Data Mining of Lung Microbiota in Cystic Fibrosis Patients

Changing profile of bacterial infection and microbiome in cystic fibrosis: when to use antibiotics in the era of CFTR-modulator therapy

The advent of cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy, especially the triple therapy combining the drugs elexacaftor, tezacaftor, ivacaftor (ETI), has significantly changed the course of the disease in people with cystic fibrosis (pwCF). ETI, which is approved for the majority (80-90%) of pwCF, partially restores CFTR channel function, resulting in improved mucociliary clearance and, consequently, improved lung function, respiratory symptoms and pulmonary exacerbations. The bacterial burden of classical CF pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus is reduced without reaching eradication in the majority of infected patients. Limited data is available on less common or emerging bacterial pathogens. ETI has a positive effect on the lung microbiome but does not fully restore it to a healthy state. Due to the significant reduction in sputum production under ETI, respiratory samples such as deep-throat swabs are commonly taken, despite their inadequate representation of lower respiratory tract pathogens. Currently, there are still unanswered questions related to this new therapy, such as the clinical impact of infection with cystic fibrosis (CF) pathogens, the value of molecular diagnostic tests, the durability of the effects on respiratory infection and the role of fungal and viral infections. This article reviews the changes in bacterial lung infections and the microbiome in CF to provide evidence for the use of antibiotics in the era of ETI.

Recreating chronic respiratory infections in vitro using physiologically relevant models

Despite the need for effective treatments against chronic respiratory infections (often caused by pathogenic biofilms), only a few new antimicrobials have been introduced to the market in recent decades. Although different factors impede the successful advancement of antimicrobial candidates from the bench to the clinic, a major driver is the use of poorly predictive model systems in preclinical research. To bridge this translational gap, significant efforts have been made to develop physiologically relevant models capable of recapitulating the key aspects of the airway microenvironment that are known to influence infection dynamics and antimicrobial activity in vivo In this review, we provide an overview of state-of-the-art cell culture platforms and ex vivo models that have been used to model chronic (biofilm-associated) airway infections, including air-liquid interfaces, three-dimensional cultures obtained with rotating-wall vessel bioreactors, lung-on-a-chips and ex vivo pig lungs. Our focus is on highlighting the advantages of these infection models over standard (abiotic) biofilm methods by describing studies that have benefited from these platforms to investigate chronic bacterial infections and explore novel antibiofilm strategies. Furthermore, we discuss the challenges that still need to be overcome to ensure the widespread application of in vivo-like infection models in antimicrobial drug development, suggesting possible directions for future research. Bearing in mind that no single model is able to faithfully capture the full complexity of the (infected) airways, we emphasise the importance of informed model selection in order to generate clinically relevant experimental data.

The past, present and future of polymicrobial infection research: Modelling, eavesdropping, terraforming and other stories

Over the last two centuries, great advances have been made in microbiology as a discipline. Much of this progress has come about as a consequence of studying the growth and physiology of individual microbial species in well-defined laboratory media; so-called "axenic growth". However, in the real world, microbes rarely live in such "splendid isolation" (to paraphrase Foster) and more often-than-not, share the niche with a plethora of co-habitants. The resulting interactions between species (and even between kingdoms) are only very poorly understood, both on a theoretical and experimental level. Nevertheless, the last few years have seen significant progress, and in this review, we assess the importance of polymicrobial infections, and show how improved experimental traction is advancing our understanding of these. A particular focus is on developments that are allowing us to capture the key features of polymicrobial infection scenarios, especially as those associated with the human airways (both healthy and diseased).

Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis

Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies. Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses. Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively. Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis.

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.

Antibiotic susceptibility of cystic fibrosis lung microbiome members in a multispecies biofilm

The lungs of cystic fibrosis (CF) patients are often chronically colonized by multiple microbial species that can form biofilms, including the major CF pathogen Pseudomonas aeruginosa. Herewith, lower microbial diversity in CF airways is typically associated with worse health outcomes. In an attempt to treat CF lung infections patients are frequently exposed to antibiotics, which may affect microbial diversity. This study aimed at understanding if common antibiotics that target P. aeruginosa influence microbial diversity. To this end, a microaerophilic multispecies biofilm model of frequently co-isolated members of the CF lung microbiome (Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus anginosus, Achromobacter xylosoxidans, Rothia mucilaginosa, and Gemella haemolysans) was exposed to antipseudomonal antibiotics. We found that antibiotics that affected several dominant species (i.e. ceftazidime, tobramycin) resulted in higher species evenness compared to colistin, which is only active against P. aeruginosa. Furthermore, susceptibility of individual species in the multispecies biofilm following antibiotic treatment was compared to that of the respective single-species biofilms, showing no differences. Adding three anaerobic species (Prevotella melaninogenica, Veillonella parvula, and Fusobacterium nucleatum) to the multispecies biofilm did not influence antibiotic susceptibility. In conclusion, our study demonstrates antibiotic-dependent effects on microbial community diversity of multispecies biofilms comprised of CF microbiome members.

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.

Antimicrobial Treatment Provides a Competitive Advantage to Mycobacterium abscessus in a Dual-Species Biofilm with Pseudomonas aeruginosa

The physiological factors that contribute to Mycobacterium abscessus lung infections remain unclear. We determined whether antibiotic treatment targeting a major cystic fibrosis pathogen (i.e., Pseudomonas aeruginosa) could provide the ideal conditions for the establishment of M. abscessus infection. Our data showed that P. aeruginosa inhibited M. abscessus biofilm formation under control conditions and that antimicrobial therapy selectively targeting P. aeruginosa diminished this competitive interaction, thereby increasing M. abscessus survival.

Selective pressures during chronic infection drive microbial competition and cooperation

Chronic infections often contain complex mixtures of pathogenic and commensal microorganisms ranging from aerobic and anaerobic bacteria to fungi and viruses. The microbial communities present in infected tissues are not passively co-existing but rather actively interacting with each other via a spectrum of competitive and/or cooperative mechanisms. Competition versus cooperation in these microbial interactions can be driven by both the composition of the microbial community as well as the presence of host defense strategies. These interactions are typically mediated via the production of secreted molecules. In this review, we will explore the possibility that microorganisms competing for nutrients at the host–pathogen interface can evolve seemingly cooperative mechanisms by controlling the production of subsets of secreted virulence factors. We will also address interspecies versus intraspecies utilization of community resources and discuss the impact that this phenomenon might have on co-evolution at the host–pathogen interface.

Metagenomics in ophthalmology: current findings and future prospectives

Less than 1% of all microorganisms of the available environmental microbiota can be cultured with the currently available techniques. Metagenomics is a new methodology of high-throughput DNA sequencing, able to provide taxonomic and functional profiles of microbial communities without the necessity to culture microbes in the laboratory. Metagenomics opens to a ‘hypothesis-free’ approach, giving important details for future research and treatment of ocular diseases in ophthalmology, such as ocular infection and ocular surface diseases.

Dysbiosis of the Salivary Microbiome Is Associated With Non-smoking Female Lung Cancer and Correlated With Immunocytochemistry Markers

Background: Association between oral bacteria and increased risk of lung cancer have been reported in several previous studies, however, the potential association between salivary microbiome and lung cancer in non-smoking women have not been evaluated. There is also no report on the relationship between immunocytochemistry markers and salivary microbiota.

Method: In this study, we assessed the salivary microbiome of 75 non-smoking female lung cancer patients and 172 matched healthy individuals using 16S rRNA gene amplicon sequencing. We also calculated the Spearman's rank correlation coefficient between salivary microbiota and three immunohistochemical markers (TTF-1, Napsin A and CK7).

Result: We analyzed the salivary microbiota of 247 subjects and found that non-smoking female lung cancer patients exhibited oral microbial dysbiosis. There was significantly lower microbial diversity and richness in lung cancer patients when compared to the control group (Shannon index, P < 0.01; Ace index, P < 0.0001). Based on the analysis of similarities, the composition of the microbiota in lung cancer patients also differed from that of the control group (r = 0.454, P < 0.001, unweighted UniFrac; r = 0.113, P < 0.01, weighted UniFrac). The bacterial genera Sphingomonas (P < 0.05) and Blastomonas (P < 0.0001) were relatively higher in non-smoking female lung cancer patients, whereas Acinetobacter (P < 0.001) and Streptococcus (P < 0.01) were higher in controls. Based on Spearman's correlation analysis, a significantly positive correlation can be observed between CK7 and Enterobacteriaceae (r = 0.223, P < 0.05). At the same time, Napsin A was positively associated with genera Blastomonas (r = 0.251, P < 0.05). TTF-1 exhibited a significantly positive correlation with Enterobacteriaceae (r = 0.262, P < 0.05). Functional analysis from inferred metagenomes indicated that oral microbiome in non-smoking female lung cancer patients were related to cancer pathways, p53 signaling pathway, apoptosis and tuberculosis.

Conclusions: The study identified distinct salivary microbiome profiles in non-smoking female lung cancer patients, revealed potential correlations between salivary microbiome and immunocytochemistry markers used in clinical diagnostics, and provided proof that salivary microbiota can be an informative source for discovering non-invasive lung cancer biomarkers.

Airway microbiota in patients with paediatric cystic fibrosis: Relationship with clinical status

INTRODUCTION

New massive sequencing techniques make it possible to determine the composition of airway microbiota in patients with cystic fibrosis (CF). However, the relationship between the composition of lung microbiome and the clinical status of paediatric patients is still not fully understood.

MATERIAL AND METHODS

A cross-sectional observational study was conducted on induced sputum samples from children with CF and known mutation in the CFTR gene. The bacterial sequences of the 16SrRNA gene were analyzed and their association with various clinical variables studied.

RESULTS

Analysis of the 13 samples obtained showed a core microbiome made up of Staphylococcus spp., Streptococcus spp., Rothia spp., Gemella spp. and Granulicatella spp., with a small number of Pseudomonas spp. The cluster of patients with less biodiversity were found to exhibit a greater number of sequences of Staphylococcus spp., mainly Staphylococcus aureus (p 0.009) and a greater degree of lung damage.

CONCLUSION

An airway microbiome with greater biodiversity may be an indicator of less pronounced disease progression, in which case new therapeutic interventions that prevent reduction in non-pathogenic species of the airway microbiota should be studied.

Profiling of lung microbiota in the patients with obstructive sleep apnea

Lung microbiota may affect innate immunity and treatment consequence in the obstructive sleep apnea (OSA) patients. Bronchoalveolar lavage fluid (BALF) was obtained from 11 OSA patients and 8 patients with other lung diseases as control, and used for lung microbiota profiling by PCR amplification and sequencing of the microbial samples. It was demonstrated that phyla of Firmicutes, Fusobacteria, and Bacteriodetes were relatively abundant in the lung microbiota. Alpha-diversity comparison between OSA and control group revealed that Proteobacteria and Fusobacteria were significantly higher in OSA patients (0.3863 ± 0.0631 and 0.0682 ± 0.0159, respectively) than that in control group (0.119 ± 0.074 and 0.0006 ± 0.0187, respectively, P < .05 for both phyla). In contrast, Firmicutes was significantly less in OSA patients (0.1371 ± 0.0394) compared with that in the control group (0.384 ± 0.046, P < .05). Comparison within a group (ß-diversity) indicated that the top 5 phyla in the OSA lung were Proteobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Acidobacteria, while the top 5 phyla in the control group were Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Acidobacteria. These findings indicated that lung microbiota in OSA is distinct from that of non-OSA patients. Manipulation of the microbiota may be an alternative strategy to augment airway immunity and to reduce susceptibility to airway infection.

Gender differences in bronchiectasis: a real issue?

Gender differences in chronic respiratory disease, including cystic fibrosis and non-cystic fibrosis bronchiectasis are clinically apparent and of increasing importance. Differences in disease prevalence, severity and outcome are all described, however, the precise cause of the gender dichotomy and their associated underlying mechanisms have been poorly characterised. A lack of dedicated clinical and epidemiological research focused in this area has led to a paucity of data and therefore a lack of understanding of its key drivers. Diagnosis, disease pathogenesis and treatment response are all complex but important aspects of bronchiectasis with an evident gender bias. Broadening our understanding of the interplay between microbiology, host physiology and the environment in the context of chronic lung diseases, such as bronchiectasis, is critical to unravelling mechanisms driving the observed gender differences. In this review, epidemiological, biological and environmental evidence related to gender in bronchiectasis is summarised. This illustrates gender differences as a “real issue” with the objective of mapping out a future framework upon which a gender-tailored medical approach may be incorporated into the diagnosis, monitoring and treatment of bronchiectasis.

CF and non-CF bronchiectasis are complex multifactorial chronic pulmonary diseases demonstrating gender differences in their prevalence, severity and infections, some of which are attributable to sex hormoneshttp://ow.ly/beDf30jseK4

The role of multispecies social interactions in shaping Pseudomonas aeruginosa pathogenicity in the cystic fibrosis lung

Pseudomonas aeruginosa is a major pathogen in the lungs of cystic fibrosis (CF) patients. However, it is now recognised that a diverse microbial community exists in the airways comprising aerobic and anaerobic bacteria as well as fungi and viruses. This rich soup of microorganisms provides ample opportunity for interspecies interactions, particularly when considering secreted compounds. Here, we discuss how P. aeruginosa-secreted products can have community-wide effects, with the potential to ultimately shape microbial community dynamics within the lung. We focus on three well-studied traits associated with worsening clinical outcome in CF: phenazines, siderophores and biofilm formation, and discuss how secretions can shape interactions between P. aeruginosa and other commonly encountered members of the lung microbiome: Staphylococcus aureus, the Burkholderia cepacia complex, Candida albicans and Aspergillus fumigatus. These interactions may shape the evolutionary trajectory of P. aeruginosa while providing new opportunities for therapeutic exploitation of the CF lung microbiome.

Numerical analyses of intestinal microbiota by data mining

The human intestinal microbiota has a close relationship with health control and causes of diseases, and a vast number of scientific papers on this topic have been published recently. Some progress has been made in identifying the causes or species of related microbiota, and successful results of data mining are reviewed here. Humans who are targets of a disease have their own individual characteristics, including various types of noise because of their individual life style and history. The quantitatively dominant bacterial species are not always deeply connected with a target disease. Instead of conventional simple comparisons of the statistical record, here the Gini-coefficient (i.e., evaluation of the uniformity of a group) was applied to minimize the effects of various types of noise in the data. A series of results were reviewed comparatively for normal daily life, disease and technical aspects of data mining. Some representative cases (i.e., heavy smokers, Crohn’s disease, coronary artery disease and prediction accuracy of diagnosis) are discussed in detail. In conclusion, data mining is useful for general diagnostic applications with reasonable cost and reproducibility.

Individual Patterns of Complexity in Cystic Fibrosis Lung Microbiota, Including Predator Bacteria, over a 1-Year Period

Cystic fibrosis (CF) lung microbiota composition has recently been redefined by the application of next-generation sequencing (NGS) tools, identifying, among others, previously undescribed anaerobic and uncultivable bacteria. In the present study, we monitored the fluctuations of this ecosystem in 15 CF patients during a 1-year follow-up period, describing for the first time, as far as we know, the presence of predator bacteria in the CF lung microbiome. In addition, a new computational model was developed to ascertain the hypothetical ecological repercussions of a prey-predator interaction in CF lung microbial communities. Fifteen adult CF patients, stratified according to their pulmonary function into mild (n = 5), moderate (n = 9), and severe (n = 1) disease, were recruited at the CF unit of the Ramón y Cajal University Hospital (Madrid, Spain). Each patient contributed three or four induced sputum samples during a 1-year follow-up period. Lung microbiota composition was determined by both cultivation and NGS techniques and was compared with the patients’ clinical variables. Results revealed a particular microbiota composition for each patient that was maintained during the study period, although some fluctuations were detected without any clinical correlation. For the first time, Bdellovibrio and Vampirovibrio predator bacteria were shown in CF lung microbiota and reduced-genome bacterial parasites of the phylum Parcubacteria were also consistently detected. The newly designed computational model allows us to hypothesize that inoculation of predators into the pulmonary microbiome might contribute to the control of chronic colonization by CF pathogens in early colonization stages.

The application of NGS to sequential samples of CF patients demonstrated the complexity of the organisms present in the lung (156 species) and the constancy of basic individual colonization patterns, although some differences between samples from the same patient were observed, probably related to sampling bias. Bdellovibrio and Vampirovibrio predator bacteria were found for the first time by NGS as part of the CF lung microbiota, although their ecological significance needs to be clarified. The newly designed computational model allows us to hypothesize that inoculation of predators into the lung microbiome can eradicate CF pathogens in early stages of the process. Our data strongly suggest that lower respiratory microbiome fluctuations are not necessarily related to the patient’s clinical status.

Microbiota, Immune Subversion, and Chronic Inflammation

Several host-adapted pathogens and commensals have evolved mechanisms to evade the host innate immune system inducing a state of low-grade inflammation. Epidemiological studies have also documented the association of a subset of these microorganisms with chronic inflammatory disorders. In this review, we summarize recent studies demonstrating the role of the microbiota in chronic inflammatory diseases and discuss how specific microorganisms subvert or inhibit protective signaling normally induced by toll-like receptors (TLRs). We highlight our work on the oral pathogen Porphyromonas gingivalis and discuss the role of microbial modulation of lipid A structures in evasion of TLR4 signaling and resulting systemic immunopathology associated with atherosclerosis. P. gingivalis intrinsically expresses underacylated lipid A moieties and can modify the phosphorylation of lipid A, leading to altered TLR4 signaling. Using P. gingivalis mutant strains expressing distinct lipid A moieties, we demonstrated that expression of antagonist lipid A was associated with P. gingivalis-mediated systemic inflammation and immunopathology, whereas strains expressing agonist lipid A exhibited modest systemic inflammation. Likewise, mice deficient in TLR4 were more susceptible to vascular inflammation after oral infection with P. gingivalis wild-type strain compared to mice possessing functional TLR4. Collectively, our studies support a role for P. gingivalis-mediated dysregulation of innate and adaptive responses resulting in immunopathology and systemic inflammation. We propose that anti-TLR4 interventions must be designed with caution, given the balance between the protective and destructive roles of TLR signaling in response to microbiota and associated immunopathologies.

Microbiome engineering: Current applications and its future

Microbiomes exist in all ecosystems and are composed of diverse microbial communities. Perturbation to microbiomes brings about undesirable phenotypes in the hosts, resulting in diseases and disorders, and disturbs the balance of the associated ecosystems. Engineering of microbiomes can be used to modify structures of the microbiota and restore ecological balance. Consequently, microbiome engineering has been employed for improving human health and agricultural productivity. The importance and current applications of microbiome engineering, particularly in humans, animals, plants and soil is reviewed. Furthermore, we explore the challenges in engineering microbiome and the future of this field, thus providing perspectives and outlook of microbiome engineering.