Disruption of Cross-Feeding Inhibits Pathogen Growth in the Sputa of Patients with Cystic Fibrosis

Iron content of commercial mucin contributes to compositional stability of a cystic fibrosis airway synthetic microbiota community

In vitro culture models of mucosal environments are used to elucidate the mechanistic roles of the microbiota in human health. These models often include commercial mucins to reflect the in-situ role of mucins as an attachment site and nutrient source for the microbiota. Two types of mucins are commercially available: porcine gastric mucin (PGM) and bovine submaxillary mucin (BSM). These commercial mucins have been shown to contain iron, an essential element required by the microbiota as a co-factor for a variety of metabolic functions. In these mucin preparations, the concentration of available iron can exceed physiological concentrations present in the native environment. This unexpected source of iron influences experimental outcomes, including shaping the interactions between co-existing microbes in synthetic microbial communities used to elucidate the multispecies interactions within native microbiota. In this work, we leveraged the well-characterized iron-dependent production of secondary metabolites by the opportunistic pathogen Pseudomonas aeruginosa to aid in the development of a simple, low-cost, reproducible workflow to remove iron from commercial mucins. Using the mucosal environment of the cystic fibrosis (CF) airway as a model system, we show that P. aeruginosa is canonically responsive to iron concentration in the chemically defined synthetic CF medium complemented with semi-purified PGM, and community composition of a clinically relevant, synthetic CF airway microbial community is modulated, in part, by iron concentration in PGM.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. Understanding PEx ecology is challenged by high inter-patient variability in airway microbial community profiles. We analyze bacterial communities in 880 CF sputum samples collected during an observational prospective cohort study and develop microbiome descriptors to model community reorganization prior to and during 18 PEx. We identify two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx show hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx display stochasticity and increased diversity. A simulation of antimicrobial treatment predicts better efficacy for hierarchically organized communities. This link between PEx, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.

Metabolic interplay between Proteus mirabilis and Enterococcus faecalis facilitates polymicrobial biofilm formation and invasive disease

Polymicrobial biofilms play an important role in the development and pathogenesis of CAUTI. Proteus mirabilis and Enterococcus faecalis are common CAUTI pathogens that persistently co-colonize the catheterized urinary tract and form biofilms with increased biomass and antibiotic resistance. In this study, we uncover the metabolic interplay that drives biofilm enhancement and examine the contribution to CAUTI severity. Through compositional and proteomic biofilm analyses, we determined that the increase in biofilm biomass stems from an increase in the protein fraction of the polymicrobial biofilm matrix. We further observed an enrichment in proteins associated with ornithine and arginine metabolism in polymicrobial biofilms compared to single-species biofilms. We show that L-ornithine secretion by E. faecalis promotes arginine biosynthesis in P. mirabilis, and that disruption of this metabolic interplay abrogates the biofilm enhancement we see in vitro and leads to significant decreases in infection severity and dissemination in a murine CAUTI model.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. An understanding of the microbial underpinnings of PEx is challenged by high inter-patient variability in airway microbial community profiles. We analyzed bacterial communities in 880 CF sputum samples and developed microbiome descriptors to model community reorganization prior to and during 18 PEx. We identified two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx showed hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx displayed stochasticity and increased diversity. A simulation of antimicrobial treatment predicted better efficacy for hierarchically organized communities. This link between PEx type, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.

Co-occurring microflora and mucin drive Pseudomonas aeruginosa diversification and pathoadaptation

While several environmental factors contribute to the evolutionary diversification of the pathogenic bacterium Pseudomonas aeruginosa during cystic fibrosis lung infections, relatively little is known about the impact of the surrounding microbiota. By using in vitro experimental evolution, we show that the presence of Stenotrophomonas maltophilia, Staphylococcus aureus, or them both, prevent the evolution of loss of virulence, which repeatedly occurs in the absence of these species due to mutations in regulators of the Pseudomonas Quinolone Signal quorum sensing system, vqsM and pqsR. Moreover, the strength of the effect of co-occurring species is attenuated through changes in the physical environment by the addition of mucin, resulting in selection for phenotypes resembling those evolved in the absence of the co-occurring species. Together, our findings show that variation in mucosal environment and the surrounding polymicrobial environment can determine the evolutionary trajectory of P. aeruginosa, partly explaining its diversification and pathoadaptation from acute to chronic phenotype during cystic fibrosis lung infections.

Translating eco-evolutionary biology into therapy to tackle antibiotic resistance

Antibiotic resistance is currently one of the most important public health problems. The golden age of antibiotic discovery ended decades ago, and new approaches are urgently needed. Therefore, preserving the efficacy of the antibiotics currently in use and developing compounds and strategies that specifically target antibiotic-resistant pathogens is critical. The identification of robust trends of antibiotic resistance evolution and of its associated trade-offs, such as collateral sensitivity or fitness costs, is invaluable for the design of rational evolution-based, ecology-based treatment approaches. In this Review, we discuss these evolutionary trade-offs and how such knowledge can aid in informing combination or alternating antibiotic therapies against bacterial infections. In addition, we discuss how targeting bacterial metabolism can enhance drug activity and impair antibiotic resistance evolution. Finally, we explore how an improved understanding of the original physiological function of antibiotic resistance determinants, which have evolved to reach clinical resistance after a process of historical contingency, may help to tackle antibiotic resistance.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease (COPD). Intermittent pulmonary exacerbations (PEx) in these conditions are associated with lung function decline and higher mortality rates. An understanding of the microbial underpinnings of PEx is challenged by high inter-patient variability in airway microbial community profiles. We analyzed 880 near-daily CF sputum samples and developed non-standard microbiome descriptors to model community reorganization prior and during 18 PEx. We identified two communal microbial regimes with opposing ecology and dynamics. Whereas pathogen-governed dysbiosis showed hierarchical community organization and reduced diversity, anaerobic bloom dysbiosis displayed stochasticity and increased diversity. Microbiome organization modulated the relevance of pathogens and a simulation of antimicrobial treatment predicted better efficacy for hierarchically organized microbiota. This causal link between PEx, microbiome organization, and treatment success advances the development of personalized dysbiosis management in CF and, potentially, other obstructive lung diseases.

Development of a Polymicrobial Checkerboard Assay as a Tool for Determining Combinatorial Antibiotic Effectiveness in Polymicrobial Communities

The checkerboard assay is a well-established tool used to determine the antimicrobial effects of two compounds in combination. Usually, data collected from the checkerboard assay use visible turbidity and optical density as a readout. While helpful in traditional checkerboard assays, these measurements become less useful in a polymicrobial context as they do not enable assessment of the drug effects on the individual members of the community. The methodology described herein allows for the determination of cell viability through selective and differential plating of each individual species in a community while retaining much of the high-throughput nature of a turbidity-based analysis and requiring no specialized equipment. This methodology further improves turbidity-based measurements by providing a distinction between bacteriostatic versus bactericidal concentrations of antibiotics. Herein, we use this method to demonstrate that the clinically used antibiotic combination of ceftazidime and gentamicin works synergistically against Pseudomonas aeruginosa in monoculture but antagonistically in a polymicrobial culture also containing Acinetobacter baumannii, Staphylococcus aureus, and Enterococcus faecalis, highlighting the fundamental importance of this methodology in improving clinical practices. We propose that this method could be implemented in clinical microbiology laboratories with minimal impact on the overall time for diagnosis.

Community composition shapes microbial-specific phenotypes in a cystic fibrosis polymicrobial model system

Interspecies interactions can drive the emergence of unexpected microbial phenotypes that are not observed when studying monocultures. The cystic fibrosis (CF) lung consists of a complex environment where microbes, living as polymicrobial biofilm-like communities, are associated with negative clinical outcomes for persons with CF (pwCF). However, the current lack of in vitro models integrating the microbial diversity observed in the CF airway hampers our understanding of why polymicrobial communities are recalcitrant to therapy in this disease. Here, integrating computational approaches informed by clinical data, we built a mixed community of clinical relevance to the CF lung composed of Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus sanguinis, and Prevotella melaninogenica. We developed and validated this model biofilm community with multiple isolates of these four genera. When challenged with tobramycin, a front-line antimicrobial used to treat pwCF, the microorganisms in the polymicrobial community show altered sensitivity to this antibiotic compared to monospecies biofilms. We observed that wild-type P. aeruginosa is sensitized to tobramycin in a mixed community versus monoculture, and this observation holds across a range of community relative abundances. We also report that LasR loss-of-function, a variant frequently detected in the CF airway, drives tolerance of P. aeruginosa to tobramycin specifically in the mixed community. Our data suggest that the molecular basis of this community-specific recalcitrance to tobramycin for the P. aeruginosa lasR mutant is increased production of phenazines. Our work supports the importance of studying a clinically relevant model of polymicrobial biofilms to understand community-specific traits relevant to infections.

Limited Impact of SARS-CoV-2 on the Human Naso-Oropharyngeal Microbiota in Hospitalized Patients

Numerous studies have reported dysbiosis in the naso- and/or oro-pharyngeal microbiota of COVID-19 patients compared with healthy individuals; however, only a few small-scale studies have also included a disease control group. In this study, we characterized and compared the bacterial communities of pooled nasopharyngeal and throat swabs from hospitalized COVID-19 patients (n = 76), hospitalized non-COVID-19 patients with respiratory symptoms or related illnesses (n = 69), and local community controls (n = 76) using 16S rRNA gene V3-V4 amplicon sequencing. None of the subjects received antimicrobial therapy within 2 weeks prior to sample collection. Both COVID-19 and non-COVID-19 hospitalized patients differed in the composition, alpha and beta diversity, and metabolic potential of the naso-oropharyngeal microbiota compared with local controls. However, the microbial communities in the two hospitalized patient groups did not differ significantly from each other. Differential abundance analysis revealed the enrichment of nine bacterial genera in the COVID-19 patients compared with local controls; however, six of them were also enriched in the non-COVID-19 patients. Bacterial genera uniquely enriched in the COVID-19 patients included Alloprevotella and Solobacterium. In contrast, Mogibacterium and Lactococcus were dramatically decreased in COVID-19 patients only. Association analysis revealed that Alloprevotella in COVID-19 patients was positively correlated with the level of the inflammation biomarker C-reactive protein. Our findings reveal a limited impact of SARS-CoV-2 on the naso-oropharyngeal microbiota in hospitalized patients and suggest that Alloprevotella and Solobacterium are more specific biomarkers for COVID-19 detection. IMPORTANCE Our results showed that while both COVID-19 and non-COVID-19 hospitalized patients differed in the composition, alpha and beta diversity, and metabolic potential of the naso-oropharyngeal microbiota compared with local controls, the microbial communities in the two hospitalized patient groups did not differ significantly from each other, indicating a limited impact of SARS-CoV-2 on the naso-oropharyngeal microbiota in hospitalized patients. Besides, we identified Alloprevotella and Solobacterium as bacterial genera uniquely enriched in COVID-19 patients, which may serve as more specific biomarkers for COVID-19 detection.

Physiology and pathophysiology of human airway mucus

The mucus clearance system is the dominant mechanical host defense system of the human lung. Mucus is cleared from the lung by cilia and airflow, including both two-phase gas-liquid pumping and cough-dependent mechanisms, and mucus transport rates are heavily dependent on mucus concentration. Importantly, mucus transport rates are accurately predicted by the gel-on-brush model of the mucociliary apparatus from the relative osmotic moduli of the mucus and periciliary-glycocalyceal (PCL-G) layers. The fluid available to hydrate mucus is generated by transepithelial fluid transport. Feedback interactions between mucus concentrations and cilia beating, via purinergic signaling, coordinate Na+ absorptive vs Cl- secretory rates to maintain mucus hydration in health. In disease, mucus becomes hyperconcentrated (dehydrated). Multiple mechanisms derange the ion transport pathways that normally hydrate mucus in muco-obstructive lung diseases, e.g., cystic fibrosis (CF), chronic obstructive pulmonary disease (COPD), non-CF bronchiectasis (NCFB), and primary ciliary dyskinesia (PCD). A key step in muco-obstructive disease pathogenesis is the osmotic compression of the mucus layer onto the airway surface with the formation of adherent mucus plaques and plugs, particularly in distal airways. Mucus plaques create locally hypoxic conditions and produce airflow obstruction, inflammation, infection, and, ultimately, airway wall damage. Therapies to clear adherent mucus with hydrating and mucolytic agents are rational, and strategies to develop these agents are reviewed.

Complex and unexpected outcomes of antibiotic therapy against a polymicrobial infection

Antibiotics are our primary approach to treating complex infections, yet we have a poor understanding of how these drugs affect microbial communities. To better understand antimicrobial effects on host-associated microbial communities we treated cultured sputum microbiomes from people with cystic fibrosis (pwCF, n = 24) with 11 different antibiotics, supported by theoretical and mathematical modeling-based predictions in a mucus-plugged bronchiole microcosm. Treatment outcomes we identified in vitro that were predicted in silico were: 1) community death, 2) community resistance, 3) pathogen killing, and 4) fermenter killing. However, two outcomes that were not predicted when antibiotics were applied were 5) community profile shifts with little change in total bacterial load (TBL), and 6) increases in TBL. The latter outcome was observed in 17.8% of samples with a TBL increase of greater than 20% and 6.8% of samples with an increase greater than 40%, demonstrating significant increases in community carrying capacity in the presence of an antibiotic. An iteration of the mathematical model showed that TBL increase was due to antibiotic-mediated release of pH-dependent inhibition of pathogens by anaerobe fermentation. These dynamics were verified in vitro when killing of fermenters resulted in a higher community carrying capacity compared to a no antibiotic control. Metagenomic sequencing of sputum samples during antibiotic therapy revealed similar dynamics in clinical samples. This study shows that the complex microbial ecology dictates the outcomes of antibiotic therapy against a polymicrobial infection.

Antibiotics Drive Expansion of Rare Pathogens in a Chronic Infection Microbiome Model

Chronic (long-lasting) infections are globally a major and rising cause of morbidity and mortality. Unlike typical acute infections, chronic infections are ecologically diverse, characterized by the presence of a polymicrobial mix of opportunistic pathogens and human-associated commensals. To address the challenge of chronic infection microbiomes, we focus on a particularly well-characterized disease, cystic fibrosis (CF), where polymicrobial lung infections persist for decades despite frequent exposure to antibiotics. Epidemiological analyses point to conflicting results on the benefits of antibiotic treatment yet are confounded by the dependency of antibiotic exposures on prior pathogen presence, limiting their ability to draw causal inferences on the relationships between antibiotic exposure and pathogen dynamics. To address this limitation, we develop a synthetic infection microbiome model representing CF metacommunity diversity and benchmark on clinical data. We show that in the absence of antibiotics, replicate microbiome structures in a synthetic sputum medium are highly repeatable and dominated by oral commensals. In contrast, challenge with physiologically relevant antibiotic doses leads to substantial community perturbation characterized by multiple alternate pathogen-dominant states and enrichment of drug-resistant species. These results provide evidence that antibiotics can drive the expansion (via competitive release) of previously rare opportunistic pathogens and offer a path toward microbiome-informed conditional treatment strategies.

IMPORTANCE We develop and clinically benchmark an experimental model of the cystic fibrosis (CF) lung infection microbiome to investigate the impacts of antibiotic exposures on chronic, polymicrobial infections. We show that a single experimental model defined by metacommunity data can partially recapitulate the diversity of individual microbiome states observed across a population of people with CF. In the absence of antibiotics, we see highly repeatable community structures, dominated by oral microbes. Under clinically relevant antibiotic exposures, we see diverse and frequently pathogen-dominated communities, and a nonevolutionary enrichment of antimicrobial resistance on the community scale, mediated by competitive release. The results highlight the potential importance of nonevolutionary (community-ecological) processes in driving the growing global crisis of increasing antibiotic resistance.

Antibiotic resistance in chronic respiratory diseases: from susceptibility testing to the resistome

The development of resistome analysis, i.e. the comprehensive analysis of antibiotic-resistance genes (ARGs), is enabling a better understanding of the mechanisms of antibiotic-resistance emergence. The respiratory microbiome is a dynamic and interactive network of bacteria, with a set of ARGs that could influence the response to antibiotics. Viruses such as bacteriophages, potential carriers of ARGs, may also form part of this respiratory resistome. Chronic respiratory diseases (CRDs) such as cystic fibrosis, severe asthma, chronic obstructive pulmonary disease and bronchiectasis, managed with long-term antibiotic therapies, lead to multidrug resistance. Antibiotic susceptibility testing provides a partial view of the bacterial response to antibiotics in the complex lung environment. Assessing the ARG network would allow personalised, targeted therapeutic strategies and suitable antibiotic stewardship in CRDs, depending on individual resistome and microbiome signatures. This review summarises the influence of pulmonary antibiotic protocols on the respiratory microbiome, detailing the variable consequences according to antibiotic class and duration of treatment. The different resistome-profiling methods are explained to clarify their respective place in antibiotic-resistance analysis in the lungs. Finally, this review details current knowledge on the respiratory resistome related to therapeutic strategies and provides insight into the application of resistome analysis to counter the emergence of multidrug-resistant respiratory pathogens.

Gram-negative bacteria act as a reservoir for aminoglycoside antibiotics that interact with host factors to enhance bacterial killing in a mouse model of pneumonia

In vitro exposure of multiple Gram-negative bacteria to an aminoglycoside (AG) antibiotic has previously been demonstrated to result in bacterial alterations that interact with host factors to suppress Gram-negative pneumonia. However, the mechanisms resulting in suppression are not known. Here, the hypothesis that Gram-negative bacteria bind and retain AGs, which are introduced into the lung and interact with host defenses to affect bacterial killing, was tested. Following in vitro exposure of one of several, pathogenic Gram-negative bacteria to the AG antibiotics kanamycin or gentamicin, AGs were detected in bacterial cell pellets (up to 208 μg/mL). Using inhibitors of AG binding and internalization, the bacterial outer membrane was implicated as the predominant kanamycin and gentamicin reservoir. Following intranasal administration of gentamicin-bound bacteria or gentamicin solution at the time of infection with live, AG-naïve bacteria, gentamicin was detected in the lungs of infected mice (up to 8 μg/g). Co-inoculation with gentamicin-bound bacteria resulted in killing of AG-naïve bacteria by up to 3-log₁₀, mirroring the effects of intranasal gentamicin treatment. In vitro killing of AG-naïve bacteria mediated by kanamycin-bound bacteria required the presence of detergents or pulmonary surfactant, suggesting that increased bacterial killing inside the murine lung is facilitated by the detergent component of pulmonary surfactant. These findings demonstrate that Gram-negative bacteria bind and retain AGs that can interact with host-derived pulmonary surfactant to enhance bacterial killing in the lung. This may help explain why AGs appear to have unique efficacy in the lung and might expand their clinical utility.

Inter-species interactions alter antibiotic efficacy in bacterial communities

The efficacy of antibiotic treatments targeting polymicrobial communities is not well predicted by conventional in vitro susceptibility testing based on determining minimum inhibitory concentration (MIC) in monocultures. One reason for this is that inter-species interactions can alter the community members' susceptibility to antibiotics. Here we quantify, and identify mechanisms for, community-modulated changes of efficacy for clinically relevant antibiotics against the pathogen Pseudomonas aeruginosa in model cystic fibrosis (CF) lung communities derived from clinical samples. We demonstrate that multi-drug resistant Stenotrophomonas maltophilia can provide high levels of antibiotic protection to otherwise sensitive P. aeruginosa. Exposure protection to imipenem was provided by chromosomally encoded metallo-β-lactamase that detoxified the environment; protection was dependent upon S. maltophilia cell density and was provided by S. maltophilia strains isolated from CF sputum, increasing the MIC of P. aeruginosa by up to 16-fold. In contrast, the presence of S. maltophilia provided no protection against meropenem, another routinely used carbapenem. Mathematical ordinary differential equation modelling shows that the level of exposure protection provided against different carbapenems can be explained by differences in antibiotic efficacy and inactivation rate. Together, these findings reveal that exploitation of pre-occurring antimicrobial resistance, and inter-specific competition, can have large impacts on pathogen antibiotic susceptibility, highlighting the importance of microbial ecology for designing successful antibiotic treatments for multispecies communities.

Putting microbial interactions back into community contexts

Microbial interactions are key aspects of the biology of microbiomes. Recently, there has been a shift in the field towards studying interactions in more representative contexts, whether using multispecies model microbial communities or by looking at interactions in situ. Across diverse microbial systems, these studies have begun to identify common interaction mechanisms. These mechanisms include interactions related to toxic molecules, nutrient competition and cross-feeding, access to metals, signaling pathways, pH changes, and interactions within biofilms. Leveraging technological innovations, many of these studies have used an interdisciplinary approach combining genetic, metabolomic, imaging, and/or microfluidic techniques to gain insight into mechanisms of microbial interactions and into the impact of these interactions on microbiomes.

Multi-Omics Study of Keystone Species in a Cystic Fibrosis Microbiome

Ecological networking and in vitro studies predict that anaerobic, mucus-degrading bacteria are keystone species in cystic fibrosis (CF) microbiomes. The metabolic byproducts from these bacteria facilitate the colonization and growth of CF pathogens like Pseudomonas aeruginosa. Here, a multi-omics study informed the control of putative anaerobic keystone species during a transition in antibiotic therapy of a CF patient. A quantitative metagenomics approach combining sequence data with epifluorescence microscopy showed that during periods of rapid lung function loss, the patient's lung microbiome was dominated by the anaerobic, mucus-degrading bacteria belonging to Streptococcus, Veillonella, and Prevotella genera. Untargeted metabolomics and community cultures identified high rates of fermentation in these sputa, with the accumulation of lactic acid, citric acid, and acetic acid. P. aeruginosa utilized these fermentation products for growth, as indicated by quantitative transcriptomics data. Transcription levels of P. aeruginosa genes for the utilization of fermentation products were proportional to the abundance of anaerobic bacteria. Clindamycin therapy targeting Gram-positive anaerobes rapidly suppressed anaerobic bacteria and the accumulation of fermentation products. Clindamycin also lowered the abundance and transcription of P. aeruginosa, even though this patient's strain was resistant to this antibiotic. The treatment stabilized the patient's lung function and improved respiratory health for two months, lengthening by a factor of four the between-hospitalization time for this patient. Killing anaerobes indirectly limited the growth of P. aeruginosa by disrupting the cross-feeding of fermentation products. This case study supports the hypothesis that facultative anaerobes operated as keystone species in this CF microbiome. Personalized multi-omics may become a viable approach for routine clinical diagnostics in the future, providing critical information to inform treatment decisions.

Ecology and evolution of antimicrobial resistance in bacterial communities

Accumulating evidence suggests that the response of bacteria to antibiotics is significantly affected by the presence of other interacting microbes. These interactions are not typically accounted for when determining pathogen sensitivity to antibiotics. In this perspective, we argue that resistance and evolutionary responses to antibiotic treatments should not be considered only a trait of an individual bacteria species but also an emergent property of the microbial community in which pathogens are embedded. We outline how interspecies interactions can affect the responses of individual species and communities to antibiotic treatment, and how these responses could affect the strength of selection, potentially changing the trajectory of resistance evolution. Finally, we identify key areas of future research which will allow for a more complete understanding of antibiotic resistance in bacterial communities. We emphasise that acknowledging the ecological context, i.e. the interactions that occur between pathogens and within communities, could help the development of more efficient and effective antibiotic treatments.

Nasopharyngeal Microbial Communities of Patients Infected With SARS-CoV-2 That Developed COVID-19

BACKGROUND

SARS-CoV-2 is an RNA virus causing COVID-19. The clinical characteristics and epidemiology of COVID-19 have been extensively investigated, however, only one study so far focused on the patient's nasopharynx microbiota. In this study we investigated the nasopharynx microbial community of patients that developed different severity levels of COVID-19. We performed 16S ribosomal DNA sequencing from nasopharyngeal swab samples obtained from SARS-CoV-2 positive (56) and negative (18) patients in the province of Alicante (Spain) in their first visit to the hospital. Positive SARS-CoV-2 patients were observed and later categorized in mild (symptomatic without hospitalization), moderate (hospitalization), and severe (admission to ICU). We compared the microbiota diversity and OTU composition among severity groups and built bacterial co-abundance networks for each group.

RESULTS

Statistical analysis indicated differences in the nasopharyngeal microbiome of COVID19 patients. 62 OTUs were found exclusively in SARS-CoV-2 positive patients, mostly classified as members of the phylum Bacteroidota (18) and Firmicutes (25). OTUs classified as Prevotella were found to be significantly more abundant in patients that developed more severe COVID-19. Furthermore, co-abundance analysis indicated a loss of network complexity among samples from patients that later developed more severe symptoms.

CONCLUSION

Our study shows that the nasopharyngeal microbiome of COVID-19 patients showed differences in the composition of specific OTUs and complexity of co-abundance networks. Taxa with differential abundances among groups could serve as biomarkers for COVID-19 severity. Nevertheless, further studies with larger sample sizes should be conducted to validate these results.

One versus Many: Polymicrobial Communities and the Cystic Fibrosis Airway

Culture-independent studies have revealed that chronic lung infections in persons with cystic fibrosis (pwCF) are rarely limited to one microbial species. Interactions among bacterial members of these polymicrobial communities in the airways of pwCF have been reported to modulate clinically relevant phenotypes. Furthermore, it is clear that a single polymicrobial community in the context of CF airway infections cannot explain the diversity of clinical outcomes. While large 16S rRNA gene-based studies have allowed us to gain insight into the microbial composition and predicted functional capacities of communities found in the CF lung, here we argue that in silico approaches can help build clinically relevant in vitro models of polymicrobial communities that can in turn be used to experimentally test and validate computationally generated hypotheses. Furthermore, we posit that combining computational and experimental approaches will enhance our understanding of mechanisms that drive microbial community function and identify new therapeutics to target polymicrobial infections.

From genotype to phenotype: adaptations of Pseudomonas aeruginosa to the cystic fibrosis environment

Pseudomonas aeruginosa is one of the main microbial species colonizing the lungs of cystic fibrosis patients and is responsible for the decline in respiratory function. Despite the hostile pulmonary environment, P. aeruginosa is able to establish chronic infections thanks to its strong adaptive capacity. Various longitudinal studies have attempted to compare the strains of early infection with the adapted strains of chronic infection. Thanks to new '-omics' techniques, convergent genetic mutations, as well as transcriptomic and proteomic dysregulations have been identified. As a consequence of this evolution, the adapted strains of P. aeruginosa have particular phenotypes that promote persistent infection.

Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists

The explosion of microbiome analyses has helped identify individual microorganisms and microbial communities driving human health and disease, but how these communities function is still an open question. For example, the role for the incredibly complex metabolic interactions among microbial species cannot easily be resolved by current experimental approaches such as 16S rRNA gene sequencing, metagenomics and/or metabolomics. Resolving such metabolic interactions is particularly challenging in the context of polymicrobial communities where metabolite exchange has been reported to impact key bacterial traits such as virulence and antibiotic treatment efficacy. As novel approaches are needed to pinpoint microbial determinants responsible for impacting community function in the context of human health and to facilitate the development of novel anti-infective and antimicrobial drugs, here we review, from the viewpoint of experimentalists, the latest advances in metabolic modeling, a computational method capable of predicting metabolic capabilities and interactions from individual microorganisms to complex ecological systems. We use selected examples from the literature to illustrate how metabolic modeling has been utilized, in combination with experiments, to better understand microbial community function. Finally, we propose how such combined, cross-disciplinary efforts can be utilized to drive laboratory work and drug discovery moving forward.

Extracellular Metabolism Sets the Table for Microbial Cross-Feeding

The transfer of nutrients between cells, or cross-feeding, is a ubiquitous feature of microbial communities with emergent properties that influence our health and orchestrate global biogeochemical cycles. Cross-feeding inevitably involves the externalization of molecules. Some of these molecules directly serve as cross-fed nutrients, while others can facilitate cross-feeding. Altogether, externalized molecules that promote cross-feeding are diverse in structure, ranging from small molecules to macromolecules. The functions of these molecules are equally diverse, encompassing waste products, enzymes, toxins, signaling molecules, biofilm components, and nutrients of high value to most microbes, including the producer cell. As diverse as the externalized and transferred molecules are the cross-feeding relationships that can be derived from them. Many cross-feeding relationships can be summarized as cooperative but are also subject to exploitation. Even those relationships that appear to be cooperative exhibit some level of competition between partners. In this review, we summarize the major types of actively secreted, passively excreted, and directly transferred molecules that either form the basis of cross-feeding relationships or facilitate them. Drawing on examples from both natural and synthetic communities, we explore how the interplay between microbial physiology, environmental parameters, and the diverse functional attributes of extracellular molecules can influence cross-feeding dynamics. Though microbial cross-feeding interactions represent a burgeoning field of interest, we may have only begun to scratch the surface.

Clinical Implications of Polymicrobial Synergism Effects on Antimicrobial Susceptibility

With the development of next generation sequencing technologies in recent years, it has been demonstrated that many human infectious processes, including chronic wounds, cystic fibrosis, and otitis media, are associated with a polymicrobial burden. Research has also demonstrated that polymicrobial infections tend to be associated with treatment failure and worse patient prognoses. Despite the importance of the polymicrobial nature of many infection states, the current clinical standard for determining antimicrobial susceptibility in the clinical laboratory is exclusively performed on unimicrobial suspensions. There is a growing body of research demonstrating that microorganisms in a polymicrobial environment can synergize their activities associated with a variety of outcomes, including changes to their antimicrobial susceptibility through both resistance and tolerance mechanisms. This review highlights the current body of work describing polymicrobial synergism, both inter- and intra-kingdom, impacting antimicrobial susceptibility. Given the importance of polymicrobial synergism in the clinical environment, a new system of determining antimicrobial susceptibility from polymicrobial infections may significantly impact patient treatment and outcomes.

Ignoring social distancing: advances in understanding multi-species bacterial interactions

Almost every ecosystem on this planet is teeming with microbial communities made of diverse bacterial species. At a reductionist view, many of these bacteria form pairwise interactions, but, as the field of view expands, the neighboring organisms and the abiotic environment can play a crucial role in shaping the interactions between species. Over the years, a strong foundation of knowledge has been built on isolated pairwise interactions between bacteria, but now the field is advancing toward understanding how cohabitating bacteria and natural surroundings affect these interactions. Use of bottom-up approaches, piecing communities together, and top-down approaches that deconstruct communities are providing insight on how different species interact. In this review, we highlight how studies are incorporating more complex communities, mimicking the natural environment, and recurring findings such as the importance of cooperation for stability in harsh environments and the impact of bacteria-induced environmental pH shifts. Additionally, we will discuss how omics are being used as a top-down approach to identify previously unknown interspecies bacterial interactions and the challenges of these types of studies for microbial ecology.

The Lung Microbiome of Three Young Brazilian Patients With Cystic Fibrosis Colonized by Fungi

Microbial communities infiltrate the respiratory tract of cystic fibrosis patients, where chronic colonization and infection lead to clinical decline. This report aims to provide an overview of the diversity of bacterial and fungal species from the airway secretion of three young CF patients with severe pulmonary disease. The bacterial and fungal microbiomes were investigated by culture isolation, metataxonomics, and metagenomics shotgun. Virulence factors and antibiotic resistance genes were also explored. A. fumigatus was isolated from cultures and identified in high incidence from patient sputum samples. Candida albicans, Penicillium sp., Hanseniaspora sp., Torulaspora delbrueckii, and Talaromyces amestolkiae were isolated sporadically. Metataxonomics and metagenomics detected fungal reads (Saccharomyces cerevisiae, A. fumigatus, and Schizophyllum sp.) in one sputum sample. The main pathogenic bacteria identified were Staphylococcus aureus, Pseudomonas aeruginosa, Burkholderia cepacia complex, and Achromobacter xylosoxidans. The canonical core CF microbiome is composed of species from the genera Streptococcus, Neisseria, Rothia, Prevotella, and Haemophilus. Thus, the airways of the three young CF patients presented dominant bacterial genera and interindividual variability in microbial community composition and diversity. Additionally, a wide diversity of virulence factors and antibiotic resistance genes were identified in the CF lung microbiomes, which may be linked to the clinical condition of the CF patients. Understanding the microbial community is crucial to improve therapy because it may have the opposite effect, restructuring the pathogenic microbiota. Future studies focusing on the influence of fungi on bacterial diversity and microbial interactions in CF microbiomes will be welcome to fulfill this huge gap of fungal influence on CF physiopathology.

Phage cocktail strategies for the suppression of a pathogen in a cross-feeding coculture

Cocktail combinations of bacteria-infecting viruses (bacteriophages) can suppress pathogenic bacterial growth. However, predicting how phage cocktails influence microbial communities with complex ecological interactions, specifically cross-feeding interactions in which bacteria exchange nutrients, remains challenging. Here, we used experiments and mathematical simulations to determine how to best suppress a model pathogen, E. coli, when obligately cross-feeding with S. enterica. We tested whether the duration of pathogen suppression caused by a two-lytic phage cocktail was maximized when both phages targeted E. coli, or when one phage targeted E. coli and the other its cross-feeding partner, S. enterica. Experimentally, we observed that cocktails targeting both cross-feeders suppressed E. coli growth longer than cocktails targeting only E. coli. Two non-mutually exclusive mechanisms could explain these results: (i) we found that treatment with two E. coli phage led to the evolution of a mucoid phenotype that provided cross-resistance against both phages, and (ii) S. enterica set the growth rate of the coculture, and therefore, targeting S. enterica had a stronger effect on pathogen suppression. Simulations suggested that cross-resistance and the relative growth rates of cross-feeders modulated the duration of E. coli suppression. More broadly, we describe a novel bacteriophage cocktail strategy for pathogens that cross-feed.

Weakest-Link Dynamics Predict Apparent Antibiotic Interactions in a Model Cross-Feeding Community

With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, in which multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen.

With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, in which multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen. This study tests whether patterns of antibiotic interactions (synergy, antagonism, or independence/additivity) in monoculture can be used to predict antibiotic interactions in an obligate cross-feeding coculture. Using our previously described weakest-link hypothesis, we hypothesized antibiotic interactions in coculture based on the interactions we observed in monoculture. We then compared our predictions to observed antibiotic interactions in coculture. We tested the interactions between 10 previously identified antibiotic combinations using checkerboard assays. Although our antibiotic combinations interacted differently than predicted in our monocultures, our monoculture results were generally sufficient to predict coculture patterns based solely on the weakest-link hypothesis. These results suggest that combination therapy for cross-feeding multispecies infections may be successfully designed based on antibiotic interaction patterns for their component species.