Evidence map›Paper›PMID 35418297›Full record

ArticleBiotechnology for biofuels and bioproducts2022

Integration of Aspergillus niger transcriptomic profile with metabolic model identifies potential targets to optimise citric acid production from lignocellulosic hydrolysate.

Daniel J Upton, Mehak Kaushal, Caragh Whitehead, Laura Faas, Leonardo D Gomez, Simon J McQueen-Mason, Shireesh Srivastava, A Jamie Wood

Open access · goldAbstract read
In one paragraph

Article in Biotechnology for biofuels and bioproducts, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 43% 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

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 2 institutions in 2 countries.

Daniel J UptonDepartment of Biology, University of York, Wentworth Way, York, YO10 5DD, UK. daniel.upton@york.ac.uk.ORCID http://orcid.org/0000-0001-6411-2798
Mehak KaushalSystems Biology for Biofuel Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), ICGEB Campus, Aruna Asaf Ali Marg, New Delhi, 110067, India.
Caragh WhiteheadDepartment of Biology, University of York, Wentworth Way, York, YO10 5DD, UK.
Laura FaasDepartment of Biology, University of York, Wentworth Way, York, YO10 5DD, UK.
Leonardo D GomezDepartment of Biology, University of York, Wentworth Way, York, YO10 5DD, UK.
Simon J McQueen-MasonDepartment of Biology, University of York, Wentworth Way, York, YO10 5DD, UK.
Shireesh SrivastavaSystems Biology for Biofuel Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), ICGEB Campus, Aruna Asaf Ali Marg, New Delhi, 110067, India.
A Jamie WoodDepartment of Biology, University of York, Wentworth Way, York, YO10 5DD, UK.
University of York · GBInternational Centre for Genetic Engineering and Biotechnology · IN

Funding

Biotechnology and Biological Sciences Research Council BB/S01196X/1Department of Biotechnology , Ministry of Science and Technology BT/IN/Indo-UK/SSY/07/2018-19Innovate UK 104336
6 · The paper itself

Abstract

backgroundCitric acid is typically produced industrially by Aspergillus niger-mediated fermentation of a sucrose-based feedstock, such as molasses. The fungus Aspergillus niger has the potential to utilise lignocellulosic biomass, such as bagasse, for industrial-scale citric acid production, but realising this potential requires strain optimisation. Systems biology can accelerate strain engineering by systematic target identification, facilitated by methods for the integration of omics data into a high-quality metabolic model. In this work, we perform transcriptomic analysis to determine the temporal expression changes during fermentation of bagasse hydrolysate and develop an evolutionary algorithm to integrate the transcriptomic data with the available metabolic model to identify potential targets for strain engineering.

resultsThe novel integrated procedure matures our understanding of suboptimal citric acid production and reveals potential targets for strain engineering, including targets consistent with the literature such as the up-regulation of citrate export and pyruvate carboxylase as well as novel targets such as the down-regulation of inorganic diphosphatase.

conclusionsIn this study, we demonstrate the production of citric acid from lignocellulosic hydrolysate and show how transcriptomic data across multiple timepoints can be coupled with evolutionary and metabolic modelling to identify potential targets for further engineering to maximise productivity from a chosen feedstock. The in silico strategies employed in this study can be applied to other biotechnological goals, assisting efforts to harness the potential of microorganisms for bio-based production of valuable chemicals.

Indexed as

Aspergillus nigerCitric acidFermentationMetabolic modellingTranscriptomics

Identifiers

PMID35418297
PMCPMC8756645
OpenAlexW4206534386

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.