Comparison of whole blood and peripheral blood mononuclear cell gene expression for evaluation of the perioperative inflammatory response in patients with advanced heart failure
Heart failure (HF) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced HF (AdHF) patients. Perioperatively, multiorgan dysfunction (MOD) is linked to the effects of device implantation, augmented by preexisting HF. Early recognition o...
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creator | Bondar, Galyna Cadeiras, Martin Wisniewski, Nicholas Maque, Jetrina Chittoor, Jay Chang, Eleanor Bakir, Maral Starling, Charlotte Shahzad, Khurram Ping, Peipei Reed, Elaine Deng, Mario |
description | Heart failure (HF) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced HF (AdHF) patients. Perioperatively, multiorgan dysfunction (MOD) is linked to the effects of device implantation, augmented by preexisting HF. Early recognition of MOD allows for better diagnosis, treatment, and risk prediction. Gene expression profiling (GEP) was used to evaluate clinical phenotypes of peripheral blood mononuclear cells (PBMC) transcriptomes obtained from patients' blood samples. Whole blood (WB) samples are clinically more feasible, but their performance in comparison to PBMC samples has not been determined.
We collected blood samples from 31 HF patients (57±15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25-75% IQR 7-14 days) and subjected to Illumina single color Human BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of MOD into low (≤ 4 points), intermediate (5-11), and high (≥ 12) risk categories correlating with GEP.
Results indicate that the direction of change in GEP of individuals with MOD as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups.
GEP analysis of WB has the potential to become a clinical tool for immune-monitoring in patients with MOD. |
doi_str_mv | 10.1371/journal.pone.0115097 |
format | Article |
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We collected blood samples from 31 HF patients (57±15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25-75% IQR 7-14 days) and subjected to Illumina single color Human BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of MOD into low (≤ 4 points), intermediate (5-11), and high (≥ 12) risk categories correlating with GEP.
Results indicate that the direction of change in GEP of individuals with MOD as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups.
GEP analysis of WB has the potential to become a clinical tool for immune-monitoring in patients with MOD.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0115097</identifier><identifier>PMID: 25517110</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Apoptosis ; Biology and Life Sciences ; Biomarkers - metabolism ; Blood ; Care and treatment ; Case-Control Studies ; Diagnosis ; Gene expression ; Gene Expression Profiling - methods ; Gene Ontology ; Genes ; Genetic research ; Genomes ; Genomics ; Heart ; Heart diseases ; Heart failure ; Heart Failure - blood ; Heart Failure - surgery ; Heart surgery ; Humans ; Implantation ; Inflammation ; Inflammation - blood ; Inflammation - etiology ; Inflammation - genetics ; Inflammatory response ; Intensive care ; Learning algorithms ; Leukocytes (mononuclear) ; Leukocytes, Mononuclear - metabolism ; Machine learning ; Medicine and Health Sciences ; Middle Aged ; Ontology ; Patients ; Perioperative Period - adverse effects ; Peripheral blood mononuclear cells ; Research and Analysis Methods ; RNA, Messenger - genetics ; RNA, Messenger - metabolism ; Sepsis ; Surgery ; Transplants & implants</subject><ispartof>PloS one, 2014-12, Vol.9 (12), p.e115097-e115097</ispartof><rights>COPYRIGHT 2014 Public Library of Science</rights><rights>2014 Bondar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2014 Bondar et al 2014 Bondar et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-e6da0271eb473773abb7279391d864cae609b09984d760ebe5316cfe03e829ce3</citedby><cites>FETCH-LOGICAL-c692t-e6da0271eb473773abb7279391d864cae609b09984d760ebe5316cfe03e829ce3</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/PMC4269402/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269402/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,725,778,782,862,883,2098,2917,23853,27911,27912,53778,53780,79355,79356</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25517110$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Sussman, Mark A.</contributor><creatorcontrib>Bondar, Galyna</creatorcontrib><creatorcontrib>Cadeiras, Martin</creatorcontrib><creatorcontrib>Wisniewski, Nicholas</creatorcontrib><creatorcontrib>Maque, Jetrina</creatorcontrib><creatorcontrib>Chittoor, Jay</creatorcontrib><creatorcontrib>Chang, Eleanor</creatorcontrib><creatorcontrib>Bakir, Maral</creatorcontrib><creatorcontrib>Starling, Charlotte</creatorcontrib><creatorcontrib>Shahzad, Khurram</creatorcontrib><creatorcontrib>Ping, Peipei</creatorcontrib><creatorcontrib>Reed, Elaine</creatorcontrib><creatorcontrib>Deng, Mario</creatorcontrib><title>Comparison of whole blood and peripheral blood mononuclear cell gene expression for evaluation of the perioperative inflammatory response in patients with advanced heart failure</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Heart failure (HF) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced HF (AdHF) patients. Perioperatively, multiorgan dysfunction (MOD) is linked to the effects of device implantation, augmented by preexisting HF. Early recognition of MOD allows for better diagnosis, treatment, and risk prediction. Gene expression profiling (GEP) was used to evaluate clinical phenotypes of peripheral blood mononuclear cells (PBMC) transcriptomes obtained from patients' blood samples. Whole blood (WB) samples are clinically more feasible, but their performance in comparison to PBMC samples has not been determined.
We collected blood samples from 31 HF patients (57±15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25-75% IQR 7-14 days) and subjected to Illumina single color Human BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of MOD into low (≤ 4 points), intermediate (5-11), and high (≥ 12) risk categories correlating with GEP.
Results indicate that the direction of change in GEP of individuals with MOD as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups.
GEP analysis of WB has the potential to become a clinical tool for immune-monitoring in patients with MOD.</description><subject>Analysis</subject><subject>Apoptosis</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers - metabolism</subject><subject>Blood</subject><subject>Care and treatment</subject><subject>Case-Control Studies</subject><subject>Diagnosis</subject><subject>Gene expression</subject><subject>Gene Expression Profiling - methods</subject><subject>Gene Ontology</subject><subject>Genes</subject><subject>Genetic research</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Heart</subject><subject>Heart diseases</subject><subject>Heart failure</subject><subject>Heart Failure - blood</subject><subject>Heart Failure - surgery</subject><subject>Heart surgery</subject><subject>Humans</subject><subject>Implantation</subject><subject>Inflammation</subject><subject>Inflammation - blood</subject><subject>Inflammation - etiology</subject><subject>Inflammation - genetics</subject><subject>Inflammatory response</subject><subject>Intensive care</subject><subject>Learning algorithms</subject><subject>Leukocytes (mononuclear)</subject><subject>Leukocytes, Mononuclear - metabolism</subject><subject>Machine learning</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Ontology</subject><subject>Patients</subject><subject>Perioperative Period - adverse effects</subject><subject>Peripheral blood mononuclear cells</subject><subject>Research and Analysis Methods</subject><subject>RNA, Messenger - genetics</subject><subject>RNA, Messenger - metabolism</subject><subject>Sepsis</subject><subject>Surgery</subject><subject>Transplants & implants</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9tq3DAQhk1padK0b1BaQaG0F7vVyZJ1Uwihh0Ag0NOtkO3xWkG2XMneJI_VN6ycdUK25KIYbDP65h_NKcteErwmTJIPF34KvXHrwfewxoTkWMlH2SFRjK4Exezxvf-D7FmMFxjnrBDiaXZA85xIQvBh9ufEd4MJNvoe-QZdtt4BKp33NTJ9jQYIdmghGLcYO9_7fqocmIAqcA5toAcEV0OAGG0SaXxAsDVuMqPdaY4t3Oj49ErGLSDbN850nRl9uEbJMaUQZysa0jn0Y0SXdmyRqbemr6BGbYo2osZYNwV4nj1pjIvwYvkeZT8_f_px8nV1dv7l9OT4bFUJRccViNpgKgmUXDIpmSlLSaViitSF4JUBgVWJlSp4LQWGEnJGRNUAZlBQVQE7yl7vdAfno16qHTURTOaccM4Tcbojam8u9BBsZ8K19sbqG4MPG53ubVOxdElFDbkqWKnyFNwogktRSEabGmRDm6T1cYk2lR3UVapCKvqe6P5Jb1u98VvNqVAc0yTwbhEI_vcEcdSdjXOHTA9-urm3UpLyNBNH2Zt_0IezW6iNSQmklvkUt5pF9TEnBaesUDO1foBKTw2drdJoNjbZ9xze7zkkZoSrcWOmGPXp92__z57_2mff3mPTxLixjd5N8xTGfZDvwCr4GAM0d0UmWM-bdVsNPW-WXjYrub2636A7p9tVYn8Bgq8i2Q</recordid><startdate>20141217</startdate><enddate>20141217</enddate><creator>Bondar, Galyna</creator><creator>Cadeiras, Martin</creator><creator>Wisniewski, Nicholas</creator><creator>Maque, Jetrina</creator><creator>Chittoor, Jay</creator><creator>Chang, Eleanor</creator><creator>Bakir, Maral</creator><creator>Starling, Charlotte</creator><creator>Shahzad, Khurram</creator><creator>Ping, Peipei</creator><creator>Reed, Elaine</creator><creator>Deng, Mario</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20141217</creationdate><title>Comparison of whole blood and peripheral blood mononuclear cell gene expression for evaluation of the perioperative inflammatory response in patients with advanced heart failure</title><author>Bondar, Galyna ; Cadeiras, Martin ; Wisniewski, Nicholas ; Maque, Jetrina ; Chittoor, Jay ; Chang, Eleanor ; Bakir, Maral ; Starling, Charlotte ; Shahzad, Khurram ; Ping, Peipei ; Reed, Elaine ; Deng, Mario</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-e6da0271eb473773abb7279391d864cae609b09984d760ebe5316cfe03e829ce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Analysis</topic><topic>Apoptosis</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers - metabolism</topic><topic>Blood</topic><topic>Care and treatment</topic><topic>Case-Control Studies</topic><topic>Diagnosis</topic><topic>Gene expression</topic><topic>Gene Expression Profiling - methods</topic><topic>Gene Ontology</topic><topic>Genes</topic><topic>Genetic research</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Heart</topic><topic>Heart diseases</topic><topic>Heart failure</topic><topic>Heart Failure - blood</topic><topic>Heart Failure - surgery</topic><topic>Heart surgery</topic><topic>Humans</topic><topic>Implantation</topic><topic>Inflammation</topic><topic>Inflammation - blood</topic><topic>Inflammation - etiology</topic><topic>Inflammation - genetics</topic><topic>Inflammatory response</topic><topic>Intensive care</topic><topic>Learning algorithms</topic><topic>Leukocytes (mononuclear)</topic><topic>Leukocytes, Mononuclear - metabolism</topic><topic>Machine learning</topic><topic>Medicine and Health Sciences</topic><topic>Middle Aged</topic><topic>Ontology</topic><topic>Patients</topic><topic>Perioperative Period - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bondar, Galyna</au><au>Cadeiras, Martin</au><au>Wisniewski, Nicholas</au><au>Maque, Jetrina</au><au>Chittoor, Jay</au><au>Chang, Eleanor</au><au>Bakir, Maral</au><au>Starling, Charlotte</au><au>Shahzad, Khurram</au><au>Ping, Peipei</au><au>Reed, Elaine</au><au>Deng, Mario</au><au>Sussman, Mark A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of whole blood and peripheral blood mononuclear cell gene expression for evaluation of the perioperative inflammatory response in patients with advanced heart failure</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2014-12-17</date><risdate>2014</risdate><volume>9</volume><issue>12</issue><spage>e115097</spage><epage>e115097</epage><pages>e115097-e115097</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Heart failure (HF) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced HF (AdHF) patients. Perioperatively, multiorgan dysfunction (MOD) is linked to the effects of device implantation, augmented by preexisting HF. Early recognition of MOD allows for better diagnosis, treatment, and risk prediction. Gene expression profiling (GEP) was used to evaluate clinical phenotypes of peripheral blood mononuclear cells (PBMC) transcriptomes obtained from patients' blood samples. Whole blood (WB) samples are clinically more feasible, but their performance in comparison to PBMC samples has not been determined.
We collected blood samples from 31 HF patients (57±15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25-75% IQR 7-14 days) and subjected to Illumina single color Human BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of MOD into low (≤ 4 points), intermediate (5-11), and high (≥ 12) risk categories correlating with GEP.
Results indicate that the direction of change in GEP of individuals with MOD as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups.
GEP analysis of WB has the potential to become a clinical tool for immune-monitoring in patients with MOD.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25517110</pmid><doi>10.1371/journal.pone.0115097</doi><oa>free_for_read</oa></addata></record> |
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language | eng |
recordid | cdi_plos_journals_1637541444 |
source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
subjects | Analysis Apoptosis Biology and Life Sciences Biomarkers - metabolism Blood Care and treatment Case-Control Studies Diagnosis Gene expression Gene Expression Profiling - methods Gene Ontology Genes Genetic research Genomes Genomics Heart Heart diseases Heart failure Heart Failure - blood Heart Failure - surgery Heart surgery Humans Implantation Inflammation Inflammation - blood Inflammation - etiology Inflammation - genetics Inflammatory response Intensive care Learning algorithms Leukocytes (mononuclear) Leukocytes, Mononuclear - metabolism Machine learning Medicine and Health Sciences Middle Aged Ontology Patients Perioperative Period - adverse effects Peripheral blood mononuclear cells Research and Analysis Methods RNA, Messenger - genetics RNA, Messenger - metabolism Sepsis Surgery Transplants & implants |
title | Comparison of whole blood and peripheral blood mononuclear cell gene expression for evaluation of the perioperative inflammatory response in patients with advanced heart failure |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T18%3A31%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Comparison%20of%20whole%20blood%20and%20peripheral%20blood%20mononuclear%20cell%20gene%20expression%20for%20evaluation%20of%20the%20perioperative%20inflammatory%20response%20in%20patients%20with%20advanced%20heart%20failure&rft.jtitle=PloS%20one&rft.au=Bondar,%20Galyna&rft.date=2014-12-17&rft.volume=9&rft.issue=12&rft.spage=e115097&rft.epage=e115097&rft.pages=e115097-e115097&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0115097&rft_dat=%3Cgale_plos_%3EA418423894%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1637541444&rft_id=info:pmid/25517110&rft_galeid=A418423894&rft_doaj_id=oai_doaj_org_article_b26de5983b954caa910b68732fde7f2f&rfr_iscdi=true |