Loading…
Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients
Understanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine in...
Saved in:
Published in: | Genome medicine 2017-02, Vol.9 (1), p.21-21, Article 21 |
---|---|
Main Authors: | , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c526t-990f8a62db12286875bc183a53b09dba1e4b5ab0a42aa7b7756762ee3ca880f3 |
---|---|
cites | cdi_FETCH-LOGICAL-c526t-990f8a62db12286875bc183a53b09dba1e4b5ab0a42aa7b7756762ee3ca880f3 |
container_end_page | 21 |
container_issue | 1 |
container_start_page | 21 |
container_title | Genome medicine |
container_volume | 9 |
creator | Galloway-Peña, Jessica R Smith, Daniel P Sahasrabhojane, Pranoti Wadsworth, W Duncan Fellman, Bryan M Ajami, Nadim J Shpall, Elizabeth J Daver, Naval Guindani, Michele Petrosino, Joseph F Kontoyiannis, Dimitrios P Shelburne, Samuel A |
description | Understanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine intra-patient temporal microbiota variability, the factors driving such variability, and its clinical impact in an extensive longitudinal cohort of hospitalized cancer patients during chemotherapy.
The stool (n = 365) and oral (n = 483) samples of 59 patients with acute myeloid leukemia (AML) undergoing induction chemotherapy (IC) were sampled from initiation of chemotherapy until neutrophil recovery. Microbiome characterization was performed via analysis of 16S rRNA gene sequencing. Temporal variability was determined using coefficients of variation (CV) of the Shannon diversity index (SDI) and unweighted and weighted UniFrac distances per patient, per site. Measurements of intra-patient temporal variability and patient stability categories were analyzed for their correlations with genera abundances. Groups of patients were analyzed to determine if patients with adverse outcomes had significantly different levels of microbiome temporal variability. Potential clinical drivers of microbiome temporal instability were determined using multivariable regression analyses.
Our cohort evidenced a high degree of intra-patient temporal instability of stool and oral microbial diversity based on SDI CV. We identified statistically significant differences in the relative abundance of multiple taxa amongst individuals with different levels of microbiota temporal stability. Increased intra-patient temporal variability of the oral SDI was correlated with increased risk of infection during IC (P = 0.02), and higher stool SDI CVs were correlated with increased risk of infection 90 days post-IC (P = 0.04). Total days on antibiotics was significantly associated with increased temporal variability of both oral microbial diversity (P = 0.03) and community structure (P = 0.002).
These data quantify the longitudinal variability of the oral and gut microbiota in AML patients, show that increased variability was correlated with adverse clinical outcomes, and offer the possibility of using stabilizing taxa as a method of focused microbiome repletion. Furthermore, these results support the importance of longitudinal microbiome sampling and analyses, rather than one time measurements, in rese |
doi_str_mv | 10.1186/s13073-017-0409-1 |
format | article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5331640</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1873393410</sourcerecordid><originalsourceid>FETCH-LOGICAL-c526t-990f8a62db12286875bc183a53b09dba1e4b5ab0a42aa7b7756762ee3ca880f3</originalsourceid><addsrcrecordid>eNqNkTtvFTEQhS0EIiHwA2iQJRqaBb_tbZDQFS8pEk0KOjP2enMd7a4X2xsp-fX4ckMUqKg80pw5PjMfQi8peUupUe8K5UTzjlDdEUH6jj5Cp1RL1fW9-P74QX2CnpVyRYgSTOin6IQZJqSR6hT92O0hg68hx1uoMS04jThlmDAsA77cKp6jz8nFNAdcw7z-7l1DjuDiFOsNjgvep7LGClO8DQP2sPiQ8drcwlLLc_RkhKmEF3fvGbr49PFi96U7__b56-7DeeclU7WlJKMBxQZHGTPKaOk8NRwkd6QfHNAgnARHQDAA7XTbTCsWAvdgDBn5GXp_tF03N4fBt69bULvmOEO-sQmi_buzxL29TNdWck6VIM3gzZ1BTj-3UKqdY_FhmmAJaSuWGq2NFFL2_yPlvOeCHlxf_yO9Slte2iEOKtHoCSWaih5V7dKl5DDe56bEHkjbI2nbSNsDaUvbzKuHC99P_EHLfwFrHaYX</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1874073464</pqid></control><display><type>article</type><title>Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients</title><source>Publicly Available Content Database</source><source>PubMed Central</source><creator>Galloway-Peña, Jessica R ; Smith, Daniel P ; Sahasrabhojane, Pranoti ; Wadsworth, W Duncan ; Fellman, Bryan M ; Ajami, Nadim J ; Shpall, Elizabeth J ; Daver, Naval ; Guindani, Michele ; Petrosino, Joseph F ; Kontoyiannis, Dimitrios P ; Shelburne, Samuel A</creator><creatorcontrib>Galloway-Peña, Jessica R ; Smith, Daniel P ; Sahasrabhojane, Pranoti ; Wadsworth, W Duncan ; Fellman, Bryan M ; Ajami, Nadim J ; Shpall, Elizabeth J ; Daver, Naval ; Guindani, Michele ; Petrosino, Joseph F ; Kontoyiannis, Dimitrios P ; Shelburne, Samuel A</creatorcontrib><description>Understanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine intra-patient temporal microbiota variability, the factors driving such variability, and its clinical impact in an extensive longitudinal cohort of hospitalized cancer patients during chemotherapy.
The stool (n = 365) and oral (n = 483) samples of 59 patients with acute myeloid leukemia (AML) undergoing induction chemotherapy (IC) were sampled from initiation of chemotherapy until neutrophil recovery. Microbiome characterization was performed via analysis of 16S rRNA gene sequencing. Temporal variability was determined using coefficients of variation (CV) of the Shannon diversity index (SDI) and unweighted and weighted UniFrac distances per patient, per site. Measurements of intra-patient temporal variability and patient stability categories were analyzed for their correlations with genera abundances. Groups of patients were analyzed to determine if patients with adverse outcomes had significantly different levels of microbiome temporal variability. Potential clinical drivers of microbiome temporal instability were determined using multivariable regression analyses.
Our cohort evidenced a high degree of intra-patient temporal instability of stool and oral microbial diversity based on SDI CV. We identified statistically significant differences in the relative abundance of multiple taxa amongst individuals with different levels of microbiota temporal stability. Increased intra-patient temporal variability of the oral SDI was correlated with increased risk of infection during IC (P = 0.02), and higher stool SDI CVs were correlated with increased risk of infection 90 days post-IC (P = 0.04). Total days on antibiotics was significantly associated with increased temporal variability of both oral microbial diversity (P = 0.03) and community structure (P = 0.002).
These data quantify the longitudinal variability of the oral and gut microbiota in AML patients, show that increased variability was correlated with adverse clinical outcomes, and offer the possibility of using stabilizing taxa as a method of focused microbiome repletion. Furthermore, these results support the importance of longitudinal microbiome sampling and analyses, rather than one time measurements, in research and future clinical practice.</description><identifier>ISSN: 1756-994X</identifier><identifier>EISSN: 1756-994X</identifier><identifier>DOI: 10.1186/s13073-017-0409-1</identifier><identifier>PMID: 28245856</identifier><language>eng</language><publisher>England: BioMed Central</publisher><subject>Aged ; Anti-Bacterial Agents - pharmacology ; Antineoplastic Agents - pharmacology ; Antineoplastic Agents - therapeutic use ; Bacteria - genetics ; Bacteria - isolation & purification ; Bacteria - metabolism ; Feces - microbiology ; Female ; Gastrointestinal Microbiome - drug effects ; Humans ; Leukemia, Myeloid, Acute - drug therapy ; Leukemia, Myeloid, Acute - microbiology ; Male ; Middle Aged ; RNA, Ribosomal, 16S ; Saliva - microbiology ; Sequence Analysis, DNA</subject><ispartof>Genome medicine, 2017-02, Vol.9 (1), p.21-21, Article 21</ispartof><rights>Copyright BioMed Central 2017</rights><rights>The Author(s). 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-990f8a62db12286875bc183a53b09dba1e4b5ab0a42aa7b7756762ee3ca880f3</citedby><cites>FETCH-LOGICAL-c526t-990f8a62db12286875bc183a53b09dba1e4b5ab0a42aa7b7756762ee3ca880f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5331640/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1874073464?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28245856$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Galloway-Peña, Jessica R</creatorcontrib><creatorcontrib>Smith, Daniel P</creatorcontrib><creatorcontrib>Sahasrabhojane, Pranoti</creatorcontrib><creatorcontrib>Wadsworth, W Duncan</creatorcontrib><creatorcontrib>Fellman, Bryan M</creatorcontrib><creatorcontrib>Ajami, Nadim J</creatorcontrib><creatorcontrib>Shpall, Elizabeth J</creatorcontrib><creatorcontrib>Daver, Naval</creatorcontrib><creatorcontrib>Guindani, Michele</creatorcontrib><creatorcontrib>Petrosino, Joseph F</creatorcontrib><creatorcontrib>Kontoyiannis, Dimitrios P</creatorcontrib><creatorcontrib>Shelburne, Samuel A</creatorcontrib><title>Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients</title><title>Genome medicine</title><addtitle>Genome Med</addtitle><description>Understanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine intra-patient temporal microbiota variability, the factors driving such variability, and its clinical impact in an extensive longitudinal cohort of hospitalized cancer patients during chemotherapy.
The stool (n = 365) and oral (n = 483) samples of 59 patients with acute myeloid leukemia (AML) undergoing induction chemotherapy (IC) were sampled from initiation of chemotherapy until neutrophil recovery. Microbiome characterization was performed via analysis of 16S rRNA gene sequencing. Temporal variability was determined using coefficients of variation (CV) of the Shannon diversity index (SDI) and unweighted and weighted UniFrac distances per patient, per site. Measurements of intra-patient temporal variability and patient stability categories were analyzed for their correlations with genera abundances. Groups of patients were analyzed to determine if patients with adverse outcomes had significantly different levels of microbiome temporal variability. Potential clinical drivers of microbiome temporal instability were determined using multivariable regression analyses.
Our cohort evidenced a high degree of intra-patient temporal instability of stool and oral microbial diversity based on SDI CV. We identified statistically significant differences in the relative abundance of multiple taxa amongst individuals with different levels of microbiota temporal stability. Increased intra-patient temporal variability of the oral SDI was correlated with increased risk of infection during IC (P = 0.02), and higher stool SDI CVs were correlated with increased risk of infection 90 days post-IC (P = 0.04). Total days on antibiotics was significantly associated with increased temporal variability of both oral microbial diversity (P = 0.03) and community structure (P = 0.002).
These data quantify the longitudinal variability of the oral and gut microbiota in AML patients, show that increased variability was correlated with adverse clinical outcomes, and offer the possibility of using stabilizing taxa as a method of focused microbiome repletion. Furthermore, these results support the importance of longitudinal microbiome sampling and analyses, rather than one time measurements, in research and future clinical practice.</description><subject>Aged</subject><subject>Anti-Bacterial Agents - pharmacology</subject><subject>Antineoplastic Agents - pharmacology</subject><subject>Antineoplastic Agents - therapeutic use</subject><subject>Bacteria - genetics</subject><subject>Bacteria - isolation & purification</subject><subject>Bacteria - metabolism</subject><subject>Feces - microbiology</subject><subject>Female</subject><subject>Gastrointestinal Microbiome - drug effects</subject><subject>Humans</subject><subject>Leukemia, Myeloid, Acute - drug therapy</subject><subject>Leukemia, Myeloid, Acute - microbiology</subject><subject>Male</subject><subject>Middle Aged</subject><subject>RNA, Ribosomal, 16S</subject><subject>Saliva - microbiology</subject><subject>Sequence Analysis, DNA</subject><issn>1756-994X</issn><issn>1756-994X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNkTtvFTEQhS0EIiHwA2iQJRqaBb_tbZDQFS8pEk0KOjP2enMd7a4X2xsp-fX4ckMUqKg80pw5PjMfQi8peUupUe8K5UTzjlDdEUH6jj5Cp1RL1fW9-P74QX2CnpVyRYgSTOin6IQZJqSR6hT92O0hg68hx1uoMS04jThlmDAsA77cKp6jz8nFNAdcw7z-7l1DjuDiFOsNjgvep7LGClO8DQP2sPiQ8drcwlLLc_RkhKmEF3fvGbr49PFi96U7__b56-7DeeclU7WlJKMBxQZHGTPKaOk8NRwkd6QfHNAgnARHQDAA7XTbTCsWAvdgDBn5GXp_tF03N4fBt69bULvmOEO-sQmi_buzxL29TNdWck6VIM3gzZ1BTj-3UKqdY_FhmmAJaSuWGq2NFFL2_yPlvOeCHlxf_yO9Slte2iEOKtHoCSWaih5V7dKl5DDe56bEHkjbI2nbSNsDaUvbzKuHC99P_EHLfwFrHaYX</recordid><startdate>20170228</startdate><enddate>20170228</enddate><creator>Galloway-Peña, Jessica R</creator><creator>Smith, Daniel P</creator><creator>Sahasrabhojane, Pranoti</creator><creator>Wadsworth, W Duncan</creator><creator>Fellman, Bryan M</creator><creator>Ajami, Nadim J</creator><creator>Shpall, Elizabeth J</creator><creator>Daver, Naval</creator><creator>Guindani, Michele</creator><creator>Petrosino, Joseph F</creator><creator>Kontoyiannis, Dimitrios P</creator><creator>Shelburne, Samuel A</creator><general>BioMed Central</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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>5PM</scope></search><sort><creationdate>20170228</creationdate><title>Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients</title><author>Galloway-Peña, Jessica R ; Smith, Daniel P ; Sahasrabhojane, Pranoti ; Wadsworth, W Duncan ; Fellman, Bryan M ; Ajami, Nadim J ; Shpall, Elizabeth J ; Daver, Naval ; Guindani, Michele ; Petrosino, Joseph F ; Kontoyiannis, Dimitrios P ; Shelburne, Samuel A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c526t-990f8a62db12286875bc183a53b09dba1e4b5ab0a42aa7b7756762ee3ca880f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Aged</topic><topic>Anti-Bacterial Agents - pharmacology</topic><topic>Antineoplastic Agents - pharmacology</topic><topic>Antineoplastic Agents - therapeutic use</topic><topic>Bacteria - genetics</topic><topic>Bacteria - isolation & purification</topic><topic>Bacteria - metabolism</topic><topic>Feces - microbiology</topic><topic>Female</topic><topic>Gastrointestinal Microbiome - drug effects</topic><topic>Humans</topic><topic>Leukemia, Myeloid, Acute - drug therapy</topic><topic>Leukemia, Myeloid, Acute - microbiology</topic><topic>Male</topic><topic>Middle Aged</topic><topic>RNA, Ribosomal, 16S</topic><topic>Saliva - microbiology</topic><topic>Sequence Analysis, DNA</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Galloway-Peña, Jessica R</creatorcontrib><creatorcontrib>Smith, Daniel P</creatorcontrib><creatorcontrib>Sahasrabhojane, Pranoti</creatorcontrib><creatorcontrib>Wadsworth, W Duncan</creatorcontrib><creatorcontrib>Fellman, Bryan M</creatorcontrib><creatorcontrib>Ajami, Nadim J</creatorcontrib><creatorcontrib>Shpall, Elizabeth J</creatorcontrib><creatorcontrib>Daver, Naval</creatorcontrib><creatorcontrib>Guindani, Michele</creatorcontrib><creatorcontrib>Petrosino, Joseph F</creatorcontrib><creatorcontrib>Kontoyiannis, Dimitrios P</creatorcontrib><creatorcontrib>Shelburne, Samuel A</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest Biological Science Journals</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Genome medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Galloway-Peña, Jessica R</au><au>Smith, Daniel P</au><au>Sahasrabhojane, Pranoti</au><au>Wadsworth, W Duncan</au><au>Fellman, Bryan M</au><au>Ajami, Nadim J</au><au>Shpall, Elizabeth J</au><au>Daver, Naval</au><au>Guindani, Michele</au><au>Petrosino, Joseph F</au><au>Kontoyiannis, Dimitrios P</au><au>Shelburne, Samuel A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients</atitle><jtitle>Genome medicine</jtitle><addtitle>Genome Med</addtitle><date>2017-02-28</date><risdate>2017</risdate><volume>9</volume><issue>1</issue><spage>21</spage><epage>21</epage><pages>21-21</pages><artnum>21</artnum><issn>1756-994X</issn><eissn>1756-994X</eissn><abstract>Understanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine intra-patient temporal microbiota variability, the factors driving such variability, and its clinical impact in an extensive longitudinal cohort of hospitalized cancer patients during chemotherapy.
The stool (n = 365) and oral (n = 483) samples of 59 patients with acute myeloid leukemia (AML) undergoing induction chemotherapy (IC) were sampled from initiation of chemotherapy until neutrophil recovery. Microbiome characterization was performed via analysis of 16S rRNA gene sequencing. Temporal variability was determined using coefficients of variation (CV) of the Shannon diversity index (SDI) and unweighted and weighted UniFrac distances per patient, per site. Measurements of intra-patient temporal variability and patient stability categories were analyzed for their correlations with genera abundances. Groups of patients were analyzed to determine if patients with adverse outcomes had significantly different levels of microbiome temporal variability. Potential clinical drivers of microbiome temporal instability were determined using multivariable regression analyses.
Our cohort evidenced a high degree of intra-patient temporal instability of stool and oral microbial diversity based on SDI CV. We identified statistically significant differences in the relative abundance of multiple taxa amongst individuals with different levels of microbiota temporal stability. Increased intra-patient temporal variability of the oral SDI was correlated with increased risk of infection during IC (P = 0.02), and higher stool SDI CVs were correlated with increased risk of infection 90 days post-IC (P = 0.04). Total days on antibiotics was significantly associated with increased temporal variability of both oral microbial diversity (P = 0.03) and community structure (P = 0.002).
These data quantify the longitudinal variability of the oral and gut microbiota in AML patients, show that increased variability was correlated with adverse clinical outcomes, and offer the possibility of using stabilizing taxa as a method of focused microbiome repletion. Furthermore, these results support the importance of longitudinal microbiome sampling and analyses, rather than one time measurements, in research and future clinical practice.</abstract><cop>England</cop><pub>BioMed Central</pub><pmid>28245856</pmid><doi>10.1186/s13073-017-0409-1</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1756-994X |
ispartof | Genome medicine, 2017-02, Vol.9 (1), p.21-21, Article 21 |
issn | 1756-994X 1756-994X |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5331640 |
source | Publicly Available Content Database; PubMed Central |
subjects | Aged Anti-Bacterial Agents - pharmacology Antineoplastic Agents - pharmacology Antineoplastic Agents - therapeutic use Bacteria - genetics Bacteria - isolation & purification Bacteria - metabolism Feces - microbiology Female Gastrointestinal Microbiome - drug effects Humans Leukemia, Myeloid, Acute - drug therapy Leukemia, Myeloid, Acute - microbiology Male Middle Aged RNA, Ribosomal, 16S Saliva - microbiology Sequence Analysis, DNA |
title | Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T11%3A53%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Characterization%20of%20oral%20and%20gut%20microbiome%20temporal%20variability%20in%20hospitalized%20cancer%20patients&rft.jtitle=Genome%20medicine&rft.au=Galloway-Pe%C3%B1a,%20Jessica%20R&rft.date=2017-02-28&rft.volume=9&rft.issue=1&rft.spage=21&rft.epage=21&rft.pages=21-21&rft.artnum=21&rft.issn=1756-994X&rft.eissn=1756-994X&rft_id=info:doi/10.1186/s13073-017-0409-1&rft_dat=%3Cproquest_pubme%3E1873393410%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c526t-990f8a62db12286875bc183a53b09dba1e4b5ab0a42aa7b7756762ee3ca880f3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1874073464&rft_id=info:pmid/28245856&rfr_iscdi=true |