Evidence map›Paper›PMID 25342967›Full record

ArticleBiotechnology for biofuels2014

Inference of phenotype-defining functional modules of protein families for microbial plant biomass degraders.

Sebastian Ga Konietzny, Phillip B Pope, Aaron Weimann, Alice C McHardy

Open access · goldAbstract read
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Article in Biotechnology for biofuels, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
4.0field-weighted citation impact, top 6% 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

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 55 citations in OpenAlex.

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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

4 authors at 3 institutions in 2 countries.

Sebastian Ga KonietznyMax-Planck Research Group for Computational Genomics and Epidemiology, Max-Planck Institute for Informatics, University Campus E1 4, Saarbrücken, 66123 Germany ; Department of Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, 40225 Germany.
Phillip B PopeDepartment of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Post Office Box 5003, 1432 Ås, Norway.
Aaron WeimannDepartment of Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, 40225 Germany.
Alice C McHardyMax-Planck Research Group for Computational Genomics and Epidemiology, Max-Planck Institute for Informatics, University Campus E1 4, Saarbrücken, 66123 Germany ; Department of Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, 40225 Germany.
Heinrich Heine University Düsseldorf · DEMax Planck Institute for Informatics · DENorwegian University of Life Sciences · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEfficient industrial processes for converting plant lignocellulosic materials into biofuels are a key to global efforts to come up with alternative energy sources to fossil fuels. Novel cellulolytic enzymes have been discovered in microbial genomes and metagenomes of microbial communities. However, the identification of relevant genes without known homologs, and the elucidation of the lignocellulolytic pathways and protein complexes for different microorganisms remain challenging.

resultsWe describe a new computational method for the targeted discovery of functional modules of plant biomass-degrading protein families, based on their co-occurrence patterns across genomes and metagenome datasets, and the strength of association of these modules with the genomes of known degraders. From approximately 6.4 million family annotations for 2,884 microbial genomes, and 332 taxonomic bins from 18 metagenomes, we identified 5 functional modules that are distinctive for plant biomass degraders, which we term "plant biomass degradation modules" (PDMs). These modules incorporate protein families involved in the degradation of cellulose, hemicelluloses, and pectins, structural components of the cellulosome, and additional families with potential functions in plant biomass degradation. The PDMs were linked to 81 gene clusters in genomes of known lignocellulose degraders, including previously described clusters of lignocellulolytic genes. On average, 70% of the families of each PDM were found to map to gene clusters in known degraders, which served as an additional confirmation of their functional relationships. The presence of a PDM in a genome or taxonomic metagenome bin furthermore allowed us to accurately predict the ability of any particular organism to degrade plant biomass. For 15 draft genomes of a cow rumen metagenome, we used cross-referencing to confirmed cellulolytic enzymes to validate that the PDMs identified plant biomass degraders within a complex microbial community.

conclusionsFunctional modules of protein families that are involved in different aspects of plant cell wall degradation can be inferred from co-occurrence patterns across (meta-)genomes with a probabilistic topic model. PDMs represent a new resource of protein families and candidate genes implicated in microbial plant biomass degradation. They can also be used to predict the plant biomass degradation ability for a genome or taxonomic bin. The method is also suitable for characterizing other microbial phenotypes.

Indexed as

Feature rankingGene clustersLatent Dirichlet allocationLDA(Ligno)cellulose degradationPectin degradationPhenotype-based identification of functional modulesPlant biomass degradationPolysaccharide utilization lociProbabilistic topic models

Identifiers

PMID25342967
PMCPMC4189754
OpenAlexW2169961674

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LicenceCC BY
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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.