (2023) Artificial microbial consortia for bioproduction processes

    Engineering in Life Sciences 23:e2100152.

    https://doi.org/10.1002/elsc.202100152

    • PubMed
    • Google Scholar
47. 1. Nadal Jimenez P
    2. Koch G
    3. Thompson JA
    4. Xavier KB
    5. Cool RH
    6. Quax WJ

    (2012) The multiple signaling systems regulating virulence in Pseudomonas aeruginosa

    Microbiology and Molecular Biology Reviews 76:46–65.

    https://doi.org/10.1128/MMBR.05007-11

    • Google Scholar
48. 1. Nguyen AT
    2. Oglesby-Sherrouse AG

    (2016) Interactions between Pseudomonas aeruginosa and Staphylococcus aureus during co-cultivations and polymicrobial infections

    Applied Microbiology and Biotechnology 100:6141–6148.

    https://doi.org/10.1007/s00253-016-7596-3

    • PubMed
    • Google Scholar
49. 1. Norbury W
    2. Herndon DN
    3. Tanksley J
    4. Jeschke MG
    5. Finnerty CC
    6. Society SSCotSI

    (2016) Infection in burns

    Surgical Infections 17:250–255.

    https://doi.org/10.1089/sur.2013.134

    • PubMed
    • Google Scholar
50. 1. Noto MJ
    2. Burns WJ
    3. Beavers WN
    4. Skaar EP

    (2017) Mechanisms of pyocyanin toxicity and genetic determinants of resistance in Staphylococcus aureus

    Journal of Bacteriology 199:e00221-17.

    https://doi.org/10.1128/JB.00221-17

    • PubMed
    • Google Scholar
51. 1. Olff H
    2. Ritchie ME

    (1998) Effects of herbivores on grassland plant diversity

    Trends in Ecology & Evolution 13:261–265.

    https://doi.org/10.1016/S0169-5347(98)01364-0

    • Google Scholar
52. 1. Orazi G
    2. Ruoff KL
    3. O’Toole GA

    (2019) Pseudomonas aeruginosa increases the sensitivity of biofilm-grown Staphylococcus aureus to membrane-targeting antiseptics and antibiotics

    MBio 10:e01501-19.

    https://doi.org/10.1128/mBio.01501-19

    • PubMed
    • Google Scholar
53. 1. Palmer KL
    2. Aye LM
    3. Whiteley M

    (2007) Nutritional cues control Pseudomonas aeruginosa multicellular behavior in cystic fibrosis sputum

    Journal of Bacteriology 189:8079–8087.

    https://doi.org/10.1128/JB.01138-07

    • Google Scholar
54. 1. Pastar I
    2. Nusbaum AG
    3. Gil J
    4. Patel SB
    5. Chen J
    6. Valdes J
    7. Stojadinovic O
    8. Plano LR
    9. Tomic-Canic M
    10. Davis SC
    11. Otto M

    (2013) Interactions of methicillin resistant Staphylococcus aureus USA300 and Pseudomonas aeruginosa in polymicrobial wound infection

    PLOS ONE 8:e56846.

    https://doi.org/10.1371/journal.pone.0056846

    • Google Scholar
55. 1. Pereira FC
    2. Berry D

    (2017) Microbial nutrient niches in the gut

    Environmental Microbiology 19:1366–1378.

    https://doi.org/10.1111/1462-2920.13659

    • PubMed
    • Google Scholar
56. 1. Perez AC
    2. Pang B
    3. King LB
    4. Tan L
    5. Murrah KA
    6. Reimche JL
    7. Wren JT
    8. Richardson SH
    9. Ghandi U
    10. Swords WE

    (2014) Residence of Streptococcus pneumoniae and Moraxella catarrhalis within polymicrobial biofilm promotes antibiotic resistance and bacterial persistence in vivo

    Pathogens and Disease 70:280–288.

    https://doi.org/10.1111/2049-632X.12129

    • PubMed
    • Google Scholar
57. 1. Peters BM
    2. Jabra-Rizk MA
    3. O’May GA
    4. Costerton JW
    5. Shirtliff ME

    (2012) Polymicrobial interactions: impact on pathogenesis and human disease

    Clinical Microbiology Reviews 25:193–213.

    https://doi.org/10.1128/CMR.00013-11

    • PubMed
    • Google Scholar
58. 1. Price CE
    2. Brown DG
    3. Limoli DH
    4. Phelan VV
    5. O’Toole GA

    (2020) Exogenous alginate protects Staphylococcus aureus from killing by Pseudomonas aeruginosa

    Journal of Bacteriology 202:e00559-19.

    https://doi.org/10.1128/JB.00559-19

    • PubMed
    • Google Scholar
59. 1. Qazi S
    2. Middleton B
    3. Muharram SH
    4. Cockayne A
    5. Hill P
    6. O’Shea P
    7. Chhabra SR
    8. Cámara M
    9. Williams P

    (2006) N-Acylhomoserine lactones antagonize virulence gene expression and quorum sensing in Staphylococcus aureus

    Infection and Immunity 74:910–919.

    https://doi.org/10.1128/IAI.74.2.910-919.2006

    • PubMed
    • Google Scholar
60. 1. Quinn RJ
    2. Barraza I
    3. García-Diéguez L
    4. Pajon C
    5. Krausfeldt LE
    6. Ibrahim K
    7. Enzinna LA
    8. Thorn ME
    9. Eldakar OT
    10. Craddock TJA
    11. Smith RP

    (2021) Periodically disturbing the spatial structure of biofilms can affect the production of an essential virulence factor in Pseudomonas aeruginosa

    MSystems 6:e0096121.

    https://doi.org/10.1128/mSystems.00961-21

    • PubMed
    • Google Scholar
61. 1. Rajan S
    2. Saiman L

    (2002) Pulmonary infections in patients with cystic fibrosis

    Seminars in Respiratory Infections 17:47–56.

    https://doi.org/10.1053/srin.2002.31690

    • PubMed
    • Google Scholar
62. 1. Rojo F

    (2010) Carbon catabolite repression in Pseudomonas: optimizing metabolic versatility and interactions with the environment

    FEMS Microbiology Reviews 34:658–684.

    https://doi.org/10.1111/j.1574-6976.2010.00218.x

    • PubMed
    • Google Scholar
63. 1. Roxburgh SH
    2. Shea K
    3. Wilson JB

    (2004) The intermediate disturbance hypothesis: patch dynamics and mechanisms of species coexistence

    Ecology 85:359–371.

    https://doi.org/10.1890/03-0266

    • Google Scholar
64. 1. Rudin L
    2. Sjöström J-E
    3. Lindberg M
    4. Philipson L

    (1974) Factors affecting competence for transformation in Staphylococcus aureus

    Journal of Bacteriology 118:155–164.

    https://doi.org/10.1128/jb.118.1.155-164.1974

    • Google Scholar
65. 1. Sagel SD
    2. Gibson RL
    3. Emerson J
    4. McNamara S
    5. Burns JL
    6. Wagener JS
    7. Ramsey BW
    8. Inhaled Tobramycin in Young Children Study Group
    9. Cystic Fibrosis Foundation Therapeutics Development Network

    (2009) Impact of Pseudomonas and Staphylococcus infection on inflammation and clinical status in young children with cystic fibrosis

    The Journal of Pediatrics 154:183–188.

    https://doi.org/10.1016/j.jpeds.2008.08.001

    • PubMed
    • Google Scholar
66. 1. Sauer U
    2. Hatzimanikatis V
    3. Hohmann HP
    4. Manneberg M
    5. van Loon AP
    6. Bailey JE

    (1996) Physiology and metabolic fluxes of wild-type and riboflavin-producing Bacillus subtilis

    Applied and Environmental Microbiology 62:3687–3696.

    https://doi.org/10.1128/aem.62.10.3687-3696.1996

    • Google Scholar
67. 1. Serino L
    2. Reimmann C
    3. Visca P
    4. Beyeler M
    5. Chiesa VD
    6. Haas D

    (1997) Biosynthesis of pyochelin and dihydroaeruginoic acid requires the iron-regulated pchdcba operon in Pseudomonas aeruginosa

    Journal of Bacteriology 179:248–257.

    https://doi.org/10.1128/jb.179.1.248-257.1997

    • PubMed
    • Google Scholar
68. 1. Serra R
    2. Grande R
    3. Butrico L
    4. Rossi A
    5. Settimio UF
    6. Caroleo B
    7. Amato B
    8. Gallelli L
    9. de Franciscis S

    (2015) Chronic wound infections: the role of Pseudomonas aeruginosa and Staphylococcus aureus

    Expert Review of Anti-Infective Therapy 13:605–613.

    https://doi.org/10.1586/14787210.2015.1023291

    • PubMed
    • Google Scholar
69. 1. Seth AK
    2. Geringer MR
    3. Galiano RD
    4. Leung KP
    5. Mustoe TA
    6. Hong SJ

    (2012) Quantitative comparison and analysis of species-specific wound biofilm virulence using an in vivo, rabbit-ear model

    Journal of the American College of Surgeons 215:388–399.

    https://doi.org/10.1016/j.jamcollsurg.2012.05.028

    • PubMed
    • Google Scholar
70. 1. Smith AC
    2. Rice A
    3. Sutton B
    4. Gabrilska R
    5. Wessel AK
    6. Whiteley M
    7. Rumbaugh KP

    (2017) Albumin inhibits Pseudomonas aeruginosa quorum sensing and alters polymicrobial interactions

    Infection and Immunity 85:e00116-17.

    https://doi.org/10.1128/IAI.00116-17

    • PubMed
    • Google Scholar
71. 1. Stacy A
    2. Everett J
    3. Jorth P
    4. Trivedi U
    5. Rumbaugh KP
    6. Whiteley M

    (2014) Bacterial fight-and-flight responses enhance virulence in a polymicrobial infection

    PNAS 111:7819–7824.

    https://doi.org/10.1073/pnas.1400586111

    • Google Scholar
72. 1. Stouthamer AH
    2. Bettenhaussen CW

    (1975) Determination of the efficiency of oxidative phosphorylation in continuous cultures of Aerobacter aerogenes

    Archives of Microbiology 102:187–192.

    https://doi.org/10.1007/BF00428367

    • PubMed
    • Google Scholar
73. Book

    1. Strutz T

    (2011) Data Fitting and Uncertainty

    Springer.

    https://doi.org/10.1007/978-3-8348-9813-5

    • Google Scholar
74. 1. Thomas WE
    2. Nilsson LM
    3. Forero M
    4. Sokurenko EV
    5. Vogel V

    (2004) Shear-Dependent `` stick-and-roll'' adhesion of type 1 fimbriated Escherichia coli

    Molecular Microbiology 53:1545–1557.

    https://doi.org/10.1111/j.1365-2958.2004.04226.x

    • PubMed
    • Google Scholar
75. Conference

    1. Tisue S
    2. Wilensky U

    (2004)

    Netlogo: A simple environment for modeling complexity

    International conference on complex systems.

    • Google Scholar
76. 1. Treffon J
    2. Block D
    3. Moche M
    4. Reiss S
    5. Fuchs S
    6. Engelmann S
    7. Becher D
    8. Langhanki L
    9. Mellmann A
    10. Peters G
    11. Kahl BC

    (2018) Adaptation of Staphylococcus aureus to airway environments in patients with cystic fibrosis by upregulation of superoxide dismutase M and iron-scavenging proteins

    The Journal of Infectious Diseases 217:1453–1461.

    https://doi.org/10.1093/infdis/jiy012

    • PubMed
    • Google Scholar
77. 1. Wakeman CA
    2. Moore JL
    3. Noto MJ
    4. Zhang Y
    5. Singleton MD
    6. Prentice BM
    7. Gilston BA
    8. Doster RS
    9. Gaddy JA
    10. Chazin WJ
    11. Caprioli RM
    12. Skaar EP

    (2016) The innate immune protein calprotectin promotes Pseudomonas aeruginosa and Staphylococcus aureus interaction

    Nature Communications 7:11951.

    https://doi.org/10.1038/ncomms11951

    • PubMed
    • Google Scholar
78. 1. Wallace RJ
    2. Holms WH

    (1986) Maintenance coefficients and rates of turnover of cell material in Escherichia coli ML308 at different growth temperatures

    FEMS Microbiology Letters 37:317–320.

    https://doi.org/10.1111/j.1574-6968.1986.tb01816.x

    • Google Scholar
79. 1. Wang P
    2. Robert L
    3. Pelletier J
    4. Dang WL
    5. Taddei F
    6. Wright A
    7. Jun S

    (2010) Robust growth of Escherichia coli

    Current Biology 20:1099–1103.

    https://doi.org/10.1016/j.cub.2010.04.045

    • PubMed
    • Google Scholar
80. 1. Waters CM
    2. Bassler BL

    (2005) Quorum sensing: cell-to-cell communication in bacteria

    Annual Review of Cell and Developmental Biology 21:319–346.

    https://doi.org/10.1146/annurev.cellbio.21.012704.131001

    • PubMed
    • Google Scholar
81. 1. Wilson CE
    2. Lopatkin AJ
    3. Craddock TJA
    4. Driscoll WW
    5. Eldakar OT
    6. Lopez JV
    7. Smith RP

    (2017) Cooperation and competition shape ecological resistance during periodic spatial disturbance of engineered bacteria

    Scientific Reports 7:440.

    https://doi.org/10.1038/s41598-017-00588-9

    • Google Scholar
82. 1. Wright ES
    2. Gupta R
    3. Vetsigian KH

    (2021) Multi-stable bacterial communities exhibit extreme sensitivity to initial conditions

    FEMS Microbiology Ecology 97:fiab073.

    https://doi.org/10.1093/femsec/fiab073

    • PubMed
    • Google Scholar

Author details

1. Camryn Pajon

   Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States

   Contribution

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

   Contributed equally with

   Marla C Fortoul

   Competing interests

   No competing interests declared

2. Marla C Fortoul

   Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States

   Contribution

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

   Contributed equally with

   Camryn Pajon

   Competing interests

   No competing interests declared

3. Gabriela Diaz-Tang

   Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Estefania Marin Meneses

   Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Ariane R Kalifa

   Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Elinor Sevy

   Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Taniya Mariah

   Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States

   Contribution

   Formal analysis, Methodology

   Competing interests

   No competing interests declared

8. Brandon Toscan

   Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

9. Maili Marcelin

   Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

10. Daniella M Hernandez

    Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Melissa M Marzouk

    Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

12. Allison J Lopatkin

    1. Department of Biology, Barnard College, Columbia University, New York, United States
    2. Data Science Institute, Columbia University, New York, United States
    3. Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, United States

    Contribution

    Software, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

13. Omar Tonsi Eldakar

    Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States

    Contribution

    Conceptualization, Software, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

14. Robert P Smith

    1. Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, United States
    2. Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    rsmith@nova.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2744-7390

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