Transcriptional Analyses of Acute Exposure to Methylmercury on Erythrocytes of Loggerhead
      Sea Turtle

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Abstract

To understand changes in enzyme activity and gene expression as biomarkers of exposure to methylmercury, we exposed loggerhead turtle erythrocytes (RBCs) to concentrations of 0, 1, and 5 mg L ⁻¹ of MeHg and de novo transcriptome were assembled using RNA-seq. The analysis of differentially expressed genes (DEGs) indicated that 79 unique genes were dysregulated (39 upregulated and 44 downregulated genes). The results showed that MeHg altered gene expression patterns as a response to the cellular stress produced, reflected in cell cycle regulation, lysosomal activity, autophagy, calcium regulation, mitochondrial regulation, apoptosis, and regulation of transcription and translation. The analysis of DEGs showed a low response of the antioxidant machinery to MeHg, evidenced by the fact that genes of early response to oxidative stress were not dysregulated. The RBCs maintained a constitutive expression of proteins that represented a good part of the defense against reactive oxygen species (ROS) induced by MeHg.

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Trimmomatic: a flexible trimmer for Illumina sequence data

Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)

Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.

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Fast gapped-read alignment with Bowtie 2.

Ben Langmead, Steven L Salzberg (2022)

As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

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RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Bo Li, Colin Dewey (2011)

Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

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Dianne Jolley: Role: Academic Editor

Journal

Journal ID (nlm-ta): Toxics

Journal ID (iso-abbrev): Toxics

Journal ID (publisher-id): toxics

Title: Toxics

Publisher: MDPI

ISSN (Electronic): 2305-6304

Publication date (Electronic): 29 March 2021

Publication date Collection: April 2021

Volume: 9

Issue: 4

Electronic Location Identifier: 70

Affiliations

[1 ]Department of Natural and Environmental Science, Marine Biology Program, Faculty of Science and Engineering, Genetics, Molecular Biology and Bioinformatic Research Group–GENBIMOL, Jorge Tadeo Lozano University, Cra. 4 No 22-61, Bogotá 110311, Colombia; mariad.rodriguezb@ 123456utadeo.edu.co

[2 ]Faculty of Sciences, Department of Biology, Pontificia Universidad Javeriana, Calle 45, Cra. 7, Bogotá 110231, Colombia

[3 ]Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Calle 45, Cra. 30, Bogotá 111321, Colombia; ampinzonv@ 123456unal.edu.co

[4 ]IDEASA Research Group-Environment and Sustainability, Institute of Environmental Studies and Services, Sergio Arboleda University, Bogotá 111711, Colombia; ellie.lopez@ 123456usa.edu.co

[5 ]NCBI, NLM, NIH Computational Biology Branch, Building 38A, Room 6S614M 8600 Rockville Pike, MSC 6075, Bethesda, MD 20894-6075, USA; marino@ 123456ncbi.nlm.nih.gov

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[* ]Correspondence: javier.hernandez@ 123456utadeo.edu.co

Author information

Javier Hernández-Fernández https://orcid.org/0000-0001-8442-9266

Pilar Rodríguez-Becerra https://orcid.org/0000-0003-3843-1490

Leonardo Mariño-Ramírez https://orcid.org/0000-0002-5716-8512

Article

Publisher ID: toxics-09-00070

DOI: 10.3390/toxics9040070

PMC ID: 8066450

PubMed ID: 33805397

SO-VID: 62030a83-1f08-4d7c-af59-8f827fbde9b8

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/).

Transcriptional Analyses of Acute Exposure to Methylmercury on Erythrocytes of Loggerhead Sea Turtle

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UCL: UN SDG 14 Life Below Water

Most cited references 148

Trimmomatic: a flexible trimmer for Illumina sequence data

Fast gapped-read alignment with Bowtie 2.

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

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