Evidence map›Paper›PMID 31196115›Full record

ArticleMicrobial cell factories2019

Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data.

Sara A Amin, Elizabeth Chavez, Vladimir Porokhin, Nikhil U Nair, Soha Hassoun

Open access · goldAbstract read
In one paragraph

Article in Microbial cell factories, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.1field-weighted citation impact, top 23% 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

12 citing papers in PubMed, 25 citations in OpenAlex.

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  12. The era of big data: Genome-scale modelling meets machine learning.Computational and structural biotechnology journal · 2020
    Review
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

5 authors at 2 institutions in 1 country.

Sara A AminDepartment of Computer Science, Tufts University, Medford, MA, USA.
Elizabeth ChavezDepartment of Biology, University of North Carolina, Chapel Hill, NC, USA.
Vladimir PorokhinDepartment of Computer Science, Tufts University, Medford, MA, USA.
Nikhil U NairDepartment of Chemical and Biological Engineering, Tufts University, Medford, MA, USA. nikhil.nair@tufts.edu.
Soha HassounDepartment of Computer Science, Tufts University, Medford, MA, USA. soha.hassoun@tufts.edu.ORCID http://orcid.org/0000-0001-9477-2199
Tufts University · USUniversity of North Carolina at Chapel Hill · US

Funding

Metabolic engineering in humans: Altered gut microbes as a therapeutic platformDP2HD091798 · NICHD · TUFTS UNIVERSITY MEDFORD · PI NAIR, NIKHIL UNNI · 2016 to 2016
$2.3M
A Machine-Learning Based Software Widget for Resolving Metabolite IdentitiesR03CA211839 · NCI · TUFTS UNIVERSITY MEDFORD · PI LEE, KYONGBUM · 2016 to 2016
$148k
Toward Altered Probiotic Therapy for PhenylketonuriaR03HD090444 · NICHD · TUFTS UNIVERSITY MEDFORD · PI NAIR, NIKHIL UNNI · 2017 to 2018
$144k
National Science Foundation 1421972NCI NIH HHS R03 CA211839NICHD NIH HHS DP2 HD091798NICHD NIH HHS R03 HD090444NIH HHS 1DP2HD091798NIH HHS 1R03CA211839-01
6 · The paper itself

Abstract

backgroundMetabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous-i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity.

resultsOur workflow utilizes PROXIMAL-a tool that uses reactant-product transformation patterns from the KEGG database-to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in Escherichia coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB).

conclusionsWe utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but may have not been documented in iML1515 or other databases. We provide detailed analysis of 23 predicted reactions and 16 associated metabolites. Interestingly, nine of these metabolites, which are in ECMDB, have not previously been documented in any other E. coli databases. Four of the predicted reactions provide putative transformations parallel to those already in iML1515. We suggest adding predicted metabolites and reactions to iML1515 to create an extended metabolic model (EMM) for E. coli.

Indexed as

Databases, ProteinEscherichia coliEscherichia coli ProteinsMetabolomeMetabolomicsModels, BiologicalEscherichia coli ProteinsEnzyme activity predictionEnzyme promiscuityExtended metabolic modelMetabolic engineeringSystems biology

Identifiers

PMID31196115
PMCPMC6567437
OpenAlexW2950223937

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.