Association of Phosphatidylinositol-Specific Phospholipase C with Calcium-Induced Biomineralization in the Coccolithophore  Emiliania huxleyi

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Abstract

Biomineralization by calcifying microalgae is a precisely controlled intracellular calcification process that produces delicate calcite scales (or coccoliths) in the coccolithophore Emiliania huxleyi (Haptophycea). Despite its importance in biogeochemical cycles and the marine environment globally, the underlying molecular mechanism of intracellular coccolith formation, which requires calcium, bicarbonate, and coccolith-polysaccharides, remains unclear. In E. huxleyi CCMP 371, we demonstrated that reducing the calcium concentration from 10 (ambient seawater) to 0.1 mM strongly restricted coccolith production, which was then recovered by adding 10 mM calcium, irrespective of inorganic phosphate conditions, indicating that coccolith production could be finely controlled by the calcium supply. Using this strain, we investigated the expression of differentially expressed genes (DEGs) to observe the cellular events induced by changes in calcium concentrations. Intriguingly, DEG analysis revealed that the phosphatidylinositol-specific phospholipase C (PI-PLC) gene was upregulated and coccolith production by cells was blocked by the PI-PLC inhibitor U73122 under conditions closely associated with calcium-induced calcification. These findings imply that PI-PLC plays an important role in the biomineralization process of the coccolithophore E. huxleyi.

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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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STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Damian Szklarczyk, Annika L Gable, David Lyon … (2018)

Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

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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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Journal

Journal ID (nlm-ta): Microorganisms

Journal ID (iso-abbrev): Microorganisms

Journal ID (publisher-id): microorganisms

Title: Microorganisms

Publisher: MDPI

ISSN (Electronic): 2076-2607

Publication date (Electronic): 10 September 2020

Publication date Collection: September 2020

Volume: 8

Issue: 9

Electronic Location Identifier: 1389

Affiliations

[1 ]Department of Life Science, Research Institute for Natural Sciences, Hanyang University, Seoul 04763, Korea; oynam@ 123456hanyang.ac.kr

[2 ]Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan; iwanes6803@ 123456biol.tsukuba.ac.jp (I.S.); emilhux@ 123456biol.tsukuba.ac.jp (Y.S.)

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[* ]Correspondence: esjin@ 123456hanyang.ac.kr ; Tel.: +82-2-2220-2561

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EonSeon Jin https://orcid.org/0000-0001-5691-0124

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Publisher ID: microorganisms-08-01389

DOI: 10.3390/microorganisms8091389

PMC ID: 7563939

PubMed ID: 32927844

SO-VID: c0160e5e-df86-4f63-ae76-0e97e97b8ce1

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/).

Association of Phosphatidylinositol-Specific Phospholipase C with Calcium-Induced Biomineralization in the Coccolithophore Emiliania huxleyi

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Most cited references 60

Trimmomatic: a flexible trimmer for Illumina sequence data

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

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

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