MSACL 2018 US Abstract

Topic: Tissue Imaging & Analysis

3D Volume Cartography of Diseased Human Lungs

Alexey Melnik (Presenter)
UCSD

Bio: Alexey V. Melnik received his BS and MS degrees in analytical chemistry from Far Eastern Federal University, Russia, where he developed a mass spectrometry tools for the discovery of novel drug leads. In 2013 he joined Prof. Pieter C. Dorrestein's lab at the Skaggs School of Pharmacy and Pharmaceutical Sciences at UCSD. His research concentrates on the development of mass spectrometry tools and workflows to study human microbiome. He is currently integrating modern mass spectrometry and microbial sequencing approaches to study complex microbial interactions and its metabolism

Authorship: Alexey Melnik(1), Neha Garg(1), Embriette Hyde(2), Ming Wang(3), Ricardo Silva(1), Ivan Protsyuk(4), Amina Bouslimani(1), William Comstock(1), Richard Wong(6), Gregory Humphrey(2), Gail Ackerman(2), Tal Luzzatto-Knaan(1), Rob Quinn(1), Timothy Spivey(7), Sharon Brouha(7), Nuno Bandeira(3), Forest Rohwer(5), Douglas Conrad(8),Theodore Alexandrov(1,4), Rob Knight(2,3), Pieter Dorrestein(1)
Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA. 2. Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA. 3. Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA 92093, USA. 4. European Molecular Biology Laboratory, Heidelberg 69117, Germany. 5. Biology Department, San Diego State University, San Diego, CA, USA. 6. Department of Pathology, University of California, San Diego, La Jolla, CA 92093, USA. 7 Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA. 8 Department of Medicine, University of California at San Diego, La Jolla, CA

Short Abstract

Our knowledge about the distributions of pathogenic microbes, antibiotics and their metabolites in Cystic Fibrosis affected lungs is limited. As a result, lungs are often treated as homogeneous entities. To gain insight into the chemical environment of the microbial habitat in human lungs, we created digitized maps of six explanted lungs from three patients with cystic fibrosis onto the 3D models generated from CT scans at both the chemical and microbial level. Except for the most dominant genera, the microbial constitution, also reflected in the chemistry, varied to a great extent in the lung. The chemical diversity between lobes of the same individual was sometimes greater than the inter-individual variation. The spatial maps revealed that the distribution of pharmaceuticals, microbial molecules, and host factors is unique to each patient and heterogeneously localized.

Long Abstract

Introduction

In order to understand pathophysiology and better devise treatment strategies, one must understand the distribution of an administered drug through its target organ. Unfortunately, it is challenging to measure the distribution of chemicals in-vivo. An assumption that organs such as human lung are homogenous, and therefore the drug distribution is homogenous as well, is patently incorrect. For example, in the infectious lung disease cystic fibrosis (CF), there is little to no knowledge of the physicochemical properties of microbial and metabolite distributions through the lung, including medications. To address this, we analyzed the microbiome and metabolome of explant lungs from three CF patients (6 lungs in total). We created 3D models of explant lungs from CT scans and mapped molecular and microbial distributions onto the models.

Methods

Explant lungs were collected from 3 individuals. Each lobe of the lungs were sectioned horizontally and then each alternating slice was further sub-sectioned. Each resulted in a 1-2 cm3 slice that were swabbed for 16S rRNA sequencing analysis and then extracted for metabolomics. 3D images of the lungs were reconstructed from CT scans collected from individuals prior to the surgery. 3D models were sectioned to mimic the procedure upon the sample collection and coordinates of each subsections were picked. Metabolomics features were extracted using mzMine and microbial features were obtained using qiita2 and then mapped onto constructed 3D models. Microbial isolates were prepared for selected pathogenic organisms and profiled with the same LC-MS method.

Results

Metabolomics data revealed the vast amount of antibiotics administered to the patients both before and during the surgery. The inventory of antibiotics was taken using GNPS online platform by comparing of fragmentation spectra to library entries using cosine score similarity. The data indicated the non-comprehensive nature of the clinical record on medications used, showing a compendium of not accounted for over the counter drugs. The distribution of the same drugs were highly variable between the patients. Sometimes the penetration of the drug for a single patient was different between right and left lung pointing out the heterogeneity of the diseased organ.

Two out of three patients were colonized primarily by the Pseudomonas pathogen, while one patient’s lungs were colonized with Stenotrophomonas. The data revealed the clear differences in the chemical cues produced by microbes in vivo in the lung and in-vitro from the cultures. A larger diversity of quinolones from P. aeruginosa was detected in cultured isolates compared to the lung tissue of all patients dominated by Pseudomonas. One of the quinolone common to all patients at m/z 268.170, has never been reported before and based on MS1 and MS2 data, this quinolone’s structure was proposed to contain two double bonds in the alkyl side chain as opposed to one double bond found in unsaturated quinolones described up to date. Additionally, patients that are dominated by Pseudomonas, show individualized phenotypes with respect to the expression of biofilm forming factors. For example, rhamnolipids, the Pseudomonas biosurfactant were not detected in the lungs of patients colonized by this pathogen. Other microbes were unevenly distributed in the different lobes of lungs. Such compartmentalization of microbial population within patients as well as variation between patients is a hallmark of a complex diseased organ such as CF lung. The distribution of microbes, as well as their virulence factors, revealed the strong evidence of microbial interactions within lungs. For example, Staphylococcus aureus was found to induce quinolone and biofilm production by Pseudomonas aeruginosa. Thus it is reasonable to hypothesize that both specific microbial interactions in the lungs and different penetration of antibiotics could result in metabolic divergence creating isolated regions of enhanced biofilm formation and tissue damage that is often observed in CF patients as well as a potential for developing of antibiotic resistance in areas where target pathogen prevalence remains high after administering of treatment. 3D volume cartography reveals these trends by visualizing existing microbial and chemical distributions.

Conclusions & Discussion

Cystic fibrosis is a devastating genetic disease which affects millions of people worldwide. In this work we presented the findings of spatial distributions of microbes, medications and its metabolites throughout CF lungs. We have found that the difference between patient’s own left and right lung, may be greater than inter-patient variations, both in terms of microbial distributions and chemistries. In-depth analysis revealed a patient-specific metabolism of prescribed medications and microbial compartmentalisation resulting in metabolic divergence governed by microbial spatial interactions. Identification of these interactions may enable future precision approaches when targeted, localized treatments are administered to maximize treatment efficacy and decrease possibility of antibiotic resistance development. In general, a paradigm shift of considering localized regions of divergent microbial and chemical distributions is an important next step for better disease management of polymicrobial infections.


References & Acknowledgements:

1. Garg N, E. al. Three-Dimensional Microbiome and Metabolome Cartography of a Diseased Human Lung. - PubMed - NCBI.

2. Quinn RA, E. al. Ecological networking of cystic fibrosis lung infections. - PubMed - NCBI


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