Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning

Autores
Prochetto, Santiago; Stegmayer, Georgina; Studer, Anthony J; Reinheimer, Renata
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Kranz syndrome is a set of leaf anatomical and functional characteristics of species using C4 photosynthesis. The current model for the evolution of C4 photosynthesis from a C3 ancestor proposes a series of gradual anatomical changes followed by a biochemical adaptation of the C4 cycle enzymatic machinery. In this work, leaf anatomical traits from closely related C3, C4 and intermediate species (Proto-Kranz, PK) were analyzed together with gene expression data to discover potential drivers for the establishment of Kranz anatomy using unsupervised machine learning. Species-specific Self-Organizing Maps (SOM) were developed to group features (genes and phenotypic traits) into clusters (neurons) according to their expression along the leaf developmental gradient. The analysis with SOM allowed us to identify candidate genes as enablers of key anatomical traits differentiation related to the area of mesophyll (M) and bundle sheath (BS) cells, vein density, and the interface between M and BS cells. At the same time, we identified a small subset of genes that displaced together with the change in the area of the BS cell along evolution suggesting a salient role in the origin of Kranz anatomy in grasses.
Fil: Prochetto, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Studer, Anthony J. University of Illinois. Urbana - Champaign; Estados Unidos
Fil: Reinheimer, Renata. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentina
Materia
Leaf anatomy
Self organizing maps
Photosynthesis
Transcription factors
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/265171
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/265171
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learningProchetto, SantiagoStegmayer, GeorginaStuder, Anthony JReinheimer, RenataLeaf anatomySelf organizing mapsPhotosynthesisTranscription factorshttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Kranz syndrome is a set of leaf anatomical and functional characteristics of species using C4 photosynthesis. The current model for the evolution of C4 photosynthesis from a C3 ancestor proposes a series of gradual anatomical changes followed by a biochemical adaptation of the C4 cycle enzymatic machinery. In this work, leaf anatomical traits from closely related C3, C4 and intermediate species (Proto-Kranz, PK) were analyzed together with gene expression data to discover potential drivers for the establishment of Kranz anatomy using unsupervised machine learning. Species-specific Self-Organizing Maps (SOM) were developed to group features (genes and phenotypic traits) into clusters (neurons) according to their expression along the leaf developmental gradient. The analysis with SOM allowed us to identify candidate genes as enablers of key anatomical traits differentiation related to the area of mesophyll (M) and bundle sheath (BS) cells, vein density, and the interface between M and BS cells. At the same time, we identified a small subset of genes that displaced together with the change in the area of the BS cell along evolution suggesting a salient role in the origin of Kranz anatomy in grasses.Fil: Prochetto, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Studer, Anthony J. University of Illinois. Urbana - Champaign; Estados UnidosFil: Reinheimer, Renata. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaCold Spring Harbor Laboratory Press2024-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/265171Prochetto, Santiago; Stegmayer, Georgina; Studer, Anthony J; Reinheimer, Renata; Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning; Cold Spring Harbor Laboratory Press; bioRxiv; 2-2024; 1-372692-8205CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1101/2024.01.31.578221info:eu-repo/semantics/altIdentifier/url/http://biorxiv.org/lookup/doi/10.1101/2024.01.31.578221info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:50:49Zoai:ri.conicet.gov.ar:11336/265171instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:50:50.214CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning
title Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning
spellingShingle Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning
Prochetto, Santiago
Leaf anatomy
Self organizing maps
Photosynthesis
Transcription factors
title_short Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning
title_full Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning
title_fullStr Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning
title_full_unstemmed Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning
title_sort Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning
dc.creator.none.fl_str_mv Prochetto, Santiago
Stegmayer, Georgina
Studer, Anthony J
Reinheimer, Renata
author Prochetto, Santiago
author_facet Prochetto, Santiago
Stegmayer, Georgina
Studer, Anthony J
Reinheimer, Renata
author_role author
author2 Stegmayer, Georgina
Studer, Anthony J
Reinheimer, Renata
author2_role author
author
author
dc.subject.none.fl_str_mv Leaf anatomy
Self organizing maps
Photosynthesis
Transcription factors
topic Leaf anatomy
Self organizing maps
Photosynthesis
Transcription factors
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Kranz syndrome is a set of leaf anatomical and functional characteristics of species using C4 photosynthesis. The current model for the evolution of C4 photosynthesis from a C3 ancestor proposes a series of gradual anatomical changes followed by a biochemical adaptation of the C4 cycle enzymatic machinery. In this work, leaf anatomical traits from closely related C3, C4 and intermediate species (Proto-Kranz, PK) were analyzed together with gene expression data to discover potential drivers for the establishment of Kranz anatomy using unsupervised machine learning. Species-specific Self-Organizing Maps (SOM) were developed to group features (genes and phenotypic traits) into clusters (neurons) according to their expression along the leaf developmental gradient. The analysis with SOM allowed us to identify candidate genes as enablers of key anatomical traits differentiation related to the area of mesophyll (M) and bundle sheath (BS) cells, vein density, and the interface between M and BS cells. At the same time, we identified a small subset of genes that displaced together with the change in the area of the BS cell along evolution suggesting a salient role in the origin of Kranz anatomy in grasses.
Fil: Prochetto, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Studer, Anthony J. University of Illinois. Urbana - Champaign; Estados Unidos
Fil: Reinheimer, Renata. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentina
description Kranz syndrome is a set of leaf anatomical and functional characteristics of species using C4 photosynthesis. The current model for the evolution of C4 photosynthesis from a C3 ancestor proposes a series of gradual anatomical changes followed by a biochemical adaptation of the C4 cycle enzymatic machinery. In this work, leaf anatomical traits from closely related C3, C4 and intermediate species (Proto-Kranz, PK) were analyzed together with gene expression data to discover potential drivers for the establishment of Kranz anatomy using unsupervised machine learning. Species-specific Self-Organizing Maps (SOM) were developed to group features (genes and phenotypic traits) into clusters (neurons) according to their expression along the leaf developmental gradient. The analysis with SOM allowed us to identify candidate genes as enablers of key anatomical traits differentiation related to the area of mesophyll (M) and bundle sheath (BS) cells, vein density, and the interface between M and BS cells. At the same time, we identified a small subset of genes that displaced together with the change in the area of the BS cell along evolution suggesting a salient role in the origin of Kranz anatomy in grasses.
publishDate 2024
dc.date.none.fl_str_mv 2024-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/265171
Prochetto, Santiago; Stegmayer, Georgina; Studer, Anthony J; Reinheimer, Renata; Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning; Cold Spring Harbor Laboratory Press; bioRxiv; 2-2024; 1-37
2692-8205
CONICET Digital
CONICET
url http://hdl.handle.net/11336/265171
identifier_str_mv Prochetto, Santiago; Stegmayer, Georgina; Studer, Anthony J; Reinheimer, Renata; Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning; Cold Spring Harbor Laboratory Press; bioRxiv; 2-2024; 1-37
2692-8205
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1101/2024.01.31.578221
info:eu-repo/semantics/altIdentifier/url/http://biorxiv.org/lookup/doi/10.1101/2024.01.31.578221
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Cold Spring Harbor Laboratory Press
publisher.none.fl_str_mv Cold Spring Harbor Laboratory Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846083033528008704
score 13.22299

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