Integrated Transcriptomics, Metabolomics, and Lipidomics Profiling in Rat Lung, Blood, and Serum for Assessment of Laser Printer-Emitted Nanoparticle Inhalation Exposure-Induced Disease Risks

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Abstract

Laser printer-emitted nanoparticles (PEPs) generated from toners during printing represent one of the most common types of life cycle released particulate matter from nano-enabled products. Toxicological assessment of PEPs is therefore important for occupational and consumer health protection. Our group recently reported exposure to PEPs induces adverse cardiovascular responses including hypertension and arrythmia via monitoring left ventricular pressure and electrocardiogram in rats. This study employed genome-wide mRNA and miRNA profiling in rat lung and blood integrated with metabolomics and lipidomics profiling in rat serum to identify biomarkers for assessing PEPs-induced disease risks. Whole-body inhalation of PEPs perturbed transcriptional activities associated with cardiovascular dysfunction, metabolic syndrome, and neural disorders at every observed time point in both rat lung and blood during the 21 days of exposure. Furthermore, the systematic analysis revealed PEPs-induced transcriptomic changes linking to other disease risks in rats, including diabetes, congenital defects, auto-recessive disorders, physical deformation, and carcinogenesis. The results were also confirmed with global metabolomics profiling in rat serum. Among the validated metabolites and lipids, linoleic acid, arachidonic acid, docosahexanoic acid, and histidine showed significant variation in PEPs-exposed rat serum. Overall, the identified PEPs-induced dysregulated genes, molecular pathways and functions, and miRNA-mediated transcriptional activities provide important insights into the disease mechanisms. The discovered important mRNAs, miRNAs, lipids and metabolites may serve as candidate biomarkers for future occupational and medical surveillance studies. To the best of our knowledge, this is the first study systematically integrating in vivo, transcriptomics, metabolomics, and lipidomics to assess PEPs inhalation exposure-induced disease risks using a rat model.

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Predicting effective microRNA target sites in mammalian mRNAs

Vikram Agarwal, George Bell, Jin-Wu Nam … (2015)

MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks. DOI: http://dx.doi.org/10.7554/eLife.05005.001

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Most mammalian mRNAs are conserved targets of microRNAs.

Robin C. Friedman, Kyle Farh, Christopher Burge … (2009)

MicroRNAs (miRNAs) are small endogenous RNAs that pair to sites in mRNAs to direct post-transcriptional repression. Many sites that match the miRNA seed (nucleotides 2-7), particularly those in 3' untranslated regions (3'UTRs), are preferentially conserved. Here, we overhauled our tool for finding preferential conservation of sequence motifs and applied it to the analysis of human 3'UTRs, increasing by nearly threefold the detected number of preferentially conserved miRNA target sites. The new tool more efficiently incorporates new genomes and more completely controls for background conservation by accounting for mutational biases, dinucleotide conservation rates, and the conservation rates of individual UTRs. The improved background model enabled preferential conservation of a new site type, the "offset 6mer," to be detected. In total, >45,000 miRNA target sites within human 3'UTRs are conserved above background levels, and >60% of human protein-coding genes have been under selective pressure to maintain pairing to miRNAs. Mammalian-specific miRNAs have far fewer conserved targets than do the more broadly conserved miRNAs, even when considering only more recently emerged targets. Although pairing to the 3' end of miRNAs can compensate for seed mismatches, this class of sites constitutes less than 2% of all preferentially conserved sites detected. The new tool enables statistically powerful analysis of individual miRNA target sites, with the probability of preferentially conserved targeting (P(CT)) correlating with experimental measurements of repression. Our expanded set of target predictions (including conserved 3'-compensatory sites), are available at the TargetScan website, which displays the P(CT) for each site and each predicted target.

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Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

Gordon K. Smyth (2004)

The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.

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Author and article information

Journal

Journal ID (nlm-ta): Int J Mol Sci

Journal ID (iso-abbrev): Int J Mol Sci

Journal ID (publisher-id): ijms

Title: International Journal of Molecular Sciences

Publisher: MDPI

ISSN (Electronic): 1422-0067

Publication date (Electronic): 16 December 2019

Publication date Collection: December 2019

Volume: 20

Issue: 24

Electronic Location Identifier: 6348

Affiliations

[1 ]West Virginia University Cancer Institute/School of Public Health, West Virginia University, Morgantown, WV 26506, USA; qiye@ 123456mix.wvu.edu

[2 ]Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore; poh_tuang_yeow@ 123456ntu.edu.sg (T.Y.P.); schotirmall@ 123456ntu.edu.sg (S.H.C.); sshen@ 123456hsph.harvard.edu (S.S.); dchris@ 123456hsph.harvard.edu (D.C.C.)

[3 ]Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA; svp097@ 123456mail.harvard.edu (S.P.); kwng@ 123456ntu.edu.sg (K.W.N.); pdemokri@ 123456hsph.harvard.edu (P.D.)

[4 ]Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505, USA; woe7@ 123456cdc.gov (M.T.F.); yaq2@ 123456cdc.gov (Y.Q.)

[5 ]Singapore Phenome Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore; tham_wk@ 123456ntu.edu.sg (W.K.T.); ssadav@ 123456ntu.edu.sg (S.S.A.)

[6 ]Key Lab for Modern Toxicology, Department of Epidemiology and Biostatistics and Ministry of Education (MOE), School of Public Health, Nanjing Medical University, Nanjing 210029, China; ywei@ 123456njmu.edu.cn

[7 ]School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore

[8 ]Environmental Chemistry and Materials Centre, Nanyang Environment & Water Research Institute, Singapore 637141, Singapore

[9 ]Office of Hazard Identification and Reduction, U.S. Consumer Product Safety Commission, Rockville, MD 20814, USA; tthomas@ 123456cpsc.gov

Author notes

[* ]Correspondence: lguo@ 123456hsc.wvu.edu ; Tel.: +1-304-293-6455

Author information

Nancy Lan Guo https://orcid.org/0000-0002-8050-6268

Mariana T. Farcas https://orcid.org/0000-0003-2404-9434

Sanjay H. Chotirmall https://orcid.org/0000-0003-0417-7607

David C. Christiani https://orcid.org/0000-0002-0301-0242

Article

Publisher ID: ijms-20-06348

DOI: 10.3390/ijms20246348

PMC ID: 6940784

PubMed ID: 31888290

SO-VID: 1d2fa14d-94ea-4533-bf90-9403b2c1a704

License:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

History

Date received : 02 December 2019

Date accepted : 13 December 2019

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Integrated Transcriptomics, Metabolomics, and Lipidomics Profiling in Rat Lung, Blood, and Serum for Assessment of Laser Printer-Emitted Nanoparticle Inhalation Exposure-Induced Disease Risks

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Most cited references 87

Predicting effective microRNA target sites in mammalian mRNAs

Most mammalian mRNAs are conserved targets of microRNAs.

Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

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