Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

Sganzerla Martinez, Gustavo; Garduno, Alexis; Toloue Ostadgavahi, Ali; Hewins, Benjamin; Dutt, Mansi; Kumar, Anuj; Martin-Loeches, Ignacio; Kelvin, David J

Sganzerla Martinez, Gustavo; Garduno, Alexis; Toloue Ostadgavahi, Ali; Hewins, Benjamin; Dutt, Mansi; Kumar, Anuj; Martin-Loeches, Ignacio; Kelvin, David J.

Afiliación

Sganzerla Martinez G; Microbiology and Immunology, Dalhousie University, Halifax, NS B3H 4H7, Canada.
Garduno A; Department of Pediatrics, Izaak Walton Killam (IWK) Health Center, Canadian Center for Vaccinology, Halifax, NS B3H 4H7, Canada.
Toloue Ostadgavahi A; Department of Immunology, Shantou University Medical College, Shantou 512025, China.
Hewins B; Department of Clinical Medicine, Trinity College Dublin, D08 NHY1 Dublin, Ireland.
Dutt M; Department of Intensive Care Medicine, St. James's Hospital, D08 NHY1 Dublin, Ireland.
Kumar A; Microbiology and Immunology, Dalhousie University, Halifax, NS B3H 4H7, Canada.
Martin-Loeches I; Department of Pediatrics, Izaak Walton Killam (IWK) Health Center, Canadian Center for Vaccinology, Halifax, NS B3H 4H7, Canada.
Kelvin DJ; Department of Immunology, Shantou University Medical College, Shantou 512025, China.

Int J Mol Sci ; 25(11)2024 May 29.

Article en En | MEDLINE | ID: mdl-38892107

ABSTRACT

ABSTRACT

A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRÉ£Î´ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.

Asunto(s)

COVID-19; Aprendizaje Automático; RNA-Seq; Humanos; COVID-19/genética; COVID-19/virología; COVID-19/diagnóstico; RNA-Seq/métodos; Biomarcadores; SARS-CoV-2/genética; Análisis de la Célula Individual/métodos; Sepsis/genética; Sepsis/diagnóstico; Sepsis/sangre; Transcriptoma; Perfilación de la Expresión Génica/métodos; Análisis de Secuencia de ARN/métodos; Análisis de Expresión Génica de una Sola Célula

Palabras clave

artificial intelligence; marker genes; sepsis; single-cell RNA sequencing

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático / RNA-Seq / COVID-19 Límite: Humans Idioma: En Revista: Int J Mol Sci Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Suiza

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