case study cystic fibrosis (cer)

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 07 June 2024

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

• Stefanie Widder   ORCID: orcid.org/0000-0003-0733-5666 1 ,
• Lisa A. Carmody 2 ,
• Kristopher Opron 3 ,
• Linda M. Kalikin 2 ,
• Lindsay J. Caverly 2 &
• John J. LiPuma 2  

Nature Communications volume  15 , Article number:  4889 ( 2024 ) Cite this article

157 Accesses

29 Altmetric

Metrics details

• Computational models

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

Similar content being viewed by others

Microbiota in health and diseases

Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes

Key recommendations for primary care from the 2022 Global Initiative for Asthma (GINA) update

Introduction.

Obstructive lung diseases, such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease (COPD), are characterized by chronic polymicrobial bacterial infection of the airways. Intermittent increases in signs and symptoms of respiratory dysfunction, so-called pulmonary exacerbations (PEx), are associated with lung disease progression and mortality in these conditions 1 , 2 , 3 . Despite their importance, the pathophysiologic events underlying PEx are unclear but generally believed to involve transient perturbation of host-microbial dynamics in the airways. Management of these events typically involves frequent, often aggressive, antibiotic treatment, which is intended to decrease bacterial burden and blunt host inflammatory response that contributes to lung pathology. This care carries considerable cost and treatment burden and is limited by drug toxicity and ever-increasing antimicrobial drug resistance 4 . In CF, therapies that modulate the activity of the dysfunctional CF transmembrane conductance regulator (CFTR), the primary cellular defect in CF, have reduced the frequency of PEx for many, but not all, people with CF 5 , 6 . Thus, a better understanding of PEx remains a high priority in efforts to improve care and enhance the quality of life for persons with obstructive lung conditions 7 .

Dysbiosis defines disease-associated alterations of the microbiome that affect the taxonomic composition as well as the functional activity of the microbial community 8 . This serves as an umbrella term for a variety of non-exclusive community characteristics, including diversity loss, symbiont loss, or pathobiont blooms. As such, the label dysbiosis may be of limited applicability in describing microbial dynamics in chronic obstructive lung diseases insofar as the pulmonary microbiome in these conditions displays a markedly different ecology from that in healthy lungs, and can be considered, by definition, to represent a dysbiotic state 9 , 10 . Nevertheless, given that a pathologic microbiome persists even during periods of relative clinical stability, a better characterization of its reorganization patterns to classify (relative) pulmonary dysbiosis into distinct types could provide opportunities for improved management of PEx in chronic pulmonary conditions 8 , 10 , 11 .

The search, in cross-sectional studies, for common motifs in microbial community processes that drive PEx, particularly in CF, has been hampered by subject-specific microbiome configurations. Longitudinal sampling strategies have revealed highly individual taxonomic profiles with context-dependent metabolic activities and signaling in numerous studies 12 , 13 , 14 , 15 . Unpredictable and ill-defined onset of PEx, as well as personalized antimicrobial treatment schemes to manage PEx, further complicate analyses 16 . A strategy that is capable of consolidating process communalities against the background of natural case variability is therefore required 17 .

A number of studies on microbial community networks have found that the precise configuration of dependencies among members define their community role, as well as the dynamical behavior of the microbiome 18 , 19 , 20 . Moreover, the formation of network clusters (i.e., the coexistence of microbial sub-communities) modulates robustness to external perturbation including antimicrobial therapy 21 , 22 . Recently, time evolution of gut, vaginal, and oral microbiomes were modeled using alternative community descriptors anchored in information theory 23 . Switching patterns of microbiota that emerged as tradeoff between perturbation, accessible niches and internal forces were identified. The impact of complex community organization on medically relevant microbial behaviors such as pathogen virulence or resilience to antimicrobial therapy is understudied and remains largely unexplored for clinical applications.

In this study, we hypothesized that discernable microbial patterns exist that delimit different types of PEx in CF. We developed non-standard descriptors that aggregated ecological and compositional properties of the CF lung microbiome and used these to identify PEx types with communal patterns. We then analyzed the organization of the CF microbiome in these backgrounds and revealed two fundamental dysbiosis states: a hierarchical community reorganization controlled by the dominant pathogen, and a stochastic reorganization with blooming anaerobic taxa and high taxonomic turnover. Of note, the behavior of a focal pathogen was markedly different with different community hierarchy. Lastly, we modeled targeted antimicrobial treatment on data-inferred co-occurrence networks and observed that distinct community organizations significantly determined treatment outcomes.

Results and discussion

Compositional characterization of pex time series.

We aggregated a collection of 880 sputum samples from 11 adults with CF, comprising 18 PEx time series. The characteristics of the study subjects and sputum samples are provided in Table  1 , and as supplementary information in the Source Data file/Sample Data; sample inclusion criteria are provided in Tables  S1 and S2 . Subjects and sputum samples were chosen from a larger dataset that had been generated during the course of a long-term observational study 15 , 24 , 25 , 26 , 27 . Subjects were selected from this larger dataset based on the availability of near-daily sputum samples (i.e., samples available from at least 60% of days) that spanned periods of clinical stability culminating with a PEx that prompted antibiotic treatment by the subject’s care team. More specifically, samples in each PEx time series were collected from 60 days prior to one day prior to the initiation of antibiotic treatment for PEx (Fig.  S1 ). The time frame of 60 days prior to the start of PEx treatment was selected to accommodate potential changes in the lung microbiome preceding symptom onset, together with changes occurring during the acute PEx phase. As we have done in previous studies 15 , 24 , 28 , 29 , 30 , 31 , 32 , 33 , samples were further characterized based on the subject’s clinical state at the time of collection: baseline samples were those collected between 60 and 15 days prior to the start of antibiotic treatment; exacerbation samples were collected between 14 and one day prior to the start of antibiotic treatment. Neither samples obtained during acute antibiotic treatment for PEx (treatment samples) nor within three weeks after PEx treatment stopped (recovery samples) were included in this analysis. Chronic (maintenance) antibiotic therapies such as inhaled tobramycin and aztreonam, and oral azithromycin used on each day were recorded. A mean of 49 (SD, 7.3) sputum samples were analyzed per PEx time series.

Identifying common microbiological features of PEx in CF is challenged by the pronounced subject specificity of the lung microbiome, which typically overshadows potential communalities. Accordingly, we identified 1949 amplicon sequence variants (ASVs) among the 880 samples, with only eight ASVs present in every subject. For further analyses, the dataset was denoised to 194 core ASVs by removing taxa present with an average relative abundance below 0.0075%. To quantify the degree of subject specificity in the dataset, a PERMANOVA test was performed to calculate the effect sizes of clinical and demographic covariates on data variance. Covariates included subject, subject age, subject sex, clinical state (baseline health or exacerbation of symptoms 34 ), and zygosity of the cftr F508del allele. As expected, we found that individual subject was a strong predictor for ASV covariance (PERMANOVA, \({\omega }^{2}=0.51\) , \(\,{{{{{\rm{p}}}}}} \, < \, 0.001\) ), followed, to lesser effects, by age (PERMANOVA, \({\omega }^{2}=0.023\) , \(\,{{{{{\rm{p}}}}}} \, < \, 0.001\) ) and clinical state (PERMANOVA, \({\omega }^{2}=0.003\) , \(\,{{{{{\rm{p}}}}}} \, < \, 0.003\) ).

Identifying distinct PEx types using non-standard descriptors

To reduce the subject-specific microbiome bias, we abandoned ASV composition as the sole sample descriptor, assembling the 194 core ASVs into five higher-order groups. The first group comprised conventional CF pathogens ( Pseudomonas, Staphylococcus, Burkholderia, Haemophilus, Achromobacter , and Stenotrophomonas ) based on the prominent role these species are believed to play in CF lung disease 35 , 36 . Three groups were categorized reflecting species oxygen requirement for growth 37 , considering that the CF lung microbiome is strongly conditioned by local oxygen gradients: strictly aerobic, strictly anaerobic, and facultatively anaerobic. The fifth group comprised uncultivated taxa with unknown oxygen requirements. A detailed list of how ASVs distribute across the groups is provided as supplementary information in the Source Data file.

Building on these five ASV categories, we assembled the following non-standard descriptors for every sputum sample: (i) the ratio of CF pathogens to strict anaerobes, (ii) the relative abundance of the most abundant CF pathogen, (iii) the Shannon diversity index of the core ASVs, (iv) the Chao1 richness of the core ASVs, and (v) a community typing using Dirichlet multinomial mixtures (DMM). The DMM model was implemented using the five ASV groups as input and identified six community classes. Two DMM community classes were dominated by CF pathogens, three by anaerobes, and one by facultatively anaerobic organisms. Community classes, selection of Dirichlet components, class distribution over the cohort and class-wise sample compositions are presented in Fig.  S2 .

Using this suite of descriptors, we implemented a second PERMANOVA model and found that the variance explained by this model was reduced compared to that based solely on ASVs (PERMANOVA, \({R}^{2}=0.62\) and \({R}^{2}=0.78\) , respectively). Most importantly, the effect size of subject bias decreased by 51% (Fig.  1A ). Subsequently, data were ordinated independent of clinical state using principal component analysis (Fig.  1B ), and the first three principal components were used to group similar samples. K-mer clustering identified three distinct PEx clusters or types using \({\chi }^{2}\) statistics (Fig.  1C, D ). ASVs in PEx clusters are detailed in the Source Data file.

A Covariate bias explaining variance of microbiome data ( \(n=880\) samples). Two PERMANOVA models contrasted the covariate effect sizes for ASV count data and non-standard sample descriptors. Partial values served as estimators of effect sizes \({\omega }^{2}\) . Subject covariates included subject, age group (<31, 31–37, 38–52 years), clinical state (baseline, exacerbation), sex (female, male), F508del CFTR mutation zygosity (homozygous, heterozygous, n.a.), CFTR mutation (F508del +/+; 3 groups F508del -/+ and one other). B Principal component analysis using non-standard sample descriptors ( \({{{{{\rm{explained\; variance}}}}}}=84.3\,\%\) , two principle components (PCs) are shown). Model variables included Shannon diversity (Shannon), Chao1 richness (Chao1), relative abundance of the dominant pathogen (pat), ratio of counts of CF pathogens and anaerobes (pat/an), and sample classification by Dirichlet multinomial mixture model (DMM) ( n  = 789 samples). Sample coloring by subject according to legend. C Sample-wise, hierarchical k-mer clustering and distance tree of ordinated data (PC1-3 indicate principle components). Subject ID and age group, as well as Pearson correlation coefficients of the samples are depicted for additional information. Color code of cohort and age group according to legend. D Identification of optimal k-mer number. The dependency between information gain and increasing cluster number \(k\) is shown. First slope saturation served as a cutoff for the minimal number of clusters. Source data for Fig. 1 are provided in the Source Data file.

Having identified three robust clusters, hereafter referred to as PEx types, we next analyzed the distribution of DMM communities among these. PEx Type 1 (hereafter called PAT) comprised communities dominated by conventional CF pathogens, including Pseudomonas , Burkholderia , Achromobacter , Haemophilus, Staphylococcus , and Stenotrophomonas . PEx Type 2 (AN1) and Type 3 (AN2), on the other hand, were driven by three distinct anaerobic community configurations. These results suggested that species-sorting occurred in subjects’ lungs according to oxygen requirements 38 . Importantly, PEx proceeded in both aerobic and anaerobic communities.

To assign subjects and their PEx time series to a single PEx type, we performed Spearman’s rank association (Figs.  S3A and S3B ). Two time series were excluded from further analyses due to inconclusive association to a single type (time series 9, 12). The reduced number of 789 samples distributed as 286, 254, and 249; the number of subjects as 4, 3, and 4; and the number of PEx as 6, 5, and 5 to PEx types PAT, AN1, and AN2, respectively (Fig.  S3C and S3D ). We found that sample association with PEx type PAT was remarkably stable both at the level of individual PEx time series (60 days), as well as with subjects over time. On the contrary, more transition events were observed between PEx types AN1 and AN2 (Fig.  S4A and S4B ). Overall, subjects showed a tendency to persist either in PAT or in AN1 or AN2 despite recurrent antibiotic treatment between time series (treatment samples excluded, Fig.  S4C ).

In summary, aggregated measures of sample diversity, ecology and function were used to reduce the organism-driven subject bias and group PEx trajectories with similar properties. We identified three communal PEx types among subjects, termed PAT, AN1 and AN2, that displayed distinguishable microbiomes.

Temporal behavior of microbiomes in distinct exacerbation regimes

We studied the configuration of the lung microbiota in and between the identified PEx types and modeled common reorganization patterns over time as the community proceeded towards the start of PEx treatment. To elucidate underlying ecological processes, we first asked whether PEx types could be simply explained by the DMM community classes, i.e., different community compositions 28 , 39 and whether oxygen availability could motivate shifts in microbiome configurations 40 . We assessed the distribution and temporal change of DMM community classes previously modeled from coarse-grained ASV groups (Fig.  2A , S2 ). Unexpectedly, no significant temporal evolution of DMM communities was observed within PEx types, indicating that the overall proportions of pathogens, anaerobes, facultative anaerobes, and aerobes persisted over most of the PEx cycle with few exceptions. These sporadic shifts occurred only between comparable community classes, i.e., due to continuous transitions (increase or decrease) of taxonomic groups.

A Time evolution of representative community compositions in three PEx types (PAT, AN1, AN2). Sample compositions were encoded by ASV groups and classified by a Dirichlet multinomial mixture (DMM) model into six community classes. Three classes were dominated by strictly anaerobic taxa, two by classical CF pathogens and one by facultative anaerobes (color coding according to legend). Community classes were independent of time towards start of PEx treatment but differed between PEx types ( \(n=789\) samples). B Time evolution of microbiome diversity towards start of PEx treatment ( \(n=789\) samples). Microbiomes in all PEx types showed significant time dependency (LMM, \({{{{{{\rm{p}}}}}}}_{{{{{{\rm{PAT}}}}}}}=1.26{{{{{\rm{e}}}}}}-3,\,{{{{{{\rm{p}}}}}}}_{{{{{{\rm{AN}}}}}}1}=4.6{{{{{\rm{e}}}}}}-2,\,{{{{{{\rm{p}}}}}}}_{{{{{{\rm{AN}}}}}}2}=3.6{{{{{\rm{e}}}}}}-2\) ) with Shannon diversity decreasing in pathogen-dominated and increasing in anaerobe-dominated PEx types towards treatment. C Time evolution of community richness towards PEx treatment ( \(n=789\) samples). Chao1 richness changed significantly with time in pathogen-dominated and in one anaerobic PEx type (LMM, \({{{{{{\rm{p}}}}}}}_{{{{{{\rm{PAT}}}}}}}=\,4.19{{{{{\rm{e}}}}}}-5,\,{{{{{{\rm{p}}}}}}}_{{{{{{\rm{AN}}}}}}1}=2.45{{{{{\rm{e}}}}}}-2\) ). Boxes in B , C depict the interquartile range; the horizontal line represents the median value. Whiskers extend to the minimum and maximum values, excluding outliers, defined as 1.5 times the interquartile range. D Community turnover \(T\) relative to the previous 20 days for three PEx types. Samples were grouped according to their relative collection term prior to PEx treatment and mean values are plotted (in blue, mean of samples collected <24 days before treatment; in green, 24–60 days). Aitchison distance from focal sample to \(n\) previously collected samples (sampling distance) was plotted, the slope of a linear fit was used to quantify \(T\) . Linear fit was represented with 95% confidence intervals of estimates (gray). \(T\) was significantly reduced in pathogen-dominated microbiomes and significantly increased in anaerobe-dominated microbiomes in a 23-day-interval before PEX antibiotic treatment (one-sided ANCOVA for every PEX type, not shown in figure, \(\,{{{{{{\rm{p}}}}}}}_{{{{{{\rm{PAT}}}}}}}=\,2.07{{{{{\rm{e}}}}}}-8\) ; \({{{{{{\rm{p}}}}}}}_{{{{{{\rm{AN}}}}}}1}=9.75e-5\) ; \({{{{{{\rm{p}}}}}}}_{{{{{{\rm{AN}}}}}}2} < 2e-16\) ). Source data for Fig. 2 are provided in the Source Data file.

Several studies have investigated microbiome structure and rearrangement prior to PEx with inconsistent results 29 , 30 , 41 , 42 . Neither pathogen load nor other recurrent organisms were consistent predictors for imminent PEx across larger patient cohorts. Here, we stratified the microbiota by the identified PEx types and analyzed diversity and richness over time in trajectories with similar properties. Mixed effect models were implemented to test time dependencies of Shannon and Chao1 for the three PEx types and corrected for confounders (subject and PEx cycle) (Fig.  2B, C ). All PEx types displayed significant diversity evolution across samples culminating in antibiotic treatment (LMM, \({{{{{{\rm{p}}}}}}}_{{{{{{\rm{PAT}}}}}}}=\,1.26{{{{{\rm{e}}}}}}-3\) , \({{{{{{\rm{p}}}}}}}_{{{{{{\rm{AN}}}}}}1}=\,4.46{{{{{\rm{e}}}}}}-2\) , \({{{{{{\rm{p}}}}}}}_{{{{{{\rm{AN}}}}}}2}=\,3.67{{{{{\rm{e}}}}}}-2\) ). The analog analysis for Chao1 identified PAT and AN1 to exhibit significant dependency with time (LMM, \({{{{{{\rm{p}}}}}}}_{{{{{{\rm{PAT}}}}}}}=\,4.33{{{{{\rm{e}}}}}}-5\) , \({{{{{{\rm{p}}}}}}}_{{{{{{\rm{AN}}}}}}1}=\,2.5{{{{{\rm{e}}}}}}-2\) ).

Interestingly, richness and diversity decreased towards treatment for the pathogen-dominated communities (PAT) and increased for anaerobic PEx types (AN1 and AN2). Furthermore, it is important to note that the time dependency of richness and diversity was consistently small and ranged between \({\eta }^{2}=\{0.02,\,0.06\}\) for all PEx types. In short, we revealed that diversity evolves opposingly, as the microbial communities approached PEx treatment. Of note, despite the modest effect size, these results have the potential to explain the inconclusive reports of previous studies that were conducted without consideration of PEx regimes 7 .

Species turnover displays antagonistic patterns in pathogen or anaerobe communities

Evidence suggests that changes in airway microbial community structures may precede the onset of clinical symptoms of PEx by days or even weeks 15 , 41 , 43 . To determine the most likely time interval for such changes, samples were systematically grouped by collection time (days before the initiation of antibiotic treatment for PEx) and tested for significant differences in Shannon diversity and Chao1 richness (Fig.  S5 ). A split into 1–23 and 24–60 days before PEx treatment showed statistically significant relative changes in all three PEx types, in accordance with the previous result indicating diversity and time dependency.

Community turnover \(T\) describes the rate of species compositional change over time as defined by Ontiveros and colleagues 44 . We employed Aitchison distance to quantify community dissimilarity over time and assessed turnover as the slope of a fitted, linear model. We analyzed turnover \(T\) during onset of (1–23 days prior to treatment) and prior to (24–60 days) PEx by evaluating Aitchison distance between any two samples collected in an interval of one to 20 days in a subject-wise manner (Fig.  2D ). Overall, dissimilarity was smaller in the pathogen-dominated PEx type PAT (ANCOVA, \({{{{{\rm{p}}}}}} < 0.001\) ) and turnover \(T\) was reduced during the 23 days compared to 24–60 days prior to PEx treatment (LMM, \({T}_{ < 24}=0.17;{T}_{24-60}=0.26;{{{{{\rm{p}}}}}} < 0.001\) for both tests). Interestingly, the anaerobic PEx types AN1 and AN2 again exhibited antagonistic patterns, with increased species turnover shortly before PEx treatment (ANCOVA, \({{{{{\rm{p}}}}}} < 0.001\) for both tests). Together, the previous results suggested two PEx regimes (PAT vs AN) with antagonistic temporal behavior.

Characteristic community reorganizations stratify pulmonary dysbiosis types

The detailed organization of interactions and dependencies throughout an ecological community predefines its emergent, dynamical capabilities 45 . In particular, resilience to perturbations such as antimicrobial treatment, community robustness, and the stabilizing effect of keystone organisms were previously attributed to properties of dependency networks 18 , 19 , 22 . Therefore, it is not only important to identify the most relevant CF pathogen in the airway microbiome, but to understand how the background community organization impacts the focal driver organism, modulates its virulence, and contributes to stability.

To study community organization, we inferred co-occurrence networks from sample subsets of individual PEx time series. In detail, for every network, 20 consecutively collected samples were used for robust inference 18 and a sliding window was employed to work across the individual PEx time series (with a step size of one sample). This approach yielded 589 co-occurrence networks, where topology changes between successive networks were caused by the substitution of a single sample. The resulting graphs were subsequently analyzed by PEx type ( \({n}_{{PAT}}=\,222\) , \(\,{n}_{{AN}1}=\,192\) , \({n}_{{AN}2}=\,175\) ; detailed description in Table  S2 ).

We studied the topology of the largest network components, defined as the ensemble of nodes belonging to the biggest connected subgraph of the network and, therefore, expected to be the most impactful for microbiome dynamics 46 . For PEx type PAT, a reduced number of organisms and associations were observed in the largest component, as well as increased betweenness centrality (Wilcoxon, \({{{{{\rm{p}}}}}} < 0.001\) for each pairwise test; Fig.  3A–C ) in contrast to PEx types AN1 and AN2. Graph betweenness centrality measures the extent of centralized organization reinforcing effective communication patterns 47 .

589 co-occurrence networks were inferred by PEx time series, properties of the largest network component are depicted. A Boxplots of community sizes for three PEx types (PAT, AN1, AN2, \(n=589\) ). Pathogen-dominated communities were significantly smaller than anaerobic ones (two-sided Wilcoxon, \({{{{{{\rm{p}}}}}}}_{{{{\rm{PAT}}}}\;{{{\rm{vs}}}}\;{{{\rm{AN2}}}}}, < \, 2.2{{{{{\rm{e}}}}}}-16\) , \({{{{{\rm{p}}}}}}_{{{{\rm{AN1}}}}\;{{{\rm{vs}}}}\;{{{\rm{AN2}}}}} \, < \, 1.7{{{{{\rm{e}}}}}}-9\) , \({{{{{{\rm{p}}}}}}}_{{{{\rm{PAT}}}}\;{{{\rm{vs}}}}\;{{{\rm{AN1}}}}} \, < \, 2.2{{{{{\rm{e}}}}}}-16\) ). B Boxplots of edge numbers for three PEX types ( \(n=589\) ). AN1 and AN2 displayed significantly more co-occurrences than PAT (two-sided Wilcoxon, \({{{{{{\rm{p}}}}}}}_{{{{\rm{PAT}}}}\;{{{\rm{vs}}}}\;{{{\rm{AN2}}}}} \, < \, 2.2{{{{{\rm{e}}}}}}-16\) , \({{{{{{\rm{p}}}}}}}_{{{{\rm{AN1}}}}\;{{{\rm{vs}}}}\;{{{\rm{AN2}}}}} \, < \, 1.2{{{{{\rm{e}}}}}}-3\) , \({{{{{{\rm{p}}}}}}}_{{{{{\rm{PAT}}}}\;{{{\rm{vs}}}}\;{{{\rm{AN1}}}}}} \, < \, 2.2{{{{{\rm{e}}}}}}-16\) ). C Information flow depicted as betweenness centrality for three PEx types ( \(n=589\) ). Betweenness centrality in PAT microbiomes was significantly higher than in AN1 or AN2 communities (two-sided Wilcoxon, \({{{{{{\rm{p}}}}}}}_{{{{\rm{PAT}}}}\;{{{\rm{vs}}}}\;{{{\rm{AN2}}}}} \, < \, 2.2{{{{{\rm{e}}}}}}-16\) , \({{{{{{\rm{p}}}}}}}_{{{{\rm{AN1}}}}\;{{{\rm{vs}}}}\;{{{\rm{AN2}}}}} \, < \, 0.52\) , \({{{{{{\rm{p}}}}}}}_{{{{\rm{PAT}}}}\;{{{\rm{vs}}}}\;{{{\rm{AN1}}}}} \, < \, 2.2{{{{{\rm{e}}}}}}-16\) ). Boxes in A – C depict the interquartile range; the horizontal line represents the median value. Whiskers extend to the minimum and maximum values, excluding outliers, defined as 1.5 times the interquartile range. D Degree distribution over all co-occurrence networks in respective PEx type. Probability \(P\) of finding a network with degree \(k\) in logarithmic scale; linear regression line depicted with model parameters; correlation of regression with data is reported. Power law in all three PEx types with comparable slopes \(s=\{-0.71,-0.7,-0.6\}\) . E Clustering distribution over all co-occurrence networks in respective PEx type (color code according to legend). The distribution of clustering coefficients \({CC}\) by network degree \(k\) is plotted in logarithmic scale. Linear regression line is depicted with model parameters; correlation of regression with data is reported. Clustering for nodes with higher degree declined 3.5 times faster in pathogen-dominated organization than in anaerobe communities ( \(s=\left\{-0.97,-0.28,\,-0.27\right\}\) , respectively). F Organisms residing in most hierarchical community positions. Top hierarchical positions were defined as nodes with highest 10% of all node degrees and lowest 10% of all clustering coefficients in the respective graph. \(P\) indicates probability of ASV on this rank (minimal appearance cutoff  \(P=0.05\) ) for three PEx types (color code according to legend). Source data for Fig. 3 are provided in the Source Data file.

In analogy to interaction networks, we furthermore examined network hierarchy of the microbial co-occurrence networks across the PEx types. In the seminal work of Barabasi and Oltvai on biological interactions networks, a “quantifiable signature of network hierarchy” was defined as “the dependency of the clustering coefficient \({{{{{\rm{CC}}}}}}\) on the degree \(k\) of a node, which follows \({{{{{\rm{CC}}}}}}\left(k\right) \sim {k}^{-1}\) ” 48 . As a result, highly connected hub nodes should display low clustering, if located on top of the network hierarchy. To test this in co-occurrence networks, degree distributions \(P(k)\) together with clustering distributions \({{{{{\rm{CC}}}}}}\left(k\right)\) were inferred and compared by the slope of a power law fit across all co-occurrence networks within the same PEx type (Fig.  3D, E ). While the fit to degree distributions showed similar slopes (LM, \({\alpha }_{{PAT}}=-0.71\) , \({\alpha }_{{AN}1}=-0.6\) , \({\alpha }_{{AN}2}=-0.69\) , \({R}_{{PAT}}=-0.64\) , \({R}_{{AN}1}=-0.67\) , \({R}_{{AN}2}=-0.73\) , \({{{{{\rm{p}}}}}} < 0.001\) ), the slope of the node clustering distribution differed significantly between PEx types (LM, \({\alpha }_{{PAT}}=-0.97\) , \({\alpha }_{{AN}1}=-0.27\) , \({\alpha }_{{AN}2}=-0.28\) ). The pathogen-driven PEx type PAT displayed the strongest descent of clustering with degree k, was indeed approximating -1, and hence indicated a clear microbial hierarchy. These compelling results supported the hypothesis of a pronounced, hierarchical dysbiosis type. Moreover, the anaerobic PEx types AN1 and AN2 also exhibited weaker correlation between fits and data (LM, \({R}_{{PAT}}=-\!0.61\) , \({R}_{{AN}1}=-\!0.3\) , \({R}_{{AN}2}=-\!0.29\) , \({{{{{\rm{p}}}}}} < 0.001\) ) suggesting flat organization and unpronounced community structure.

To confirm that these results were driven by PEx types rather than sample diversity, we implemented independent linear mixed effect models for every graph readout, corrected for subject and calculated effect sizes of PEx types and covariates Shannon diversity and Chao1 richness. We found that betweenness centrality, clustering, number of vertices, and number of edges significantly depended on PEx types with effect sizes being 2.9 times, 5.5 times, 8.9 times and 3.4 larger than the most effective diversity measures, respectively (LMM, \(\,{{{{{{\rm{p}}}}}}}_{{{{{{\rm{between}}}}}}}=4.0e-3,\,{{{{{{\rm{p}}}}}}}_{{{{{{\rm{cluster}}}}}}}=4.8e-2,\,{{{{{{\rm{p}}}}}}}_{{{{{{\rm{\#vertex}}}}}}}=2.7e-7,\,{{{{{{\rm{p}}}}}}}_{{{{{{\rm{\#edge}}}}}}}=7.1e-11\) ). Clustering and betweenness centrality were statistically independent of tested diversity measures, while both diversities influenced edge numbers and richness graph size to a minor extent (Fig.  S6 ).

Next, we investigated which organisms preferentially occupied the most hierarchical positions in the communities and therefore likely controlled the overall microbiome dynamics. For each network, we identified the most hierarchical nodes, which were defined as nodes \(i\) with a degree \({k}_{i} \, > \, 90\%\) and a clustering coefficient \({{{{{{\rm{CC}}}}}}}_{{{{{{\rm{i}}}}}}} < 10\%\) of all nodes in the graph. ASV frequencies were then assessed on these positions (Fig.  3F ). In PAT communities, Pseudomona s, Staphylococcus and Streptococcus were not only masters in hierarchy, but also belonged to the most abundant ASV group in the samples. In AN2, a stronger variation of taxa was observed in the most hierarchical nodes. Of note, one third of the ranking was occupied by various ASVs belonging to the genus Prevotella in these communities. In Fig.  S7 , we visualized three representative PEx type communities with exemplary hierarchies using the Sugiyama algorithm for hierarchical graphs 49 , which corroborated these findings. We concluded that in anaerobic communities, individual species were less relevant for overall microbiome dynamics, and microbiome organization was increasingly stochastic and less well picked up by co-occurrence analysis. On the contrary, in the pathogen-driven PEx type, the network hierarchies were conserved, occupied by a few key organisms and well supported by a simple, centralized community organization.

To contextualize these observations, we examined the identified configurations in contrast to microbiota in lung homeostasis. The definition of dysbiosis employed in this work defines a reorganization of the microbiota in the disease microenvironment. In healthy lungs, the pulmonary microbiome shows neutral community dynamics 50 , 51 , 52 . After Hubbell, diversity and abundance distributions of neutral communities can be explained by stochastic immigration and extinction events alone 53 . On the contrary, in chronic lung disease, microbial interactions, local replication, and environmental adaptation become key for diversity and community dynamics, and the impact of dispersal diminishes 13 , 50 . Consequently, if neutrality is a property of microbial eubiosis in the lung, then microbial interactions can be considered a hallmark of dysbiosis of the pulmonary microbiota. Together with metabolic adaptations, such interactions promote outgrowth of certain taxa to high relative abundances. Accordingly, we propose that both observed community states resemble different fundamental kinds of dysbiosis: the first, a structured, interacting community under the governance of an abundant, conventional CF pathogen, and the second, a globally successful functional guild that gains abundance by adapting to selective environmental pressures. Of note, similar community archetypes characterized by species-sorting or mass effects were described in metacommunity theory, a framework for ecological community assembly and dynamics 54 . The transition between the two metacommunity archetypes was explained by changes in dispersal due to altered spatial arrangements 38 . Here, we speculate that subject-specific mucus accumulation and decreasing oxygen availability in the lung microenvironment determine CF dysbiosis states in equivalent ways.

Importantly, both community states are robust maladaptations to the disease conditions of the lung, which raises the question whether negative loops exist in the system that enable their dynamical stability 13 , 55 . We hypothesize that in the first state, functional adaptations of the dominant pathogens together with antimicrobial defense against microbial competitors provide important negative feedback, whereas limitations of available niche space stabilize the second regime.

CF pathogens drive PEx dynamics in hierarchical, but not in flat community organization

The virulence of pathogenic bacteria depends on microbial interactions and the biochemistry of the microenvironment among other factors. For example, Pseudomonas aeruginosa tightly regulates biofilm formation, as well as the production of siderophores and exotoxins based on iron availability and oxygen levels 56 , 57 . Moreover, the fermentation products 2,3-butanediol and lactic acid produced by anaerobic members of the CF microbiome were reported to trigger quorum-sensing and further virulence 58 , 59 . Conversely, synergistic interactions such as metabolic cross-feeding affect pathogen growth and lower the tolerance of P. aeruginosa to antimicrobial treatment independent of intrinsic antibiotic resistance profiles 60 , 61 .

The insight that bacterial organization appeared markedly distinct in the identified PEx types raised the important question of whether microbiome organization could modulate pathogen importance or interfere with treatment outcomes in a foreseeable manner. As a first step, page rank was used as a statistical descriptor for network importance to compare the importance of conventional pathogens, strictly anaerobic, facultatively anaerobic, and strictly aerobic taxa in the community. We found that CF pathogens were differentially important for the CF community, displaying significantly higher page rank in hierarchical than in flat community organization (Fig.  4A ). Next, pathogen dynamics in different community organizations were assessed using time series information. Previously, we demonstrated that stochastic ecological processes can be distinguished from interaction-driven processes by Fourier spectra inferred from the abundance changes of microbiota 62 . Here, the spectrum of every ASV per PEx time series was determined, and their noise color was inferred. White color indicated stochastic, whereas pink noise pointed to self-organized underlying processes. We observed that pathogens exhibited interaction-driven dynamics (pink noise) in steep hierarchies only (Fig.  4B ). In flat anaerobe-dominated organization, pathogens instead showed stochastic behavior (white noise, AN2) or else anaerobic taxa  dominated community dynamics (AN1). These results supported the hypothesis that CF pathogen activity depends on the community background and its positioning within (Fig.  S7 ). Indeed, in graph theory it was demonstrated repeatedly that the topology of interaction networks was intimately linked with its dynamics 63 , 64 , 65 . Moreover, the particular configuration of microbial interactions has also been identified as a key element for robust composition forecasting 66 . In complex networks, interaction topology not only determined the importance of hubs for systems dynamics 20 , but also controllability of dynamics supporting our conclusions 67 .

A Community importance of organisms by ASV groups in different community backgrounds (C, CF pathogens; A, strict aerobes; F, facultative anaerobes; N, strict anaerobes). Page rank of nodes in co-occurrence networks for three PEx types (PAT, AN1, AN2) are presented ( \(n=589\) networks). Pathogens are significantly more important than other ASV groups in pathogen-dominated microbiomes; aerobic community members are significantly less important in AN1 (two-sided Wilcoxon, PAT \({{{{{{\rm{p}}}}}}}_{{{{{\rm{C}}}}\;{{{\rm{vs}}}}\;{{{\rm{N}}}}}} \, < \, 7.4{{{{{\rm{e}}}}}}-8\) , \({{{{{{\rm{p}}}}}}}_{{{{{\rm{C}}}}\;{{{\rm{vs}}}}\;{{{\rm{F}}}}}} \, < \, 6.1{{{{{\rm{e}}}}}}-11\) , \({{{{{{\rm{p}}}}}}}_{{{{{\rm{C}}}}\;{{{\rm{vs}}}}\;{{{\rm{A}}}}}} \, < \, 2.8{{{{{\rm{e}}}}}}-9\) ; AN1 \({{{{{{\rm{p}}}}}}}_{{{{{\rm{C}}}}\;{{{\rm{vs}}}}\;{{{\rm{N}}}}}} \, < \, 3.0{{{{{\rm{e}}}}}}-2\) , \({{{{{{\rm{p}}}}}}}_{{{{{\rm{C}}}}\;{{{\rm{vs}}}}\;{{{\rm{F}}}}}} \, < \, 3.1{{{{{\rm{e}}}}}}-1\) , \({{{{{{\rm{p}}}}}}}_{{{{{\rm{C}}}}\;{{{\rm{vs}}}}\;{{{\rm{A}}}}}} \, < \, 1.8{{{{{\rm{e}}}}}}-6\) ). No significant differences were detected in AN2 (two-sided Wilcoxon, \({{{{{{\rm{p}}}}}}}_{{{{{\rm{C}}}}\;{{{\rm{vs}}}}\;{{{\rm{N}}}}}} \, < \, 8.4{{{{{\rm{e}}}}}}-2\) , \({{{{{{\rm{p}}}}}}}_{{{{{\rm{C}}}}\;{{{\rm{vs}}}}\;{{{\rm{F}}}}}} \, < \, 3.4{{{{{\rm{e}}}}}}-1\) , \({{{{{{\rm{p}}}}}}}_{{{{{\rm{C}}}}\;{{{\rm{vs}}}}\;{{{\rm{A}}}}}} \, < \, 2.2{{{{{\rm{e}}}}}}-1\) ). B Log2-change of noise colors generated by pathogenic and anaerobic organisms in three PEx types (PAT, AN1, AN2). Noise color was assessed by ASV, \(n=424\) were significant. The log2-ratio of pathogens to anaerobes (pat/an) associated with white or pink dynamics is plotted; green dashed line depicts shift of driver organisms (pathogens if \({{{{{\rm{y}}}}}} > 0{{{{{\rm{;}}}}}}\) anaerobes if \({{{{{\rm{y}}}}}} \, < \, 0\) ). White noise is the repercussion of stochastic time dynamics; pink noise the result of self-organized processes. CF pathogens displayed (self-)organized behavior in PAT only ( \({{{{{{\rm{y}}}}}}}_{{{{{{\rm{pink}}}}}}} \, > \, 0\) ), and stochastic behavior ( \({{{{{{\rm{y}}}}}}}_{{{{{{\rm{white}}}}}}} \, > \, 0\) , AN2) or were irrelevant ( \({{{{{{\rm{y}}}}}}}_{{{{{{\rm{white}}}}}}} < \, 0,\,{{{{{{\rm{y}}}}}}}_{{{{{{\rm{pink}}}}}}} < 0\) , AN1) in anaerobic communities. Boxes in A , B depict the interquartile range; the horizontal line represents the median value. Whiskers extend to the minimum and maximum values, excluding outliers, defined as 1.5 times the interquartile range. C – E Simulations of targeted pathogen removal from data-derived co-occurrence networks. The most abundant pathogen in the sample was removed from the largest component and the resulting community perturbation was assessed ( \(n=313\) networks). Probability densities of the effects are presented: C the relative size reduction (number of nodes) of the largest component; D the relative increase of modularity; and E the breaking of the largest component into smaller, unconnected components (relative difference of components). Hierarchical community organization in PAT is more strongly disrupted by focal treatment than is flat organization in AN1 and AN2. Source data for Fig. 4 are provided in the Source Data file.

To clarify the relevance of community organization for PEx treatment strategies, we asked whether community (network) organization could influence pathogen importance and modulate treatment outcomes. Recently, it was shown that dynamical fluctuation in response to external damage depended on the local network architecture of single-layered and multiplex networks 63 , 64 , 65 . In an analog, simplified approach, we modeled the response of our empirical co-occurrence networks to focal depletion of the dominant pathogen by antibiotic treatment. We hypothesized that community organization affected the degree of network disruption and, consequently, community dynamics and likely treatment outcomes. In the model, the most abundant pathogen was removed from the major component of 313 networks ( \({{{{{{\rm{NWs}}}}}}}_{{{{{{\rm{PAT}}}}}}}=152\) , \({{{{{{\rm{NWs}}}}}}}_{{{{{{\rm{AN}}}}}}1}=53\) , \({{{{{{\rm{NWs}}}}}}}_{{{{{{\rm{AN}}}}}}2}=108\) ) and resulting network disruption was assessed by monitoring modularity change, breakup into subcomponents, and size reduction after single node removal (Fig.  4C–E ). While total pathogen removal may not always be achieved in practice, microbial interactions were expected to abate together with strain abundance. Here we modeled the extreme case for the purpose of hypothesis testing. We found that pathogen elimination from steeper background hierarchies resulted in significantly stronger topology disruption supported by all three topological parameters (Kolmogorov-Smirnov statistic in Table  S3 ). We observed stronger change in modularity, increased disruption into unconnected subcomponents, as well as more pronounced size loss of the biggest connected component. Indeed, the depletion of the same organism resulted in divergent effects for the overall community architectures. The markedly different outcomes might serve as indicators for the degree of niche rearrangement after antibiotic treatment. We hypothesized that maintained niche accessibility should benefit repopulation after depletion, while niche reorganization could instigate the establishment of different community configurations. Although these data-derived hypotheses call for rigorous experimental testing, they are in line with previous clinical and experimental observations reporting altered responses of focal organisms to antibiotic treatment in different background communities 68 , 69 . In fact, response to antimicrobials may be recognized as an emergent property of the entire microbiome 70 .

We concluded that the relevance of CF pathogens for microbial community dynamics and, by extension, likely also clinical course, was crucially shaped by community organization of the CF microbiome. Moreover, targeted treatment of pathogens resulted in distinct responses as a function of microbiome hierarchy, i.e., steep or flat community background.

The importance of airway microbial community dynamics in CF

Our study on the human lung microbiome showcases the importance of community organization for understanding microbiome dynamics in homeostasis and dysbiosis. Using CF as a model disease, we employed functional/ecological coarse-graining to analyze both temporal and organizational aspects of pulmonary infection and its relevance for therapy outcomes.

We identified two archetypes of dysbiosis in the CF lung (driven by pathogens or anaerobes), characterized their community structures, and discussed stabilizing factors in the context of current graph and ecological theory. It is important to realize that the identified ecological features can cancel out if analyzed cross-sectionally due to their antagonistic nature. This might explain the difficulties with establishing robust predictors for PEx thus far 7 .

We modeled the focal depletion of the most abundant pathogen in empirical co-occurrence networks and recognized that distinct background communities shaped the outcome of this treatment simulation. We concluded that the relevance of pathogenic taxa for microbiome dynamics, disease progression, and treatment effect is systematically linked to the organization of the background microbiome.

Our insights are limited to the ecological dynamics in airway microbiota observed in 11 adult subjects. Despite the large dataset investigated (880 samples, which comprise a tidy and comparable subset of a collection incorporating >21 patient years of near-daily sampling), this study cannot possibly reveal the full complexity of airway microbial ecology in CF. For example, more research is required to determine applicability to children with CF. Furthermore, none of the subjects in our study were receiving CFTR modulator therapy at the time of sample collection. How this therapy will impact CF airway microbiology is the subject of on-going studies. Nevertheless, we believe our model and the observations made in this study contribute to generating novel hypotheses regarding CF lung pathology, thereby building theory for targeted dysbiosis management, enhancing antibiotic stewardship, and advancing personalized medicine.

Study cohort, sample collection, and sample inclusion criteria

This observational prospective cohort study complied with all ethical regulations and was approved by the Institutional Review Board of the University of Michigan Medical School (HUM00037056) on March 17, 2011 (and renewed annually).

To obtain informed consent from participants, individuals who had previously expressed interest in participating in the study met face-to-face with a member of the study team before or after a regularly scheduled clinic appointment. The study team member reviewed the consent form with the individual in its entirety and answered any questions. Signatures of the individual and study team member were obtained on two copies of the consent form, one to go home with the individual and the other for study records. Five female and six male adult persons with CF were included in a balanced study design (sex determined by self-report) in this research. The study cohort is further characterized in Table  1 . Expectorated sputum was collected as part of a long-term study of CF airway microbiota between 2011 and 2020. Subjects collected daily sputum samples at home, which were stored at either 4 °C for up to 35 days (mean 13 days), or −20 °C for up to 56 days (mean 25 days), before shipment to the University of Michigan on ice packs or dry ice for immediate storage at −80 °C. Electronic medical records were reviewed for subject demographic and clinical data. Among the 880 sputum samples and DNA sequences included in this study, 283 were reported previously (NCBI BioProjects PRJNA520924 and PRJNA611611) 24 , 25 . Sample inclusion criteria applied for downstream analyses are detailed in Tables  S1 and S2 .

DNA extraction

Sputum samples were thawed on ice and homogenized with 10% Sputolysin (MilliporeSigma, Burlington, MA, USA). Samples were treated with bacterial lysis buffer (Roche Diagnostics Corp., Indianapolis, IN, USA), lysozyme (MilliporeSigma), and lysostaphin (MilliporeSigma) as previously described 31 , followed by mechanical disruption by glass bead beating and digestion in proteinase K (Qiagen Sciences, Germantown, MD, USA). DNA was extracted and purified using the MagNA Pure nucleic acid purification platform (Roche Diagnostics Corp., Indianapolis, IN, USA) according to the manufacturer’s protocol.

Sequencing controls, protocol and taxonomic annotation

DNA libraries were prepared by the University of Michigan Microbial Systems Molecular Biology Laboratory. Human Microbiome Project (HMP) or Zymo (Zymo Research, Irvine, CA, USA) mock community standards were included on each sequencing plate. Negative water controls were included on each sequencing run, and reagent controls were prepared and sequenced with each new lot of reagents used in DNA extraction. In brief, the V4 region of the bacterial 16 S rRNA gene was amplified using touchdown PCR with barcoded dual-index primers (forward primer GTGCCAGCMGCCGCGGTAA, reverse primer TAATCTWTGGGVHCATCAGG 71 ). Each PCR reaction was comprised of 1 μl of DNA template plus 4 μM equimolar primer set (5 μl), Accuprime High-Fidelity Taq (0.15 μl), 10× Accuprime PCR II buffer (2 μl), sterile PCR-grade water (11.85 μl). Touchdown PCR was performed consisting of 2 min at 95 °C, followed by 20 cycles of 95 °C for 20 s, 60 °C (starting from 60 °C, the annealing temperature decreased 0.3 °C each cycle) for 15 s, and 72 °C for 5 min, followed by 20 cycles of 95 °C for 20 s, 55 °C for 15 s, and 72 °C for 5 min and a final 72 °C for 10 min. The resulting amplicon libraries were normalized using a SequelPrep normalization plate kit (Life Technologies, catalog no. A10510-01), and concentrations measured using a Kapa Biosystems Library Quantification Kit (Kapa Biosystems, Wilmington MA) prior to being sequenced on an Illumina sequencing platform using a MiSeq Reagent Kit V2 (Illumina Inc., San Diego, CA, USA). The final load concentration was 4.0–5.5 pM with a 15% PhiX spike to add diversity, resulting in approximately 2x250bp reads (minimum depth 1031, average depth 15408.76 reads).

Annotation was performed using the dada2 pipeline in R according to the “Atacama soil microbiome” tutorial ( https://docs.qiime2.org/2021.11/tutorials ) using SILVA v138 for taxonomic assignments 72 . Samples were processed separately by subject through sample inference to reduce batch effects introduced across multiple sequencing runs, then merged for the remaining processing steps. The dataset was denoised removing all ASVs with <0.0075% average abundance across all samples as previously described 73 and subsequently rarified using R package vegan 74 . Sequencing data, taxonomic information and clinical metadata were organized in phyloseq objects for further analysis 75 . Sequencing error rates were determined by comparing 43 mock community profiles to reference sequences in mothur (v1.48) 71 using the seq.error command, which measures error as the sum of mismatches to the reference divided by the sum of bases in the query. In 24 sequencing runs, the median mock community error rate was 0.037% (range 0.012–0.690%).

Community typing with Dirichlet multinomial mixture models

To stratify representative community classes across subjects, Dirchlet multinomial mixtures (DMM) were inferred employing ASV groups as the taxonomic level 39 . Counts from ASVs were summarized into five groups: CF pathogens, strict anaerobes, facultative anaerobes, strict aerobes and unknown according to the oxygen requirements of the respective taxon 37 . Next, the total dataset was subject-stratified to avoid bias. Thirty-six random data subsets with 650 samples each were generated by sampling with replacement from the total data collection. Subsequently, models with 1-25 DMMs were inferred stepwise for each subset using the R package Dirichlet Multinomial 76 . Laplace approximation, BIC, and AIC were queried independently to identify the optimal number of DMMs.

Variance testing and ordination

To quantify the impact of covariates on the lung microbiome, PERMANOVA was performed on rarified ASV data and the identical data were remodeled by non-standard sample descriptors 74 . Bray-Curtis distance was employed for ASVs, and Euclidian distance for scaled and centered non-standard descriptors. The model was designed to test the marginal effects of the individual covariates (function adonis2(), parameter setting by = margin). For comparison, effect sizes ( \({\omega }^{2}\) ) of the covariates were calculated using the adonis_omegaSq() function from the MicEco package 77 . Principal component analysis was performed on scaled and centralized sample descriptors in R. Sample descriptors included Shannon diversity, Chao1 richness, relative abundance of the most abundant CF pathogen, the ratio of CF pathogen counts to counts from anaerobic taxa and the classification to a particular DMM community class.

PEx type clustering and sample classification

Hierarchical k-mer clustering of samples was conducted on the first three principal components of the ordinated sample data using the package pheatmap 78 . Pearson correlation was employed as a similarity measure. Next, an \({\chi }^{2}\) contingency test was used to identify the best k for the classified sputum samples. Subsequently, entire PEx time series and networks were assigned to a single k-mer cluster by majority vote of the included samples. Two PEx time series were excluded from further analysis, because frequent type transitions prevented a conclusive association to a single PEx type. Detailed inclusion criteria are explained in Tables  S1 and S2 .

Statistical modeling of time behavior

Linear mixed effect models to determine the time dependency of Shannon diversity and Chao1 richness were built using time groups (<24 days and 24–60 days before PEx antibiotic treatment; PEx types PAT and AN1) or 5-day intervals (PEx type AN2) as fixed effects and subject, age group, as well as PEx cycle as random effects (package lmerTest ) 79 . Standardized effect sizes ( \({\omega }^{2}\) ) of predictors were calculated using the package effect size 80 .

ANCOVA models were implemented using time groups (<24 days and 24–60 days before PEx treatment) as categorical predictors, sampling distance as numerical covariate, and Aitchison distance as dependent variable. Aitchison distances were calculated using R package robCompositions 81 .

Graphical representations of boxplots and regression models were generated using ggplot2 82 and ggpubr 83 .

Co-occurrence network inference and network statistics

Co-occurrence networks were inferred from 20 samples collected on consecutive days with SparCC in a sliding window along each PEx time series 84 . Missing samples were imputed using R package seqtime 62 and jump size for the sample window was set to 1.

For downstream analysis on the largest network components, only strong ( \(\rho \, > \, |0.2|\) ) and statistically significant ( \({{{{{\rm{p}}}}}} \, < \, 0.01\) after FDR correction) co-occurrence edges were included, as well as networks with < 10 imputed samples. Topological properties were assessed using R package igraph 49 .

Node degree and node clustering distributions were calculated across all networks classified in the same PEx type. To identify power laws and their respective slopes, linear regressions were performed on log-transformed data using R package ggplot2 82 .

For comparing the effect size of PEx clusters with covariates Shannon and Chao1 on graph topology, we first calculated clustering coefficient, betweenness centrality, node counts, and edge counts for each largest component and averaged Shannon and Chao1 of all samples included for inference of the respective network. Next, we implemented independent LMMs, corrected for subject and assessed the effects sizes (partial \({\eta }^{2}\) ) as described previously.

To quantify the presence of certain ASVs in top hierarchical network positions, we first selected positions with the relative highest degree (>90% of all degree values) and relative lowest clustering (<10% of all clustering coefficients). Subsequently, the frequency of ASVs on these positions was counted, normalized, and ranked. All calculations were performed in R.

Noise analysis of ASV time behavior

For each PEx time series, noise colors of participating ASVs were inferred using seqtime as previously described 62 . In short, missing samples were interpolated, and rounded to counts, negative interpolation values were set to 0 counts. Subsequently, the wrapper function identifyNoisetypes() performed a spectral density estimate and calculated a linear fit to the resulting periodogram (log frequency vs log spectral density) of the ASV time series. According to the slope of the fit, ASV time series were classified into categorical noise color groups. Noise colors were plotted as ratios of pathogens vs anaerobic ASVs with similar color (relative abundances) in the same sample.

Pathogen removal

To identify the dominant pathogen by network, the subset of samples used to infer the individual co-occurrence network was queried and the pathogen with highest cumulative abundance was selected. Only networks with the dominant pathogen locating to the largest component were used for further analysis. We calculated modularity, the number of unconnected components and node size of the largest component independently for each co-occurrence network before and after pathogen removal. All parameters were normalized to the corresponding value before node removal. Density distributions of the normalized parameters were scaled and plotted with ggplot2 function geom_density() 82 . To test for difference of the cumulative parameter distributions, two-sided Kolmogorov–Smirnov tests were performed (ks.test(), stats package) 85 .

Statistics and reproducibility

The study data were collected in an exploratory, prospective cohort study design. No statistical method was used to predetermine sample size. Subject inclusion criteria are described in the main text; samples included to individual analyses are detailed in tables  S1 , S2 . The experiments were not randomized. The Investigators were not blinded to allocation during experiments and outcome assessment.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The generated sequencing data (FASTq files) are available as NCBI BioProjects under the accession numbers PRJNA987026 , PRJNA520924 , and PRJNA611611 . Moreover, data are presented disaggregated by sex of the sample donor for every figure in the Source Data file. Furthermore, a list of ASVs as they appear in ASV groups and PEx types was added to Supplementary information/Source Data file.  Source data are provided with this paper.

Code availability

The R script collection and input data for this study are hosted at GitHub https://github.com/swidder/pex_types 86 . Large input data for code execution are hosted at Zenodo [ https://zenodo.org/records/11109986 ].

de Boer, K. et al. Exacerbation frequency and clinical outcomes in adult patients with cystic fibrosis. Thorax 66 , 680–685 (2011).

Article   PubMed   Google Scholar  

Choi, H. & Chalmers, J. D. Bronchiectasis exacerbation: a narrative review of causes, risk factors, management and prevention. Ann. Transl. Med. 11 , 25 (2023).

Article   CAS   PubMed   Google Scholar  

Wedzicha, J. A. & Seemungal, T. A. COPD exacerbations: defining their cause and prevention. Lancet 370 , 786–796 (2007).

Article   PubMed   PubMed Central   Google Scholar  

Flume, P. A. et al. Cystic fibrosis pulmonary guidelines: treatment of pulmonary exacerbations. Am. J. Respir. Crit. Care Med. 180 , 802–808 (2009).

Shteinberg, M. & Taylor-Cousar, J. L. Impact of CFTR modulator use on outcomes in people with severe cystic fibrosis lung disease. Eur. Respir. Rev. 29 , https://doi.org/10.1183/16000617.0112-2019 (2020).

Lopes-Pacheco, M. CFTR modulators: the changing face of cystic fibrosis in the era of precision medicine. Front. Pharm. 10 , 1662 (2019).

Article   CAS   Google Scholar  

Thornton, C. S., Acosta, N., Surette, M. G. & Parkins, M. D. Exploring the cystic fibrosis lung microbiome: making the most of a sticky situation. J. Pediatr. Infect. Dis. Soc. 11 , S13–S22 (2022).

Article   Google Scholar  

Levy, M., Kolodziejczyk, A. A., Thaiss, C. A. & Elinav, E. Dysbiosis and the immune system. Nat. Rev. Immunol. 17 , 219–232 (2017).

Walker, A. W. & Hoyles, L. Human microbiome myths and misconceptions. Nat. Microbiol. 8 , 1392–1396 (2023).

Natalini, J. G., Singh, S. & Segal, L. N. The dynamic lung microbiome in health and disease. Nat. Rev. Microbiol. 21 , 222–235 (2023).

Agusti, A. et al. Precision medicine in airway diseases: moving to clinical practice. Eur. Respir. J. 50 , https://doi.org/10.1183/13993003.01655-2017 (2017).

Dmitrijeva, M. et al. Strain-Resolved Dynamics of the Lung Microbiome in Patients with Cystic Fibrosis. mBio 12 , https://doi.org/10.1128/mBio.02863-20 (2021).

Dickson, R. P., Erb-Downward, J. R. & Huffnagle, G. B. Homeostasis and its disruption in the lung microbiome. Am. J. Physiol. Lung Cell Mol. Physiol. 309 , L1047–1055, (2015).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Fodor, A. A. et al. The adult cystic fibrosis airway microbiota is stable over time and infection type, and highly resilient to antibiotic treatment of exacerbations. PLoS One 7 , e45001 (2012).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Carmody, L. A. et al. The daily dynamics of cystic fibrosis airway microbiota during clinical stability and at exacerbation. Microbiome 3 , 12 (2015).

Cuthbertson, L. et al. Lung function and microbiota diversity in cystic fibrosis. Microbiome 8 , 45 (2020).

Si, J., Choi, Y., Raes, J., Ko, G. & You, H. J. Sputum bacterial metacommunities in distinguishing heterogeneity in respiratory health and disease. Front. Microbiol. 13 , 719541 (2022).

Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front Microbiol. 5 , 219 (2014).

Wei, Z. et al. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat. Commun. 6 , 8413 (2015).

Article   ADS   CAS   PubMed   Google Scholar  

Harush, U. & Barzel, B. Dynamic patterns of information flow in complex networks. Nat. Commun. 8 , 2181 (2017).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Lam, T. J. & Ye, Y. Meta-analysis of microbiome association networks reveal patterns of dysbiosis in diseased microbiomes. Sci. Rep. 12 , 17482 (2022).

Palla, G., Barabasi, A. L. & Vicsek, T. Quantifying social group evolution. Nature 446 , 664–667 (2007).

Long, C. et al. Structured community transitions explain the switching capacity of microbial systems. Proc. Natl Acad. Sci. USA 121 , e2312521121 (2024).

Caverly, L. J. et al. Measures of cystic fibrosis airway microbiota during periods of clinical stability. Ann. Am. Thorac. Soc. 16 , 1534–1542 (2019).

Lu, J. et al. Parallel analysis of cystic fibrosis sputum and saliva reveals overlapping communities and an opportunity for sample decontamination. mSystems 5 , e00296-20 (2020).

Carmody, L. A. et al. Changes in airway bacterial communities occur soon after initiation of antibiotic treatment of pulmonary exacerbations in cystic fibrosis. J. Cyst. Fibros. 21 , 766–768 (2022).

Thornton, C. S. et al. Quantifying variation in home spirometry in people with cystic fibrosis during baseline health, and associations with clinical outcomes. J. Cyst. Fibros . 23 , 321–328 (2023).

Widder, S. et al. Association of bacterial community types, functional microbial processes and lung disease in cystic fibrosis airways. ISME J. 16 , 905–914 (2022).

Carmody, L. A. et al. Changes in cystic fibrosis airway microbiota at pulmonary exacerbation. Ann. Am. Thorac. Soc. 10 , 179–187 (2013).

Carmody, L. A. et al. Fluctuations in airway bacterial communities associated with clinical states and disease stages in cystic fibrosis. PLoS One 13 , e0194060 (2018).

Zhao, J. et al. Decade-long bacterial community dynamics in cystic fibrosis airways. Proc. Natl Acad. Sci. USA 109 , 5809–5814 (2012).

Quinn, R. A. et al. Ecological networking of cystic fibrosis lung infections. NPJ Biofilms Microbiomes 2 , 4 (2016).

Caverly, L. J. & LiPuma, J. J. Good cop, bad cop: anaerobes in cystic fibrosis airways. Eur. Respir. J. 52 , https://doi.org/10.1183/13993003.01146-2018 (2018).

Caverly, L. J. & LiPuma, J. J. Cystic fibrosis respiratory microbiota: unraveling complexity to inform clinical practice. Expert Rev. Respir. Med. 12 , 857–865 (2018).

Thornton, C. S., Caverly, L. J. & LiPuma, J. J. Coming up for air: the role of anaerobes in cystic fibrosis. Ann. Am. Thorac. Soc. 19 , 713–716 (2022).

Thornton, C. S. et al. Airway bacterial community composition in persons with advanced cystic fibrosis lung disease. J. Cyst. Fibros. 22 , 623–629 (2023).

Bergey, D. H. & Holt, J. G. Bergey’s Manual of Systematic Bacteriology . (Springer, New York, NY, 2005).

Suzuki, Y. & Economo, E. P. From species sorting to mass effects: spatial network structure mediates the shift between metacommunity archetypes. Ecography 44 , 715–726 (2020).

Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS One 7 , e30126 (2012).

Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30 , R1176–R1188 (2020).

Layeghifard, M. et al. Microbiome networks and change-point analysis reveal key community changes associated with cystic fibrosis pulmonary exacerbations. NPJ Biofilms Microbiomes 5 , 4 (2019).

Stressmann, F. A. et al. Does bacterial density in cystic fibrosis sputum increase prior to pulmonary exacerbation? J. Cyst. Fibros. 10 , 357–365 (2011).

Raghuvanshi, R. et al. High-resolution longitudinal dynamics of the cystic fibrosis sputum microbiome and metabolome through antibiotic therapy. mSystems 5 , 3 (2020).

Ontiveros, V. J., Capitan, J. A., Casamayor, E. O. & Alonso, D. The characteristic time of ecological communities. Ecology 102 , e03247 (2021).

Thebault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329 , 853–856 (2010).

Girvan, M. & Newman, M. E. Community structure in social and biological networks. Proc. Natl Acad. Sci. USA 99 , 7821–7826 (2002).

Article   ADS   MathSciNet   CAS   PubMed   PubMed Central   Google Scholar  

Freeman, L. C. Centrality in social networks i: conceptual clarification. Soc. Netw. 1 , 215–239 (1978).

Barabasi, A. L. & Oltvai, Z. N. Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5 , 101–113 (2004).

Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal Complex Syst . 1695, 1–9 (2006).

Venkataraman, A. et al. Application of a neutral community model to assess structuring of the human lung microbiome. mBio 6 , e02284-14 (2015).

Morris, A. et al. Comparison of the respiratory microbiome in healthy nonsmokers and smokers. Am. J. Respir. Crit. Care Med. 187 , 1067–1075 (2013).

Sloan, W. T. et al. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ. Microbiol. 8 , 732–740 (2006).

Hubbell, S. The unified neutral theory of biodiversity and biogeography (Princeton University Press, 2001).

Leibold, M. et al. The metacommunity concept: a framework for multi-scale community ecology. Ecol. Lett. 7 , 601–613 (2004).

Gonze, D., Lahti, L., Raes, J. & Faust, K. Multi-stability and the origin of microbial community types. ISME J. 11 , 2159–2166 (2017).

Gaines, J. M. et al. Regulation of the Pseudomonas aeruginosa toxA, regA and ptxR genes by the iron-starvation sigma factor PvdS under reduced levels of oxygen. Microbiology 153 , 4219–4233 (2007).

Berlutti, F. et al. Iron availability influences aggregation, biofilm, adhesion and invasion of Pseudomonas aeruginosa and Burkholderia cenocepacia. Int. J. Immunopathol. Pharm. 18 , 661–670 (2005).

Venkataraman, A., Rosenbaum, M. A., Werner, J. J., Winans, S. C. & Angenent, L. T. Metabolite transfer with the fermentation product 2,3-butanediol enhances virulence by Pseudomonas aeruginosa. ISME J. 8 , 1210–1220 (2014).

Phan, J., Gallagher, T., Oliver, A., England, W. E. & Whiteson, K. Fermentation products in the cystic fibrosis airways induce aggregation and dormancy-associated expression profiles in a CF clinical isolate of Pseudomonas aeruginosa. FEMS Microbiol. Lett. 365 , fny082 (2018).

Flynn, J. M. et al. Disruption of cross-feeding inhibits pathogen growth in the sputa of patients with cystic fibrosis. mSphere 5 , e00081-20 (2020).

Adamowicz, E. M., Flynn, J., Hunter, R. C. & Harcombe, W. R. Cross-feeding modulates antibiotic tolerance in bacterial communities. ISME J. 12 , 2723–2735 (2018).

Faust, K. et al. Signatures of ecological processes in microbial community time series. Microbiome 6 , 120 (2018).

Coghi, F., Radicchi, F. & Bianconi, G. Controlling the uncertain response of real multiplex networks to random damage. Phys. Rev. E 98 , 062317 (2018).

Article   ADS   CAS   Google Scholar  

Bianconi, G. Fluctuations in percolation of sparse complex networks. Phys. Rev. E 96 , 012302 (2017).

Article   ADS   PubMed   Google Scholar  

Bianconi, G. Rare events and discontinuous percolation transitions. Phys. Rev. E 97 , 022314 (2018).

Daugaard, U., Munch, S. B., Inauen, D., Pennekamp, F. & Petchey, O. L. Forecasting in the face of ecological complexity: Number and strength of species interactions determine forecast skill in ecological communities. Ecol. Lett. 25 , 1974–1985 (2022).

Nepusz, T. & Vicsek, T. Controlling edge dynamics in complex networks. Nat. Phys. 8 , 568–573 (2012).

Hromada, S. & Venturelli, O. S. Gut microbiota interspecies interactions shape the response of Clostridioides difficile to clinically relevant antibiotics. PLoS Biol. 21 , e3002100 (2023).

Smith, A. L., Fiel, S. B., Mayer-Hamblett, N., Ramsey, B. & Burns, J. L. Susceptibility testing of Pseudomonas aeruginosa isolates and clinical response to parenteral antibiotic administration: lack of association in cystic fibrosis. Chest 123 , 1495–1502 (2003).

Bottery, M. J., Pitchford, J. W. & Friman, V. P. Ecology and evolution of antimicrobial resistance in bacterial communities. ISME J. 15 , 939–948 (2021).

Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ. Microbiol. 79 , 5112–5120 (2013).

Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13 , 581–583 (2016).

Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473 , 174–180 (2011).

Oksanen, J. et al. vegan: community ecology package. (2019).

McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8 , e61217 (2013).

Morgan, M. DirichletMultinomial: Dirichlet-multinomial mixture model machine learning for microbiome data. (2021).

Russel, J. MicEco R package. (2022).

Kolde, R. pheatmap: Pretty Heatmaps. (2019).

Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82 , 1–26 (2017).

Ben-Shachar, M. S., Lüdecke, S. & Makowski, D. effectsize: estimation of effect size indices and standardized parameters. J. Open Source Softw. 5 , 2815 (2020).

Article   ADS   Google Scholar  

Filzmoser, P., Hron, K. & Templ, M. Applied compositional data analysis. With worked examples in R . (Springer International Publishing, Cham, Switzerland, 2018).

Wickham, H. ggplot2: elegant graphics for data analysis . Springer-Verlag, New York (2016).

Kassambara, A. ggpubr: ‘ggplot2’ based publication ready plots . (2020).

Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8 , e1002687 (2012).

R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2013).

Widder, S. et al. Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis. https://doi.org/10.5281/zenodo.11110106 (2024).

Download references

Acknowledgements

We gratefully acknowledge the individuals who provided the sputum samples used in this study. This work was supported by National Institutes of Health grants R01HL136647 and R56HL126756 and Cystic Fibrosis Foundation grants LIPUMA13I0 and LIPUMA15P0 (J.J.L.). SW was supported by the Austrian Science Fund (FWF) Elise Richter project V585-B31. We thank Ginestra Bianco for the critical discussion of the presented network models and Philipp Starkl for valuable feedback on figure design.

Author information

Authors and affiliations.

Department of Medicine 1, Research Division Infection Biology, Medical University of Vienna, 1090, Vienna, Austria

Stefanie Widder

Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA

Lisa A. Carmody, Linda M. Kalikin, Lindsay J. Caverly & John J. LiPuma

Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, 48109, USA

Kristopher Opron

You can also search for this author in PubMed   Google Scholar

Contributions

S.W. designed the overall study and analyses. J.J.L. and S.W. engaged in the acquisition of the financial support for the study leading to this publication. S.W. and K.O. performed the data analyses. J.J.L., L.A.C., L.J.C., L.M.K. planned the daily sputum study, coordinated with participants, and collected samples, meta- and 16 S rRNA gene amplicon sequencing data. S.W., J.J.L., K.O., L.A.C., and L.J.C. participated in discussions related to this work. S.W. and J.J.L. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Stefanie Widder .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Communications thanks Christopher van der Gast, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Peer review file, reporting summary, source data, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Widder, S., Carmody, L.A., Opron, K. et al. Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis. Nat Commun 15 , 4889 (2024). https://doi.org/10.1038/s41467-024-49150-y

Download citation

Received : 08 November 2023

Accepted : 23 May 2024

Published : 07 June 2024

DOI : https://doi.org/10.1038/s41467-024-49150-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

People with Certain Medical Conditions

This information is intended for a general audience. Healthcare professionals should see Underlying Medical Conditions Associated with Higher Risk for Severe COVID-19  for more detailed information.

What You Need to Know

• A person with any of the medical conditions listed below is more likely to get very sick with COVID-19. If you have one of these conditions, talk with your healthcare provider about how best to protect yourself from severe illness from COVID-19.
• The list below does not include all possible conditions that put you at higher risk of severe illness from COVID-19. If you have questions about a condition not included on this list, talk to your healthcare provider about how best to manage your condition and protect yourself from COVID-19.
• If you have symptoms consistent with COVID-19 and you are aged 50 years or older OR are at high risk of getting very sick, you may be eligible for treatment . Contact your healthcare provider and start treatment within the first 5-7 days after you first develop symptoms. You can also visit a Test to Treat location. Treatment can reduce your risk of hospitalization by more than 50% and also reduces the risk of death.
• Staying up to date with COVID-19 vaccines and following preventive measures for COVID-19  are important. This is especially important if you are older or have severe health conditions or more than one health condition, including those on the list below.
• Approved and authorized COVID-19 vaccines are safe and effective .
• Some people who are immunocompromised , or people with weakened immune systems, may get additional doses of COVID-19 vaccine.

Medical Conditions

Additional information on children and teens.

Based on the current evidence, a person with any of the conditions listed below is more likely to get very sick from COVID-19. This means that a person with one or more of these conditions who gets very sick from COVID-19 (has severe illness from COVID-19) is more likely to:

• Be hospitalized
• Need intensive care
• Require a ventilator to help them breathe

In addition:

• Older adults are at highest risk of getting very sick from COVID-19. More than 81% of COVID-19 deaths occur in people over age 65. The number of deaths among people over age 65 is 97 times higher than the number of deaths among people ages 18-29 years.
• A person’s risk of severe illness from COVID-19 increases as the number of underlying medical conditions they have increases.
• Studies have shown people from racial and ethnic minority groups are also dying from COVID-19 at younger ages. People in racial and ethnic minority groups are often younger when they develop chronic medical conditions and may be more likely to have more than one medical condition.
• People with disabilities are more likely than those without disabilities to have chronic health conditions, live in shared group (also called “congregate”) settings, and face more barriers in accessing health care . Studies have shown that some people with certain disabilities are more likely to get COVID-19 and have worse outcomes.

Staying up to date with COVID-19 vaccines and taking COVID-19 prevention actions  are important. This is especially important if you are older or have severe health conditions or more than one health condition, including those on this list. Learn more about how CDC develops COVID-19 vaccination recommendations. If you have a medical condition, learn more about Actions You Can Take .

• The conditions on this list are in alphabetical order. They are  not in order of risk.
• CDC completed a review for each medical condition on this list. This was done to ensure that these conditions met criteria for inclusion on this list. CDC conducts ongoing reviews of additional underlying conditions. If other medical conditions have enough evidence, they might be added to the list.
• Because we are learning more about COVID-19 every day, this list  does not include all medical conditions that place a person at higher risk of severe illness from COVID-19. Rare medical conditions, including many conditions that mostly affect children, may not be included on the list below. We will update the list as we learn more.
• A person with a condition that is not listed may still be at greater risk of getting very sick from COVID-19 than other people who do not have the condition. It is important that you talk with your healthcare provider about your risk .

Having cancer   can make you more likely to get very sick from COVID-19. Treatments for many types of cancer can weaken your body’s ability to fight off disease. At this time, based on available studies, having a history of cancer may increase your risk.

Get more information:

• COVID-19: What People with Cancer Should Know – National Cancer Institute

Chronic kidney disease

Having chronic kidney disease of any stage can make you more likely to get very sick from COVID-19.

• Chronic Kidney Disease
• National Kidney Foundation: Kidney Disease and COVID-19

Chronic liver disease

Having chronic liver disease can make you more likely to get very sick from COVID-19. Chronic liver disease can include alcohol-related liver disease, non-alcoholic fatty liver disease, autoimmune hepatitis, and cirrhosis (or scarring of the liver).

• Liver Disease
• American Liver Foundation: Your Liver and COVID-19

Chronic lung diseases

Having a chronic lung disease can make you more likely to get very sick from COVID-19. Chronic lung diseases can include:

• Asthma, if it’s moderate to severe
• Bronchiectasis (thickening of the lungs’ airways)
• Bronchopulmonary dysplasia (chronic lung disease affecting newborns)
• Chronic obstructive pulmonary disease (COPD), including emphysema and chronic bronchitis
• Having damaged or scarred lung tissue known as interstitial lung disease (including idiopathic pulmonary fibrosis)
• Pulmonary embolism (blood clot in the lungs)
• Pulmonary hypertension (high blood pressure in the lungs)
• People with Moderate to Severe Asthma
• American Lung Association: Controlling Chronic Lung Diseases Amid COVID-19
• Cystic fibrosis

Having cystic fibrosis, with or without lung or other solid organ transplant (like kidney, liver, intestines, heart, and pancreas) can make you more likely to get very sick from COVID-19.

• Cystic Fibrosis Foundation: CF and Coronavirus (COVID-19)

Dementia or other neurological conditions

Having neurological conditions, such as dementia, can make you more likely to get very sick from COVID-19.

• Alzheimer’s Association: COVID-19, Alzheimer’s and Dementia

Diabetes (type 1 or type 2)

Having either type 1 or type 2 diabetes can make you more likely to get very sick from COVID-19.

• American Diabetes Association: How COVID-19 Impacts People with Diabetes

Disabilities

People with some types of disabilities may be more likely to get very sick from COVID-19 because of underlying medical conditions, living in congregate settings, or systemic health and social inequities, including:

• People with any type of disability that makes it more difficult to do certain activities or interact with the world around them, including people who need help with self-care or daily activities
• People with attention-deficit/hyperactivity disorder (ADHD)
• People with cerebral palsy
• People with birth defects
• People with intellectual and developmental disabilities
• People with learning disabilities
• People with spinal cord injuries
• People with Down syndrome
• People with Disabilities

Heart conditions

Having heart conditions such as heart failure, coronary artery disease, cardiomyopathies, and possibly high blood pressure (hypertension) can make you more likely to get very sick from COVID-19.

• Heart Disease
• American Heart Association: COVID-19
• NHLBI Information and Resources on COVID-19

HIV infection

Having HIV (Human Immunodeficiency Virus) infection can make you more likely to get very sick from COVID-19.

• HIV Infection
• Interim Guidance for COVID-19 and Persons with HIV

Immunocompromised condition or weakened immune system

Some people are immunocompromised or have a weakened immune system because of a medical condition or a treatment for a condition. This includes people who have cancer and are on chemotherapy, or who have had a solid organ transplant, like a kidney transplant or heart transplant, and are taking medication to keep their transplant. Other people have to use certain types of medicines for a long time, like corticosteroids, that weaken their immune system. One example is called primary immunodeficiency. Being immunocompromised can make you more likely to get very sick from COVID-19 or be sick for a longer period of time.

People who are immunocompromised or are taking medicines that weaken their immune system may not be protected even if they are  up to date on their vaccines . Talk with your healthcare provider about what additional precautions may be necessary when respiratory viruses are causing a lot of  illness in your community . Additionally, people who are moderately or severely immunocompromised may get additional doses of updated COVID-19 vaccine. Because the immune response following COVID-19 vaccination may differ in people who are moderately or severely immunocompromised, specific guidance has been developed.

People who are moderately or severely immunocompromised, are aged 12 and older, and who weigh at least 88 pounds may be eligible to get Pemivibart (Pemgarda™) , a monoclonal antibody authorized to help protect against COVID-19. Pemgarda may provide another layer of protection against COVID-19 in addition to protection provided through vaccination, and can be given at least 2 weeks after receiving a COVID-19 vaccine. Pemgarda is not a treatment for COVID-19. Talk to your healthcare provider to see if Pemgarda is right for you.

• Types of Primary Immune Deficiency Diseases
• Jeffrey Modell Foundation
• Immune Deficiency Foundation
• Primary Immunodeficiency (PI)

Mental health conditions

Having mood disorders, including depression, and schizophrenia spectrum disorders can make you more likely to get very sick from COVID-19.

• National Institute of Mental Health (NIMH) Shareable Resources on Coping with COVID-19
• National Institute of Mental Health (NIMH) Depression
• Mood Disorders

Overweight and obesity

Overweight (defined as a  body mass index (BMI) is 25 kg/m 2  or higher, but under 30 kg/m 2 ), obesity (BMI is 30 kg/m 2  or higher, but under 40 kg/m 2 ), or severe obesity (BMI is 40 kg/m 2  or higher), can make you more likely to get very sick from COVID-19. The risk of severe illness from COVID-19 increases sharply with higher BMI.

• Overweight and Obesity
• Obesity, Race/Ethnicity, and COVID-19
• Obesity Action Coalition: COVID-19 and Obesity

Physical inactivity

People who do little or no physical activity are more likely to get very sick from COVID-19 than those who are physically active. Being physically active is important to being healthy. Get more information on physical activity and health, physical activity recommendations, how to become more active, and how to create activity-friendly communities:

• Physical Activity
• Physical Activity Guidelines for Americans , 2nd edition
• Move Your Way ®
• Active People, Healthy Nation SM : Strategies to Increase Physical Activity
• National Center on Health, Physical Activity and Disability – Building Healthy Inclusive Communities

Pregnant and recently pregnant people (for at least 42 days following end of pregnancy) are more likely to get very sick from COVID-19 compared with non-pregnant people.

• Pregnant and Recently Pregnant People

Sickle cell disease or thalassemia

Having hemoglobin blood disorders like sickle cell disease or thalassemia (inherited red blood cell disorders) can make you more likely to get very sick from COVID-19.

• Sickle Cell Disease
• Thalassemia

Smoking, current or former

Being a current or former cigarette smoker can make you more likely to get very sick from COVID-19. If you currently smoke, quit. If you used to smoke, don’t start again. If you’ve never smoked, don’t start.

• Smoking and Tobacco Use
• Tips From Former Smokers
• Health Benefits of Quitting Smoking

Solid organ or blood stem cell transplant

Having had a solid organ or blood stem cell transplant, which includes bone marrow transplants, can make you more likely to get very sick from COVID-19.

• Transplant Safety
• COVID-19 Resources for Transplant Community

Stroke or cerebrovascular disease

Having cerebrovascular disease, such as having a stroke which affects blood flow to the brain, can make you more likely to get very sick from COVID-19.

• COVID19 Stroke Podcast Series for Patients and Caregivers 

Substance use disorders

Having a substance use disorder (such as alcohol, opioid, or cocaine use disorder) can make you more likely to get very sick from COVID-19.

• How to Recognize a Substance Use Disorder
• Drug Overdose

Tuberculosis

Having tuberculosis (TB) can make you more likely to get very sick from COVID-19.

• Basic TB Facts
• Public Health Emergencies

People of all ages, including children, can get very sick from COVID-19. Children with underlying medical conditions are at increased risk for getting very sick compared to children without underlying medical conditions.

Current evidence suggests that children with medical complexity, with genetic, neurologic, or metabolic conditions, or with congenital heart disease can be at increased risk for getting very sick from COVID-19. Like adults, children with obesity, diabetes, asthma or chronic lung disease, sickle cell disease, or who are immunocompromised can also be at increased risk for getting very sick from COVID-19. Check out COVID-19 Vaccines for Children and Teens  for more information on vaccination information for children.

• COVID-19 Vaccines for Children and Teens

Continue medications and preventive care

• Continue your medicines and do not change your treatment plan without talking to your healthcare provider.
• Have at least a 30-day supply of prescription and non-prescription medicines. Talk to a healthcare provider, insurer, or pharmacist about getting an extra supply (i.e., more than 30 days) of prescription medicines, if possible, to reduce your trips to the pharmacy.
• Follow your current treatment plan (e.g., Asthma Action Plan , dialysis schedule, blood sugar testing, nutrition, and exercise recommendations) to keep your medical condition(s) under control.
• When possible, keep your appointments (e.g., vaccinations and blood pressure checks) with your healthcare provider. Check with your healthcare provider about safety precautions for office visits and ask about telemedicine or virtual healthcare appointment options.

To receive email updates about COVID-19, enter your email address:

Exit Notification / Disclaimer Policy

• The Centers for Disease Control and Prevention (CDC) cannot attest to the accuracy of a non-federal website.
• Linking to a non-federal website does not constitute an endorsement by CDC or any of its employees of the sponsors or the information and products presented on the website.
• You will be subject to the destination website's privacy policy when you follow the link.
• CDC is not responsible for Section 508 compliance (accessibility) on other federal or private website.

May 30, 2024

A general passion

Image credit: pgen.1011291

research article

Conserved signalling functions for Mps1, Mad1 and Mad2 in the Cryptococcus neoformans spindle checkpoint

Mps1-dependent phosphorylation of C-terminal Mad1 residues is a critical step in Cryptococcus spindle checkpoint signalling. 

Image credit: pgen.1011302

Recently Published Articles

• Pseudomonas copper resistance">Cross-regulation and cross-talk of conserved and accessory two-component regulatory systems orchestrate Pseudomonas copper resistance
• Growth deficiency in a mouse model of Kabuki syndrome 2 bears mechanistic similarities to Kabuki syndrome 1
• Anopheles gambiae : Insight into the cellular dynamics of a novel synthetic sex ratio distorter">Y chromosome shredding in Anopheles gambiae : Insight into the cellular dynamics of a novel synthetic sex ratio distorter

Current Issue May 2024

Adaptations to nitrogen availability drive ecological divergence of chemosynthetic symbionts

The importance of nitrogen availability in driving the ecological diversification of chemosynthetic symbiont species and the role that bacterial symbionts may play in the adaptation of marine organisms to changing environmental conditions.

Image credit: pgen.1011295

Paramutation at the maize pl1 locus is associated with RdDM activity at distal tandem repeats

pl1 paramutation depends on trans-chromosomal RNA-directed DNA methylation operating at a discrete cis-linked and copy-number-dependent transcriptional regulatory element.

Image credit: pgen.1011296

Research Article

Genomic analyses of Symbiomonas scintillans show no evidence for endosymbiotic bacteria but does reveal the presence of giant viruses

A multi-gene tree showed the three SsV genome types branched within highly supported clades with each of BpV2, OlVs, and MpVs, respectively.

Image credit: pgen.1011218

A natural bacterial pathogen of C . elegans uses a small RNA to induce transgenerational inheritance of learned avoidance

A mechanism of learning and remembering pathogen avoidance likely happens in the wild. 

Image credit: pgen.1011178

Spoink , a LTR retrotransposon, invaded D. melanogaster populations in the 1990s

Evidence of Spoink retrotransposon's horizontal transfer into D. melanogaster populations post-1993, suggesting its origin from D.willistoni .

Image credit: pgen.1011201

Comparison of clinical geneticist and computer visual attention in assessing genetic conditions

Understanding AI, specifically Deep Learning, in facial diagnostics for genetic conditions can enhance the design and utilization of AI tools.

Image credit: pgen.1011168

Maintenance of proteostasis by Drosophila Rer1 is essential for competitive cell survival and Myc-driven overgrowth

Loss of Rer1 induces proteotoxic stress, leading to cell competition and elimination ...

Image credit: pgen.1011171

Anthracyclines induce cardiotoxicity through a shared gene expression response signature

TOP2i induce thousands of shared gene expression changes in cardiomyocytes.

Image credit: pgen.1011164

New PLOS journals accepting submissions

Five new journals unified in addressing global health and environmental challenges are now ready to receive submissions: PLOS Climate , PLOS Sustainability and Transformation , PLOS Water , PLOS Digital Health , and PLOS Global Public Health

COVID-19 Collection

The COVID-19 Collection highlights all content published across the PLOS journals relating to the COVID-19 pandemic.

Submit your Lab and Study Protocols to PLOS ONE !

PLOS ONE is now accepting submissions of Lab Protocols, a peer-reviewed article collaboration with protocols.io, and Study Protocols, an article that credits the work done prior to producing and publishing results.

PLOS Reviewer Center

A collection of free training and resources for peer reviewers of PLOS journals—and for the peer review community more broadly—drawn from research and interviews with staff editors, editorial board members, and experienced reviewers.

Ten Simple Rules

PLOS Computational Biology 's "Ten Simple Rules" articles provide quick, concentrated guides for mastering some of the professional challenges research scientists face in their careers.

Welcome New Associate Editors!

PLOS Genetics welcomes several new Associate Editors to our board: Nicolas Bierne, Julie Simpson, Yun Li, Hongbin Ji, Hongbing Zhang, Bertrand Servin, & Benjamin Schwessinger

Expanding human variation at PLOS Genetics

The former Natural Variation section at PLOS Genetics relaunches as Human Genetic Variation and Disease. Read the editors' reasoning behind this change.

PLOS Genetics welcomes new Section Editors

Quanjiang Ji (ShanghaiTech University) joined the editorial board and Xiaofeng Zhu (Case Western Reserve University) was promoted as new Section Editors for the PLOS Genetics Methods section.

PLOS Genetics editors elected to National Academy of Sciences

Congratulations to Associate Editor Michael Lichten and Consulting Editor Nicole King, who are newly elected members of the National Academy of Sciences.

Harmit Malik receives Novitski Prize

Congratulations to Associate Editor Harmit Malik, who was awarded the Edward Novitski Prize by the Genetics Society of America for his work on genetic conflict. Harmit has also been elected as a new member of the American Academy of Arts & Sciences.

Publish with PLOS

• Submission Instructions
• Submit Your Manuscript

Connect with Us

• PLOS Genetics on Twitter
• PLOS on Facebook

Get new content from PLOS Genetics in your inbox

Thank you you have successfully subscribed to the plos genetics newsletter., sorry, an error occurred while sending your subscription. please try again later..

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Respir Med Case Rep

Case report: Three adult brothers with cystic fibrosis (delF508-delF508) maintain unusually preserved clinical profile in the absence of standard CF care

We present three cases in this report. Three adult brothers, homozygous for the delF508 cystic fibrosis mutation, have maintained an unusually preserved clinical condition even though they did not attend a CF Clinic during their childhood, do not attend a CF Clinic now, and do not follow standard CF care guidelines. The brothers use an alternative CF treatment regimen on which they have maintained normal lung function, height/weight, and bloodwork, and they utilize less than half the recommended dosage of pancreatic enzymes. The brothers culture only methicillin-sensitive Staphylococcus aureus, and have never cultured any other bacteria. Highly effective modulator therapies, such as elexacaftor/tezacaftor/ivacaftor, do not substantially reduce infection and inflammation in vivo in CF patients, and thus these three case reports are of special note in terms of suggesting adjunct therapeutic approaches. Finally, these three cases also raise important questions about standard CF care guidelines.

• • Three adult brothers, delF508 cystic fibrosis (CF) homozygotes, maintain unusually preserved clinical condition absent standard CF care.
• • An alternative CF treatment regimen has kept their lung function, weight/height, and lab parameters normal, with low pancreatic enzyme dose.
• • The brothers culture only methicillin-sensitive Staphylococcus aureus, and have never cultured any other bacteria.
• • Highly effective modulator therapies (HEMT) for CF do not substantially reduce infection and inflammation in vivo; these cases are thus of note.
• • These cases also raise important questions about standard CF care guidelines.

1. Introduction

Cystic fibrosis (CF) is a serious and life-shortening genetic disorder affecting approximately 70,000 persons worldwide [ 1 ]. Respiratory failure is the foremost cause of death in CF patients, and lung transplantation is often considered in end-stage CF disease. For those born with CF in the last five years, median predicted survival age is now 44, which is decades longer than survival rates in the recent past [ 2 ]. Indeed, new advances in CF modulator therapy and CF gene therapy may eventually provide a normal life expectancy for these individuals.

A key approach in fighting the ravages of CF while waiting for more advanced treatments to be developed has been to slow the inexorable decline in lung function. Typical rate of lung function decline in CF is approximately −1.2 to −1.6 FEV1% per year [ 3 ]. Rate of decline is strongly associated with type of CF mutation. The three most severe classes of CFTR, Classes I, II, and III, represent defects in protein production, protein processing, and protein regulation, respectively [ 4 ]. The most common CF-causing mutation is delF508, occurring in 70% of cases, which is a Class II mutation [ 5 ]. Being homozygous for the delF508 mutation confers a severe phenotype, including pancreatic insufficiency and a steeper rate of decline in lung function over time [ 6 ]. In the United States, it is estimated that approximately 50% of those with cystic fibrosis are homozygous for delF508 [ 7 ]. Standard clinical care for severe mutation cases is often aggressive, including but not limited to daily airway clearance, use of pancreatic enzymes at the level of 500-2,500 lipase units/kg/meal (and enteric feeding if adequate weight percentile cannot be maintained), common and repeated use of oral, inhaled and intravenous antibiotics, daily intake of water-miscible versions of fat-soluble vitamins, and quarterly CF Clinic visits where lung function parameters and cultures of lung bacteria and fungi are assessed [ 8 , 9 ]. Pulmonary exacerbations often result in hospitalization, which may occur one or more times per year, typically lasting 14–21 days and including intensive antibiotic treatment and chest physical therapy. Everyday treatment burden is high, with estimates of 2–3 hours per day, with adherence at an estimated 50% or less [ 10 ]. The mean annual cost of standard supportive CF care in the US in 2016 (in 2019 dollars), before CFTR modulator therapies, was estimated to average $77,143, with severe non-transplant cases experiencing multiple pulmonary exacerbations costing on average triple or quadruple that amount [ 11 ]. With the average cost of elexacaftor/tezacaftor/ivacaftor (Trikafta) treatment currently over $311,000 per year, average standard supportive CF care costs were expected to double in 2019 [ 12 ] and increase further over time, perhaps quadrupling, with wider adoption of that treatment by all eligible patients.

Here we report on three adult brothers who are delF508 homozygotes, and yet who have maintained an unusually preserved clinical profile in the absence of standard CF clinical care. At the time of this writing, Brother A is 23 years old, Brother B is 21 years old, and Brother C is 18 years old. They are full-blooded siblings.

2. Case reports

2.1. brother a.

Brother A, now aged 23, was born full-term weighing 10 lbs. 2 oz. to a carrier mother experiencing gestational diabetes who subsequently breastfed him. His weight percentile decreased significantly over time, and at 6 months, after a course of oral antibiotics for a suspected ear infection, he developed a severe Vitamin K deficiency manifesting in quarter-sized black bruises on his body, as well as Pseudo-Bartter Syndrome. He was hospitalized until IV fluids stabilized his condition and normalized his electrolytes. Vitamin K shots were also administered. At 9 months of age, he was diagnosed with cystic fibrosis, and the genetic mutation analysis identified him as a delF508 homozygote. Between the time of his hospitalization and his diagnosis, he suffered from malnutrition with accompanying protein edema and his weight percentile, which had been over 97th percentile when born, was under the 5th percentile adjusted for age and sex. Once started on pancreatic enzymes (CREON 5) after diagnosis, his weight percentile increased to approximately the 30th percentile.

Approximately one year after diagnosis, the parents of Brother A elected to depart from standard CF care, including an election to stop attending the CF Clinic, while continuing to be under the care of their family pediatrician. The treatment plan for the brothers is described in detail in a later section. The only prescription medicine taken during his childhood and continuing to this day remains CREON 5/6, with Brother A utilizing 4 CREON 5/6 per meal, less than half the lowest recommended dose for his weight. In the teen years, Brother A experienced three episodes of heat exhaustion requiring IV fluid stabilization in an emergency room, has had one endoscopic sinus cleaning for sinus pain at age 20, and also underwent an appendectomy for appendicitis at age 23, but otherwise has had no major clinical issues, though exhibiting digital clubbing. Brother A played ice hockey throughout his childhood and teen years. His height, weight, lung function, and lab results at age 23 are provided in Table 1 .

Clinical parameters, Brother A.

2.2. Brother B

Brother B, now aged 21, was born full-term, weighing 8 lbs. 8 oz., the mother supplementing with oral glutathione (GSH) during the pregnancy and subsequently breastfeeding him. Brother B has never attended a CF Clinic, was diagnosed at 2 weeks of age, and was under the care of the family's pediatrician only. Brother B's only prescription medication during his childhood was CREON 5/6, just as with Brother A, utilizing 4 capsules per meal. Brother B has never needed to be hospitalized or have surgery or antibiotics. While Brother B does not exhibit digital clubbing; when recovering from respiratory viruses, he does manifest a cough that lingers longer than it lingers for his brothers, though the cough ultimately resolves. Brother B played ice hockey in childhood and teen years, as well as participated in gymnastics, cross-country running, track and field, and weight-lifting. His height, weight, lung function, and lab results at age 21 are provided in Table 2 .

Clinical parameters, Brother B.

2.3. Brother C

Brother C, now aged 18, was born full-term weighing 9 lbs. 2 oz., the mother supplementing with oral glutathione (GSH) during the pregnancy and subsequently breastfeeding him. Brother C has never attended a CF Clinic, was diagnosed at 2 weeks of age, and was under the care of the family's pediatrician only. Brother C's only prescription medication during his childhood was CREON 5/6, just as with Brothers A and B, utilizing 4 capsules per meal. Brother C has never needed to be hospitalized, or have surgery or antibiotics. Brother C does not exhibit digital clubbing. Brother C played ice hockey in childhood and teen years, as well as participated in gymnastics. His height, weight, lung function, and lab results at age 18 are provided in Table 3 .

Clinical parameters, Brother C.

3. Description of treatment

Given the severity of the genotype involved and the almost complete non-adherence to standard CF guidelines (with the exception of a significantly lower-than-average dose of prescription pancreatic enzymes and a standard dose of water-miscible fat soluble vitamins), the preserved clinical profile of these three brothers is noteworthy. However, the family developed a regimen that went well beyond pancreatic enzymes and water-miscible vitamins. The treatment regimen is provided in Table 4 .

Description of Daily Regimen.

4. Discussion

There are several possibilities for the preserved clinical status of these three brothers in the absence of standard CF care:

• a) They avoided the CF Clinic setting. Recent research [ 13 ] has shown that Pseudomonas infections are more prevalent and lung function lower among CF patients in standard care versus CF patients in a telemedicine setting. It is possible these three brothers benefitted from not attending a standard CF Clinic, especially since during their childhood years at the turn of the century, Clinic infection control was not emphasized. For example, during Brother A's first few CF Clinic visits as an infant, families were expected to wait together in a communal area with communal toys, and health care professionals at the Clinic wore neither masks nor gloves as they moved from exam room to exam room.
• b) With the exception of Brother A, Brothers B and C have used no antibiotics at all. Brother A has only used antibiotics three times in his life; the first use in infancy precipitated Pseudo-Bartter Syndrome, leading to his diagnosis with cystic fibrosis. The other two uses were incident to endoscopic sinus scraping and an appendectomy. Recent research has shown the importance of the gut microbiome in maintenance of health (including respiratory function), digestion and immune signaling, and this is true in the case of cystic fibrosis as well [ [14] , [15] , [16] ]. As David Pride, Associate Director of Microbiology at UC San Diego, notes in an address to the 2019 North American Cystic Fibrosis Conference [ 17 ], “It is important to preserve our microbiomes because they play important roles in preventing pathogens from establishing infections, in the development of our immune systems to recognize and kill pathogens, and in metabolic processes such as the digestion of foods. Indiscriminate uses of antibiotics can have profound and long-lasting effects upon our microbiomes by killing many of the bacteria that make up our microbiome; thus, limiting their use may aid in keeping us healthy.”
• Prevalent, sometimes chronic, antibiotic use among CF patients results in a significant gut dysbiosis [ 18 ]. In addition, it has been noted that aggressive antibiotic use in CF, usually incident to the first manifestation of Staphylococcus aureus (SA), may allow Pseudomonas aeruginosa a greater foothold [ 19 ], and that aggressive treatment of Pseudomonas may, in turn, promote drug resistance and may allow additional bacteria, such as Stenotrophomonas maltophilia, an opportunity to proliferate [ 20 ]. Perhaps a preserved gut microbiome due to non-use of antibiotics may have played a role in the brothers' preserved clinical condition; this may also help account for the brothers’ significantly lower level of need for pancreatic enzymes. Perhaps also the decision not to aggressively treat their light to moderate growth of methicillin-sensitive SA may have precluded additional bacteria, including drug-resistant bacteria, from emerging.
• c) Other standard daily CF treatments were not employed, either, which might help account for their preserved clinical condition. For example, the brothers do not use bronchodilators; and beta-2 agonist bronchodilators, such as albuterol, have recently been shown to significantly reduce delF508 CFTR activation [ 21 ]. This reduction is even evident when CFTR modulators are used, with the finding of a more than 60% reduction of modulator-corrected CFTR activation in vitro, “sufficient to abrogate VX809/VX770 modulation of F508-del CFTR” [ 21 ]. In addition, the brothers do not use DNase, which has been associated with increased levels of neutrophil elastase in past research [ 22 ]. Last, after Brother A transitioned to his new treatment regimen at approximately 23 months of age, chest percussive therapy (CPT) was discontinued, and neither Brother B nor C underwent CPT at all. A Cochrane meta-review found that while CPT constituted the lion's share of treatment time burden in CF, the evidence that outcomes of CPT differed from no CPT was “very low quality” [ 23 ].
• d) Glutathione (GSH) is heavily emphasized in the brothers' daily regimen. Levels of GSH are strongly decreased in the extracellular milieu of CF patients, as its efflux from epithelial cells is compromised by CFTR mutation [ 24 ]. In the non-CF research literature, GSH in its ratio of reduced to oxidized forms (GSH:GSSG) has been shown to be the foundation of redox signaling in the body; GSH is also the body's primary water-soluble antioxidant and a potent mucolytic, and conserves NO through formation of GSNO. Given its pivotal roles, it is not surprising to find that GSH deficiency is noted in several other severe respiratory illnesses besides CF, including ARDS, COPD, IIP, IPF, IRDS, and DFA, and GSH deficiency is a key catalyst for (and GSH dosing a key treatment of) cachexia [ 24 ]. The use of GSH in the treatment of CF may reduce systemic inflammation, lessen the viscosity of mucus, and catalyze the efficacy of the immune system, including through GSNO. Indeed, a clinical study by Visca et al. found significantly increased BMI [ 25 ], significantly increased lung function [ 26 ], and even improved bacteriological results [ 27 ] from the daily use of oral glutathione in children with CF at a dose of 30 mg/lb body weight/day, spread out over 3–4 doses, over a time period of 6 months. In addition, the parents of these brothers noted a sudden increase in both saliva and appetite in Brother A after glutathione (GSH) was introduced when he was two years of age. Brothers B and C, on GSH from two weeks of birth (and with the mother supplementing with oral glutathione throughout pregnancy with these two brothers), never displayed low saliva or low appetite. The preserved clinical status of these three brothers may perhaps be related to this glutathione-heavy regimen.
• e) Other aspects of the brothers' regimen may offset their disease condition. The use of probiotics [ 28 ], the heavy emphasis on antioxidants in addition to glutathione (such as C, CoQ10, Alpha-lipoic acid, D, E, etc. [ 29 ]), amino acids (such as cysteine [ 30 ], carnitine [ 31 ], choline [ 32 , 33 ], taurine [ 34 ], and glycine [ 35 ]), curcumin [ 36 ], and additional digestive support beyond enzymes (lecithin, bile acid). It is possible that some or all of these supplementation efforts also helped to preserve the clinical status of the three brothers. In addition, exclusive breastfeeding of CF infants has been linked to significantly higher FEV1 at age 5 (difference significant at p ≤ 0.001 between breastfed and formula fed CF infants), perhaps contributing to the preservation of lung function beyond that time frame [ 37 ].
• f) Modifier alleles may be present. While no in-depth analysis of the brothers' genetic profile has been performed beyond the identification of their CF mutations, there are known modifier alleles that serve to lessen (or exacerbate) the severity of CF (see, for example [ 38 ]). It is possible all three brothers inherited some propitious set of modifier alleles.

5. Conclusion

In conclusion, while it is encouraging and heartening that new CF therapies, such as elexacaftor/tezacaftor/ivacaftor (Trikafta) and other HEMT (highly effective modulator therapies), now exist, it is instructive to consider how this family was able to preserve the clinical condition of three brothers, all delF508 homozygotes, in the absence of those therapies, and even in the absence of standard CF care. While HEMT certainly increase CFTR activity, there is substantially less effect on infection and inflammation in vivo [ 39 ]. As recently noted by Singh et al., “[I]f infection and inflammation become uncoupled from CFTR activity in established disease [due to HEMT use], drugs targeting CFTR may need to be initiated very early in life, or used in combination with agents that suppress infection and inflammation ” [ 39 ; emphasis ours]. These case reports may speak to that proposition.

Furthermore, each possible explanation for that preservation is an occasion for reflection on the current standard of CF care. We may feel to ask questions such as, “From the point of view of the patient's health, is the entire concept of the CF Clinic inherently flawed? Is the frequent, sometimes chronic, use of antibiotics and certain other medications in CF care a real double-edged sword for CF patients, with disadvantages possibly outweighing advantages in many cases? Are there measures we can take now, relatively inexpensive measures such as the use of glutathione (GSH) and other antioxidants and amino acids, that will help preserve the clinical status of CF patients, and that might synergize with cutting-edge treatments such as CFTR modulators to improve and safeguard health to an even greater degree, and which should be initiated as early in life as possible, possibly while the fetus is still in utero ?” The experience of these three brothers, so removed from standard CF care and yet so well preserved in their clinical status, highlights the need to consider such questions more urgently than we perhaps have heretofore considered them.

Funding sources

This work was supported by the Utah Valley Institute of Cystic Fibrosis, for publication costs only.

Acknowledgements

The author wishes to acknowledge Valerie M. Hudson, who assisted with the writing of this article.

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons

Margin Size

• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

Case Study: Cystic Fibrosis - CER

• Last updated
• Save as PDF
• Page ID 26446

This page is a draft and is under active development. 

\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

\( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

\( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

\( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

\( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

\( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

\( \newcommand{\Span}{\mathrm{span}}\)

\( \newcommand{\id}{\mathrm{id}}\)

\( \newcommand{\kernel}{\mathrm{null}\,}\)

\( \newcommand{\range}{\mathrm{range}\,}\)

\( \newcommand{\RealPart}{\mathrm{Re}}\)

\( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

\( \newcommand{\Argument}{\mathrm{Arg}}\)

\( \newcommand{\norm}[1]{\| #1 \|}\)

\( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

\( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

\( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

\( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

\( \newcommand{\vectorC}[1]{\textbf{#1}} \)

\( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

\( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

\( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

Part I: A​ ​Case​ ​of​ ​Cystic​ ​Fibrosis

Dr. Weyland examined a six month old infant that had been admitted to University Hospital earlier in the day. The baby's parents had brought young Zoey to the emergency room because she had been suffering from a chronic cough. In addition, they said that Zoey sometimes would "wheeze" a lot more than they thought was normal for a child with a cold. Upon arriving at the emergency room, the attending pediatrician noted that salt crystals were present on Zoey's skin and called Dr. Weyland, a pediatric pulmonologist. Dr. Weyland suspects that baby Zoey may be suffering from cystic fibrosis.

CF affects more than 30,000 kids and young adults in the United States. It disrupts the normal function of epithelial cells — cells that make up the sweat glands in the skin and that also line passageways inside the lungs, pancreas, and digestive and reproductive systems.

The inherited CF gene directs the body's epithelial cells to produce a defective form of a protein called CFTR (or cystic fibrosis transmembrane conductance regulator) found in cells that line the lungs, digestive tract, sweat glands, and genitourinary system.

When the CFTR protein is defective, epithelial cells can't regulate the way that chloride ions pass across cell membranes. This disrupts the balance of salt and water needed to maintain a normal thin coating of mucus inside the lungs and other passageways. The mucus becomes thick, sticky, and hard to move, and can result in infections from bacterial colonization.

• "Woe to that child which when kissed on the forehead tastes salty. He is bewitched and soon will die" This is an old saying from the eighteenth century and describes one of the symptoms of CF (salty skin). Why do you think babies in the modern age have a better chance of survival than babies in the 18th century?
• What symptoms lead Dr. Weyland to his initial diagnosis?
• Consider the graph of infections, which organism stays relatively constant in numbers over a lifetime. What organism is most likely affecting baby Zoey?
• What do you think is the most dangerous time period for a patient with CF? Justify your answer.

Part​ ​II:​ ​ ​CF​ ​is​ ​a​ ​disorder​ ​of​ ​the​ ​cell​ ​membrane.

Imagine a door with key and combination locks on both sides, back and front. Now imagine trying to unlock that door blind-folded. This is the challenge faced by David Gadsby, Ph.D., who for years struggled to understand the highly intricate and unusual cystic fibrosis chloride channel – a cellular doorway for salt ions that is defective in people with cystic fibrosis.

His findings, reported in a series of three recent papers in the Journal of General Physiology, detail the type and order of molecular events required to open and close the gates of the cystic fibrosis chloride channel, or as scientists call it, the cystic fibrosis transmembrane conductance regulator (CFTR).

Ultimately, the research may have medical applications, though ironically not likely for most cystic fibrosis patients. Because two-thirds of cystic fibrosis patients fail to produce the cystic fibrosis channel altogether, a cure for most is expected to result from research focused on replacing the lost channel.

5. Suggest a molecular fix for a mutated CFTR channel. How would you correct it if you had the ability to tinker with it on a molecular level?

6. Why would treatment that targets the CFTR channel not be effective for 2⁄3 of those with cystic fibrosis?

7. Sweat glands cool the body by releasing perspiration (sweat) from the lower layers of the skin onto the surface. Sodium and chloride (salt) help carry water to the skin's surface and are then reabsorbed into the body. Why does a person with cystic fibrosis have salty tasting skin?

Part​ ​III:​ ​No​ ​cell​ ​is​ ​an​ ​island

Like people, cells need to communicate and interact with their environment to survive. One way they go about this is through pores in their outer membranes, called ion channels, which provide charged ions, such as chloride or potassium, with their own personalized cellular doorways. But, ion channels are not like open doors; instead, they are more like gateways with high-security locks that are opened and closed to carefully control the passage of their respective ions.

In the case of CFTR, chloride ions travel in and out of the cell through the channel’s guarded pore as a means to control the flow of water in and out of cells. In cystic fibrosis patients, this delicate salt/water balance is disturbed, most prominently in the lungs, resulting in thick coats of mucus that eventually spur life-threatening infections. Shown below are several mutations linked to CFTR:

8. Which mutation do you think would be easiest to correct. Justify your answer. 9. Consider what you know about proteins, why does the “folding” of the protein matter?

Part​ ​IV:​ ​Open​ ​sesame

Among the numerous ion channels in cell membranes, there are two principal types: voltage-gated and ligand-gated. Voltage-gated channels are triggered to open and shut their doors by changes in the electric potential difference across the membrane. Ligand-gated channels, in contrast, require a special “key” to unlock their doors, which usually comes in the form of a small molecule.

CFTR is a ligand-gated channel, but it’s an unusual one. Its “key” is ATP, a small molecule that plays a critical role in the storage and release of energy within cells in the body. In addition to binding the ATP, the CFTR channel must snip a phosphate group – one of three “P’s” – off the ATP molecule to function. But when, where and how often this crucial event takes place has remains obscure.

10. Compare the action of the ligand-gated channel to how an enzyme works.

11. Consider the model of the membrane channel, What could go wrong to prevent the channel from opening?

12. Where is ATP generated in the cell? How might ATP production affect the symptoms of cystic fibrosis?

13. Label the image below to show how the ligand-gated channel for CFTR works. Include a summary.

Part​ ​V:​ Can​ ​a​ ​Drug​ ​Treat​ ​Zoey’s​ ​Condition?

Dr. Weyland confirmed that Zoey does have cystic fibrosis and called the parents in to talk about potential treatments. “Good news, there are two experimental drugs that have shown promise in CF patients. These drugs can help Zoey clear the mucus from his lungs. Unfortunately, the drugs do not work in all cases.” The doctor gave the parents literature about the drugs and asked them to consider signing Zoey up for trials.

The​ ​Experimental​ ​Drugs

Ivacaftor TM is a potentiator that increases CFTR channel opening time. We know from the cell culture studies that this increases chloride transport by as much as 50% from baseline and restores it closer to what we would expect to observe in wild type CFTR. Basically, the drug increases CFTR activity by unlocking the gate that allows for the normal flow of salt and fluids.

In early trials, 144 patients all of whom were age over the age of 12 were treated with 150 mg of Ivacaftor twice daily. The total length of treatment was 48 weeks. Graph A shows changes in FEV (forced expiratory volume) with individuals using the drug versus a placebo. Graph B shows concentrations of chloride in patient’s sweat.

14. What is FEV? Describe a way that a doctor could take a measurement of FEV.

15. Why do you think it was important to have placebos in both of these studies?

16. Which graph do you think provides the most compelling evidence for the effectiveness of Ivacafor? Defend your choice.

17. Take a look at the mutations that can occur in the cell membrane proteins from Part III. For which mutation do you think Ivacaftor will be most effective? Justify your answer.

18. Would you sign Zoey up for clinical trials based on the evidence? What concerns would a parent have before considering an experimental drug?

Part​ ​VI:​ ​Zoey’s​ ​Mutation

Dr. Weyland calls a week later to inform the parents that genetic tests show that Zoey chromosomes show that she has two copies of the F508del mutation. This mutation, while the most common type of CF mutation, is also one that is difficult to treat with just Ivacaftor. There are still some options for treatment.

In people with the most common CF mutation, F508del, a series of problems prevents the CFTR protein from taking its correct shape and reaching its proper place on the cell surface. The cell recognizes the protein as not normal and targets it for degradation before it makes it to the cell surface. In order to treat this problem, we need to do two things: first, an agent to get the protein to the surface, and then ivacaftor (VX-770) to open up the channel and increase chloride transport. VX-809 has been identified as a way to help with the trafficking of the protein to the cell surface. When added VX-809 is added to ivacaftor (now called Lumacaftor,) the protein gets to the surface and also increases in chloride transport by increasing channel opening time.

In early trials, experiments were done in-vitro, where studies were done on cell cultures to see if the drugs would affect the proteins made by the cell. General observations can be made from the cells, but drugs may not work on an individual’s phenotype. A new type of research uses ex-vivo experiments, where rectal organoids (mini-guts) were grown from rectal biopsies of the patient that would be treated with the drug. Ex-vivo experiments are personalized medicine, each person may have different correctors and potentiators evaluated using their own rectal organoids. The graph below shows how each drug works for 8 different patients (#1-#8)

19. Compare ex-vivo trials to in-vitro trials.

20. One the graph, label the group that represents Ivacaftor and Lumacaftor. What is the difference between these two drugs?

21. Complete a CER Chart. If the profile labeled #7 is Zoey, rank the possible drug treatments in order of their effectiveness for her mutation. This is your CLAIM. Provide EVIDENCE​ to support your claim. Provide REASONING​ that explains why this treatment would be more effective than other treatments and why what works for Zoey may not work for other patients. This is where you tie the graph above to everything you have learned in this case. Attach a page.