A GMR enzymatic assay for quantifying nuclease and peptidase activity.

Hydrolytic enzymes play crucial roles in cellular processes, and dysregulation of their activities is implicated in various physiological and pathological conditions. These enzymes cleave substrates such as peptide bonds, phosphodiester bonds, glycosidic bonds, and other esters. Detecting aberrant hydrolase activity is vital for understanding disease mechanisms and developing targeted therapeutic interventions. This study introduces a novel approach to measuring hydrolase activity using giant magnetoresistive (GMR) spin valve sensors. These sensors change resistance in response to magnetic fields, and here, they are functionalized with specific substrates for hydrolases conjugated to magnetic nanoparticles (MNPs). When a hydrolase cleaves its substrate, the tethered magnetic nanoparticle detaches, causing a measurable shift in the sensor's resistance. This design translates hydrolase activity into a real-time, activity-dependent signal. The assay is simple, rapid, and requires no washing steps, making it ideal for point-of-care settings. Unlike fluorescent methods, it avoids issues like autofluorescence and photobleaching, broadening its applicability to diverse biofluids. Furthermore, the sensor array contains 80 individually addressable sensors, allowing for the simultaneous measurement of multiple hydrolases in a single reaction. The versatility of this method is demonstrated with substrates for nucleases, Bcu I and DNase I, and the peptidase, human neutrophil elastase. To demonstrate a clinical application, we show that neutrophil elastase in sputum from cystic fibrosis patients hydrolyze the peptide-GMR substrate, and the cleavage rate strongly correlates with a traditional fluorogenic substrate. This innovative assay addresses challenges associated with traditional enzyme measurement techniques, providing a promising tool for real-time quantification of hydrolase activities in diverse biological contexts.

Early Evidence of Chronic Obstructive Pulmonary Disease Obscured by Race-Specific Prediction Equations.

Rationale: The identification of early chronic obstructive pulmonary disease (COPD) is essential to appropriately counsel patients regarding smoking cessation, provide symptomatic treatment, and eventually develop disease-modifying treatments. Disease severity in COPD is defined using race-specific spirometry equations. These may disadvantage non-White individuals in diagnosis and care. Objectives: Determine the impact of race-specific equations on African American (AA) versus non-Hispanic White individuals. Methods: Cross-sectional analyses of the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease) cohort were conducted, comparing non-Hispanic White (n = 6,766) and AA (n = 3,366) participants for COPD manifestations. Measurements and Main Results: Spirometric classifications using race-specific, multiethnic, and "race-reversed" prediction equations (NHANES [National Health and Nutrition Examination Survey] and Global Lung Function Initiative "Other" and "Global") were compared, as were respiratory symptoms, 6-minute-walk distance, computed tomography imaging, respiratory exacerbations, and St. George's Respiratory Questionnaire. Application of different prediction equations to the cohort resulted in different classifications by stage, with NHANES and Global Lung Function Initiative race-specific equations being minimally different, but race-reversed equations moving AA participants to more severe stages and especially between the Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage 0 and preserved ratio impaired spirometry groups. Classification using the established NHANES race-specific equations demonstrated that for each of GOLD stages 1-4, AA participants were younger, had fewer pack-years and more current smoking, but had more exacerbations, shorter 6-minute-walk distance, greater dyspnea, and worse BODE (body mass index, airway obstruction, dyspnea, and exercise capacity) scores and St. George's Respiratory Questionnaire scores. Differences were greatest in GOLD stages 1 and 2. Race-reversed equations reclassified 774 AA participants (43%) from GOLD stage 0 to preserved ratio impaired spirometry. Conclusions: Race-specific equations underestimated disease severity among AA participants. These effects were particularly evident in early disease and may result in late detection of COPD.

Compounding Achromobacter Phages for Therapeutic Applications.

Achromobacter species colonization of Cystic Fibrosis respiratory airways is an increasing concern. Two adult patients with Cystic Fibrosis colonized by Achromobacter xylosoxidans CF418 or Achromobacter ruhlandii CF116 experienced fatal exacerbations. Achromobacter spp. are naturally resistant to several antibiotics. Therefore, phages could be valuable as therapeutics for the control of Achromobacter. In this study, thirteen lytic phages were isolated and characterized at the morphological and genomic levels for potential future use in phage therapy. They are presented here as the Achromobacter Kumeyaay phage collection. Six distinct Achromobacter phage genome clusters were identified based on a comprehensive phylogenetic analysis of the Kumeyaay collection as well as the publicly available Achromobacter phages. The infectivity of all phages in the Kumeyaay collection was tested in 23 Achromobacter clinical isolates; 78% of these isolates were lysed by at least one phage. A cryptic prophage was induced in Achromobacter xylosoxidans CF418 when infected with some of the lytic phages. This prophage genome was characterized and is presented as Achromobacter phage CF418-P1. Prophage induction during lytic phage preparation for therapy interventions require further exploration. Large-scale production of phages and removal of endotoxins using an octanol-based procedure resulted in a phage concentrate of 1 × 109 plaque-forming units per milliliter with an endotoxin concentration of 65 endotoxin units per milliliter, which is below the Food and Drugs Administration recommended maximum threshold for human administration. This study provides a comprehensive framework for the isolation, bioinformatic characterization, and safe production of phages to kill Achromobacter spp. in order to potentially manage Cystic Fibrosis (CF) pulmonary infections.

Race-Specific Spirometry Equations Do Not Improve Models of Dyspnea and Quantitative Chest CT Phenotypes.

BACKGROUND: Race-specific spirometry reference equations are used globally to interpret lung function for clinical, research, and occupational purposes, but inclusion of race is under scrutiny. RESEARCH QUESTION: Does including self-identified race in spirometry reference equation formation improve the ability of predicted FEV1 values to explain quantitative chest CT abnormalities, dyspnea, or Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification? STUDY DESIGN AND METHODS: Using data from healthy adults who have never smoked in both the National Health and Nutrition Survey (2007-2012) and COPDGene study cohorts, race-neutral, race-free, and race-specific prediction equations were generated for FEV1. Using sensitivity/specificity, multivariable logistic regression, and random forest models, these equations were applied in a cross-sectional analysis to populations of individuals who currently smoke and individuals who formerly smoked to determine how they affected GOLD classification and the fit of models predicting quantitative chest CT phenotypes or dyspnea. RESULTS: Race-specific equations showed no advantage relative to race-neutral or race-free equations in models of quantitative chest CT phenotypes or dyspnea. Race-neutral reference equations reclassified up to 19% of Black participants into more severe GOLD classes, while race-neutral/race-free equations may improve model fit for dyspnea symptoms relative to race-specific equations. INTERPRETATION: Race-specific equations offered no advantage over race-neutral/race-free equations in three distinct explanatory models of dyspnea and chest CT scan abnormalities. Race-neutral/race-free reference equations may improve pulmonary disease diagnoses and treatment in populations highly vulnerable to lung disease.

Immunization with lytic polysaccharide monooxygenase CbpD induces protective immunity against Pseudomonas aeruginosa pneumonia.

Pseudomonas aeruginosa (PA) CbpD belongs to the lytic polysaccharide monooxygenases (LPMOs), a family of enzymes that cleave chitin or related polysaccharides. Here, we demonstrate a virulence role of CbpD in PA pneumonia linked to impairment of host complement function and opsonophagocytic clearance. Following intratracheal challenge, a PA ΔCbpD mutant was more easily cleared and produced less mortality than the wild-type parent strain. The x-ray crystal structure of the CbpD LPMO domain was solved to subatomic resolution (0.75Å) and its two additional domains modeled by small-angle X-ray scattering and Alphafold2 machine-learning algorithms, allowing structure-based immune epitope mapping. Immunization of naive mice with recombinant CbpD generated high IgG antibody titers that promoted human neutrophil opsonophagocytic killing, neutralized enzymatic activity, and protected against lethal PA pneumonia and sepsis. IgG antibodies generated against full-length CbpD or its noncatalytic M2+CBM73 domains were opsonic and protective, even in previously PA-exposed mice, while antibodies targeting the AA10 domain were not. Preexisting antibodies in PA-colonized cystic fibrosis patients primarily target the CbpD AA10 catalytic domain. Further exploration of LPMO family proteins, present across many clinically important and antibiotic-resistant human pathogens, may yield novel and effective vaccine antigens.

Bacteriophage Therapy of Multidrug-resistant Achromobacter in an 11-Year-old Boy With Cystic Fibrosis Assessed by Metagenome Analysis.

BACKGROUND: Cystic fibrosis (CF) is a genetic disease associated with lung disease characterized by chronic pulmonary infection, increasingly caused by multiple drug-resistant pathogens after repeated antibiotic exposure, limiting antibiotic treatment options. Bacteriophages can provide a pathogen-specific bactericidal treatment used with antibiotics to improve microbiologic and clinical outcomes in CF. METHODS: Achromobacter species isolates from sputum of a chronically infected person with CF, were assessed for susceptibility to bacteriophages: 2 highly active, purified bacteriophages were administered intravenously every 8 hours, in conjunction with a 14-day piperacillin/tazobactam course for CF exacerbation. Sputum and blood were collected for metagenome analysis during treatment, with sputum analysis at 1-month follow-up. Assessments of clinical status, pulmonary status and laboratory evaluation for safety were conducted. RESULTS: Bacteriophage administration was well-tolerated, with no associated clinical or laboratory adverse events. Metagenome analysis documented an 86% decrease in the relative proportion of Achromobacter DNA sequence reads in sputum and a 92% decrease in blood, compared with other bacterial DNA reads, comparing pretreatment and posttreatment samples. Bacteriophage DNA reads were detected in sputum after intravenous administration during treatment, and at 1-month follow-up. Reversal of antibiotic resistance to multiple antibiotics occurred in some isolates during treatment. Stabilization of lung function was documented at 1-month follow-up. CONCLUSIONS: Bacteriophage/antibiotic treatment decreased the host pulmonary bacterial burden for Achromobacter assessed by metagenome analysis of sputum and blood, with ongoing bacteriophage replication documented in sputum at 1-month follow-up. Prospective controlled studies are needed to define the dose, route of administration and duration of bacteriophage therapy for both acute and chronic infection in CF.

Is it time to end race and ethnicity adjustment for pediatric pulmonary function tests?

The continued inclusion of race in spirometry reference equations is a topic of intense debate for adult lung function, but less discussion has focused on implications for children. Obtaining accurate estimates of children's lung function is an important component of the diagnosis of childhood respiratory illnesses, including asthma, cystic fibrosis, and interstitial lung disease. Given the higher burden among racial/ethnic minorities for many respiratory illnesses, it is critical to avoid racial bias in interpreting lung function. We recommend against the continued use of race-specific reference equations for a number of reasons. The original reference populations used to develop the equations were comprised of children with restricted racial diversity, relatively small sample sizes, and likely included some unhealthy children. Moreover, there is no scientific justification for innate racial differences in lung function, as there is no clear physiological or genetic explanation for the disparities. Alternatively, many environmental factors harm lung development, including allergens from pests, asbestos, lead, prenatal smoking, and air pollution, as well as preterm birth and childhood respiratory illnesses, which are all more common among minority racial groups. Race-neutral equations may provide a temporary solution, but still rely on the racial diversity of the reference populations used to build them. Ultimately researchers must uncover the underlying factors truly driving racial differences in lung function.

Use of the Spirometric "Fixed-Ratio" Underdiagnoses COPD in African-Americans in a Longitudinal Cohort Study.

BACKGROUND: COPD diagnosis is tightly linked to the fixed-ratio spirometry criteria of FEV1/FVC < 0.7. African-Americans are less often diagnosed with COPD. OBJECTIVE: Compare COPD diagnosis by fixed-ratio with findings and outcomes by race. DESIGN: Genetic Epidemiology of COPD (COPDGene) (2007-present), cross-sectional comparing non-Hispanic white (NHW) and African-American (AA) participants for COPD diagnosis, manifestations, and outcomes. SETTING: Multicenter, longitudinal US cohort study. PARTICIPANTS: Current or former smokers with ≥ 10-pack-year smoking history enrolled at 21 clinical centers including over-sampling of participants with known COPD and AA. Exclusions were pre-existing non-COPD lung disease, except for a history of asthma. MEASUREMENTS: Subject diagnosis by conventional criteria. Mortality, imaging, respiratory symptoms, function, and socioeconomic characteristics, including area deprivation index (ADI). Matched analysis (age, sex, and smoking status) of AA vs. NHW within participants without diagnosed COPD (GOLD 0; FEV1 ≥ 80% predicted and FEV1/FVC ≥ 0.7). RESULTS: Using the fixed ratio, 70% of AA (n = 3366) were classified as non-COPD, versus 49% of NHW (n = 6766). AA smokers were younger (55 vs. 62 years), more often current smoking (80% vs. 39%), with fewer pack-years but similar 12-year mortality. Density distribution plots for FEV1 and FVC raw spirometry values showed disproportionate reductions in FVC relative to FEV1 in AA that systematically led to higher ratios. The matched analysis demonstrated GOLD 0 AA had greater symptoms, worse DLCO, spirometry, BODE scores (1.03 vs 0.54, p < 0.0001), and greater deprivation than NHW. LIMITATIONS: Lack of an alternative diagnostic metric for comparison. CONCLUSIONS: The fixed-ratio spirometric criteria for COPD underdiagnosed potential COPD in AA participants when compared to broader diagnostic criteria. Disproportionate reductions in FVC relative to FEV1 leading to higher FEV1/FVC were identified in these participants and associated with deprivation. Broader diagnostic criteria for COPD are needed to identify the disease across all populations.

Erratum: Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network.

[This corrects the article DOI: 10.1148/ryct.2021200477.].

Unsupervised Learning Identifies Computed Tomographic Measurements as Primary Drivers of Progression, Exacerbation, and Mortality in Chronic Obstructive Pulmonary Disease.

Rationale: Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome with phenotypic manifestations that tend to be distributed along a continuum. Unsupervised machine learning based on broad selection of imaging and clinical phenotypes may be used to identify primary variables that define disease axes and stratify patients with COPD. Objectives: To identify primary variables driving COPD heterogeneity using principal component analysis and to define disease axes and assess the prognostic value of these axes across three outcomes: progression, exacerbation, and mortality. Methods: We included 7,331 patients between 39 and 85 years old, of whom 40.3% were Black and 45.8% were female smokers with a mean of 44.6 pack-years, from the COPDGene (Genetic Epidemiology of COPD) phase I cohort (2008-2011) in our analysis. Out of a total of 916 phenotypes, 147 continuous clinical, spirometric, and computed tomography (CT) features were selected. For each principal component (PC), we computed a PC score based on feature weights. We used PC score distributions to define disease axes along which we divided the patients into quartiles. To assess the prognostic value of these axes, we applied logistic regression analyses to estimate 5-year (n = 4,159) and 10-year (n = 1,487) odds of progression. Cox regression and Kaplan-Meier analyses were performed to estimate 5-year and 10-year risk of exacerbation (n = 6,532) and all-cause mortality (n = 7,331). Results: The first PC, accounting for 43.7% of variance, was defined by CT measures of air trapping and emphysema. The second PC, accounting for 13.7% of variance, was defined by spirometric and CT measures of vital capacity and lung volume. The third PC, accounting for 7.9% of the variance, was defined by CT measures of lung mass, airway thickening, and body habitus. Stratification of patients across each disease axis revealed up to 3.2-fold (95% confidence interval [CI] 2.4, 4.3) greater odds of 5-year progression, 5.4-fold (95% CI 4.6, 6.3) greater risk of 5-year exacerbation, and 5.0-fold (95% CI 4.2, 6.0) greater risk of 10-year mortality between the highest and lowest quartiles. Conclusions: Unsupervised learning analysis of the COPDGene cohort reveals that CT measurements may bolster patient stratification along the continuum of COPD phenotypes. Each of the disease axes also individually demonstrate prognostic potential, predictive of future forced expiratory volume in 1 second decline, exacerbation, and mortality.

Bone Marrow Transplantation Rescues Monocyte Recruitment Defect and Improves Cystic Fibrosis in Mice.

Cystic fibrosis (CF) is an inherited life-threatening disease accompanied by repeated lung infections and multiorgan inflammation that affects tens of thousands of people worldwide. The causative gene, cystic fibrosis transmembrane conductance regulator (CFTR), is mutated in CF patients. CFTR functions in epithelial cells have traditionally been thought to cause the disease symptoms. Recent work has shown an additional defect: monocytes from CF patients show a deficiency in integrin activation and adhesion. Because monocytes play critical roles in controlling infections, defective monocyte function may contribute to CF progression. In this study, we demonstrate that monocytes from CFTRΔF508 mice (CF mice) show defective adhesion under flow. Transplanting CF mice with wild-type (WT) bone marrow after sublethal irradiation replaced most (60-80%) CF monocytes with WT monocytes, significantly improved survival, and reduced inflammation. WT/CF mixed bone marrow chimeras directly demonstrated defective CF monocyte recruitment to the bronchoalveolar lavage and the intestinal lamina propria in vivo. WT mice reconstituted with CF bone marrow also show lethality, suggesting that the CF defect in monocytes is not only necessary but also sufficient to cause disease. We also show that monocyte-specific knockout of CFTR retards weight gains and exacerbates dextran sulfate sodium-induced colitis. Our findings show that providing WT monocytes by bone marrow transfer rescues mortality in CF mice, suggesting that similar approaches may mitigate disease in CF patients.

Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network.

PURPOSE: To develop a deep learning-based algorithm to stage the severity of chronic obstructive pulmonary disease (COPD) through quantification of emphysema and air trapping on CT images and to assess the ability of the proposed stages to prognosticate 5-year progression and mortality. MATERIALS AND METHODS: In this retrospective study, an algorithm using co-registration and lung segmentation was developed in-house to automate quantification of emphysema and air trapping from inspiratory and expiratory CT images. The algorithm was then tested in a separate group of 8951 patients from the COPD Genetic Epidemiology study (date range, 2007-2017). With measurements of emphysema and air trapping, bivariable thresholds were determined to define CT stages of severity (mild, moderate, severe, and very severe) and were evaluated for their ability to prognosticate disease progression and mortality using logistic regression and Cox regression. RESULTS: On the basis of CT stages, the odds of disease progression were greatest among patients with very severe disease (odds ratio [OR], 2.67; 95% CI: 2.02, 3.53; P < .001) and were elevated in patients with moderate disease (OR, 1.50; 95% CI: 1.22, 1.84; P = .001). The hazard ratio of mortality for very severe disease at CT was 2.23 times the normal ratio (95% CI: 1.93, 2.58; P < .001). When combined with Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging, patients with GOLD stage 2 disease had the greatest odds of disease progression when the CT stage was severe (OR, 4.48; 95% CI: 3.18, 6.31; P < .001) or very severe (OR, 4.72; 95% CI: 3.13, 7.13; P < .001). CONCLUSION: Automated CT algorithms can facilitate staging of COPD severity, have diagnostic performance comparable with that of spirometric GOLD staging, and provide further prognostic value when used in conjunction with GOLD staging.Supplemental material is available for this article.© RSNA, 2021See also commentary by Kalra and Ebrahimian in this issue.

Multi-dimensional clinical phenotyping of a national cohort of adult cystic fibrosis patients.

BACKGROUND: Cystic Fibrosis (CF) is a multi-systemic disorder resulting from genetic variation in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene which can result in bronchiectasis, chronic sinusitis, pancreatic malabsorption, cholestatic liver disease and distal intestinal obstructive syndrome. This study generates multi-dimensional clinical phenotypes that capture the complexity and spectrum of the disease manifestations seen in adult CF patients using statistically robust techniques. METHODS: Pre-transplant clinical data from adult (age ≥18 years) CF patients (n = 992) seen in six regionally distinct US CF centers between 1/1/2014 and 6/30/2015 were included. Demographic, spirometry, nutritional, microbiological and therapy data were used to generate clusters using the Random Forests statistical-learning and Partitioning around Medoids (PAM) clustering algorithms. Five commonly measured demographic, physiological and nutritional parameters were needed to create the final phenotypes that are highly similar to a regionally matched group of patients from the CF Foundation Patient Registry RESULTS: This approach identified high-risk phenotypes with expected characteristics including high rates of pancreatic insufficiency, diabetes and Pseudomonas aeruginosa colonization. It also identified unexpected populations including a) a male-dominated, well-nourished group with good lung function with a high prevalence of severe genotypes (i.e. 60% subjects had two minimal function CFTR variations), b) and an older, "survivor" phenotype that had high rates of chronic P. aeruginosa infection. CONCLUSIONS: This study identified recognizable phenotypes that capture the clinical complexity in a statistically robust manner and which may aide in the identification of specific genetic and environmental factors responsible for these disease manifestation patterns.

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads.

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing.

Molecular and Microbial Microenvironments in Chronically Diseased Lungs Associated with Cystic Fibrosis.

To visualize the personalized distributions of pathogens and chemical environments, including microbial metabolites, pharmaceuticals, and their metabolic products, within and between human lungs afflicted with cystic fibrosis (CF), we generated three-dimensional (3D) microbiome and metabolome maps of six explanted lungs from three cystic fibrosis patients. These 3D spatial maps revealed that the chemical environments differ between patients and within the lungs of each patient. Although the microbial ecosystems of the patients were defined by the dominant pathogen, their chemical diversity was not. Additionally, the chemical diversity between locales in the lungs of the same individual sometimes exceeded interindividual variation. Thus, the chemistry and microbiome of the explanted lungs appear to be not only personalized but also regiospecific. Previously undescribed analogs of microbial quinolones and antibiotic metabolites were also detected. Furthermore, mapping the chemical and microbial distributions allowed visualization of microbial community interactions, such as increased production of quorum sensing quinolones in locations where Pseudomonas was in contact with Staphylococcus and Granulicatella, consistent with in vitro observations of bacteria isolated from these patients. Visualization of microbe-metabolite associations within a host organ in early-stage CF disease in animal models will help elucidate the complex interplay between the presence of a given microbial structure, antibiotics, metabolism of antibiotics, microbial virulence factors, and host responses.IMPORTANCE Microbial infections are now recognized to be polymicrobial and personalized in nature. Comprehensive analysis and understanding of the factors underlying the polymicrobial and personalized nature of infections remain limited, especially in the context of the host. By visualizing microbiomes and metabolomes of diseased human lungs, we reveal how different the chemical environments are between hosts that are dominated by the same pathogen and how community interactions shape the chemical environment or vice versa. We highlight that three-dimensional organ mapping methods represent hypothesis-building tools that allow us to design mechanistic studies aimed at addressing microbial responses to other microbes, the host, and pharmaceutical drugs.

Cystic Fibrosis Rapid Response: Translating Multi-omics Data into Clinically Relevant Information.

Pulmonary exacerbations are the leading cause of death in cystic fibrosis (CF) patients. To track microbial dynamics during acute exacerbations, a CF rapid response (CFRR) strategy was developed. The CFRR relies on viromics, metagenomics, metatranscriptomics, and metabolomics data to rapidly monitor active members of the viral and microbial community during acute CF exacerbations. To highlight CFRR, a case study of a CF patient is presented, in which an abrupt decline in lung function characterized a fatal exacerbation. The microbial community in the patient's lungs was closely monitored through the multi-omics strategy, which led to the identification of pathogenic shigatoxigenic Escherichia coli (STEC) expressing Shiga toxin. This case study illustrates the potential for the CFRR to deconstruct complicated disease dynamics and provide clinicians with alternative treatments to improve the outcomes of pulmonary exacerbations and expand the life spans of individuals with CF.IMPORTANCE Proper management of polymicrobial infections in patients with cystic fibrosis (CF) has extended their life span. Information about the composition and dynamics of each patient's microbial community aids in the selection of appropriate treatment of pulmonary exacerbations. We propose the cystic fibrosis rapid response (CFRR) as a fast approach to determine viral and microbial community composition and activity during CF pulmonary exacerbations. The CFRR potential is illustrated with a case study in which a cystic fibrosis fatal exacerbation was characterized by the presence of shigatoxigenic Escherichia coli The incorporation of the CFRR within the CF clinic could increase the life span and quality of life of CF patients.

Median regression spline modeling of longitudinal FEV1 measurements in cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients.

RATIONALE: Clinical phenotyping, therapeutic investigations as well as genomic, airway secretion metabolomic and metagenomic investigations can benefit from robust, nonlinear modeling of FEV1 in individual subjects. We demonstrate the utility of measuring FEV1 dynamics in representative cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) populations. METHODS: Individual FEV1 data from CF and COPD subjects were modeled by estimating median regression splines and their predicted first and second derivatives. Classes were created from variables that capture the dynamics of these curves in both cohorts. RESULTS: Nine FEV1 dynamic variables were identified from the splines and their predicted derivatives in individuals with CF (n = 177) and COPD (n = 374). Three FEV1 dynamic classes (i.e. stable, intermediate and hypervariable) were generated and described using these variables from both cohorts. In the CF cohort, the FEV1 hypervariable class (HV) was associated with a clinically unstable, female-dominated phenotypes while stable FEV1 class (S) individuals were highly associated with the male-dominated milder clinical phenotype. In the COPD cohort, associations were found between the FEV1 dynamic classes, the COPD GOLD grades, with exacerbation frequency and symptoms. CONCLUSION: Nonlinear modeling of FEV1 with splines provides new insights and is useful in characterizing CF and COPD clinical phenotypes.

Three-Dimensional Microbiome and Metabolome Cartography of a Diseased Human Lung.

Our understanding of the spatial variation in the chemical and microbial makeup of an entire human organ remains limited, in part due to the size and heterogeneity of human organs and the complexity of the associated metabolome and microbiome. To address this challenge, we developed a workflow to enable the cartography of metabolomic and microbiome data onto a three-dimensional (3D) organ reconstruction built off radiological images. This enabled the direct visualization of the microbial and chemical makeup of a human lung from a cystic fibrosis patient. We detected host-derived molecules, microbial metabolites, medications, and region-specific metabolism of medications and placed it in the context of microbial distributions in the lung. Our tool further created browsable maps of a 3D microbiome/metabolome reconstruction map on a radiological image of a human lung and forms an interactive resource for the scientific community.

Characterizing Lung Disease in Cystic Fibrosis with Magnetic Resonance Imaging and Airway Physiology.

Translational investigations in cystic fibrosis (CF) have a need for improved quantitative and longitudinal measures of disease status. To establish a non-invasive quantitative MRI technique to monitor lung health in patients with CF and correlate MR metrics with airway physiology as measured by multiple breath washout (MBW). Data were collected in 12 CF patients and 12 healthy controls. Regional (central and peripheral lung) measures of fractional lung water density (FLD: air to 100% fluid) were acquired both at FRC and TLC on a 1.5T MRI. The median FLD (mFLD) and the FRC-to-TLC mFLD ratio were calculated for each region at both lung volumes. Spirometry and MBW data were also acquired for each subject. Ventilation inhomogeneities were quantified by the lung clearance index (LCI) and by indices Scond* and Sacin* that assess inhomogeneities in the conducting (central) and acinar (peripheral) lung regions, respectively. MBW indices and mFLD at TLC (both regions) were significantly elevated in CF (p<0.01) compared to controls. The mFLD at TLC (central: R = 0.82) and the FRC-to-TLC mFLD ratio (peripheral: R = -0.77) were strongly correlated with Scond* and LCI. CF patients had high lung water content at TLC when compared to controls. This is likely due to the presence of retained airway secretions and airway wall edema (more water) and to limited expansions of air trapping areas (less air) in CF subjects. FRC-to-TLC ratios of mFLD strongly correlated with central ventilation inhomogeneities. These combined measures may provide a useful marker of both retained mucus and air trapping in CF lungs.

Using Cystic Fibrosis Therapies for Non-Cystic Fibrosis Bronchiectasis.

Non-cystic fibrosis bronchiectasis (NCFB) is an increasingly prevalent disease that places a significant burden on patients and health systems globally. Although many of the therapies used to treat NCFB were originally developed as cystic fibrosis (CF) therapies, not all of them have been demonstrated to be efficacious in NCFB and some may even be harmful. This article explores the evidence for which therapeutic strategies used to treat CF have been translated into the care of NCFB. The conclusion is that therapies for adult NCFB cannot be simply extrapolated from CF clinical trials, and in some instances, doing so may actually result in harm.