Microbiota and metabolite profiling reveal specific alterations in bacterial community structure and environment in the cystic fibrosis airway during exacerbation

Practical Guidance for Clinical Microbiology Laboratories: Updated guidance for processing respiratory tract samples from people with cystic fibrosis

SUMMARYThis guidance presents recommendations for clinical microbiology laboratories for processing respiratory samples from people with cystic fibrosis (pwCF). Appropriate processing of respiratory samples is crucial to detect bacterial and fungal pathogens, guide treatment, monitor the epidemiology of cystic fibrosis (CF) pathogens, and assess therapeutic interventions. Thanks to CF transmembrane conductance regulator modulator therapy, the health of pwCF has improved, but as a result, fewer pwCF spontaneously expectorate sputum. Thus, the collection of sputum samples has decreased, while the collection of other types of respiratory samples such as oropharyngeal and bronchoalveolar lavage samples has increased. To optimize the detection of microorganisms, including Pseudomonas aeruginosa, Staphylococcus aureus, Haemophilus influenzae, and Burkholderia cepacia complex; other less common non-lactose fermenting Gram-negative bacilli, e.g., Stenotrophomonas maltophilia, Inquilinus, Achromobacter, Ralstonia, and Pandoraea species; and yeasts and filamentous fungi, non-selective and selective culture media are recommended for all types of respiratory samples, including samples obtained from pwCF after lung transplantation. There are no consensus recommendations for laboratory practices to detect, characterize, and report small colony variants (SCVs) of S. aureus, although studies are ongoing to address the potential clinical impact of SCVs. Accurate identification of less common Gram-negative bacilli, e.g., S. maltophilia, Inquilinus, Achromobacter, Ralstonia, and Pandoraea species, as well as yeasts and filamentous fungi, is recommended to understand their epidemiology and clinical importance in pwCF. However, conventional biochemical tests and automated platforms may not accurately identify CF pathogens. MALDI-TOF MS provides excellent genus-level identification, but databases may lack representation of CF pathogens to the species-level. Thus, DNA sequence analysis should be routinely available to laboratories for selected clinical circumstances. Antimicrobial susceptibility testing (AST) is not recommended for every routine surveillance culture obtained from pwCF, although selective AST may be helpful, e.g., for unusual pathogens or exacerbations unresponsive to initial therapy. While this guidance reflects current care paradigms for pwCF, recommendations will continue to evolve as CF research expands the evidence base for laboratory practices.

The Role of the Microbiome in Pediatric Respiratory Diseases

Numerous studies have examined the role of the microbiome and microbiome-based therapeutics in many childhood airway and lung diseases. In this narrative review, the authors first give a brief overview of the current methods used in microbiome research. The authors then review the literature linking the microbiome with (1) early-life acute respiratory infections due to respiratory syncytial virus, (2) childhood asthma onset, (3) cystic fibrosis, and (4) bronchopulmonary dysplasia, focusing on recent studies that have used culture-independent methods to characterize the respiratory or gut microbiome in the pediatric population.

Metabolic specialization drives reduced pathogenicity in Pseudomonas aeruginosa isolates from cystic fibrosis patients

Metabolism provides the foundation for all cellular functions. During persistent infections, in adapted pathogenic bacteria metabolism functions radically differently compared with more naïve strains. Whether this is simply a necessary accommodation to the persistence phenotype or if metabolism plays a direct role in achieving persistence in the host is still unclear. Here, we characterize a convergent shift in metabolic function(s) linked with the persistence phenotype during Pseudomonas aeruginosa colonization in the airways of people with cystic fibrosis. We show that clinically relevant mutations in the key metabolic enzyme, pyruvate dehydrogenase, lead to a host-specialized metabolism together with a lower virulence and immune response recruitment. These changes in infection phenotype are mediated by impaired type III secretion system activity and by secretion of the antioxidant metabolite, pyruvate, respectively. Our results show how metabolic adaptations directly impinge on persistence and pathogenicity in this organism.

Polyamine-Mediated Sensitization of Klebsiella pneumoniae to Macrolides through a Dual Mode of Action

Chemicals bacteria encounter at the infection site could shape their stress and antibiotic responses; such effects are typically undetected under standard lab conditions. Polyamines are small molecules typically overproduced by the host during infection and have been shown to alter bacterial stress responses. We sought to determine the effect of polyamines on the antibiotic response of Klebsiella pneumoniae, a Gram-negative priority pathogen. Interestingly, putrescine and other natural polyamines sensitized K. pneumoniae to azithromycin, a macrolide protein translation inhibitor typically used for Gram-positive bacteria. This synergy was further potentiated in the physiological buffer, bicarbonate. Chemical genomic screens suggested a dual mechanism, whereby putrescine acts at the membrane and ribosome levels. Putrescine permeabilized the outer membrane of K. pneumoniae (NPN and β-lactamase assays) and the inner membrane (Escherichia coli β-galactosidase assays). Chemically and genetically perturbing membranes led to a loss of putrescine-azithromycin synergy. Putrescine also inhibited protein synthesis in an E. coli-derived cell-free protein expression assay simultaneously monitoring transcription and translation. Profiling the putrescine-azithromycin synergy against a combinatorial array of antibiotics targeting various ribosomal sites suggested that putrescine acts as tetracyclines targeting the 30S ribosomal acceptor site. Next, exploiting the natural polyamine-azithromycin synergy, we screened a polyamine analogue library for azithromycin adjuvants, discovering four azithromycin synergists with activity starting from the low micromolar range and mechanisms similar to putrescine. This work sheds light on the bacterial antibiotic responses under conditions more reflective of those at the infection site and provides a new strategy to extend the macrolide spectrum to drug-resistant K. pneumoniae.

Does "all disease begin in the gut"? The gut-organ cross talk in the microbiome

The human microbiome, a diverse ecosystem of microorganisms within the body, plays pivotal roles in health and disease. This review explores site-specific microbiomes, their role in maintaining health, and strategies for their upkeep, focusing on oral, lung, vaginal, skin, and gut microbiota, and their systemic connections. Understanding the intricate relationships between these microbial communities is crucial for unraveling mechanisms underlying human health. Recent research highlights bidirectional communication between the gut and distant microbiome sites, influencing immune function, metabolism, and disease susceptibility. Alterations in one microbiome can impact others, emphasizing their interconnectedness and collective influence on human physiology. The therapeutic potential of gut microbiota in modulating distant microbiomes offers promising avenues for interventions targeting various disorders. Through interdisciplinary collaboration and technological advancements, we can harness the power of the microbiome to revolutionize healthcare, emphasizing microbiome-centric approaches to promote holistic well-being while identifying areas for future research.

How Does Airway Surface Liquid Composition Vary in Different Pulmonary Diseases, and How Can We Use This Knowledge to Model Microbial Infections?

Growth environment greatly alters many facets of pathogen physiology, including pathogenesis and antimicrobial tolerance. The importance of host-mimicking environments for attaining an accurate picture of pathogen behaviour is widely recognised. Whilst this recognition has translated into the extensive development of artificial cystic fibrosis (CF) sputum medium, attempts to mimic the growth environment in other respiratory disease states have been completely neglected. The composition of the airway surface liquid (ASL) in different pulmonary diseases is far less well characterised than CF sputum, making it very difficult for researchers to model these infection environments. In this review, we discuss the components of human ASL, how different lung pathologies affect ASL composition, and how different pathogens interact with these components. This will provide researchers interested in mimicking different respiratory environments with the information necessary to design a host-mimicking medium, allowing for better understanding of how to treat pathogens causing infection in these environments.

Mucus Structure, Viscoelastic Properties, and Composition in Chronic Respiratory Diseases

The respiratory mucus, a viscoelastic gel, effectuates a primary line of the airway defense when operated by the mucociliary clearance. In chronic respiratory diseases (CRDs), such as asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis (CF), the mucus is overproduced and its solid content augments, changing its structure and viscoelastic properties and determining a derangement of essential defense mechanisms against opportunistic microbial (virus and bacteria) pathogens. This ensues in damaging of the airways, leading to a vicious cycle of obstruction and infection responsible for the harsh clinical evolution of these CRDs. Here, we review the essential features of normal and pathological mucus (i.e., sputum in CF, COPD, and asthma), i.e., mucin content, structure (mesh size), micro/macro-rheology, pH, and osmotic pressure, ending with the awareness that sputum biomarkers (mucins, inflammatory proteins and peptides, and metabolites) might serve to indicate acute exacerbation and response to therapies. There are some indications that old and novel treatments may change the structure, viscoelastic properties, and biomarker content of sputum; however, a wealth of work is still needed to embrace these measures as correlates of disease severity in association with (or even as substitutes of) pulmonary functional tests.

Multiomic Investigations into Lung Health and Disease

Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.

Lung microbiota and potential treatment of respiratory diseases

The unique microbiome found in the lungs has been studied and shown to be associated with both pulmonary homeostasis and lung diseases. The lung microbiome has the potential to produce metabolites that modulate host-microbe interactions. Specifically, short-chain fatty acids (SCFAs) produced by certain strains of the lung microbiota have been shown to regulate immune function and maintain gut mucosal health. In response, this review described the distribution and composition of the microbiota in lung diseases and discussed the impact of the lung microbiota on health and lung disease. In addition, the review further elaborated on the mechanism of microbial metabolites in microbial-host interaction and their application in the treatment of lung diseases. A better understanding of the interaction between the microbiota, metabolites, and host will provide potential strategies for the development of novel methods for the treatment of pulmonary microbial induced lung diseases.

Microbial Epidemiology of the Cystic Fibrosis Airways: Past, Present, and Future

Progressive obstructive lung disease secondary to chronic airway infection, coupled with impaired host immunity, is the leading cause of morbidity and mortality in cystic fibrosis (CF). Classical pathogens found in the airways of persons with CF (pwCF) include Pseudomonas aeruginosa, Staphylococcus aureus, the Burkholderia cepacia complex, Achromobacter species, and Haemophilus influenzae. While traditional respiratory-tract surveillance culturing has focused on this limited range of pathogens, the use of both comprehensive culture and culture-independent molecular approaches have demonstrated complex highly personalized microbial communities. Loss of bacterial community diversity and richness, counteracted with relative increases in dominant taxa by traditional CF pathogens such as Burkholderia or Pseudomonas, have long been considered the hallmark of disease progression. Acquisition of these classic pathogens is viewed as a harbinger of advanced disease and postulated to be driven in part by recurrent and frequent antibiotic exposure driven by frequent acute pulmonary exacerbations. Recently, CF transmembrane conductance regulator (CFTR) modulators, small molecules designed to potentiate or restore diminished protein levels/function, have been successfully developed and have profoundly influenced disease course. Despite the multitude of clinical benefits, structural lung damage and consequent chronic airway infection persist in pwCF. In this article, we review the microbial epidemiology of pwCF, focus on our evolving understanding of these infections in the era of modulators, and identify future challenges in infection surveillance and clinical management.

Crosstalk between the lung microbiome and lung cancer

The human microbiome is a complex ecosystem that mediates interaction between the human host and the environment. All of the human body is colonized by microorganisms. The lung as an organ used to be considered sterile. Recently, however, there has been a growing number of reports with evidence that the lungs are also in a state of carrying bacteria. The pulmonary microbiome is associated with many lung diseases and is increasingly reported in current studies. These include; chronic obstructive pulmonary disease (COPD), asthma, acute chronic respiratory infections, and cancers. These lung diseases are associated with reduced diversity and dysbiosis. It directly or indirectly affects the occurrence and development of lung cancer. Very few microbes directly cause cancer, while many are complicit in cancer growth, usually working through the host's immune system. This review focuses on the correlation between lung microbiota and lung cancer, and investigates the mechanism of action of lung microorganisms on lung cancer, which will provide new and reliable treatments and diagnosis of lung cancer in the future.

Pseudomonas aeruginosa utilizes the host-derived polyamine spermidine to facilitate antimicrobial tolerance

Pseudomonas aeruginosa undergoes diversification during infection of the cystic fibrosis (CF) lung. Understanding these changes requires model systems that capture the complexity of the CF lung environment. We previously identified loss-of-function mutations in the 2-component regulatory system sensor kinase gene pmrB in P. aeruginosa from CF lung infections and from experimental infection of mice. Here, we demonstrate that, while such mutations lowered in vitro minimum inhibitory concentrations for multiple antimicrobial classes, this was not reflected in increased antibiotic susceptibility in vivo. Loss of PmrB impaired aminoarabinose modification of LPS, increasing the negative charge of the outer membrane and promoting uptake of cationic antimicrobials. However, in vivo, this could be offset by increased membrane binding of other positively charged molecules present in lungs. The polyamine spermidine readily coated the surface of PmrB-deficient P. aeruginosa, reducing susceptibility to antibiotics that rely on charge differences to bind the outer membrane and increasing biofilm formation. Spermidine was elevated in lungs during P. aeruginosa infection in mice and during episodes of antimicrobial treatment in people with CF. These findings highlight the need to study antimicrobial resistance under clinically relevant environmental conditions. Microbial mutations carrying fitness costs in vitro may be advantageous during infection, where host resources can be utilized.

Exploring the Cystic Fibrosis Lung Microbiome: Making the Most of a Sticky Situation

Chronic lower respiratory tract infections are a leading contributor to morbidity and mortality in persons with cystic fibrosis (pwCF). Traditional respiratory tract surveillance culturing has focused on a limited range of classic pathogens; however, comprehensive culture and culture-independent molecular approaches have demonstrated complex communities highly unique to each individual. Microbial community structure evolves through the lifetime of pwCF and is associated with baseline disease state and rates of disease progression including occurrence of pulmonary exacerbations. While molecular analysis of the airway microbiome has provided insight into these dynamics, challenges remain including discerning not only "who is there" but "what they are doing" in relation to disease progression. Moreover, the microbiome can be leveraged as a multi-modal biomarker for both disease activity and prognostication. In this article, we review our evolving understanding of the role these communities play in pwCF and identify challenges in translating microbiome data to clinical practice.

Metabolomics of airways disease in cystic fibrosis

While discovery metabolomic studies have identified many potential biomarkers of cystic fibrosis (CF) airways disease, relatively few have been validated. We review the recent literature to identify the most promising metabolomic findings as those repeatedly observed over multiple studies. Reproducible metabolomic findings include increased airway amino acids and small peptides in CF airways, as well as changes in phospholipids and sphingolipids. Other commonly altered pathways include adenosine metabolism, polyamine synthesis, and oxidative stress. These pathways represent potential biomarkers and therapeutic targets, though findings require reevaluation in the era of highly effective modulator therapies. Analysis of airway biomarkers in exhaled breath holds promise for non-invasive detection, though technical challenges will need to be overcome.

Metabolic basis for the evolution of a common pathogenic Pseudomonas aeruginosa variant

Microbes frequently evolve in reproducible ways. Here, we show that differences in specific metabolic regulation rather than inter-strain interactions explain the frequent presence of lasR loss-of-function (LOF) mutations in the bacterial pathogen Pseudomonas aeruginosa. While LasR contributes to virulence through its role in quorum sensing, lasR mutants have been associated with more severe disease. A model based on the intrinsic growth kinetics for a wild type strain and its LasR- derivative, in combination with an experimental evolution based genetic screen and further genetics analyses, indicated that differences in metabolism were sufficient to explain the rise of these common mutant types. The evolution of LasR- lineages in laboratory and clinical isolates depended on activity of the two-component system CbrAB, which modulates substrate prioritization through the catabolite repression control pathway. LasR- lineages frequently arise in cystic fibrosis lung infections and their detection correlates with disease severity. Our analysis of bronchoalveolar lavage fluid metabolomes identified compounds that negatively correlate with lung function, and we show that these compounds support enhanced growth of LasR- cells in a CbrB-controlled manner. We propose that in vivo metabolomes contribute to pathogen evolution, which may influence the progression of disease and its treatment.

Network Analysis to Identify Multi-Omic Correlations in the Lower Airways of Children With Cystic Fibrosis

The leading cause of morbidity and mortality in cystic fibrosis (CF) is progressive lung disease secondary to chronic airway infection and inflammation; however, what drives CF airway infection and inflammation is not well understood. By providing a physiological snapshot of the airway, metabolomics can provide insight into these processes. Linking metabolomic data with microbiome data and phenotypic measures can reveal complex relationships between metabolites, lower airway bacterial communities, and disease outcomes. In this study, we characterize the airway metabolome in bronchoalveolar lavage fluid (BALF) samples from persons with CF (PWCF) and disease control (DC) subjects and use multi-omic network analysis to identify correlations with the airway microbiome. The Biocrates targeted liquid chromatography mass spectrometry (LC-MS) platform was used to measure 409 metabolomic features in BALF obtained during clinically indicated bronchoscopy. Total bacterial load (TBL) was measured using quantitative polymerase chain reaction (qPCR). The Qiagen EZ1 Advanced automated extraction platform was used to extract DNA, and bacterial profiling was performed using 16S sequencing. Differences in metabolomic features across disease groups were assessed univariately using Wilcoxon rank sum tests, and Random forest (RF) was used to identify features that discriminated across the groups. Features were compared to TBL and markers of inflammation, including white blood cell count (WBC) and percent neutrophils. Sparse supervised canonical correlation network analysis (SsCCNet) was used to assess multi-omic correlations. The CF metabolome was characterized by increased amino acids and decreased acylcarnitines. Amino acids and acylcarnitines were also among the features most strongly correlated with inflammation and bacterial burden. RF identified strong metabolomic predictors of CF status, including L-methionine-S-oxide. SsCCNet identified correlations between the metabolome and the microbiome, including correlations between a traditional CF pathogen, Staphylococcus, a group of nontraditional taxa, including Prevotella, and a subnetwork of specific metabolomic markers. In conclusion, our work identified metabolomic characteristics unique to the CF airway and uncovered multi-omic correlations that merit additional study.

Metabolomic Biomarkers to Predict and Diagnose Cystic Fibrosis Pulmonary Exacerbations: A Systematic Review

INTRODUCTION

Metabolomics is an emerging area of research and has the potential to identify clinical biomarkers for predicting or diagnosing cystic fibrosis (CF) pulmonary exacerbations (PEx).

OBJECTIVE

To identify clinically promising metabolites across different sample sources that can be used to predict or diagnose PEx in CF.

EVIDENCE REVIEW

Searches for original literature were completed through EMBASE, MEDLINE, and all databases on the Web of Science with no restrictions on language or publication date. Gray literature was collected through Google Scholar. Additional studies were obtained by contacting authors and searching reference lists of candidate papers. The patient population included individuals with CF. Studies involving patients who underwent lung transplantation were excluded. The outcome was the prediction or diagnosis of pulmonary exacerbations from metabolites directly measured from biological samples. Search results were downloaded and imported into Covidence and duplicates were removed automatically. Any remaining duplicates were manually tagged and excluded. Two independent reviewers screened each abstract for eligibility and repeated this process for full texts. Risk of bias was conducted using QUADAS-2 by two independent reviewers. A third author resolved any remaining conflicts.

RESULTS

A combined 3974 relevant abstracts were identified and 115 full texts were assessed for eligibility. The final 25 studies underwent data extraction for study design, patient demographics, studied metabolites, concentration values, and diagnostic accuracy values. Included studies differed considerably in methodologies, sample specimen types (exhaled breath condensate [EBC], sputum, saliva, plasma, urine), and disease states. We identified 19 unique metabolites that were measured by two or more studies of which 2 have the potential to predict PEx (EBC 4-hydroxycyclohexylcarboxylic acid [4-HCHC] and lactic acid) and 6 to diagnose PEx (EBC 4-HCHC and lactic acid, sputum lactic acid and nitrate, and plasma arginine and methionine).

CONCLUSION AND RELEVANCE

This systematic review has identified promising metabolites for further study in CF. Certain metabolites may provide clinical potential in predicting or diagnosing PEx, but further validation studies are required. With better tools to aid in the earlier identification of PEx, clinicians can implement preventative measures to mitigate airway damage.Systematic Review Registration: https://www.crd.york.ac.uk/prospero/.

An Overview on the Upper and Lower Airway Microbiome in Cystic Fibrosis Patients

BACKGROUND

In cystic fibrosis patients, the mucus is an excellent place for opportunistic bacteria and pathogens to cover. Chronic infections of upper and lower airways play a critical role in the mortality of cystic fibrosis. This study aimed to introduce the microbiota profiles in patients with cystic fibrosis.

MATERIALS AND METHODS

In this study, a comprehensive literature search was done for studies on upper and lower airway microbiota in cystic fibrosis patients. International and national databases were searched for the following MeSH words: microbiota, microbiome, upper airway, lower airway, cystic fibrosis, cystic fibrosis, upper airway microbiome, lower airway microbiome, microbiome pattern in cystic fibrosis, microbiome pattern in cystic fibrosis, upper airway microbiota, lower airway microbiota, and microbiota pattern.

RESULTS

Streptococcus spp. are in significantly higher relative abundance in infants and children with cystic fibrosis; however, Pseudomonas spp. are in higher relative abundance in adults with cystic fibrosis. Molecular diagnostic techniques can be remarkably accurate in detecting microbial strains.

CONCLUSION

For the detection and isolation of most bacterial species, independent-culture methods in addition to the standard culture method are recommended, and sampling should include both upper and lower airways.

Lung Microbiome in Cystic Fibrosis

The defective mucociliary clearance due to CFTR malfunctioning causes predisposition to the colonization of pathogens responsible for the recurrent inflammation and rapid deterioration of lung function in patients with cystic fibrosis (CF). This has also a profound effect on the lung microbiome composition, causing a progressive reduction in its diversity, which has become a common characteristic of patients affected by CF. Although we know that the lung microbiome plays an essential role in maintaining lung physiology, our comprehension of how the microbial components interact with each other and the lung, as well as how these interactions change during the disease's course, is still at an early stage. Many challenges exist and many questions still to be answered, but there is no doubt that manipulation of the lung microbiome could help to develop better therapies for people affected by CF.

Metabotypes of Pseudomonas aeruginosa Correlate with Antibiotic Resistance, Virulence and Clinical Outcome in Cystic Fibrosis Chronic Infections

Pseudomonas aeruginosa (P.a) is one of the most critical antibiotic resistant bacteria in the world and is the most prevalent pathogen in cystic fibrosis (CF), causing chronic lung infections that are considered one of the major causes of mortality in CF patients. Although several studies have contributed to understanding P.a within-host adaptive evolution at a genomic level, it is still difficult to establish direct relationships between the observed mutations, expression of clinically relevant phenotypes, and clinical outcomes. Here, we performed a comparative untargeted LC/HRMS-based metabolomics analysis of sequential isolates from chronically infected CF patients to obtain a functional view of P.a adaptation. Metabolic profiles were integrated with expression of bacterial phenotypes and clinical measurements following multiscale analysis methods. Our results highlighted significant associations between P.a “metabotypes”, expression of antibiotic resistance and virulence phenotypes, and frequency of clinical exacerbations, thus identifying promising biomarkers and therapeutic targets for difficult-to-treat P.a infections

Ecological Succession of Polymicrobial Communities in the Cystic Fibrosis Airways

Antimicrobial therapies against cystic fibrosis (CF) lung infections are largely aimed at the traditional, well-studied CF pathogens such as Pseudomonas aeruginosa and Burkholderia cepacia complex, despite the fact that the CF lung harbors a complex and dynamic polymicrobial community. A clinical focus on the dominant pathogens ignores potentially important community-level interactions in disease pathology, perhaps explaining why these treatments are often less effective than predicted based on in vitro testing.

Antimicrobial therapies against cystic fibrosis (CF) lung infections are largely aimed at the traditional, well-studied CF pathogens such as Pseudomonas aeruginosa and Burkholderia cepacia complex, despite the fact that the CF lung harbors a complex and dynamic polymicrobial community. A clinical focus on the dominant pathogens ignores potentially important community-level interactions in disease pathology, perhaps explaining why these treatments are often less effective than predicted based on in vitro testing. A better understanding of the ecological dynamics of this ecosystem may enable clinicians to harness these interactions and thereby improve treatment outcomes. Like all ecosystems, the CF lung microbial community develops through a series of stages, each of which may present with distinct microbial communities that generate unique host-microbe and microbe-microbe interactions, metabolic profiles, and clinical phenotypes. While insightful models have been developed to explain some of these stages and interactions, there is no unifying model to describe how these infections develop and persist. Here, we review current perspectives on the ecology of the CF airway and present the CF Ecological Succession (CFES) model that aims to capture the spatial and temporal complexity of CF lung infection, address current challenges in disease management, and inform the development of ecologically driven therapeutic strategies.

High-Resolution Longitudinal Dynamics of the Cystic Fibrosis Sputum Microbiome and Metabolome through Antibiotic Therapy

Microbial diversity in the cystic fibrosis (CF) lung decreases over decades as pathogenic bacteria such as Pseudomonas aeruginosa take over. The dynamics of the CF microbiome and metabolome over shorter time frames, however, remain poorly studied. Here, we analyze paired microbiome and metabolome data from 594 sputum samples collected over 401 days from six adult CF subjects (subject mean = 179 days) through periods of clinical stability and 11 CF pulmonary exacerbations (CFPE). While microbiome profiles were personalized (permutational multivariate analysis of variance [PERMANOVA] r ² = 0.79, P < 0.001), we observed significant intraindividual temporal variation that was highest during clinical stability (linear mixed-effects [LME] model, P = 0.002). This included periods where the microbiomes of different subjects became highly similar (UniFrac distance, <0.05). There was a linear increase in the microbiome alpha-diversity and in the log ratio of anaerobes to pathogens with time (n = 14 days) during the development of a CFPE (LME P = 0.0045 and P = 0.029, respectively). Collectively, comparing samples across disease states showed there was a reduction of these two measures during antibiotic treatment (LME P = 0.0096 and P = 0.014, respectively), but the stability data and CFPE data were not significantly different from each other. Metabolome alpha-diversity was higher during CFPE than during stability (LME P = 0.0085), but no consistent metabolite signatures of CFPE across subjects were identified. Virulence-associated metabolites from P. aeruginosa were temporally dynamic but were not associated with any disease state. One subject died during the collection period, enabling a detailed look at changes in the 194 days prior to death. This subject had over 90% Pseudomonas in the microbiome at the beginning of sampling, and that level gradually increased to over 99% prior to death. This study revealed that the CF microbiome and metabolome of some subjects are dynamic through time. Future work is needed to understand what drives these temporal dynamics and if reduction of anaerobes correlate to clinical response to CFPE therapy.IMPORTANCE Subjects with cystic fibrosis battle polymicrobial lung infections throughout their lifetime. Although antibiotic therapy is a principal treatment for CF lung disease, we have little understanding of how antibiotics affect the CF lung microbiome and metabolome and how much the community changes on daily timescales. By analyzing 594 longitudinal CF sputum samples from six adult subjects, we show that the sputum microbiome and metabolome are dynamic. Significant changes occur during times of stability and also through pulmonary exacerbations (CFPEs). Microbiome alpha-diversity increased as a CFPE developed and then decreased during treatment in a manner corresponding to the reduction in the log ratio of anaerobic bacteria to classic pathogens. Levels of metabolites from the pathogen P. aeruginosa were also highly variable through time and were negatively associated with anaerobes. The microbial dynamics observed in this study may have a significant impact on the outcome of antibiotic therapy for CFPEs and overall subject health.

Direct Lung Sampling Indicates That Established Pathogens Dominate Early Infections in Children with Cystic Fibrosis

Culture and sequencing have produced divergent hypotheses about cystic fibrosis (CF) lung infections. Culturing suggests that CF lungs are uninfected before colonization by a limited group of CF pathogens. Sequencing suggests diverse communities of mostly oral bacteria inhabit lungs early on and diversity decreases as disease progresses. We studied the lung microbiota of CF children using bronchoscopy and sequencing, with measures to reduce contamination. We found no evidence for oral bacterial communities in lung lavages that lacked CF pathogens. Lavage microbial diversity varied widely, but decreases in diversity appeared to be driven by increased CF pathogen abundance, which reduced the signal from contaminants. Streptococcus, Prevotella, and Veillonella DNA was detected in some lavages containing CF pathogens, but DNA from these organisms was vastly exceeded by CF pathogen DNA and was not associated with inflammation. These findings support the hypothesis that established CF pathogens are primarily responsible for CF lung infections.

The Microbiome in Cystic Fibrosis Pulmonary Disease

Cystic fibrosis (CF) is a genetic disease with mutational changes leading to profound dysbiosis, both pulmonary and intestinal, from a very young age. This dysbiosis plays an important role in clinical manifestations, particularly in the lungs, affected by chronic infection. The range of microbiological tools has recently been enriched by metagenomics based on next-generation sequencing (NGS). Currently applied essentially in a gene-targeted manner, metagenomics has enabled very exhaustive description of bacterial communities in the CF lung niche and, to a lesser extent, the fungi. Aided by progress in bioinformatics, this now makes it possible to envisage shotgun sequencing and opens the door to other areas of the microbial world, the virome, and the archaeome, for which almost everything remains to be described in cystic fibrosis. Paradoxically, applying NGS in microbiology has seen a rebirth of bacterial culture, but in an extended manner (culturomics), which has proved to be a perfectly complementary approach to NGS. Animal models have also proved indispensable for validating microbiome pathophysiological hypotheses. Description of pathological microbiomes and correlation with clinical status and therapeutics (antibiotic therapy, cystic fibrosis transmembrane conductance regulator (CFTR) modulators) revealed the richness of microbiome data, enabling description of predictive and follow-up biomarkers. Although monogenic, CF is a multifactorial disease, and both genotype and microbiome profiles are crucial interconnected factors in disease progression. Microbiome-genome interactions are thus important to decipher.

Early Detection of Cystic Fibrosis Acute Pulmonary Exacerbations by Exhaled Breath Condensate Metabolomics

The most common cause of death in cystic fibrosis (CF) patients is progressive lung function decline, which is punctuated by acute pulmonary exacerbations (APEs). A major challenge is to discover biomarkers for detecting an oncoming APE and allow for pre-emptive clinical interventions. Metabolic profiling of exhaled breath condensate (EBC) samples collected from CF patients before, during, and after APEs and under stable conditions (n = 210) was performed using ultraperformance liquid chromatography (UPLC) coupled to Orbitrap mass spectrometry (MS). Negative ion mode MS data showed that classification between metabolic profiles from "pre-APE" (pending APE before the CF patient had any signs of illness) and stable CF samples was possible with good sensitivities (85.7 and 89.5%), specificities (88.4 and 84.1%), and accuracies (87.7 and 85.7%) for pediatric and adult patients, respectively. Improved classification performance was achieved by combining positive with negative ion mode data. Discriminant metabolites included two potential biomarkers identified in a previous pilot study: lactic acid and 4-hydroxycyclohexylcarboxylic acid. Some of the discriminant metabolites had microbial origins, indicating a possible role of bacterial metabolism in APE progression. The results show promise for detecting an oncoming APE using EBC metabolites, thus permitting early intervention to abort such an event.

A Unified Conceptual Framework for Metabolic Phenotyping in Diagnosis and Prognosis

Understanding metabotype (multicomponent metabolic characteristics) variation can help to generate new diagnostic and prognostic biomarkers, as well as models, with potential to impact on patient management. We present a suite of conceptual approaches for the generation, analysis, and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of (i) metabolite levels, (ii) metabolic trajectories, (iii) metabolic entropies, and (iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalized medicine, with the aim of improving clinical outcomes for patients, but the framework will have value and utility in areas of metabolic profiling well beyond this exemplar.

Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance

Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.

Cystic fibrosis (CF) is a fatal genetic disease characterized by chronic lung infections due to aberrant mucus production and the inability to clear invading pathogens. The traditional view that CF infections are caused by a single pathogen has been replaced by the realization that the CF lung usually is colonized by a complex community of bacteria, fungi, and viruses. To help unravel the complex interplay between the CF lung environment and the infecting microbial community, we developed a community metabolic model comprised of the 17 most abundant bacterial taxa, which account for >95% of reads across samples, from three published studies in which 75 sputum samples from 46 adult CF patients were analyzed by 16S rRNA gene sequencing. The community model was able to correctly predict high abundances of the “rare” pathogens Enterobacteriaceae, Burkholderia, and Achromobacter in three patients whose polymicrobial infections were dominated by these pathogens. With these three pathogens removed, the model correctly predicted that the remaining 43 patients would be dominated by Pseudomonas and/or Streptococcus. This dominance was predicted to be driven by relatively high monoculture growth rates of Pseudomonas and Streptococcus as well as their ability to efficiently consume amino acids, organic acids, and alcohols secreted by other community members. Sample-by-sample heterogeneity of community composition could be qualitatively captured through random variation of the simulated metabolic environment, suggesting that experimental studies directly linking CF lung metabolomics and 16S sequencing could provide important insights into disease progression and treatment efficacy.

IMPORTANCE Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.

Anaerobes in cystic fibrosis patients' airways

Anaerobes are known to constitute an important part of the airway microbiota in both healthy subjects and cystic fibrosis (CF) patients. Studies on the potential role of anaerobic bacteria in CF and thus their involvement in CF pathophysiology have reported contradictory results, and the question is still not elucidated. The aim of this study was to summarize anaerobe diversity in the airway microbiota and its potential role in CF, to provide an overview of the state of knowledge on anaerobe antibiotic resistances (resistome), and to investigate the detectable metabolites produced by anaerobes in CF airways (metabolome). This review emphasizes key metabolites produced by strict anaerobic bacteria (sphingolipids, fermentation-induced metabolites and metabolites involved in quorum-sensing), which may be essential for the better understanding of lung disease pathophysiology in CF.

Getting Our Fingers on the Pulse of Slow-Growing Bacteria in Hard-To-Reach Places

Chronic infections with slow-growing pathogens have plagued humans throughout history. However, assessing the identities and growth rates of bacteria in an infection has remained an elusive goal. Neubauer et al. (J. Bacteriol. 200:e00365-18, 2018, https://doi.org/10.1128/JB.00365-18) combine two cutting-edge approaches to make progress on both fronts: probing specific RNA molecules to assess the identity of actively transcribing microbes and measuring growth rates through incorporation of stable isotope labels. They found that growth rates of pathogens were relatively stable during antibacterial therapy. The article delves into a basic and unanswered question that gets to the heart of understanding infection: what are the microbial growth rates?

The Pseudomonas aeruginosa Complement of Lactate Dehydrogenases Enables Use of d- and l-Lactate and Metabolic Cross-Feeding

Lactate is thought to serve as a carbon and energy source during chronic infections. Sites of bacterial colonization can contain two enantiomers of lactate: the l-form, generally produced by the host, and the d-form, which is usually produced by bacteria, including the pulmonary pathogen Pseudomonas aeruginosa. Here, we characterize P. aeruginosa’s set of four enzymes that it can use to interconvert pyruvate and lactate, the functions of which depend on the availability of oxygen and specific enantiomers of lactate. We also show that anaerobic pyruvate fermentation triggers production of the aerobic d-lactate dehydrogenase in both liquid cultures and biofilms, thereby enabling metabolic cross-feeding of lactate over time and space between subpopulations of cells. These metabolic pathways might contribute to P. aeruginosa growth and survival in the lung.

Pseudomonas aeruginosa is the most common cause of chronic, biofilm-based lung infections in patients with cystic fibrosis (CF). Sputum from patients with CF has been shown to contain oxic and hypoxic subzones as well as millimolar concentrations of lactate. Here, we describe the physiological roles and expression patterns of P. aeruginosa lactate dehydrogenases in the contexts of different growth regimes. P. aeruginosa produces four enzymes annotated as lactate dehydrogenases, three of which are known to contribute to anaerobic or aerobic metabolism in liquid cultures. These three are LdhA, which reduces pyruvate to d-lactate during anaerobic survival, and LldE and LldD, which oxidize d-lactate and l-lactate, respectively, during aerobic growth. We demonstrate that the fourth enzyme, LldA, performs redundant l-lactate oxidation during growth in aerobic cultures in both a defined MOPS (morpholinepropanesulfonic acid)-based medium and synthetic CF sputum media. However, LldA differs from LldD in that its expression is induced specifically by the l-enantiomer of lactate. We also show that the P. aeruginosa lactate dehydrogenases perform functions in colony biofilms that are similar to their functions in liquid cultures. Finally, we provide evidence that the enzymes LdhA and LldE have the potential to support metabolic cross-feeding in biofilms, where LdhA can catalyze the production of d-lactate in the anaerobic zone, which is then used as a substrate in the aerobic zone. Together, these observations further our understanding of the metabolic pathways that can contribute to P. aeruginosa growth and survival during CF lung infection.

Cystic fibrosis respiratory microbiota: unraveling complexity to inform clinical practice

INTRODUCTION

Cystic fibrosis (CF) lung disease is characterized by chronic cycles of pulmonary infection, inflammation, and mucus obstruction, beginning early in life, and eventually leading to progressive lung damage and early mortality. During the past ~15 years, culture-independent analyses of CF respiratory samples have identified diverse bacterial communities in CF airways, and relationships between respiratory microbiota and clinical outcomes. Areas covered: This paper reviews recent advances in our understanding of the relationships between respiratory microbiota and CF lung disease. The paper focuses on measures of airway bacterial community diversity and estimates of the relative abundance of anaerobic species. Finally, this paper will review the opportunities for advancing patient care suggested by these studies and highlight some of the ongoing challenges and unmet needs in translating this knowledge into clinical practice. Expert commentary: Culture-independent analyses of respiratory microbiota have suggested new strategies for advancing CF care, but have also highlighted challenges in understanding the complexity of CF respiratory infections. Development of more sophisticated models and analytic approaches to better account for this complexity are needed to elucidate mechanistic links between CF respiratory microbiota and clinical outcomes, and to ultimately translate this knowledge into better patient care.

Interaction between the microbiome and TP53 in human lung cancer

Background

Lung cancer is the leading cancer diagnosis worldwide and the number one cause of cancer deaths. Exposure to cigarette smoke, the primary risk factor in lung cancer, reduces epithelial barrier integrity and increases susceptibility to infections. Herein, we hypothesize that somatic mutations together with cigarette smoke generate a dysbiotic microbiota that is associated with lung carcinogenesis. Using lung tissue from 33 controls and 143 cancer cases, we conduct 16S ribosomal RNA (rRNA) bacterial gene sequencing, with RNA-sequencing data from lung cancer cases in The Cancer Genome Atlas serving as the validation cohort.

Results

Overall, we demonstrate a lower alpha diversity in normal lung as compared to non-tumor adjacent or tumor tissue. In squamous cell carcinoma specifically, a separate group of taxa are identified, in which Acidovorax is enriched in smokers. Acidovorax temporans is identified within tumor sections by fluorescent in situ hybridization and confirmed by two separate 16S rRNA strategies. Further, these taxa, including Acidovorax, exhibit higher abundance among the subset of squamous cell carcinoma cases with TP53 mutations, an association not seen in adenocarcinomas.

Conclusions

The results of this comprehensive study show both microbiome-gene and microbiome-exposure interactions in squamous cell carcinoma lung cancer tissue. Specifically, tumors harboring TP53 mutations, which can impair epithelial function, have a unique bacterial consortium that is higher in relative abundance in smoking-associated tumors of this type. Given the significant need for clinical diagnostic tools in lung cancer, this study may provide novel biomarkers for early detection.

Electronic supplementary material

The online version of this article (10.1186/s13059-018-1501-6) contains supplementary material, which is available to authorized users.

Microbiome characteristics of induced sputum compared to bronchial fluid and upper airway samples

OBJECTIVE

The study of the community of microorganisms (the microbiota) in the lower airways in children is restricted to opportunistic sampling in children undergoing elective general anaesthetic. Here we tested the hypothesis that induced sputum is a valid alternative to directly sampling the lower airways to study lower airway microbiota.

METHODS

Children scheduled for elective operations were recruited. Pre-operatively a sample of induced sputum was obtained. After anaesthesia was induced, a bronchial brushing and swabs of the upper respiratory tract were obtained. Bacterial community analysis was performed by amplification of the V3-V4 16S rRNA gene region.

RESULTS

Twenty children were recruited, mean age 10.7 years. Induced sputum samples were obtained from 12 children, bronchial brushing from 14 and nasal, mouth, and throat samples in 15, 16, and 17 children. The profile of bacterial communities was similar in the mouth, throat, and sputum samples with the nose and bronchial samples being different. Actinobacteria species dominated the nose and mouth, Fusobacteria were the dominant species in the throat and sputum while Proteobacteria species dominated in bronchial samples. Forty-one percent of detected bacteria in bronchial samples were unclassified. Bacterial communities from the mouth, throat, and induced sputum were tightly clustered and were distinct from nose and those found in bronchial communities.

CONCLUSIONS

Induced sputum may not be a valid surrogate for microbiome assessment of the lower airways in all individuals. Many bacteria in bronchial samples were not recognized by standard testing, suggesting that our understanding of the lower airway microbiota in children remains rudimentary.

Fermentation products in the cystic fibrosis airways induce aggregation and dormancy-associated expression profiles in a CF clinical isolate of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a well-known dominant opportunistic pathogen in cystic fibrosis (CF) with a wide range of metabolic capacities. However, P. aeruginosa does not colonize the airways alone, and benefits from the metabolic products of neighboring cells-especially volatile molecules that can travel between different parts of the airways easily. Here, we present a study that investigates the metabolic, gene expression profiles and phenotypic responses of a P. aeruginosa clinical isolate to fermentation products lactic acid and 2,3-butanediol, metabolites that are produced by facultative anaerobic members of the CF polymicrobial community and potential biomarkers of disease progression. Although previous studies have successfully investigated the metabolic and transcriptional profiles of P. aeruginosa, most have used common lab reference strains that may differ in important ways from clinical isolates. Using transcriptomics and metabolomics with gas chromatography time of flight mass spectrometry, we observe that fermentation products induce pyocyanin production along with the expression of genes involved in P. aeruginosa amino acid utilization, dormancy and aggregative or biofilm modes of growth. These findings have important implications for how interactions within the diverse CF microbial community influence microbial physiology, with potential clinical consequences.

Metabolomic responses to lumacaftor/ivacaftor in cystic fibrosis

BACKGROUND

Cystic fibrosis (CF) is a life-limiting disease caused by a defect in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Lumacaftor/Ivacaftor is a novel CFTR modulator approved for patients that are homozygous for Phe508del CFTR, but its clinical effectiveness varies amongst patients, making it difficult to determine clinical responders. Therefore, identifying biochemical biomarkers associated with drug response are clinically important for follow-up studies.

METHODS

Serum metabolomics was performed on twenty patients with CF pre- and 6-month post-Lumacaftor/Ivacaftor response via Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS). Correlation with clinical variables was performed.

RESULTS

Metabolomics analysis demonstrated 188 differentially regulated metabolites between patients pre- and post-Lumacaftor/Ivacaftor initiation, with a predominance of lipid and amino acid alterations. The top 30 metabolites were able to differentiate pre- and post-Lumacaftor/Ivacaftor status in greater than 90% of patients via a random-forest confusion matrix. Alterations in bile acids, phospholipids, and bacteria-associated metabolites were the predominant changes associated with drug response. Importantly, changes in metabolic patterns were associated with clinical responders.

CONCLUSIONS

Selected key lipid and amino acid metabolic pathways were significantly affected by Lumacaftor/Ivacaftor initiation and similar pathways were affected in clinical responders. Targeted metabolomics may provide useful and relevant biomarkers of CFTR modulator responses.

Making It Last: Storage Time and Temperature Have Differential Impacts on Metabolite Profiles of Airway Samples from Cystic Fibrosis Patients

Metabolomics has great potential for uncovering biomarkers of the disease state in CF and many other contexts. However, sample storage timing and temperature may alter the abundance of clinically relevant metabolites. To assess whether existing samples are stable and to direct future study design, we conducted untargeted GC-MS metabolomic analysis of CF sputum samples after one or two freeze-thaw cycles and storage at 4°C and −20°C for 4 to 8 weeks. Overall, storage at −20°C and freeze-thaw cycles had little impact on metabolite profiles; however, storage at 4°C shifted metabolite abundances significantly. GC-MS profiling will aid in our understanding of the CF lung, but care should be taken in studies using sputum samples to ensure that samples are properly stored.

Metabolites of human or microbial origin have the potential to be important biomarkers of the disease state in cystic fibrosis (CF). Clinical sample collection and storage conditions may impact metabolite abundances with clinical relevance. We measured the change in metabolite composition based on untargeted gas chromatography-mass spectrometry (GC-MS) when CF sputum samples were stored at 4°C, −20°C, or −80°C with one or two freeze-thaw cycles. Daily measurements were taken for 1 week and then weekly for 4 weeks (4°C) and 8 weeks (−20°C). The metabolites in samples stored at −20°C maintained abundances similar to those found at−80°C over the course of 8 weeks (average change in Bray-Curtis distance, 0.06 ± 0.04) and were also stable after one or two freeze-thaw cycles. However, the metabolite profiles of samples stored at 4°C shifted after 1 day and continued to change over the course of 4 weeks (average change in Bray-Curtis distance, 0.31 ± 0.12). The abundances of several amino acids and other metabolites increased with time of storage at 4°C but remained constant at −20°C. Storage temperature was a significant factor driving the metabolite composition (permutational multivariate analysis of variance: r² = 0.32 to 0.49, P < 0.001). CF sputum samples stored at −20°C at the time of sampling maintain a relatively stable untargeted GC-MS profile. Samples should be frozen on the day of collection, as more than 1 day at 4°C impacts the global composition of the metabolites in the sample.

IMPORTANCE Metabolomics has great potential for uncovering biomarkers of the disease state in CF and many other contexts. However, sample storage timing and temperature may alter the abundance of clinically relevant metabolites. To assess whether existing samples are stable and to direct future study design, we conducted untargeted GC-MS metabolomic analysis of CF sputum samples after one or two freeze-thaw cycles and storage at 4°C and −20°C for 4 to 8 weeks. Overall, storage at −20°C and freeze-thaw cycles had little impact on metabolite profiles; however, storage at 4°C shifted metabolite abundances significantly. GC-MS profiling will aid in our understanding of the CF lung, but care should be taken in studies using sputum samples to ensure that samples are properly stored.

The airway microbiota in early cystic fibrosis lung disease

Infection plays a critical role in the pathogenesis of cystic fibrosis (CF) lung disease. Over the past two decades, the application of molecular and extended culture-based techniques to microbial analysis has changed our understanding of the lungs in both health and disease. CF lung disease is a polymicrobial disorder, with obligate and facultative anaerobes recovered alongside traditional pathogens in varying proportions, with some differences observed to correlate with disease stage. While healthy lungs are not sterile, differences between the lower airway microbiota of individuals with CF and disease-controls are already apparent in childhood. Understanding the evolution of the CF airway microbiota, and its relationship with clinical treatments and outcome at each disease stage, will improve our understanding of the pathogenesis of CF lung disease and potentially inform clinical management. This review summarizes current knowledge of the early development of the respiratory microbiota in healthy children and then discusses what is known about the airway microbiota in individuals with CF, including how it evolves over time and where future research priorities lie.

Insight into c-di-GMP Regulation in Anammox Aggregation in Response to Alternating Feed Loadings

Substrate concentrations generally fluctuate in wastewaters. However, how anammox biomass behaves to overcome the stress of alternating feed loadings remains unclear. Here, we combined long-term reactor operation, batch tests, 16S rRNA transcript sequencing, and metabolomics analysis to investigate the aggregation of anammox biomass under the regulation of c-di-GMP, a key second messenger, in response to alternating feed loadings. We demonstrated that the aggregation process was significantly faster under alternating loadings and was significantly correlated with higher levels of c-di-GMP and extracellular polymeric substances (EPS) production. The increase in c-di-GMP was positively correlated with a higher relative transcript expression level in the c-di-GMP pathway-dependent community. The targeted metabolomics results indicated that the increased production of fructose 6-phosphate and UDP-N-acetyl-d-glucosamine, the precursor substances for the synthesis of exopolysaccharides, was induced by higher levels of c-di-GMP. Consequently, the granulation process was accelerated via EPS production. Higher levels of intracellular hydrophobic amino acids were also positively correlated with increased extracellular protein levels, considering the significant increase in peptides under alternating loadings. On the basis of our findings, we believe that c-di-GMP regulation and EPS production of the anammox biomass are important mechanisms to enhance its tolerance against unfavorable feed stress. These results highlight the role of c-di-GMP in anammox biomass as it works to survive in unfavorable niches.

A pilot study demonstrating the altered gut microbiota functionality in stable adults with Cystic Fibrosis

Cystic Fibrosis (CF) and its treatment result in an altered gut microbiota composition compared to non-CF controls. However, the impact of this on gut microbiota functionality has not been extensively characterised. Our aim was to conduct a proof-of-principle study to investigate if measurable changes in gut microbiota functionality occur in adult CF patients compared to controls. Metagenomic DNA was extracted from faecal samples from six CF patients and six non-CF controls and shotgun metagenomic sequencing was performed on the MiSeq platform. Metabolomic analysis using gas chromatography-mass spectrometry was conducted on faecal water. The gut microbiota of the CF group was significantly different compared to the non-CF controls, with significantly increased Firmicutes and decreased Bacteroidetes. Functionality was altered, with higher pathway abundances and gene families involved in lipid (e.g. PWY 6284 unsaturated fatty acid biosynthesis (p = 0.016)) and xenobiotic metabolism (e.g. PWY-5430 meta-cleavage pathway of aromatic compounds (p = 0.004)) in CF patients compared to the controls. Significant differences in metabolites occurred between the two groups. This proof-of-principle study demonstrates that measurable changes in gut microbiota functionality occur in CF patients compared to controls. Larger studies are thus needed to interrogate this further.

Strong incidence of Pseudomonas aeruginosa on bacterial rrs and ITS genetic structures of cystic fibrosis sputa

Cystic fibrosis (CF) lungs harbor a complex community of interacting microbes, including pathogens like Pseudomonas aeruginosa. Meta-taxogenomic analysis based on V5-V6 rrs PCR products of 52 P. aeruginosa-positive (Pp) and 52 P. aeruginosa-negative (Pn) pooled DNA extracts from CF sputa suggested positive associations between P. aeruginosa and Stenotrophomonas and Prevotella, but negative ones with Haemophilus, Neisseria and Burkholderia. Internal Transcribed Spacer analyses (RISA) from individual DNA extracts identified three significant genetic structures within the CF cohorts, and indicated an impact of P. aeruginosa. RISA clusters Ip and IIIp contained CF sputa with a P. aeruginosa prevalence above 93%, and of 24.2% in cluster IIp. Clusters Ip and IIIp showed lower RISA genetic diversity and richness than IIp. Highly similar cluster IIp RISA profiles were obtained from two patients harboring isolates of a same P. aeruginosa clone, suggesting convergent evolution in the structure of their microbiota. CF patients of cluster IIp had received significantly less antibiotics than patients of clusters Ip and IIIp but harbored the most resistant P. aeruginosa strains. Patients of cluster IIIp were older than those of Ip. The effects of P. aeruginosa on the RISA structures could not be fully dissociated from the above two confounding factors but several trends in these datasets support the conclusion of a strong incidence of P. aeruginosa on the genetic structure of CF lung microbiota.

Acquisition and adaptation of the airway microbiota in the early life of cystic fibrosis patients

Cystic fibrosis (CF) is a genetic disease in which bacterial infections of the airways play a major role in the long-term clinical outcome. In recent years, a number of next-generation sequencing (NGS)-based studies aimed at deciphering the structure and composition of the airways’ microbiota. It was shown that the nasal cavity of CF patients displays dysbiosis early in life indicating a failure in the first establishment of a healthy microbiota. In contrast, within the conducting and lower airways, the establishment occurs normally first, but is sensitive to future dysbiosis including chronic infections with classical pathogens in later life. The objective of this mini-review is to give an update on the current knowledge about the development of the microbiota in the early life of CF patients. Microbial acquisition in the human airways can be described by the island model: Microbes found in the lower airways of CF patients represent “islands” that are at first populated from the upper airways reflecting the “mainland.” Colonization can be modeled following the neutral theory in which the most abundant bacteria in the mainland are also frequently found in the lower airways initially. At later times, however, the colonization process of the lower airways segregates by active selection of specific microbes. Future research should focus on those processes of microbial and host interactions to understand how microbial communities are shaped on short- and long-term scales. We point out what therapeutic consequences arise from the microbiome data obtained within ecological framework models.

Ecological networking of cystic fibrosis lung infections

In the context of a polymicrobial infection, treating a specific pathogen poses challenges because of unknown consequences on other members of the community. The presence of ecological interactions between microbes can change their physiology and response to treatment. For example, in the cystic fibrosis lung polymicrobial infection, antimicrobial susceptibility testing on clinical isolates is often not predictive of antibiotic efficacy. Novel approaches are needed to identify the interrelationships within the microbial community to better predict treatment outcomes. Here we used an ecological networking approach on the cystic fibrosis lung microbiome characterized using 16S rRNA gene sequencing and metagenomics. This analysis showed that the community is separated into three interaction groups: Gram-positive anaerobes, Pseudomonas aeruginosa, and Staphylococcus aureus. The P. aeruginosa and S. aureus groups both anti-correlate with the anaerobic group, indicating a functional antagonism. When patients are clinically stable, these major groupings were also stable, however, during exacerbation, these communities fragment. Co-occurrence networking of functional modules annotated from metagenomics data supports that the underlying taxonomic structure is driven by differences in the core metabolism of the groups. Topological analysis of the functional network identified the non-mevalonate pathway of isoprenoid biosynthesis as a keystone for the microbial community, which can be targeted with the antibiotic fosmidomycin. This study uses ecological theory to identify novel treatment approaches against a polymicrobial disease with more predictable outcomes.

Biomarkers for cystic fibrosis drug development

PURPOSE

To provide a review of the status of biomarkers in cystic fibrosis drug development, including regulatory definitions and considerations, a summary of biomarkers in current use with supportive data, current gaps, and future needs.

METHODS

Biomarkers are considered across several areas of CF drug development, including cystic fibrosis transmembrane conductance regulator modulation, infection, and inflammation.

RESULTS

Sweat chloride, nasal potential difference, and intestinal current measurements have been standardized and examined in the context of multicenter trials to quantify CFTR function. Detection and quantification of pathogenic bacteria in CF respiratory cultures (e.g.: Pseudomonas aeruginosa) are commonly used in early phase antimicrobial clinical trials, and to monitor safety of therapeutic interventions. Sputum (e.g.: neutrophil elastase, myeloperoxidase, calprotectin) and blood biomarkers (e.g.: C reactive protein, calprotectin, serum amyloid A) have had variable success in detecting response to inflammatory treatments.

CONCLUSIONS

Biomarkers are used throughout the drug development process in CF, and many have been used in early phase clinical trials to provide proof of concept, detect drug bioactivity, and inform dosing for later-phase studies. Advances in the precision of current biomarkers, and the identification of new biomarkers with 'omics-based technologies, are needed to accelerate CF drug development.

Managing Pseudomonas aeruginosa respiratory infections in cystic fibrosis

PURPOSE OF REVIEW

The current guidelines and recent clinical research in the management of Pseudomonas aeruginosa respiratory infections in cystic fibrosis (CF) are reviewed. Areas where further research is required will also be highlighted.

RECENT FINDINGS

P. aeruginosa is a key respiratory pathogen in CF. Inhaled tobramycin or colistin is recommended for early eradication to prevent establishment of chronic infection. Other antibiotic options are currently being investigated. The long-term success of eradication strategies is also now being assessed. The use of inhaled antibiotics in the management of chronic P. aeruginosa infection is an area of active investigation. Acute pulmonary exacerbations are still a major cause of morbidity and mortality. Guidelines continue to recommend combination intravenous therapy but further research is required to clarify the advantage of this approach. Multidrug resistance is common and potentially more effective antipseudomonal antibiotics may soon become available.

SUMMARY

The management of P. aeruginosa respiratory infection in CF remains a challenging area, especially in the setting of multidrug resistance. The role of inhaled antibiotics continues to be expanded. Further research is required in the key areas of eradication and management of chronic infection and acute pulmonary exacerbations to identify those treatments that optimize long-term, clinical benefits.

Gut Microbiota Predict Pulmonary Infiltrates after Allogeneic Hematopoietic Cell Transplantation

RATIONALE

Pulmonary complications (PCs) cause significant morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HCT). Shifts in gut microbiota have been linked to HCT outcomes; however, their effect on PCs is unknown.

OBJECTIVES

To investigate whether changes in gut microbiota are associated with PCs after HCT.

METHODS

A single-center observational study was performed on 94 patients who underwent HCT from 2009 to 2011 and who were previously enrolled in a protocol for 16S ribosomal RNA sequencing of fecal microbiota. The primary endpoint, PC, was defined by new abnormal parenchymal findings on chest imaging in the setting of respiratory signs and/or symptoms. Outcomes were collected up to 40 months after transplant. Clinical and microbiota risk factors for PCs and mortality were evaluated using survival analysis.

MEASUREMENTS AND MAIN RESULTS

One hundred twelve PCs occurred in 66 (70.2%) subjects. A high comorbidity index (hazard ratio [HR], 2.30; 95% confidence interval [CI], 1.30-4.00; P = 0.004), fluoroquinolones (HR, 2.29, 95% CI, 1.32-3.98; P = 0.003), low baseline diversity (HR, 2.63; 95% CI, 1.22-5.32; P = 0.015), and γ-proteobacteria domination of fecal microbiota (HR, 2.64; 95% CI, 1.10-5.65; P = 0.031), which included common respiratory pathogens, predicted PCs. In separate analyses, low baseline diversity was associated with PCs that occurred preengraftment (HR, 6.30; 95% CI, 1.42-31.80; P = 0.016), whereas γ-proteobacteria domination predicted PCs postengraftment (HR, 3.68; 95% CI, 1.49-8.21; P = 0.006) and overall mortality (HR, 3.52; 95% CI, 1.28-9.21; P = 0.016). Postengraftment PCs were also independent predictors of death (HR, 2.50; 95% CI, 1.25-5.22; P = 0.009).

CONCLUSIONS

This is the first study to demonstrate prospective changes in gut microbiota associated with PCs after HCT. Postengraftment PCs and γ-proteobacteria domination were predictive of mortality. This suggests an adverse relationship between the graft and lung, which is perhaps mediated by bacterial composition in the gut. Further study is warranted.

Metabolomics of pulmonary exacerbations reveals the personalized nature of cystic fibrosis disease

Background. Cystic fibrosis (CF) is a genetic disease that results in chronic infections of the lungs. CF patients experience intermittent pulmonary exacerbations (CFPE) that are associated with poor clinical outcomes. CFPE involves an increase in disease symptoms requiring more aggressive therapy. Methods. Longitudinal sputum samples were collected from 11 patients (n = 44 samples) to assess the effect of exacerbations on the sputum metabolome using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The data was analyzed with MS/MS molecular networking and multivariate statistics. Results. The individual patient source had a larger influence on the metabolome of sputum than the clinical state (exacerbation, treatment, post-treatment, or stable). Of the 4,369 metabolites detected, 12% were unique to CFPE samples; however, the only known metabolites significantly elevated at exacerbation across the dataset were platelet activating factor (PAF) and a related monacylglycerophosphocholine lipid. Due to the personalized nature of the sputum metabolome, a single patient was followed for 4.2 years (capturing four separate exacerbation events) as a case study for the detection of personalized biomarkers with metabolomics. PAF and related lipids were significantly elevated during CFPEs of this patient and ceramide was elevated during CFPE treatment. Correlating the abundance of bacterial 16S rRNA gene amplicons to metabolomics data from the same samples during a CFPE demonstrated that antibiotics were positively correlated to Stenotrophomonas and Pseudomonas, while ceramides and other lipids were correlated with Streptococcus, Rothia, and anaerobes. Conclusions. This study identified PAF and other inflammatory lipids as potential biomarkers of CFPE, but overall, the metabolome of CF sputum was patient specific, supporting a personalized approach to molecular detection of CFPE onset.

Microbial, host and xenobiotic diversity in the cystic fibrosis sputum metabolome

Cystic fibrosis (CF) lungs are filled with thick mucus that obstructs airways and facilitates chronic infections. Pseudomonas aeruginosa is a significant pathogen of this disease that produces a variety of toxic small molecules. We used molecular networking-based metabolomics to investigate the chemistry of CF sputa and assess how the microbial molecules detected reflect the microbiome and clinical culture history of the patients. Metabolites detected included xenobiotics, P. aeruginosa specialized metabolites and host sphingolipids. The clinical culture and microbiome profiles did not correspond to the detection of P. aeruginosa metabolites in the same samples. The P. aeruginosa molecules that were detected in sputum did not match those from laboratory cultures. The pseudomonas quinolone signal (PQS) was readily detectable from cultured strains, but absent from sputum, even when its precursor molecules were present. The lack of PQS production in vivo is potentially due to the chemical nature of the CF lung environment, indicating that culture-based studies of this pathogen may not explain its behavior in the lung. The most differentially abundant molecules between CF and non-CF sputum were sphingolipids, including sphingomyelins, ceramides and lactosylceramide. As these highly abundant molecules contain the inflammatory mediator ceramide, they may have a significant role in CF hyperinflammation. This study demonstrates that the chemical makeup of CF sputum is a complex milieu of microbial, host and xenobiotic molecules. Detection of a bacterium by clinical culturing and 16S rRNA gene profiling do not necessarily reflect the active production of metabolites from that bacterium in a sputum sample.

Understanding persistent bacterial lung infections: clinical implications informed by the biology of the microbiota and biofilms

The infections found in chronic obstructive pulmonary disease, cystic fibrosis, and bronchiectasis share a number of clinical similarities, the most striking of which is bacterial persistence despite the use of antibiotics. These infections have been clinically described using culture-based methods usually performed on sputum samples, and treatment has been directed towards the bacteria found in this manner. Unfortunately the clinical response to antibiotics is frequently not predictable based on these cultures, and the role of these cultured organisms in disease progression has been debated. The past 20 years have seen a revolution in the techniques used to describe bacterial populations and their growth patterns. These techniques have revealed these persistent lung infections are vastly more complicated than described by traditional, and still widely relied upon, sputum cultures. A better understanding of the initiation and evolution of these infections, and better clinical tools to describe them, will dramatically alter the way patients are cared for. While clinical tests to more accurately describe these infections are not yet available, the better appreciation of these infections afforded by current science should enlighten practitioners as to the care of their patients with these diseases.