Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive.

Aging is universal, yet characterizing the molecular changes that occur in aging which lead to an increased risk for neurological disease remains a challenging problem. Aging affects the prefrontal cortex (PFC), which governs executive function, learning, and memory. Previous sequencing studies have...

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Autores principales:	Joe L Webb, Simon M Moe, Andrew K Bolstad, Elizabeth M McNeill
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/46408acfea83454ba82acb25a677afa4
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spelling	oai:doaj.org-article:46408acfea83454ba82acb25a677afa42021-12-02T20:15:04ZIdentification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive.1932-620310.1371/journal.pone.0255085https://doaj.org/article/46408acfea83454ba82acb25a677afa42021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255085https://doaj.org/toc/1932-6203Aging is universal, yet characterizing the molecular changes that occur in aging which lead to an increased risk for neurological disease remains a challenging problem. Aging affects the prefrontal cortex (PFC), which governs executive function, learning, and memory. Previous sequencing studies have demonstrated that aging alters gene expression in the PFC, however the extent to which these changes are conserved across species and are meaningful in neurodegeneration is unknown. Identifying conserved, age-related genetic and morphological changes in the brain allows application of the wealth of tools available to study underlying mechanisms in model organisms such as Drosophila melanogaster. RNA sequencing data from human PFC and fly heads were analyzed to determine conserved transcriptome signatures of age. Our analysis revealed that expression of 50 conserved genes can accurately determine age in Drosophila (R2 = 0.85) and humans (R2 = 0.46). These transcriptome signatures were also able to classify Drosophila into three age groups with a mean accuracy of 88% and classify human samples with a mean accuracy of 69%. Overall, this work identifies 50 highly conserved aging-associated genetic changes in the brain that can be further studied in model organisms and demonstrates a novel approach to uncovering genetic changes conserved across species from multi-study public databases.Joe L WebbSimon M MoeAndrew K BolstadElizabeth M McNeillPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255085 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Joe L Webb Simon M Moe Andrew K Bolstad Elizabeth M McNeill Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive.
description	Aging is universal, yet characterizing the molecular changes that occur in aging which lead to an increased risk for neurological disease remains a challenging problem. Aging affects the prefrontal cortex (PFC), which governs executive function, learning, and memory. Previous sequencing studies have demonstrated that aging alters gene expression in the PFC, however the extent to which these changes are conserved across species and are meaningful in neurodegeneration is unknown. Identifying conserved, age-related genetic and morphological changes in the brain allows application of the wealth of tools available to study underlying mechanisms in model organisms such as Drosophila melanogaster. RNA sequencing data from human PFC and fly heads were analyzed to determine conserved transcriptome signatures of age. Our analysis revealed that expression of 50 conserved genes can accurately determine age in Drosophila (R2 = 0.85) and humans (R2 = 0.46). These transcriptome signatures were also able to classify Drosophila into three age groups with a mean accuracy of 88% and classify human samples with a mean accuracy of 69%. Overall, this work identifies 50 highly conserved aging-associated genetic changes in the brain that can be further studied in model organisms and demonstrates a novel approach to uncovering genetic changes conserved across species from multi-study public databases.
format	article
author	Joe L Webb Simon M Moe Andrew K Bolstad Elizabeth M McNeill
author_facet	Joe L Webb Simon M Moe Andrew K Bolstad Elizabeth M McNeill
author_sort	Joe L Webb
title	Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive.
title_short	Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive.
title_full	Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive.
title_fullStr	Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive.
title_full_unstemmed	Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive.
title_sort	identification of conserved transcriptome features between humans and drosophila in the aging brain utilizing machine learning on combined data from the nih sequence read archive.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/46408acfea83454ba82acb25a677afa4
work_keys_str_mv	AT joelwebb identificationofconservedtranscriptomefeaturesbetweenhumansanddrosophilaintheagingbrainutilizingmachinelearningoncombineddatafromthenihsequencereadarchive AT simonmmoe identificationofconservedtranscriptomefeaturesbetweenhumansanddrosophilaintheagingbrainutilizingmachinelearningoncombineddatafromthenihsequencereadarchive AT andrewkbolstad identificationofconservedtranscriptomefeaturesbetweenhumansanddrosophilaintheagingbrainutilizingmachinelearningoncombineddatafromthenihsequencereadarchive AT elizabethmmcneill identificationofconservedtranscriptomefeaturesbetweenhumansanddrosophilaintheagingbrainutilizingmachinelearningoncombineddatafromthenihsequencereadarchive
_version_	1718374578106925056

Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive.

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