Applying predictive models to decipher rhizobacterial modifications in common reed die-back affected populations

The microbiota inhabiting the soil, as well as the rhizosphere, represents a key determinant of several plant functions. Like for humans, dysbiosis of the plant-associated microbiota may be a co-causal agent in disease with still obscure eziology. In the last decades, the common reed Phragmites aust...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:The Science of the total environment 2018-11, Vol.642, p.708-722
Hauptverfasser: Bacci, Giovanni, Cerri, Martina, Lastrucci, Lorenzo, Ferranti, Francesco, Ferri, Valentina, Foggi, Bruno, Gigante, Daniela, Venanzoni, Roberto, Viciani, Daniele, Mengoni, Alessio, Reale, Lara, Coppi, Andrea
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 722
container_issue
container_start_page 708
container_title The Science of the total environment
container_volume 642
creator Bacci, Giovanni
Cerri, Martina
Lastrucci, Lorenzo
Ferranti, Francesco
Ferri, Valentina
Foggi, Bruno
Gigante, Daniela
Venanzoni, Roberto
Viciani, Daniele
Mengoni, Alessio
Reale, Lara
Coppi, Andrea
description The microbiota inhabiting the soil, as well as the rhizosphere, represents a key determinant of several plant functions. Like for humans, dysbiosis of the plant-associated microbiota may be a co-causal agent in disease with still obscure eziology. In the last decades, the common reed Phragmites australis has been deeply studied for its disappearance from natural stands, but no clear causative agents have been identified and no laboratory models of such “reed die-back syndrome” (RDBS) have been developed. In this study, we try to shed light on the RDBS, by comparing the rhizosphere microbiota of five Italian P. australis populations with different degrees of decline. Results obtained showed a biogeographical meaningful pattern of rhizosphere microbiota, coupled with an impact of RDBS. Obtained data allowed to construct a two-steps predictive model which enabled the prediction of the plant health status from the microbiota taxonomic composition, independently from their geographic location. In conclusion, this study represents one of the first overviews that statistically links RDBS to alteration of rhizosphere microbiota and suggests a model for the analysis of plant-bacteria relationships in nature. [Display omitted] •Die-back of Phragmites australis is a still obscure syndrome.•Root microbiota composition was shown to be affected by both sites and health status.•A plant health status x site x microbiota interaction was defined.•Root microbiota composition was shown to be predictable from plant health status.
doi_str_mv 10.1016/j.scitotenv.2018.06.066
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2057127860</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0048969718321375</els_id><sourcerecordid>2057127860</sourcerecordid><originalsourceid>FETCH-LOGICAL-c371t-d6c9ce81fdb0c514b6e7024bfc3d0ecf59b48ec84999f335d273c1731bb9bb93</originalsourceid><addsrcrecordid>eNqFkMFqGzEQhkVpaJykr9Dq2Ms60motrY7GtE0gkEvuYnc0SuTurlRpbXCfPjJ2fc3ww1y-fwY-Qr5ztuSMy_vtMoOfw4zTflkz3i6ZLJGfyIK3Slec1fIzWTDWtJWWWl2Tm5y3rIxq-RdyXWvNhZByQeI6xuHgp1caE1oPs98jHYPFIdM5UIvg4xsmmt78v9B3MGPy3XAkvPPQzT5MmfqJQhjHMNGEaKn1WBX0D-2cw9KwNIa4G07wHbly3ZDx63nfkpdfP182D9XT8-_HzfqpAqH4XFkJGrDlzvYMVrzpJSpWN70DYRmCW-m-aRHaRmvthFjZWgngSvC-1yXilvw4nY0p_N1hns3oM-AwdBOGXTY1Wyleq1aygqoTCinknNCZmPzYpYPhzBxtm6252DZH24bJElma385Pdv2I9tL7r7cA6xNQdOLeYzoewgmK6VTMGBv8h0_eAcJOmNE</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2057127860</pqid></control><display><type>article</type><title>Applying predictive models to decipher rhizobacterial modifications in common reed die-back affected populations</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><creator>Bacci, Giovanni ; Cerri, Martina ; Lastrucci, Lorenzo ; Ferranti, Francesco ; Ferri, Valentina ; Foggi, Bruno ; Gigante, Daniela ; Venanzoni, Roberto ; Viciani, Daniele ; Mengoni, Alessio ; Reale, Lara ; Coppi, Andrea</creator><creatorcontrib>Bacci, Giovanni ; Cerri, Martina ; Lastrucci, Lorenzo ; Ferranti, Francesco ; Ferri, Valentina ; Foggi, Bruno ; Gigante, Daniela ; Venanzoni, Roberto ; Viciani, Daniele ; Mengoni, Alessio ; Reale, Lara ; Coppi, Andrea</creatorcontrib><description>The microbiota inhabiting the soil, as well as the rhizosphere, represents a key determinant of several plant functions. Like for humans, dysbiosis of the plant-associated microbiota may be a co-causal agent in disease with still obscure eziology. In the last decades, the common reed Phragmites australis has been deeply studied for its disappearance from natural stands, but no clear causative agents have been identified and no laboratory models of such “reed die-back syndrome” (RDBS) have been developed. In this study, we try to shed light on the RDBS, by comparing the rhizosphere microbiota of five Italian P. australis populations with different degrees of decline. Results obtained showed a biogeographical meaningful pattern of rhizosphere microbiota, coupled with an impact of RDBS. Obtained data allowed to construct a two-steps predictive model which enabled the prediction of the plant health status from the microbiota taxonomic composition, independently from their geographic location. In conclusion, this study represents one of the first overviews that statistically links RDBS to alteration of rhizosphere microbiota and suggests a model for the analysis of plant-bacteria relationships in nature. [Display omitted] •Die-back of Phragmites australis is a still obscure syndrome.•Root microbiota composition was shown to be affected by both sites and health status.•A plant health status x site x microbiota interaction was defined.•Root microbiota composition was shown to be predictable from plant health status.</description><identifier>ISSN: 0048-9697</identifier><identifier>EISSN: 1879-1026</identifier><identifier>DOI: 10.1016/j.scitotenv.2018.06.066</identifier><identifier>PMID: 29913366</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Ecology ; Italy ; Microbiota ; Models, Theoretical ; Phragmites australis ; Plant Roots ; Poaceae ; Predictive models ; Reed die-back ; Rhizosphere ; Soil Microbiology</subject><ispartof>The Science of the total environment, 2018-11, Vol.642, p.708-722</ispartof><rights>2018 Elsevier B.V.</rights><rights>Copyright © 2018 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c371t-d6c9ce81fdb0c514b6e7024bfc3d0ecf59b48ec84999f335d273c1731bb9bb93</citedby><cites>FETCH-LOGICAL-c371t-d6c9ce81fdb0c514b6e7024bfc3d0ecf59b48ec84999f335d273c1731bb9bb93</cites><orcidid>0000-0003-4760-8403 ; 0000-0002-1265-8251 ; 0000-0003-4406-7816</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.scitotenv.2018.06.066$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29913366$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bacci, Giovanni</creatorcontrib><creatorcontrib>Cerri, Martina</creatorcontrib><creatorcontrib>Lastrucci, Lorenzo</creatorcontrib><creatorcontrib>Ferranti, Francesco</creatorcontrib><creatorcontrib>Ferri, Valentina</creatorcontrib><creatorcontrib>Foggi, Bruno</creatorcontrib><creatorcontrib>Gigante, Daniela</creatorcontrib><creatorcontrib>Venanzoni, Roberto</creatorcontrib><creatorcontrib>Viciani, Daniele</creatorcontrib><creatorcontrib>Mengoni, Alessio</creatorcontrib><creatorcontrib>Reale, Lara</creatorcontrib><creatorcontrib>Coppi, Andrea</creatorcontrib><title>Applying predictive models to decipher rhizobacterial modifications in common reed die-back affected populations</title><title>The Science of the total environment</title><addtitle>Sci Total Environ</addtitle><description>The microbiota inhabiting the soil, as well as the rhizosphere, represents a key determinant of several plant functions. Like for humans, dysbiosis of the plant-associated microbiota may be a co-causal agent in disease with still obscure eziology. In the last decades, the common reed Phragmites australis has been deeply studied for its disappearance from natural stands, but no clear causative agents have been identified and no laboratory models of such “reed die-back syndrome” (RDBS) have been developed. In this study, we try to shed light on the RDBS, by comparing the rhizosphere microbiota of five Italian P. australis populations with different degrees of decline. Results obtained showed a biogeographical meaningful pattern of rhizosphere microbiota, coupled with an impact of RDBS. Obtained data allowed to construct a two-steps predictive model which enabled the prediction of the plant health status from the microbiota taxonomic composition, independently from their geographic location. In conclusion, this study represents one of the first overviews that statistically links RDBS to alteration of rhizosphere microbiota and suggests a model for the analysis of plant-bacteria relationships in nature. [Display omitted] •Die-back of Phragmites australis is a still obscure syndrome.•Root microbiota composition was shown to be affected by both sites and health status.•A plant health status x site x microbiota interaction was defined.•Root microbiota composition was shown to be predictable from plant health status.</description><subject>Ecology</subject><subject>Italy</subject><subject>Microbiota</subject><subject>Models, Theoretical</subject><subject>Phragmites australis</subject><subject>Plant Roots</subject><subject>Poaceae</subject><subject>Predictive models</subject><subject>Reed die-back</subject><subject>Rhizosphere</subject><subject>Soil Microbiology</subject><issn>0048-9697</issn><issn>1879-1026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkMFqGzEQhkVpaJykr9Dq2Ms60motrY7GtE0gkEvuYnc0SuTurlRpbXCfPjJ2fc3ww1y-fwY-Qr5ztuSMy_vtMoOfw4zTflkz3i6ZLJGfyIK3Slec1fIzWTDWtJWWWl2Tm5y3rIxq-RdyXWvNhZByQeI6xuHgp1caE1oPs98jHYPFIdM5UIvg4xsmmt78v9B3MGPy3XAkvPPQzT5MmfqJQhjHMNGEaKn1WBX0D-2cw9KwNIa4G07wHbly3ZDx63nfkpdfP182D9XT8-_HzfqpAqH4XFkJGrDlzvYMVrzpJSpWN70DYRmCW-m-aRHaRmvthFjZWgngSvC-1yXilvw4nY0p_N1hns3oM-AwdBOGXTY1Wyleq1aygqoTCinknNCZmPzYpYPhzBxtm6252DZH24bJElma385Pdv2I9tL7r7cA6xNQdOLeYzoewgmK6VTMGBv8h0_eAcJOmNE</recordid><startdate>20181115</startdate><enddate>20181115</enddate><creator>Bacci, Giovanni</creator><creator>Cerri, Martina</creator><creator>Lastrucci, Lorenzo</creator><creator>Ferranti, Francesco</creator><creator>Ferri, Valentina</creator><creator>Foggi, Bruno</creator><creator>Gigante, Daniela</creator><creator>Venanzoni, Roberto</creator><creator>Viciani, Daniele</creator><creator>Mengoni, Alessio</creator><creator>Reale, Lara</creator><creator>Coppi, Andrea</creator><general>Elsevier B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4760-8403</orcidid><orcidid>https://orcid.org/0000-0002-1265-8251</orcidid><orcidid>https://orcid.org/0000-0003-4406-7816</orcidid></search><sort><creationdate>20181115</creationdate><title>Applying predictive models to decipher rhizobacterial modifications in common reed die-back affected populations</title><author>Bacci, Giovanni ; Cerri, Martina ; Lastrucci, Lorenzo ; Ferranti, Francesco ; Ferri, Valentina ; Foggi, Bruno ; Gigante, Daniela ; Venanzoni, Roberto ; Viciani, Daniele ; Mengoni, Alessio ; Reale, Lara ; Coppi, Andrea</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c371t-d6c9ce81fdb0c514b6e7024bfc3d0ecf59b48ec84999f335d273c1731bb9bb93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Ecology</topic><topic>Italy</topic><topic>Microbiota</topic><topic>Models, Theoretical</topic><topic>Phragmites australis</topic><topic>Plant Roots</topic><topic>Poaceae</topic><topic>Predictive models</topic><topic>Reed die-back</topic><topic>Rhizosphere</topic><topic>Soil Microbiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bacci, Giovanni</creatorcontrib><creatorcontrib>Cerri, Martina</creatorcontrib><creatorcontrib>Lastrucci, Lorenzo</creatorcontrib><creatorcontrib>Ferranti, Francesco</creatorcontrib><creatorcontrib>Ferri, Valentina</creatorcontrib><creatorcontrib>Foggi, Bruno</creatorcontrib><creatorcontrib>Gigante, Daniela</creatorcontrib><creatorcontrib>Venanzoni, Roberto</creatorcontrib><creatorcontrib>Viciani, Daniele</creatorcontrib><creatorcontrib>Mengoni, Alessio</creatorcontrib><creatorcontrib>Reale, Lara</creatorcontrib><creatorcontrib>Coppi, Andrea</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>The Science of the total environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bacci, Giovanni</au><au>Cerri, Martina</au><au>Lastrucci, Lorenzo</au><au>Ferranti, Francesco</au><au>Ferri, Valentina</au><au>Foggi, Bruno</au><au>Gigante, Daniela</au><au>Venanzoni, Roberto</au><au>Viciani, Daniele</au><au>Mengoni, Alessio</au><au>Reale, Lara</au><au>Coppi, Andrea</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Applying predictive models to decipher rhizobacterial modifications in common reed die-back affected populations</atitle><jtitle>The Science of the total environment</jtitle><addtitle>Sci Total Environ</addtitle><date>2018-11-15</date><risdate>2018</risdate><volume>642</volume><spage>708</spage><epage>722</epage><pages>708-722</pages><issn>0048-9697</issn><eissn>1879-1026</eissn><abstract>The microbiota inhabiting the soil, as well as the rhizosphere, represents a key determinant of several plant functions. Like for humans, dysbiosis of the plant-associated microbiota may be a co-causal agent in disease with still obscure eziology. In the last decades, the common reed Phragmites australis has been deeply studied for its disappearance from natural stands, but no clear causative agents have been identified and no laboratory models of such “reed die-back syndrome” (RDBS) have been developed. In this study, we try to shed light on the RDBS, by comparing the rhizosphere microbiota of five Italian P. australis populations with different degrees of decline. Results obtained showed a biogeographical meaningful pattern of rhizosphere microbiota, coupled with an impact of RDBS. Obtained data allowed to construct a two-steps predictive model which enabled the prediction of the plant health status from the microbiota taxonomic composition, independently from their geographic location. In conclusion, this study represents one of the first overviews that statistically links RDBS to alteration of rhizosphere microbiota and suggests a model for the analysis of plant-bacteria relationships in nature. [Display omitted] •Die-back of Phragmites australis is a still obscure syndrome.•Root microbiota composition was shown to be affected by both sites and health status.•A plant health status x site x microbiota interaction was defined.•Root microbiota composition was shown to be predictable from plant health status.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>29913366</pmid><doi>10.1016/j.scitotenv.2018.06.066</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0003-4760-8403</orcidid><orcidid>https://orcid.org/0000-0002-1265-8251</orcidid><orcidid>https://orcid.org/0000-0003-4406-7816</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0048-9697
ispartof The Science of the total environment, 2018-11, Vol.642, p.708-722
issn 0048-9697
1879-1026
language eng
recordid cdi_proquest_miscellaneous_2057127860
source MEDLINE; Elsevier ScienceDirect Journals
subjects Ecology
Italy
Microbiota
Models, Theoretical
Phragmites australis
Plant Roots
Poaceae
Predictive models
Reed die-back
Rhizosphere
Soil Microbiology
title Applying predictive models to decipher rhizobacterial modifications in common reed die-back affected populations
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T05%3A02%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Applying%20predictive%20models%20to%20decipher%20rhizobacterial%20modifications%20in%20common%20reed%20die-back%20affected%20populations&rft.jtitle=The%20Science%20of%20the%20total%20environment&rft.au=Bacci,%20Giovanni&rft.date=2018-11-15&rft.volume=642&rft.spage=708&rft.epage=722&rft.pages=708-722&rft.issn=0048-9697&rft.eissn=1879-1026&rft_id=info:doi/10.1016/j.scitotenv.2018.06.066&rft_dat=%3Cproquest_cross%3E2057127860%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2057127860&rft_id=info:pmid/29913366&rft_els_id=S0048969718321375&rfr_iscdi=true