Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes
Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomi...
Gespeichert in:
| Veröffentlicht in: | Cell 2012-03, Vol.148 (6), p.1293-1307 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1307 |
|---|---|
| container_issue | 6 |
| container_start_page | 1293 |
| container_title | Cell |
| container_volume | 148 |
| creator | Chen, Rui Mias, George I. Li-Pook-Than, Jennifer Jiang, Lihua Lam, Hugo Y.K. Chen, Rong Miriami, Elana Karczewski, Konrad J. Hariharan, Manoj Dewey, Frederick E. Cheng, Yong Clark, Michael J. Im, Hogune Habegger, Lukas Balasubramanian, Suganthi O'Huallachain, Maeve Dudley, Joel T. Hillenmeyer, Sara Haraksingh, Rajini Sharon, Donald Euskirchen, Ghia Lacroute, Phil Bettinger, Keith Boyle, Alan P. Kasowski, Maya Grubert, Fabian Seki, Scott Garcia, Marco Whirl-Carrillo, Michelle Gallardo, Mercedes Blasco, Maria A. Greenberg, Peter L. Snyder, Phyllis Klein, Teri E. Altman, Russ B. Butte, Atul J. Ashley, Euan A. Gerstein, Mark Nadeau, Kari C. Tang, Hua Snyder, Michael |
| description | Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.
[Display omitted]
► Physiological states analyzed by integrative personal omics profiling ► Extensive molecular changes revealed during different health states ► Individual disease risk predicted from integrated omics data ► Extensive heteroallele and RNA editing during healthy and disease states
A personalized medicine pilot study samples a patient's transcriptome, proteome, and metabolome multiple times over the course of 14 months and integrates this information with whole-genome sequence data to predict risk and provide a comprehensive view of healthy and disease states, including two viral infections and the onset of type 2 diabetes. |
| doi_str_mv | 10.1016/j.cell.2012.02.009 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_3341616</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0092867412001663</els_id><sourcerecordid>929120216</sourcerecordid><originalsourceid>FETCH-LOGICAL-c577t-9fb7cd015592b5e8a43f8626eca446b3f38e9c43abbcdb38ce7a7fdb05d835c43</originalsourceid><addsrcrecordid>eNp9kV1rFDEUhoModtv6B7zQudOb2eZj8gUiSLW10NJF7XXIZM5ss8wmazK7sP--GbcWvSkJBHKe983JeRF6S_CcYCLOVnMHwzCnmNA5LhvrF2hGsJZ1QyR9iWblhtZKyOYIHee8whgrzvlrdERpUxYTM3S1gJRjsEN1u_YuV4sUez_4sKx-wA7skKuv-2BLqbqJA7jtYFNlQ1fdQOddUS3uIcRxv4F8il71hYc3j-cJurv49uv8e319e3l1_uW6dlzKsdZ9K12HCeeathyUbVivBBXgbNOIlvVMgXYNs23rupYpB9LKvmsx7xTjpXCCPh98N9t2DZ2DMCY7mE3ya5v2Jlpv_q8Ef2-WcWcYa4ggohh8eDRI8fcW8mjWPk-TtAHiNhtNNaGY_iE_PkvSMtEShFRTV_SAuhRzTtA_NUSwmdIyKzMpzZSWwWVjXUTv_v3Kk-RvPAV4fwB6G41dJp_N3c_iIKaHNVO4EJ8OBJSR7zwkk52H4Eo8Cdxouuif6-ABKQqwbg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2000016784</pqid></control><display><type>article</type><title>Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes</title><source>MEDLINE</source><source>Cell Press Free Archives</source><source>Elsevier ScienceDirect Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Chen, Rui ; Mias, George I. ; Li-Pook-Than, Jennifer ; Jiang, Lihua ; Lam, Hugo Y.K. ; Chen, Rong ; Miriami, Elana ; Karczewski, Konrad J. ; Hariharan, Manoj ; Dewey, Frederick E. ; Cheng, Yong ; Clark, Michael J. ; Im, Hogune ; Habegger, Lukas ; Balasubramanian, Suganthi ; O'Huallachain, Maeve ; Dudley, Joel T. ; Hillenmeyer, Sara ; Haraksingh, Rajini ; Sharon, Donald ; Euskirchen, Ghia ; Lacroute, Phil ; Bettinger, Keith ; Boyle, Alan P. ; Kasowski, Maya ; Grubert, Fabian ; Seki, Scott ; Garcia, Marco ; Whirl-Carrillo, Michelle ; Gallardo, Mercedes ; Blasco, Maria A. ; Greenberg, Peter L. ; Snyder, Phyllis ; Klein, Teri E. ; Altman, Russ B. ; Butte, Atul J. ; Ashley, Euan A. ; Gerstein, Mark ; Nadeau, Kari C. ; Tang, Hua ; Snyder, Michael</creator><creatorcontrib>Chen, Rui ; Mias, George I. ; Li-Pook-Than, Jennifer ; Jiang, Lihua ; Lam, Hugo Y.K. ; Chen, Rong ; Miriami, Elana ; Karczewski, Konrad J. ; Hariharan, Manoj ; Dewey, Frederick E. ; Cheng, Yong ; Clark, Michael J. ; Im, Hogune ; Habegger, Lukas ; Balasubramanian, Suganthi ; O'Huallachain, Maeve ; Dudley, Joel T. ; Hillenmeyer, Sara ; Haraksingh, Rajini ; Sharon, Donald ; Euskirchen, Ghia ; Lacroute, Phil ; Bettinger, Keith ; Boyle, Alan P. ; Kasowski, Maya ; Grubert, Fabian ; Seki, Scott ; Garcia, Marco ; Whirl-Carrillo, Michelle ; Gallardo, Mercedes ; Blasco, Maria A. ; Greenberg, Peter L. ; Snyder, Phyllis ; Klein, Teri E. ; Altman, Russ B. ; Butte, Atul J. ; Ashley, Euan A. ; Gerstein, Mark ; Nadeau, Kari C. ; Tang, Hua ; Snyder, Michael</creatorcontrib><description>Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.
[Display omitted]
► Physiological states analyzed by integrative personal omics profiling ► Extensive molecular changes revealed during different health states ► Individual disease risk predicted from integrated omics data ► Extensive heteroallele and RNA editing during healthy and disease states
A personalized medicine pilot study samples a patient's transcriptome, proteome, and metabolome multiple times over the course of 14 months and integrates this information with whole-genome sequence data to predict risk and provide a comprehensive view of healthy and disease states, including two viral infections and the onset of type 2 diabetes.</description><identifier>ISSN: 0092-8674</identifier><identifier>EISSN: 1097-4172</identifier><identifier>DOI: 10.1016/j.cell.2012.02.009</identifier><identifier>PMID: 22424236</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>autoantibodies ; Diabetes Mellitus, Type 2 - genetics ; Female ; Gene Expression Profiling ; Genome, Human ; Genomics ; Humans ; Male ; medicine ; Metabolomics ; Middle Aged ; monitoring ; Mutation ; noninsulin-dependent diabetes mellitus ; phenotype ; Precision Medicine ; Proteomics ; Respiratory Syncytial Viruses - isolation & purification ; Rhinovirus - isolation & purification ; risk ; RNA editing ; transcriptomics</subject><ispartof>Cell, 2012-03, Vol.148 (6), p.1293-1307</ispartof><rights>2012 Elsevier Inc.</rights><rights>Copyright © 2012 Elsevier Inc. All rights reserved.</rights><rights>2012 Elsevier Inc. All rights reserved 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c577t-9fb7cd015592b5e8a43f8626eca446b3f38e9c43abbcdb38ce7a7fdb05d835c43</citedby><cites>FETCH-LOGICAL-c577t-9fb7cd015592b5e8a43f8626eca446b3f38e9c43abbcdb38ce7a7fdb05d835c43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.cell.2012.02.009$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,3536,27903,27904,45974</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22424236$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Rui</creatorcontrib><creatorcontrib>Mias, George I.</creatorcontrib><creatorcontrib>Li-Pook-Than, Jennifer</creatorcontrib><creatorcontrib>Jiang, Lihua</creatorcontrib><creatorcontrib>Lam, Hugo Y.K.</creatorcontrib><creatorcontrib>Chen, Rong</creatorcontrib><creatorcontrib>Miriami, Elana</creatorcontrib><creatorcontrib>Karczewski, Konrad J.</creatorcontrib><creatorcontrib>Hariharan, Manoj</creatorcontrib><creatorcontrib>Dewey, Frederick E.</creatorcontrib><creatorcontrib>Cheng, Yong</creatorcontrib><creatorcontrib>Clark, Michael J.</creatorcontrib><creatorcontrib>Im, Hogune</creatorcontrib><creatorcontrib>Habegger, Lukas</creatorcontrib><creatorcontrib>Balasubramanian, Suganthi</creatorcontrib><creatorcontrib>O'Huallachain, Maeve</creatorcontrib><creatorcontrib>Dudley, Joel T.</creatorcontrib><creatorcontrib>Hillenmeyer, Sara</creatorcontrib><creatorcontrib>Haraksingh, Rajini</creatorcontrib><creatorcontrib>Sharon, Donald</creatorcontrib><creatorcontrib>Euskirchen, Ghia</creatorcontrib><creatorcontrib>Lacroute, Phil</creatorcontrib><creatorcontrib>Bettinger, Keith</creatorcontrib><creatorcontrib>Boyle, Alan P.</creatorcontrib><creatorcontrib>Kasowski, Maya</creatorcontrib><creatorcontrib>Grubert, Fabian</creatorcontrib><creatorcontrib>Seki, Scott</creatorcontrib><creatorcontrib>Garcia, Marco</creatorcontrib><creatorcontrib>Whirl-Carrillo, Michelle</creatorcontrib><creatorcontrib>Gallardo, Mercedes</creatorcontrib><creatorcontrib>Blasco, Maria A.</creatorcontrib><creatorcontrib>Greenberg, Peter L.</creatorcontrib><creatorcontrib>Snyder, Phyllis</creatorcontrib><creatorcontrib>Klein, Teri E.</creatorcontrib><creatorcontrib>Altman, Russ B.</creatorcontrib><creatorcontrib>Butte, Atul J.</creatorcontrib><creatorcontrib>Ashley, Euan A.</creatorcontrib><creatorcontrib>Gerstein, Mark</creatorcontrib><creatorcontrib>Nadeau, Kari C.</creatorcontrib><creatorcontrib>Tang, Hua</creatorcontrib><creatorcontrib>Snyder, Michael</creatorcontrib><title>Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes</title><title>Cell</title><addtitle>Cell</addtitle><description>Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.
[Display omitted]
► Physiological states analyzed by integrative personal omics profiling ► Extensive molecular changes revealed during different health states ► Individual disease risk predicted from integrated omics data ► Extensive heteroallele and RNA editing during healthy and disease states
A personalized medicine pilot study samples a patient's transcriptome, proteome, and metabolome multiple times over the course of 14 months and integrates this information with whole-genome sequence data to predict risk and provide a comprehensive view of healthy and disease states, including two viral infections and the onset of type 2 diabetes.</description><subject>autoantibodies</subject><subject>Diabetes Mellitus, Type 2 - genetics</subject><subject>Female</subject><subject>Gene Expression Profiling</subject><subject>Genome, Human</subject><subject>Genomics</subject><subject>Humans</subject><subject>Male</subject><subject>medicine</subject><subject>Metabolomics</subject><subject>Middle Aged</subject><subject>monitoring</subject><subject>Mutation</subject><subject>noninsulin-dependent diabetes mellitus</subject><subject>phenotype</subject><subject>Precision Medicine</subject><subject>Proteomics</subject><subject>Respiratory Syncytial Viruses - isolation & purification</subject><subject>Rhinovirus - isolation & purification</subject><subject>risk</subject><subject>RNA editing</subject><subject>transcriptomics</subject><issn>0092-8674</issn><issn>1097-4172</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kV1rFDEUhoModtv6B7zQudOb2eZj8gUiSLW10NJF7XXIZM5ss8wmazK7sP--GbcWvSkJBHKe983JeRF6S_CcYCLOVnMHwzCnmNA5LhvrF2hGsJZ1QyR9iWblhtZKyOYIHee8whgrzvlrdERpUxYTM3S1gJRjsEN1u_YuV4sUez_4sKx-wA7skKuv-2BLqbqJA7jtYFNlQ1fdQOddUS3uIcRxv4F8il71hYc3j-cJurv49uv8e319e3l1_uW6dlzKsdZ9K12HCeeathyUbVivBBXgbNOIlvVMgXYNs23rupYpB9LKvmsx7xTjpXCCPh98N9t2DZ2DMCY7mE3ya5v2Jlpv_q8Ef2-WcWcYa4ggohh8eDRI8fcW8mjWPk-TtAHiNhtNNaGY_iE_PkvSMtEShFRTV_SAuhRzTtA_NUSwmdIyKzMpzZSWwWVjXUTv_v3Kk-RvPAV4fwB6G41dJp_N3c_iIKaHNVO4EJ8OBJSR7zwkk52H4Eo8Cdxouuif6-ABKQqwbg</recordid><startdate>20120316</startdate><enddate>20120316</enddate><creator>Chen, Rui</creator><creator>Mias, George I.</creator><creator>Li-Pook-Than, Jennifer</creator><creator>Jiang, Lihua</creator><creator>Lam, Hugo Y.K.</creator><creator>Chen, Rong</creator><creator>Miriami, Elana</creator><creator>Karczewski, Konrad J.</creator><creator>Hariharan, Manoj</creator><creator>Dewey, Frederick E.</creator><creator>Cheng, Yong</creator><creator>Clark, Michael J.</creator><creator>Im, Hogune</creator><creator>Habegger, Lukas</creator><creator>Balasubramanian, Suganthi</creator><creator>O'Huallachain, Maeve</creator><creator>Dudley, Joel T.</creator><creator>Hillenmeyer, Sara</creator><creator>Haraksingh, Rajini</creator><creator>Sharon, Donald</creator><creator>Euskirchen, Ghia</creator><creator>Lacroute, Phil</creator><creator>Bettinger, Keith</creator><creator>Boyle, Alan P.</creator><creator>Kasowski, Maya</creator><creator>Grubert, Fabian</creator><creator>Seki, Scott</creator><creator>Garcia, Marco</creator><creator>Whirl-Carrillo, Michelle</creator><creator>Gallardo, Mercedes</creator><creator>Blasco, Maria A.</creator><creator>Greenberg, Peter L.</creator><creator>Snyder, Phyllis</creator><creator>Klein, Teri E.</creator><creator>Altman, Russ B.</creator><creator>Butte, Atul J.</creator><creator>Ashley, Euan A.</creator><creator>Gerstein, Mark</creator><creator>Nadeau, Kari C.</creator><creator>Tang, Hua</creator><creator>Snyder, Michael</creator><general>Elsevier Inc</general><scope>6I.</scope><scope>AAFTH</scope><scope>FBQ</scope><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>7S9</scope><scope>L.6</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20120316</creationdate><title>Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes</title><author>Chen, Rui ; Mias, George I. ; Li-Pook-Than, Jennifer ; Jiang, Lihua ; Lam, Hugo Y.K. ; Chen, Rong ; Miriami, Elana ; Karczewski, Konrad J. ; Hariharan, Manoj ; Dewey, Frederick E. ; Cheng, Yong ; Clark, Michael J. ; Im, Hogune ; Habegger, Lukas ; Balasubramanian, Suganthi ; O'Huallachain, Maeve ; Dudley, Joel T. ; Hillenmeyer, Sara ; Haraksingh, Rajini ; Sharon, Donald ; Euskirchen, Ghia ; Lacroute, Phil ; Bettinger, Keith ; Boyle, Alan P. ; Kasowski, Maya ; Grubert, Fabian ; Seki, Scott ; Garcia, Marco ; Whirl-Carrillo, Michelle ; Gallardo, Mercedes ; Blasco, Maria A. ; Greenberg, Peter L. ; Snyder, Phyllis ; Klein, Teri E. ; Altman, Russ B. ; Butte, Atul J. ; Ashley, Euan A. ; Gerstein, Mark ; Nadeau, Kari C. ; Tang, Hua ; Snyder, Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c577t-9fb7cd015592b5e8a43f8626eca446b3f38e9c43abbcdb38ce7a7fdb05d835c43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>autoantibodies</topic><topic>Diabetes Mellitus, Type 2 - genetics</topic><topic>Female</topic><topic>Gene Expression Profiling</topic><topic>Genome, Human</topic><topic>Genomics</topic><topic>Humans</topic><topic>Male</topic><topic>medicine</topic><topic>Metabolomics</topic><topic>Middle Aged</topic><topic>monitoring</topic><topic>Mutation</topic><topic>noninsulin-dependent diabetes mellitus</topic><topic>phenotype</topic><topic>Precision Medicine</topic><topic>Proteomics</topic><topic>Respiratory Syncytial Viruses - isolation & purification</topic><topic>Rhinovirus - isolation & purification</topic><topic>risk</topic><topic>RNA editing</topic><topic>transcriptomics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Rui</creatorcontrib><creatorcontrib>Mias, George I.</creatorcontrib><creatorcontrib>Li-Pook-Than, Jennifer</creatorcontrib><creatorcontrib>Jiang, Lihua</creatorcontrib><creatorcontrib>Lam, Hugo Y.K.</creatorcontrib><creatorcontrib>Chen, Rong</creatorcontrib><creatorcontrib>Miriami, Elana</creatorcontrib><creatorcontrib>Karczewski, Konrad J.</creatorcontrib><creatorcontrib>Hariharan, Manoj</creatorcontrib><creatorcontrib>Dewey, Frederick E.</creatorcontrib><creatorcontrib>Cheng, Yong</creatorcontrib><creatorcontrib>Clark, Michael J.</creatorcontrib><creatorcontrib>Im, Hogune</creatorcontrib><creatorcontrib>Habegger, Lukas</creatorcontrib><creatorcontrib>Balasubramanian, Suganthi</creatorcontrib><creatorcontrib>O'Huallachain, Maeve</creatorcontrib><creatorcontrib>Dudley, Joel T.</creatorcontrib><creatorcontrib>Hillenmeyer, Sara</creatorcontrib><creatorcontrib>Haraksingh, Rajini</creatorcontrib><creatorcontrib>Sharon, Donald</creatorcontrib><creatorcontrib>Euskirchen, Ghia</creatorcontrib><creatorcontrib>Lacroute, Phil</creatorcontrib><creatorcontrib>Bettinger, Keith</creatorcontrib><creatorcontrib>Boyle, Alan P.</creatorcontrib><creatorcontrib>Kasowski, Maya</creatorcontrib><creatorcontrib>Grubert, Fabian</creatorcontrib><creatorcontrib>Seki, Scott</creatorcontrib><creatorcontrib>Garcia, Marco</creatorcontrib><creatorcontrib>Whirl-Carrillo, Michelle</creatorcontrib><creatorcontrib>Gallardo, Mercedes</creatorcontrib><creatorcontrib>Blasco, Maria A.</creatorcontrib><creatorcontrib>Greenberg, Peter L.</creatorcontrib><creatorcontrib>Snyder, Phyllis</creatorcontrib><creatorcontrib>Klein, Teri E.</creatorcontrib><creatorcontrib>Altman, Russ B.</creatorcontrib><creatorcontrib>Butte, Atul J.</creatorcontrib><creatorcontrib>Ashley, Euan A.</creatorcontrib><creatorcontrib>Gerstein, Mark</creatorcontrib><creatorcontrib>Nadeau, Kari C.</creatorcontrib><creatorcontrib>Tang, Hua</creatorcontrib><creatorcontrib>Snyder, Michael</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>AGRIS</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Cell</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Rui</au><au>Mias, George I.</au><au>Li-Pook-Than, Jennifer</au><au>Jiang, Lihua</au><au>Lam, Hugo Y.K.</au><au>Chen, Rong</au><au>Miriami, Elana</au><au>Karczewski, Konrad J.</au><au>Hariharan, Manoj</au><au>Dewey, Frederick E.</au><au>Cheng, Yong</au><au>Clark, Michael J.</au><au>Im, Hogune</au><au>Habegger, Lukas</au><au>Balasubramanian, Suganthi</au><au>O'Huallachain, Maeve</au><au>Dudley, Joel T.</au><au>Hillenmeyer, Sara</au><au>Haraksingh, Rajini</au><au>Sharon, Donald</au><au>Euskirchen, Ghia</au><au>Lacroute, Phil</au><au>Bettinger, Keith</au><au>Boyle, Alan P.</au><au>Kasowski, Maya</au><au>Grubert, Fabian</au><au>Seki, Scott</au><au>Garcia, Marco</au><au>Whirl-Carrillo, Michelle</au><au>Gallardo, Mercedes</au><au>Blasco, Maria A.</au><au>Greenberg, Peter L.</au><au>Snyder, Phyllis</au><au>Klein, Teri E.</au><au>Altman, Russ B.</au><au>Butte, Atul J.</au><au>Ashley, Euan A.</au><au>Gerstein, Mark</au><au>Nadeau, Kari C.</au><au>Tang, Hua</au><au>Snyder, Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes</atitle><jtitle>Cell</jtitle><addtitle>Cell</addtitle><date>2012-03-16</date><risdate>2012</risdate><volume>148</volume><issue>6</issue><spage>1293</spage><epage>1307</epage><pages>1293-1307</pages><issn>0092-8674</issn><eissn>1097-4172</eissn><abstract>Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.
[Display omitted]
► Physiological states analyzed by integrative personal omics profiling ► Extensive molecular changes revealed during different health states ► Individual disease risk predicted from integrated omics data ► Extensive heteroallele and RNA editing during healthy and disease states
A personalized medicine pilot study samples a patient's transcriptome, proteome, and metabolome multiple times over the course of 14 months and integrates this information with whole-genome sequence data to predict risk and provide a comprehensive view of healthy and disease states, including two viral infections and the onset of type 2 diabetes.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>22424236</pmid><doi>10.1016/j.cell.2012.02.009</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0092-8674 |
| ispartof | Cell, 2012-03, Vol.148 (6), p.1293-1307 |
| issn | 0092-8674 1097-4172 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_3341616 |
| source | MEDLINE; Cell Press Free Archives; Elsevier ScienceDirect Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | autoantibodies Diabetes Mellitus, Type 2 - genetics Female Gene Expression Profiling Genome, Human Genomics Humans Male medicine Metabolomics Middle Aged monitoring Mutation noninsulin-dependent diabetes mellitus phenotype Precision Medicine Proteomics Respiratory Syncytial Viruses - isolation & purification Rhinovirus - isolation & purification risk RNA editing transcriptomics |
| title | Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T13%3A53%3A57IST&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=Personal%20Omics%20Profiling%20Reveals%20Dynamic%20Molecular%20and%20Medical%20Phenotypes&rft.jtitle=Cell&rft.au=Chen,%20Rui&rft.date=2012-03-16&rft.volume=148&rft.issue=6&rft.spage=1293&rft.epage=1307&rft.pages=1293-1307&rft.issn=0092-8674&rft.eissn=1097-4172&rft_id=info:doi/10.1016/j.cell.2012.02.009&rft_dat=%3Cproquest_pubme%3E929120216%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2000016784&rft_id=info:pmid/22424236&rft_els_id=S0092867412001663&rfr_iscdi=true |