Evidence map›Paper›PMID 25802553›Full record

ArticleBiotechnology for biofuels2015

Dynamic metabolic profiling of the marine microalga Chlamydomonas sp. JSC4 and enhancing its oil production by optimizing light intensity.

Shih-Hsin Ho, Akihito Nakanishi, Xiaoting Ye, Jo-Shu Chang, Chun-Yen Chen, Tomohisa Hasunuma, Akihiko Kondo

Open access · goldAbstract read
In one paragraph

Article in Biotechnology for biofuels, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 79 citations in OpenAlex.

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  10. Metabolomics and lipid profile analysis of Coccomyxa melkonianii SCCA 048.Extremophiles : life under extreme conditions · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 2 countries.

Shih-Hsin HoOrganization of Advanced Science and Technology, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe, 657-8501 Japan.
Akihito NakanishiOrganization of Advanced Science and Technology, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe, 657-8501 Japan.
Xiaoting YeOrganization of Advanced Science and Technology, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe, 657-8501 Japan.
Jo-Shu ChangDepartment of Chemical Engineering, National Cheng Kung University, Tainan, 701 Taiwan ; Research Center for Energy Technology and Strategy, National Cheng Kung University, Tainan, 701 Taiwan ; Center for Bioscience and Biotechnology, National Cheng Kung University, Tainan, 701 Taiwan.
Chun-Yen ChenCenter for Bioscience and Biotechnology, National Cheng Kung University, Tainan, 701 Taiwan.
Tomohisa HasunumaOrganization of Advanced Science and Technology, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe, 657-8501 Japan.
Akihiko KondoDepartment of Chemical Science and Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe, 657-8501 Japan ; Biomass Engineering Program, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan.
Kobe University · JPNational Cheng Kung University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMarine microalgae are among the most promising lipid sources for biodiesel production because they can be grown on nonarable land without the use of potable water. Marine microalgae also harvest solar energy efficiently with a high growth rate, converting CO2 into lipids stored in the cells. Both light intensity and nitrogen availability strongly affect the growth, lipid accumulation, and fatty acid composition of oleaginous microalgae. However, very few studies have systematically examined how to optimize lipid productivity by adjusting irradiance intensity, and the metabolic dynamics that may lead to improved lipid accumulation in microalgae have not been elucidated. Little is known about the mechanism of lipid synthesis regulation in microalgae. Moreover, few studies have assessed the potential of using marine microalgae as oil producers.

resultsIn this work, a newly isolated marine microalga, Chlamydomonas sp. JSC4, was selected as a potential lipid producer, and the effect of photobioreactor operations on cell growth and lipid production was investigated. The combined effects of light intensity and nitrogen depletion stresses on growth and lipid accumulation were further explored in an effort to markedly improve lipid production and quality. The optimal lipid productivity and content attained were 312 mg L(-1) d(-1) and 43.1% per unit dry cell weight, respectively. This lipid productivity is the highest ever reported for marine microalgae. Metabolic intermediates were profiled over time to observe transient changes during lipid accumulation triggered by combined stresses. Finally, metabolite turnover was also assessed using an in vivo (13)C-labeling technique to directly measure the flow of carbon during lipid biosynthesis under stress associated with light intensity and nitrogen deficiency.

conclusionsThis work demonstrates the synergistic integration of cultivation and dynamic metabolic profiling technologies to develop a simple and effective strategy for enhancing oil production in a marine microalga. The knowledge obtained from this study could be useful in assessing the feasibility of marine microalgae biodiesel production and for understanding the links between dynamic metabolic profiles and lipid biosynthesis during the course of microalgal cultivation.

Indexed as

BiodieselCarbon fluxChlamydomonas spLight intensityLipidsMetabolite profilingMicroalgaeNitrogen depletion

Identifiers

PMID25802553
PMCPMC4369889
OpenAlexW2050021451

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.