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Automated Microbial Diagnostics01:24

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Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...

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PMkbase (version 1.0): an interactive web-based tool for tracking bacterial metabolic traits using phenotype

K Jayanth Krishnan1, Ying Hefner1, Richard Szubin1

  • 1Department of Bioengineering, University of California San Diego, La Jolla, California, USA.

Microbiology Spectrum
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

A new database, Phenotype Microarray Knowledgebase (PMkbase), provides a public repository for bacterial phenotype microarray data. This resource enables researchers to analyze metabolic traits and genomic diversity across bacterial strains.

Keywords:
PM (phenotype microarray)bacterial genomesbacterial metabolismmetabolic phenotype tracking

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Area of Science:

  • Microbiology and Genomics
  • Systems Biology and Bioinformatics

Background:

  • Bacteria exhibit significant metabolic diversity, even within the same species, which is increasingly understood through genomic sequencing.
  • Phenotype microarrays (PMs) are crucial for measuring bacterial metabolic activity and traits by assessing cellular respiration on various substrates and stressors.
  • Existing research lacks centralized public databases for warehousing, accessing, and analyzing PM data, hindering comparative studies of metabolic traits across strains.

Purpose of the Study:

  • To address the unmet need for a public repository of bacterial phenotype microarray data.
  • To provide researchers with tools for analyzing and visualizing metabolic traits and genomic information.
  • To facilitate the development of metabolic models, enrich pangenomic analyses, and guide experimental design.

Main Methods:

  • Development of the Phenotype Microarray Knowledgebase (PMkbase), an interactive database integrating PM data with sequence information.
  • PMkbase processes PM data to generate binarized activity calls and associated kinetic parameters for metabolic substrates and inhibitors.
  • The platform allows users to upload their own data for analysis, visualization, and quality control.

Main Results:

  • PMkbase houses a substantial collection of PM data, including over 9,000 nitrogen, 41,000 carbon, and 8,000 phosphorus/sulfur substrate utilization data points.
  • The database also contains over 27,000 data points on antibiotic effects across three key bacterial species: *Escherichia coli*, *Pseudomonas putida*, and *Staphylococcus aureus*.
  • Binarized activity calls and kinetic parameters are provided for analyzed substrates and inhibitors.

Conclusions:

  • PMkbase serves as a valuable, publicly accessible resource for bacterial metabolic trait data.
  • The integration of PM data with sequence information enhances the utility for comparative genomics and metabolic modeling.
  • This knowledgebase will empower researchers to better understand bacterial metabolic diversity and advance microbial research.