Evidence map›Paper›PMID 42036837›Full record

ArticleMicrobial biotechnology2026

A Bait-and-Switch Strategy Links Phenotypes to Genes Coding for Polymer-Degrading Enzymes in Intact Microbiomes.

Colleen E Yancey, Kyle D Brumfield, Jackson A Buss, Rita R Colwell, Laurence Ettwiller

Abstract read
In one paragraph

Article in Microbial biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Colleen E YanceyResearch Division, New England Biolabs, Ipswich, Massachusetts, USA.ORCID https://orcid.org/0000-0002-6078-9483
Kyle D BrumfieldUniversity of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland, USA.
Jackson A BussResearch Division, New England Biolabs, Ipswich, Massachusetts, USA.
Rita R ColwellUniversity of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland, USA.
Laurence EttwillerResearch Division, New England Biolabs, Ipswich, Massachusetts, USA.

Funding

NASA 80NSSC20K0814NASA 80NSSC22K1044National Science Foundation CBET1751854National Science Foundation CCF1918749National Science Foundation OCE1839171New England BiolabsNIEHS NIH HHS R01ES030317A
6 · The paper itself

Abstract

Natural microbial communities, with their vast diversity and complexity, are among the richest sources of untapped novel enzymes. Identifying novel enzymes can be challenging because microbiomes often lack clear, measurable phenotypes, unlike laboratory cultures where enzymatic activity can be linked to genetic elements. These constraints have left much of the functional diversity within microbiomes inaccessible to enzyme discovery efforts. Here, we present a genotype/phenotype association framework directly on microbial communities for enzyme discovery. For this, we developed a 'bait-and-switch' treatment strategy that generates measurable dual phenotypes directly within intact microbiomes. Using soil microbiomes as a test system, we applied chitin-rich compost as 'bait' to enrich chitin-degrading organisms, followed by glucose addition to functionally 'switch' the community. This treatment produced a distinct phenotypic signature: prevalence of known chitin degradation genes increases during the bait phase, and their transcripts are rapidly downregulated during the switch phase. By performing hypothesis-free association analysis of protein domains with this dual phenotype, we identified the glycoside hydrolase 18 as the most significantly associated protein domain. Experimental validation confirmed chitinase activity in 63% of tested enzymes, including candidates from unculturable bacteria and those with previously uncharacterized domain architectures. This species-independent, reference-free approach to discover novel enzymes has broad applications in microbiome engineering, biopolymer processing and systems biology, offering a generalizable strategy for functional gene discovery in complex microbial systems.

Indexed as

BacteriaChitinasesMicrobiotaChitinPhenotypeSoil MicrobiologyChitinChitinasesenzyme discoverymetagenomicsmetatranscriptomicsmicrobial communitiesmicrobiomephenotypic association

Identifiers

PMID42036837
PMCPMC13111413

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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.