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Related Concept Videos

MicroRNAs01:22

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA ends...
MicroRNAs01:22

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA ends...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
MicroRNAs01:22

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
Automated Microbial Diagnostics01:24

Automated Microbial Diagnostics

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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Probe-based Real-time PCR Approaches for Quantitative Measurement of microRNAs
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Towards a microRNA-based Johne's disease diagnostic predictive system: Preliminary results.

Paul Capewell1, Arianne Lowe2, Spiridoula Athanasiadou2

  • 1School of Molecular Biosciences, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, UK.

The Veterinary Record
|November 20, 2024
PubMed
Summary

A new study shows microRNA profiling can accurately diagnose Johne's disease in cattle. This method, using machine learning on serum microRNAs, offers a promising tool for managing this costly cattle enteritis.

Keywords:
Johne's diseasediagnosticsmicroRNApredictive modelling

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Area of Science:

  • Veterinary Medicine
  • Genomics
  • Molecular Diagnostics

Background:

  • Johne's disease, a chronic enteritis in cattle caused by Mycobacterium avium subspecies paratuberculosis (MAP), significantly impacts animal welfare and farm productivity.
  • Current diagnostic methods for Johne's disease suffer from low sensitivity, complicating effective herd management through animal screening and removal.
  • MicroRNAs, small non-coding RNAs regulating gene expression, are being investigated as potential biomarkers for mycobacterial infections like Johne's disease.

Purpose of the Study:

  • To evaluate the potential of microRNA expression profiling in cattle serum for diagnosing Johne's disease.
  • To develop and validate a machine learning-based diagnostic classifier using microRNA data.

Main Methods:

  • Serum samples were collected from 66 MAP-positive and 65 MAP-negative cattle.
  • Expression levels of 24 microRNAs, known to be affected by mycobacterial infection, were quantified.
  • A machine learning approach was employed to construct an optimal diagnostic classifier for MAP.

Main Results:

  • The developed microRNA profiling method achieved an average accuracy of 72%.
  • The classifier demonstrated an average sensitivity of 73% and specificity of 71%.
  • The area under the receiver operating characteristic curve (AUC) was 78%, indicating good diagnostic performance.

Conclusions:

  • MicroRNA profiling, when integrated with advanced predictive modeling, offers a rapid and accurate diagnostic approach for Johne's disease in cattle.
  • While promising, potential misclassification of animals due to the limitations of current diagnostics (low sensitivity) in control groups was acknowledged.
  • This technique presents a novel avenue for improving Johne's disease surveillance and control strategies in cattle populations.