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Pieter C. Dorrestein  - - - 
Top co-authors See all
Rob Knight

734 shared publications

UCSD School of Medicine

Victor Nizet

467 shared publications

Department of Pediatrics; University of California, San Diego; La Jolla California

Julia Laskin

441 shared publications

Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA

Sang Hyun Sung

224 shared publications

College of Pharmacy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea

Paul R. Jensen

207 shared publications

Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography; University of California San Diego; La Jolla, California USA

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Publication Record
Distribution of Articles published per year 
(2006 - 2019)
Total number of journals
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27
 
Publications See all
Article 0 Reads 0 Citations Neutrophilic proteolysis in the cystic fibrosis lung correlates with a pathogenic microbiome Robert A. Quinn, Sandeep Adem, Robert H. Mills, William Coms... Published: 13 February 2019
Microbiome, doi: 10.1186/s40168-019-0636-3
DOI See at publisher website ABS Show/hide abstract
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.
PREPRINT-CONTENT 1 Read 0 Citations Implementations of the chemical structural and compositional similarity metric in R and Python Asker Daniel Brejnrod, Madeleine Ernst, Piotr Dworzynski, La... Published: 11 February 2019
bioRxiv, doi: 10.1101/546150
DOI See at publisher website ABS Show/hide abstract
Tandem mass spectrometry (MS/MS) has the potential to substantially improve metabolomics by acquiring spectra of fragmented ions. These fragmentation spectra can be represented as a molecular network, by measuring cosine distances between them, thus identifying signals from the same or similar molecules. Metrics that enable comparison between pairs of samples based on their metabolite profiles are in great need. Taking inspiration from the successful phylogeny-aware beta-diversity measures used in microbiome research, integrating chemical similarity information about the features in addition to their abundances could lead to better insights when comparing metabolite profiles. Chemical Structural and Compositional Similarity (CSCS) is a recently published similarity metric comparing the full set of signals and their chemical similarity between two samples. Efficient, scalable and easily accessible implementations of this algorithm is currently lacking. Here, we present an easily accessible and scalable implementation of CSCS in both python and R, including a version not weighted by intensity information. We provide a new implementation of the CSCS algorithm that is over 300 times faster than the published implementation in R, making the algorithm suitable for large-scale metabolomics applications. We also show that adding chemical information enriches existing methods. Furthermore, the R implementation includes functions for exporting molecular networks directly from the mass spectral molecular networking platform GNPS for ease of use for downstream applications.
PREPRINT-CONTENT 0 Reads 0 Citations De Novo Peptide Sequencing Reveals a Vast Cyclopeptidome in Human Gut and Other Environments Bahar Behsaz, Hosein Mohimani, Alexey Gurevich, Andrey Prjib... Published: 16 January 2019
bioRxiv, doi: 10.1101/521872
DOI See at publisher website
PREPRINT-CONTENT 0 Reads 0 Citations QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A B... Published: 03 December 2018
doi: 10.7287/peerj.preprints.27295
DOI See at publisher website ABS Show/hide abstract
We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
PREPRINT-CONTENT 0 Reads 0 Citations QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A B... Published: 03 December 2018
doi: 10.7287/peerj.preprints.27295v2
DOI See at publisher website ABS Show/hide abstract
We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
PREPRINT-CONTENT 0 Reads 0 Citations Comprehensive mass spectrometry-guided plant specialized metabolite phenotyping reveals metabolic diversity in the cosmo... Kyo Bin Kang, Madeleine Ernst, Justin J. J. Van Der Hooft, R... Published: 07 November 2018
bioRxiv, doi: 10.1101/463620
DOI See at publisher website ABS Show/hide abstract
Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped specialized metabolite diversity, which drives many other aspects of plant biodiversity. However, until recently, large-scale studies investigating specialized metabolite diversity in an evolutionary context have been limited by the impossibility to identify chemical structures of hundreds to thousands of compounds in a time-feasible manner. Here, we introduce a workflow for large-scale, semi-automated annotation of specialized metabolites, and apply it for over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5 % based on spectral library matches alone to 42.6 % of total MS/MS molecular features extending annotations from well-known plant compound classes into the dark plant metabolomics matter. To gain insights in substructural diversity within the plant family, we also extract patterns of co-occurring fragments and neutral losses, so-called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7-O-methyltransferase activity. Our workflow provides the foundations towards the automated, high-throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.
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