Caroline Johnson

Dr. Caroline Johnson

Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, USA
orcid

Research Focus & Metrics

Academic Career Length

22Years (2005-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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16 March 2016 in Nature Reviews Molecular Cell Biology
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Journal Article
Closed access
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Caroline JohnsonCaroline Johnson
Caroline Johnson
Julijana IvanisevicJulijana Ivanisevic
Julijana Ivanisevic
GSGary Siuzdak
Gary Siuzdak

7 February 2013 in Nature Protocols
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Journal Article
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ZZZheng-Jiang Zhu
Zheng-Jiang Zhu
ASAndrew W Schultz
Andrew W Schultz
JWJunhua Wang
Junhua Wang
Caroline JohnsonCaroline Johnson
Caroline Johnson
SYSteven M Yannone
Steven M Yannone
GSGary Siuzdak
Gary Siuzdak

16 June 2014 in Analytical Chemistry
Verified
Journal Article
Open access
HGHarsha Gowda
Harsha Gowda
Julijana IvanisevicJulijana Ivanisevic
Julijana Ivanisevic
Caroline JohnsonCaroline Johnson
Caroline Johnson
MKMichael E. Kurczy
Michael E. Kurczy
HBH. Paul Benton
H. Paul Benton
GSGary Siuzdak
Gary Siuzdak
XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, “control” versus “disease” experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

1 June 2015 in Cell Metabolism
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Journal Article
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Caroline JohnsonCaroline Johnson
Caroline Johnson
CDChristine M. Dejea
Christine M. Dejea
DEDavid Edler
David Edler
LHLinh T. Hoang
Linh T. Hoang
ASAntonio F. Santidrian
Antonio F. Santidrian
GSGary Siuzdak
Gary Siuzdak

27 October 2011 in Journal of Cellular Physiology
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Journal Article
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Caroline JohnsonCaroline Johnson
Caroline Johnson
FGFrank Gonzalez
Frank Gonzalez

10 February 2012 in Annual Review of Pharmacology and Toxicology
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Journal Article
Closed access
Caroline JohnsonCaroline Johnson
Caroline Johnson
APAndrew Patterson
Andrew Patterson
JIJeffrey Robert Idle
Jeffrey Robert Idle
FGFrank Gonzalez
Frank Gonzalez
Xenobiotics are encountered by humans on a daily basis and include drugs, environmental pollutants, cosmetics, and even components of the diet. These chemicals undergo metabolism and detoxication to produce numerous metabolites, some of which have the potential to cause unintended effects such as toxicity. They can also block the action of enzymes or receptors used for endogenous metabolism or affect the efficacy and/or bioavailability of a coadministered drug. Therefore, it is essential to determine the full metabolic effects that these chemicals have on the body. Metabolomics, the comprehensive analysis of small molecules in a biofluid, can reveal biologically relevant perturbations that result from xenobiotic exposure. This review discusses the impact that genetic, environmental, and gut microflora variation has on the metabolome, and how these variables may interact, positively and negatively, with xenobiotic metabolism.