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.

To visualize the personalized distributions of pathogens, chemical environments including microbial metabolites, pharmaceuticals, and their metabolic products within and between human lungs afflicted with cystic fibrosis, we generated 3D microbiome and metabolome maps of six explanted lungs from three cystic fibrosis patients. These 3D spatial maps revealed that the chemical environments are variable between patients and within the lungs of each patient. Although the patients’ microbial ecosystems were defined by the dominant pathogen, their chemical diversity was not. Additionally, the chemical diversity between locales in lungs of the same individual sometimes exceeded inter-individual 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 was in contact with and , consistent with 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 a complex interplay between the presence of a given microbial structure, antibiotics, metabolism of antibiotics, microbial virulence factors, and host responses.

Studies of the cystic fibrosis (CF) lung microbiome have consistently shown that lung function decline is associated with decreased microbial diversity due to the dominance of opportunistic pathogens. However, how this phenomenon is reflected in the metabolites and chemical environment of lung secretions remains poorly understood. Here we investigated the microbial and molecular composition of CF sputum samples using 16S rRNA gene amplicon sequencing and untargeted tandem mass spectrometry to determine their interrelationships and associations with clinical measures of disease severity. The CF metabolome was found to exist in two states: one from patients with more severe disease that had higher molecular diversity and more Pseudomonas aeruginosa and the other from patients with better lung function having lower metabolite diversity and fewer pathogenic bacteria. The two molecular states were differentiated by the abundance and diversity of peptides and amino acids. Patients with severe disease and more pathogenic bacteria had higher levels of peptides. Analysis of the carboxyl terminal residues of these peptides indicated that neutrophil elastase and cathepsin G were responsible for their generation, and accordingly, these patients had higher levels of proteolytic activity from these enzymes in their sputum. The CF pathogen Pseudomonas aeruginosa was correlated with the abundance of amino acids and is known to primarily feed on them in the lung. In cases of severe CF lung disease, proteolysis by host enzymes creates an amino acid-rich environment that P. aeruginosa comes to dominate, which may contribute to the pathogen's persistence by providing its preferred carbon source.

Environmental microbial communities are stratified by chemical gradients that shape the structure and function of these systems. Similar chemical gradients exist in the human body, but how they influence these microbial systems is more poorly understood. Understanding these effects can be particularly important for dysbiotic shifts in microbiome structure that are often associated with disease. We show that pH and oxygen strongly partition the microbial community from a diseased human lung into two mutually exclusive communities of pathogens and anaerobes. Antimicrobial treatment disrupted this chemical partitioning, causing complex death, survival, and resistance outcomes that were highly dependent on the individual microorganism and on community stratification. These effects were mathematically modeled, enabling a predictive understanding of this complex polymicrobial system. Harnessing the power of these chemical gradients could be a drug-free method of shaping microbial communities in the human body from undesirable dysbiotic states.

Many chronic airway diseases result in mucus plugging of the airways. Lungs of an individual with cystic fibrosis are an exemplary case where their mucus-plugged bronchioles create a favorable habitat for microbial colonization. Various pathogens thrive in this environment interacting with each other and driving many of the symptoms associated with CF disease. Like any microbial community, the chemical conditions of their habitat have a significant impact on the community structure and dynamics. For example, different microorganisms thrive in differing levels of oxygen or other solute concentrations. This is also true in the CF lung, where oxygen concentrations are believed to drive community physiology and structure. The methods described here are designed to mimic the lung environment and grow pathogens in a manner more similar to that from which they cause disease. Manipulation of the chemical surroundings of these microbes is then used to study how the chemistry of lung infections governs its microbial ecology. The method, called the WinCF system, is based on artificial sputum medium and narrow capillary tubes meant to provide an oxygen gradient similar to that which exists in mucus-plugged bronchioles. Manipulating chemical conditions, such as the media pH of the sputum or antibiotics pressure, allows for visualization of the microbiological differences in those samples using colored indicators, watching for gas or biofilm production, or extracting and sequencing the nucleic acid contents of each sample.