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Related Concept Videos

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

Gustavo Sganzerla Martinez1,2,3, Alexis Garduno4,5, Ali Toloue Ostadgavahi1,2,3

  • 1Microbiology and Immunology, Dalhousie University, Halifax, NS B3H 4H7, Canada.

International Journal of Molecular Sciences
|June 19, 2024
PubMed
Summary

Artificial intelligence and single-cell RNA sequencing can detect infection severity. Key gene expression in immune cells helps differentiate between mild and severe sepsis and COVID-19, aiding early diagnosis.

Keywords:
artificial intelligencemarker genessepsissingle-cell RNA sequencing

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Area of Science:

  • Immunology
  • Computational Biology
  • Genomics

Background:

  • Infections can trigger abnormal immune responses, leading to inflammation, tissue damage, and organ failure.
  • Immune dysregulation manifests as altered immune cell populations and biomarker concentrations.
  • Early diagnosis and severity assessment of immune-dysregulated syndromes are critical.

Purpose of the Study:

  • To develop an artificial intelligence (AI) tool for early diagnosis and severity differentiation of immune-dysregulated syndromes.
  • To utilize single-cell RNA sequencing (scRNA-seq) data for immune response analysis.
  • To identify key genes and immune cell markers for infection classification.

Main Methods:

  • Built an AI classification system using scRNA-seq data.
  • Analyzed gene expression patterns in immune cells.
  • Interpreted AI decision patterns to identify significant genes and cell types.

Main Results:

  • Single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19.
  • Specific gene expression changes (CD3, CD14, CD16, FOSB, S100A12, TCRɣδ) in immune cells accurately differentiated infection severity.
  • Identified immune cell populations crucial for distinguishing infection degrees.

Conclusions:

  • AI combined with scRNA-seq offers a promising approach for early diagnosis of infection severity.
  • Identified genes serve as potential diagnostic markers for sepsis and COVID-19.
  • These findings may guide the development of targeted immunotherapeutic interventions.