samples 33 PCD, RK, SM, VN and DS guided experimental design and analysis. 34 MW converted the data in GNPS, developed spectral search and molecular explorer. 35 TT, VN and SM raised animals and guided experimental design. 36 RQ and PD wrote the manuscript 37 38Abstract 39 A mosaic of cross-phyla chemical interactions occurs between all metazoans and their 40 microbiomes. In humans, the gut harbors the heaviest microbial load, but many organs, 41 particularly those with a mucosal surface, associate with highly adapted and evolved 42 microbial consortia 1 . The microbial residents within these organ systems are increasingly well 43 characterized, yielding a good understanding of human microbiome composition, but we have 44 yet to elucidate the full chemical impact the microbiome exerts on an animal and the breadth 45 of the chemical diversity it contributes 2 . A number of molecular families are known to be 46 shaped by the microbiome including short-chain fatty acids, indoles, aromatic amino acid 47 values of the two data types onto the murine 3-D model showed how the gut samples were monosaccharides in all regions of the GI tract, which were absent in SPF animals. Instead, a 132

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 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 a complex interplay between the presence of a given microbial structure, antibiotics, metabolism of antibiotics, microbial virulence factors, and host responses.ImportanceMicrobial infections are now recognized to be polymicrobial and personalized in nature. A comprehensive analysis and understanding of the factors underlying the polymicrobial and personalized nature of infections remains limited, especially in the context of the host. By visualizing microbiomes and metabolomes of diseased human lungs, we describe 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 are hypothesis building tools that allow us to design mechanistic studies aimed at addressing microbial responses to other microbes, the host, and pharmaceutical drugs.

Novel small molecule therapies for cystic fibrosis (CF) are showing promising efficacy and becoming more widely available since recent FDA approval. The newest of these is a triple therapy of Elexacaftor-Tezacaftor-Ivacaftor (ETI). Little is known about how these drugs will affect polymicrobial lung infections, which are the leading cause of morbidity and mortality among people with CF (pwCF). We analyzed the sputum microbiome and metabolome from pwCF (n=24) before and after TKT therapy using 16S rRNA gene amplicon sequencing and untargeted metabolomics. The lung microbiome diversity, particularly its evenness, was increased (p = 0.044) and the microbiome profiles were different between individuals before and after therapy (PERMANOVA F=1.92, p=0.044). Despite these changes, the microbiomes were more similar within an individual than across the sampled population. There were no specific microbial taxa that were different in abundance before and after therapy, but collectively, the log-ratio of anaerobes to pathogens significantly decreased. The sputum metabolome also showed changes due to TKT. Beta-diversity increased after therapy (PERMANOVA F=4.22, p=0.022) and was characterized by greater variation across subjects while on treatment. This significant difference in the metabolome was driven by a decrease in peptides, amino acids, and metabolites from the kynurenine pathway. Metabolism of the three small molecules that make up TKT was extensive, including previously uncharacterized structural modifications. This study shows that TKT therapy affects both the microbiome and metabolome of airway mucus. This effect was stronger on sputum biochemistry, which may reflect changing niche spaces for microbial residency in lung mucus as the drug effects take hold, which then leads to changing microbiology.