2. How Does the Lung Microbiome in CF Patients Change with Age and Disease Stage?

From their first months of life, CF infants have a different lung microbiome (LM) composition from that of healthy subjects [18], although many studies have demonstrated that in the first decade of life, the lungs of CF subjects still preserve some diversity, as they are colonized with non-typical CF pathogens. The genera that are normally found are Streptococcus, Prevotella, Rothia, Veillonella, Gemella, Neissera, Actynomyces, and Haemophilus [18,19]. Interestingly, the microbiome composition of bronchoalveolar lavage fluid (BALF) from CF patients in the first year of life does not seem to be identical with the one present in oropharyngeal swabs or nasopharyngeal samples [18]. This has important clinical implications, since, at least in infants, it makes the upper respiratory tract niches inadequate for the determination of lung microbial diversity and, most importantly, for the identification of all pathogens. Furthermore, especially under disease conditions, the microbiome could be different between the left and right lungs [20] and in the different areas of the thick mucus layers characteristic of the CF patients [21], reinforcing the idea that analyzing the microbiomes from patients’ BALFs would make diagnosis and therapies more accurate. As described earlier, in young CF patients, the microbiome still seems to preserve some diversity, with Streptococcus being the predominant taxon; the common CF pathogens (Staphylococcus and P. aeruginosa) comprise about 50% of the total microbial community. Starting from the second decade of life, microbial diversity begin to decrease, which is probably also due to the heavy antibiotic use, and the common CF pathogens, such as P. aeruginosa and Burkholderia, become more abundant, thus decreasing microbiome diversity [19]. Their presence is also correlated with the disease’s severity. Lung function measured by FEV (forced expiratory volume) decreases with age and correlates with less microbiome diversity and dominance of these two pathogens, which, when present, decrease lung function in young CF subjects as well [19]. Another important aspect to consider is the episodes of exacerbation with an increase in respiratory symptoms, which are very common and persistent during the lives of CF patients [22]. The most logical explanation would be that these episodes are due to an increase in CF pathogens, causing a consequent reduction in microbial diversity. Unfortunately, there is no consensus in the literature about this matter, with some clinical reports demonstrating a change in diversity during exacerbation [23,24,25], while others do not report any variation in microbial composition [26,27]. Although this discordance makes our understanding of host–pathogen interaction during exacerbations more complicated, it opens new conceptual views, where different CF subjects could manifest exacerbations due to: (i) an increase in the amount of a specific pathogen; (ii) a switch in the metabolic activity of a specific bacterial family; (iii) some anaerobic bacteria that are not normally associated with the disease state that can act as pathogens under particular conditions [28]. Therefore, although a late diagnosis of CF is rare nowadays [29], an early assessment of the disease in association with lung microbiome composition could reduce the occurrence of complications later in life.

3. How Do CF Therapies Affect the Lung Microbiome?

Although the enormous progress in the development of new therapies has substantially increased the quality of life and life expectancy of CF patients, antibiotic treatment is still the first line of defense against recurrent infections in CF patients. It is used especially to treat episodes of exacerbation, which, as we described above, are frequent during the lifetimes of CF subjects [30]. Many studies have demonstrated that, although necessary, antibiotic therapies are the first cause of a reduction in microbial diversity [31,32]. CF patients are subject to continuous use of different antibiotics and can develop what is called multidrug resistance (MDR), which can help to select for a specific taxon, thus further decreasing microbial diversity [33]. Another important aspect to note is the untargeted action that antibiotics can have on non-pathogenic bacteria, applying additional pressure on the lung microbial community [34]. Furthermore, it has been shown that antibiotic therapies used to eradicate P. aeruginosa can make the conditions favorable for acquiring Aspergillus in young CF subjects [35], causing a further imbalance in microbiome composition.

In the last decade, new therapies have been developed, changing from the classical treatment of the downstream symptoms to the development of small drugs named potentiators and correctors, which can improve the CFTR processing or function in patients with some classes of CFTR mutations [36]. It is interesting to note that, alone or in combination, these drugs exert antimicrobial activity both in vitro and in vivo. For instance, Ivacaftor has been demonstrated to reduce the viability and growth of P. aeruginosa, Streptococcus pneumoniae, and Staphilococcus aureus, and its activity was potentiated in the presence of antibiotics [37]. Similar results were obtained in two other independent studies, where Ivacaftor alone or in combination with ciprofloxacin showed the same antimicrobial activity against the same pathogens [38,39]. Interestingly, the assessment of microbiome changes in CF subjects treated with Ivacaftor also showed an initial reduction in P. aeruginosa colonization, but did not achieve a complete eradication, with the P. aeruginosa level increasing after one year of treatment [40,41].

Understanding how and if these CFTR modulators directly affect the microbiome in CF patients has many clinical implications. For instance, if the correction of CFTR function would restore microbiome diversity and decrease pathogen loads, then assessing the microbiome composition could be used as a screening tool to follow disease severity and progression. If this would not be the case, then we would know that restoring CFTR function is not enough, and re-establishing the microbiome diversity could represent the key for restoring lung function. Furthermore, if pathogen loads would not decrease, antibiotic treatment in combination with these drugs would still be necessary.

4. Can We Improve Lung Function by Restoring Microbiome Diversity?

As described earlier, a decrease in microbial diversity is intimately linked with a deterioration in lung function in CF [19]. This has opened the way to the interesting hypothesis that restoring its diversity would have a beneficial effect on lung physiology, consequently improving its function. Although, at a first glance, this could appear to be a simple task, differently from the gut microbiome, considerable challenges exist in manipulating and following the changes in the lung microbiome in patients with CF. Firstly, lungs have a more complex anatomical structure than the gut [42]. Furthermore, collecting fecal samples to study the gut microbiome is not invasive and could be repeated at any time to monitor its dynamic changes. This is different for the lungs. While BALF would give a very close picture of the lung microbiome, differently from oropharyngeal or nasopharyngeal samples [18], it is an invasive procedure and cannot be performed many times in CF patients that are more prone to infection and in which the lungs could be already damaged. This greatly limits our possibilities for studying if changes in lung microbial composition correlate with lung function or if new therapies can repristinate its diversity. Many studies have, in fact, successfully manipulated the gut microbiome by studying its impact on pulmonary pathology, unraveling a gut–lung axis [43,44,45], while very few studies have tried to manipulate the lung microbiome, leaving its gut counterpart unaltered. For instance, in a proof-of-concept study, Le Noci and colleagues successfully used probiotic aerosol therapy to demonstrate its immunomodulatory effect in a mouse model of lung cancer [46], and it would be interesting to see if the same approach could be used as a combination therapy with CFTR modulators.

5. How Should We Look at the Microbiome to Understand Its Complexity?

To understand how the lung microbiome could be used to monitor CF development, predict exacerbation, and function as an ally for developing better therapies, we need to understand its complexity and what kinds of interaction microbial communities have within the lung. This means that we must look at the microbiome in a different way, and not only ask “What is there?” and “How much is there?”. The answers to these questions are, of course, an important part of the picture, but now we know that they are just the beginning. As previously anticipated, in CF, reduced diversity is linked to lung function, but what exactly does “reduced diversity” mean when we try to link it with lung function? Which physiological functions were those bacteria responsible for that are now reduced or missing? How do these bacteria communicate to maintain a healthy lung environment? What is the proportion that each bacterial family needs to maintain in the community before becoming pathogenic? As hypothesized earlier, CF exacerbations could be due to a switch in the metabolic activity of specific bacteria families that were previously inactive or to a family of non-pathogenic bacteria that, in a specific situation, can behave as a pathogen, and not due to a change in microbiome composition [26,28]. Therefore, microbiome composition studies should be followed by: (i) metagenomic analysis to assess the functional capacity of the microbiome, (ii) meta-transcriptomic analysis to assess gene expression, (iii) meta-proteomics to study the catalytic functions, and (iv) metabolomics to study the metabolic activity (Figure 1). These are the necessary steps to put in place to fully understand the microbiome’s complexity and the interactions within the lung during the disease course. Particular attention should be given to the study of the microbiome’s metabolic activity. We know that bacteria, through the production of specific metabolites, can communicate with each other and with the host organ, and can even influence molecular pathways in distal tissue [47]. Therefore, the identification of metabolites and how the metabolic activity changes during disease progression has become a piece of crucial information that can potentially lead to the identification of biomarkers. This would have a huge impact on clinical practice. In fact, although microbiome studies started more than a decade ago, understanding the large amount of data obtained from OMICS analysis and translating them into a clear message that is able to inform and guide treatments are probably the biggest challenges that the microbiome field needs to overcome.

Figure 1. A flow chart for study of the microbiome. The OMICS approaches needed to gain a full comprehension of the microbiome’s role both in heath and pathologies are illustrated here. The identification of a specific bacterial composition in pathological conditions should ideally be followed by an assessment of gene and protein expression, and especially an assessment of which metabolites are produced that can influence tissue homeostasis.