Evidence map›Paper›PMID 36798209›Full record

ArticlebioRxiv : the preprint server for biology2024

Engineering bacteriophages through deep mining of metagenomic motifs.

Phil Huss, Kristopher Kieft, Anthony Meger, Kyle Nishikawa, Karthik Anantharaman, Srivatsan Raman

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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
–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

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, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

University of Wisconsin–Madison · US

Funding

COMPUTATIONAL FRAMEWORKS FOR PHAGE DISCOVERY, ECOLOGY, AND DYNAMICS FROM METAGENOMESR35GM143024 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI ANANTHARAMAN, KARTHIK · 2021 to 2025
$1.9M
NIGMS NIH HHS R35 GM143024
6 · The paper itself

Abstract

Bacteriophages can adapt to new hosts by altering sequence motifs through recombination or convergent evolution. Where such motifs exist and what fitness advantage they confer remains largely unknown. We report a new method, Metagenomic Sequence Informed Functional Scoring (Meta-SIFT), to discover sequence motifs in metagenomic datasets that can be used to engineer phage activity. Meta-SIFT uses experimental deep mutational scanning data to create sequence profiles to enable deep mining of metagenomes for functional motifs which are otherwise invisible to searches. We experimentally tested over 17,000 Meta-SIFT derived sequence motifs in the receptor-binding protein of the T7 phage. The screen revealed thousands of T7 variants with novel host specificity with functional motifs sourced from distant families. Position, substitution and location preferences dictated specificity across a panel of 20 hosts and conditions. To demonstrate therapeutic utility, we engineered active T7 variants against foodborne pathogen

Identifiers

PMID36798209
PMCPMC9934549
OpenAlexW4319602876

What OpenQuestion holds

Textmetadata
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.