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Related Experiment Videos

Pathogen discovery from human tissue by sequence-based computational subtraction.

Yaohui Xu1, Nicole Stange-Thomann, Griffin Weber

  • 1Department of Adult Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA.

Genomics
|March 28, 2003
PubMed
Summary
This summary is machine-generated.

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Computational subtraction, a novel pathogen discovery method, successfully identified Epstein-Barr virus (EBV) in human tissue. This technique filters non-human transcripts, proving effective for detecting microbes in diseased samples.

Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Pathogen discovery is crucial for diagnosing and treating infectious diseases.
  • Current methods may face limitations in identifying novel or unexpected pathogens.
  • Computational subtraction offers a new strategy for pathogen identification.

Purpose of the Study:

  • To experimentally validate the feasibility of the computational subtraction approach for pathogen discovery.
  • To assess the efficacy of computational subtraction in identifying microbial sequences in human diseased tissue.
  • To demonstrate the potential of this method in detecting known pathogens like Epstein-Barr virus (EBV).

Main Methods:

  • Generation of a cDNA library from a post-transplant lymphoproliferative disorder (PTLD) tissue sample.

Related Experiment Videos

  • Sequencing of cDNA library and computational subtraction against the human genome.
  • Amplification of nonmatching transcripts to differentiate microbial from novel human DNA.

    • Main Results:

    • Out of 27,840 cDNA sequences, 32 nonmatching transcripts were identified.
    • 22 transcripts were identified as novel human DNA.
    • 10 transcripts were uniquely amplified from EBV-infected tissue, confirming EBV presence.

    Conclusions:

    • Computational subtraction is an experimentally feasible method for pathogen discovery.
    • The approach successfully identified Epstein-Barr virus in primary human diseased tissue.
    • This proof-of-principle study highlights the utility of computational subtraction in identifying pathogenic microbes.