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Work package 6

Multiomics in the PRIL cohort

In this work package, multiomics analyses will be applied on longitudinally collected samples from the PRIL cohort (WP4) (serum, urine, fecal, nose swabs and exhaled breath) for correlation between the subjects who developed lung damage/lung disease vs disease-free subjects despite a similar risk profile at start. This may lead to the detection of biomarkers and treatable targets for early lung damage. Preliminary gene candidates from WP2 will also be tested in the PRIL cohort samples .

Lung diseases are detected at a late stage, when it is no longer possible to cure disease. Instead the only option is to reduce symptoms and optimize patients' quality of life as much as possible. By using different –omics techniques in middle-aged patients without diagnosis of lung disease we aim to identify early markers.

Both in populations at high risk as in those at low risk, we currently can not predict who will actually develop lung disease. We do not know the biological signs in patient samples that inform us on the development of lung disease at an early stage. We expect that multiomics approaches will help identifying such signs as they provide insight into an enormous number of different molecules. Previously, concrete biological interpretation of for instance metabolomic data was limited as analytical standards were unavailable for many of the detected molecules (instead being reported as ion intensities). However, recent advances in reference standardization have improved data analysis. Also computational algorithms based on shared biological pathways will help to identify useful biomarkers.

The aim of this working package is to analyze samples of the PRIL cohort and to compare biological markers of patients who are showing early signs of lung damage (and after some years of follow-up subjects who developed a lung disease) to markers of participants who do not show early signs of lung disease (and those who did not develop lung disease in a later stage of the project).

For breathomics analysis, two volatile organic compounds (VOC) samples from exhaled breath will be obtained from each participant of the PRIL cohort during yearly spirometry testing. These samples will be analyzed by standardized Gas Chromatography-Mass Spectrometry (GC-MS) that allows identification of individual chemical compounds present in the exhaled breath samples (volatile organic compounds). In addition, the samples will be analyzed on an eNose (the SpiroNose) which is a more practical alternative.  An eNose is a diagnostic tool that can capture a signature of mixtures of VOCs in exhaled breath without a true molecular identification of these compounds. These so-called ‘breathprints’ reflect local, systemic and exogenous processes in the airways and alveoli (idem). This allows to distinguish differences in patterns of VOCs between different groups. Technological advances allow real-time and high-throughput pattern analysis of breathprints, making eNose feasible for practical use at point-of-care16.

For (epi-)genomic/transcriptomic analyses, DNA will be isolated from different inflammatory blood cells and from one of the nose swabs. DNA will be used for GWAS analyses and for methylome profiling. In addition, transcriptomic analysis of circulating platelets will be used for early detection of (pre-)malignant changes in the lung induced by environmental toxins17.

For epigenomic analysis, blood eosinophils (and possibly other inflammatory cells) will be isolated following a purification protocol using an adapted FACS sorting strategy based on Mori et al18. DNA methylation in inflammatory cells will be measured with Illumina TruSeq MethylCaptureEPIC technology, which measures DNA Methylation at >3 million CpG sites within regulatory elements.

Metabolomics has the potential to significantly enhance the knowledge gained by epigenomics and transcriptomics by integrating signatures of exposome components (exposomics) to their biological effects. The metabolomic profile in urine will be assessed via TLC/GC-MS. In the blood, thousands of small molecules, including environmental compounds, can be detected via the application of ultra-high-resolution mass spectrometry (UHRMS). A unique technology that is part of our expertise is the use of gas-chromatography UHRMS (GC-UHRMS), which provides excellent sensitivity and specificity for environmental compounds. When combined with liquid chromatography UHRMS (LC-UHRMS), which is best suited for measurements of molecules with specific functional groups or large molecules which contain these groups (e.g. lipids, fatty acids, sterols) integrated measures of both exposure and biological response are generated. This provides a framework through which environmental exposures can be linked to internal dose, biological response, and disease.

Microbiome analysis will be performed on a nose swab and fecal sample. High-throughput amplicon sequencing of the V4 variable region of the microbial 16S ribosomal RNA gene will be performed on genomic DNA extracts. Each sample sequence set will be sub-sampled to 8,700 sequences.

The deliverables of this work package is information on the association of the exposome and (epi)genetic changes, and information on the interaction between an unfavorable metabolic milieu and exposome on development of lung disease. Also, these analyses are expected to lead to the detection of biomarkers for early lung damage. Information on these biomarkers will be linked to the other biomarkers acquired in the PRIL cohort (WP5/7), and will be combined in WP8. Also, they will be integrated into population level data in the Exposome Hub (WP5).

References
16. Dragonieri et al. Electronic Nose Technology in Respiratory Diseases. Lung. 2017;195(2): 157-165.
17. Best et al. Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets.Cancer Cell. 2017;32(2):238-252.
18. Mori  Y et  al. Identification  of  the  human  eosinophil  lineage committed  progenitor:  revision  of phenotypic definition of the human common myeloid progenitor. J Exp Med. 2009;16:183-193.
 

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