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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Updated: Jul 31, 2025

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures
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Mapping human pathogens in wastewater using a metatranscriptomic approach.

João Carneiro1, Francisco Pascoal2, Miguel Semedo1

  • 1CIIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros Do Porto de Leixões, Av. General Norton de Matos, S/n, 4450-208, Matosinhos, Portugal.

Environmental Research
|May 7, 2023
PubMed
Summary
This summary is machine-generated.

Wastewater surveillance using metatranscriptomics can now rapidly and accurately detect pathogenic bacteria and viruses. This new bioinformatic workflow enhances public health monitoring in urban environments.

Keywords:
Human pathogensMetatranscriptomicsPublic healthWastewater

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Area of Science:

  • Environmental microbiology
  • Bioinformatics
  • Public health

Background:

  • Wastewater surveillance is crucial for urban public health, especially during pandemics.
  • Traditional methods like DNA/RNA isolation with qPCR/RT-qPCR detect specific pathogens.
  • Metatranscriptomics offers broader detection but generates large datasets requiring advanced analysis.

Purpose of the Study:

  • To develop a reproducible bioinformatic workflow for detecting pathogenic bacteria and viruses in wastewater using metatranscriptomic data.
  • To create a computational methodology for accurate and efficient identification of human pathogens in urban wastewater.
  • To enable rapid and sensitive wastewater monitoring for public health.

Main Methods:

  • Implementation of a bioinformatic workflow analyzing metatranscriptomic data.
  • Development of algorithms for detecting pathogenic bacteria and viruses.
  • Utilizing open-source software and efficient computational resources.

Main Results:

  • The workflow successfully mapped detected pathogenic bacteria and viruses in wastewater samples.
  • Algorithms accurately predicted the presence of potential human pathogens.
  • Active forms of both bacteria and viruses were identified in the samples.
  • The implemented method demonstrated faster analysis, higher accuracy, and sensitivity compared to existing workflows.

Conclusions:

  • The developed workflow significantly improves the speed and accuracy of wastewater-based pathogen detection.
  • This approach enhances the capability of wastewater monitoring programs for public health surveillance.
  • The open-source workflow is readily available for implementation in current monitoring systems.