Evidence map›Paper›PMID 41003669›Full record

ArticleMicrobiology spectrum2025

PMkbase (version 1.0): an interactive web-based tool for tracking bacterial metabolic traits using phenotype microarrays made interoperable with sequence information and visualizing/processing PM data.

K Jayanth Krishnan, Ying Hefner, Richard Szubin, Jonathan Monk, David T Pride, Bernhard Palsson

Abstract read
In one paragraph

Article in Microbiology spectrum, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Multi-strain analysis ofmSystems · 2026
    Article
  3. Multi-strain Analysis ofbioRxiv : the preprint server for biology · 2025
    Article
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

6 authors.

K Jayanth KrishnanDepartment of Bioengineering, University of California San Diego, La Jolla, California, USA.ORCID 0000-0002-2851-9619
Ying HefnerDepartment of Bioengineering, University of California San Diego, La Jolla, California, USA.
Richard SzubinDepartment of Bioengineering, University of California San Diego, La Jolla, California, USA.
Jonathan MonkDepartment of Bioengineering, University of California San Diego, La Jolla, California, USA.
David T PrideDepartment of Pathology, University of California San Diego, La Jolla, California, USA.
Bernhard PalssonDepartment of Bioengineering, University of California San Diego, La Jolla, California, USA.ORCID 0000-0003-2357-6785

Funding

Systems Biology Approach to Redefine Susceptibility Testing and Treatment of MDR Pathogens in the Context of Host ImmunityU01AI124316 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PALSSON, BERNHARD O · 2016 to 2020
$10.2M
NIAID NIH HHS U01 AI124316NIH HHS U01-AI124316Novo Nordisk Fonden NNF20CC0035580
6 · The paper itself

Abstract

Bacteria showcase remarkable metabolic diversity and traits, even among strains of the same species. In recent years, a large number of bacterial genomes have been sequenced, leading to the elucidation and documentation of genomic differences and commonalities across and within species. Genome-scale metabolic reconstructions, which are often defined and curated using data from phenotype microarrays, elucidate the differences in metabolic traits resulting from genomic diversity. These microarrays measure cellular respiration on a variety of carbon, nitrogen, phosphorus, and sulfur sources and various stressors and inhibitors over a period of time to determine the metabolic activity of a given strain. Despite their popularity in measuring bacterial metabolic activity and traits, no public databases that allow researchers to warehouse, access, and analyze this information currently exist. Additionally, there are no publicly available tools that allow researchers to view the variance of these metabolic traits across bacterial strains. To address this need, we present Phenotype Microarray Knowledgebase (PMkbase [version 1.0], https://pmkbase.com/), an interactive database that acts as a repository of phenotype microarray (PM) data with integrated sequence information. Binarized activity calls, along with associated kinetic parameters, are made for all metabolic substrates and inhibitors. Users can upload their own data for analysis and visualization and to perform quality checks on their experiments. PMkbase will address an unmet need to track and view bacterial metabolic traits and provide researchers with valuable information to develop metabolic models, enrich pangenomic analyses, and design new experiments.IMPORTANCEBacterial species can be differentiated by their metabolic profiles or the type of nutrients they consume. Interestingly, strains within the same species also display differences in nutrient consumption. Phenotype microarrays are a high-throughput, widely used technology to measure which substrates can be metabolized by various microbial strains and the extent to which inhibitors can affect it. Despite their widespread use, public databases to parse and access this data type at scale do not exist. PMkbase, which contains 9,024 data points for nitrogen substrate utilization, 41,664 data points for carbon substrate utilization, 8,448 data points for phosphorus/sulfur substrate utilization, and 27,264 data points on various antibiotics across three species (

Indexed as

BacteriaMicroarray AnalysisGenome, BacterialInternetPhenotypeSoftwarebacterial genomesbacterial metabolismmetabolic phenotype trackingPM (phenotype microarray)

Identifiers

PMID41003669
PMCPMC12584640

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Registered trials

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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.