DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules
Introduction Diabetic kidney disease (DKD) is the most prevalent complication in diabetic patients, which contributes to high morbidity and mortality. Urine and plasma metabolomics studies have been demonstrated to provide valuable insights for DKD. However, limited information on spatial distributi...
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| Veröffentlicht in: | Metabolomics 2020-01, Vol.16 (1), p.11-11, Article 11 |
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| creator | Zhang, Guanshi Zhang, Jialing DeHoog, Rachel J. Pennathur, Subramaniam Anderton, Christopher R. Venkatachalam, Manjeri A. Alexandrov, Theodore Eberlin, Livia S. Sharma, Kumar |
| description | Introduction
Diabetic kidney disease (DKD) is the most prevalent complication in diabetic patients, which contributes to high morbidity and mortality. Urine and plasma metabolomics studies have been demonstrated to provide valuable insights for DKD. However, limited information on spatial distributions of metabolites in kidney tissues have been reported.
Objectives
In this work, we employed an ambient desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) coupled to a novel bioinformatics platform (METASPACE) to characterize the metabolome in a mouse model of DKD.
Methods
DESI-MSI was performed for spatial untargeted metabolomics analysis in kidneys of mouse models (F1 C57BL/6J-Ins2Akita male mice at 17 weeks of age) of type 1 diabetes (T1D, n = 5) and heathy controls (n = 6).
Results
Multivariate analyses (i.e., PCA and PLS-DA (a 2000 permutation test:
P
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| doi_str_mv | 10.1007/s11306-020-1637-8 |
| format | Article |
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Diabetic kidney disease (DKD) is the most prevalent complication in diabetic patients, which contributes to high morbidity and mortality. Urine and plasma metabolomics studies have been demonstrated to provide valuable insights for DKD. However, limited information on spatial distributions of metabolites in kidney tissues have been reported.
Objectives
In this work, we employed an ambient desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) coupled to a novel bioinformatics platform (METASPACE) to characterize the metabolome in a mouse model of DKD.
Methods
DESI-MSI was performed for spatial untargeted metabolomics analysis in kidneys of mouse models (F1 C57BL/6J-Ins2Akita male mice at 17 weeks of age) of type 1 diabetes (T1D, n = 5) and heathy controls (n = 6).
Results
Multivariate analyses (i.e., PCA and PLS-DA (a 2000 permutation test:
P
< 0.001)) showed clearly separated clusters for the two groups of mice on the basis of 878 measured
m
/
z
’s in kidney cortical tissues. Specifically, mice with T1D had increased relative abundances of pseudouridine, accumulation of free polyunsaturated fatty acids (PUFAs), and decreased relative abundances of cardiolipins in cortical proximal tubules when compared with healthy controls.
Conclusion
Results from the current study support potential key roles of pseudouridine and cardiolipins for maintaining normal RNA structure and normal mitochondrial function, respectively, in cortical proximal tubules with DKD. DESI-MSI technology coupled with METASPACE could serve as powerful new tools to provide insight on fundamental pathways in DKD.</description><identifier>ISSN: 1573-3882</identifier><identifier>EISSN: 1573-3890</identifier><identifier>DOI: 10.1007/s11306-020-1637-8</identifier><identifier>PMID: 31925564</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Animal models ; Animals ; BASIC BIOLOGICAL SCIENCES ; Biochemistry ; Bioinformatics ; Biomedical and Life Sciences ; Biomedicine ; Cardiolipins - metabolism ; Cell Biology ; Computational Biology ; DESI-MSI ; Developmental Biology ; Diabetes ; Diabetes mellitus (insulin dependent) ; Diabetic kidney disease ; Diabetic Nephropathies - metabolism ; Fatty Acids, Omega-3 - metabolism ; Kidney diseases ; Kidney Tubules, Proximal - metabolism ; Kidneys ; Life Sciences ; lipid metabolism ; Male ; Mass spectroscopy ; Metabolic Networks and Pathways ; Metabolites ; Metabolome ; Metabolomics ; Mice ; Mice, Inbred C57BL ; Mitochondria ; Mitochondrial Membranes - metabolism ; Molecular Medicine ; Morbidity ; Original Article ; Polyunsaturated fatty acids ; Proximal tubules ; Pseudouridine - metabolism ; renal proximal tubule ; Ribonucleic acid ; RNA ; Spatial distribution ; Spectrometry, Mass, Electrospray Ionization ; Urine</subject><ispartof>Metabolomics, 2020-01, Vol.16 (1), p.11-11, Article 11</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020</rights><rights>Metabolomics is a copyright of Springer, (2020). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c497t-70258b8a34dbbe0f2b0c739cef97bac9d783e27dc16daf2acbadf8946bb98e3c3</citedby><cites>FETCH-LOGICAL-c497t-70258b8a34dbbe0f2b0c739cef97bac9d783e27dc16daf2acbadf8946bb98e3c3</cites><orcidid>0000-0002-7550-8525 ; 0000000275508525</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11306-020-1637-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11306-020-1637-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31925564$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/servlets/purl/1717876$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Guanshi</creatorcontrib><creatorcontrib>Zhang, Jialing</creatorcontrib><creatorcontrib>DeHoog, Rachel J.</creatorcontrib><creatorcontrib>Pennathur, Subramaniam</creatorcontrib><creatorcontrib>Anderton, Christopher R.</creatorcontrib><creatorcontrib>Venkatachalam, Manjeri A.</creatorcontrib><creatorcontrib>Alexandrov, Theodore</creatorcontrib><creatorcontrib>Eberlin, Livia S.</creatorcontrib><creatorcontrib>Sharma, Kumar</creatorcontrib><creatorcontrib>Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)</creatorcontrib><title>DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules</title><title>Metabolomics</title><addtitle>Metabolomics</addtitle><addtitle>Metabolomics</addtitle><description>Introduction
Diabetic kidney disease (DKD) is the most prevalent complication in diabetic patients, which contributes to high morbidity and mortality. Urine and plasma metabolomics studies have been demonstrated to provide valuable insights for DKD. However, limited information on spatial distributions of metabolites in kidney tissues have been reported.
Objectives
In this work, we employed an ambient desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) coupled to a novel bioinformatics platform (METASPACE) to characterize the metabolome in a mouse model of DKD.
Methods
DESI-MSI was performed for spatial untargeted metabolomics analysis in kidneys of mouse models (F1 C57BL/6J-Ins2Akita male mice at 17 weeks of age) of type 1 diabetes (T1D, n = 5) and heathy controls (n = 6).
Results
Multivariate analyses (i.e., PCA and PLS-DA (a 2000 permutation test:
P
< 0.001)) showed clearly separated clusters for the two groups of mice on the basis of 878 measured
m
/
z
’s in kidney cortical tissues. Specifically, mice with T1D had increased relative abundances of pseudouridine, accumulation of free polyunsaturated fatty acids (PUFAs), and decreased relative abundances of cardiolipins in cortical proximal tubules when compared with healthy controls.
Conclusion
Results from the current study support potential key roles of pseudouridine and cardiolipins for maintaining normal RNA structure and normal mitochondrial function, respectively, in cortical proximal tubules with DKD. DESI-MSI technology coupled with METASPACE could serve as powerful new tools to provide insight on fundamental pathways in DKD.</description><subject>Animal models</subject><subject>Animals</subject><subject>BASIC BIOLOGICAL SCIENCES</subject><subject>Biochemistry</subject><subject>Bioinformatics</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Cardiolipins - metabolism</subject><subject>Cell Biology</subject><subject>Computational Biology</subject><subject>DESI-MSI</subject><subject>Developmental Biology</subject><subject>Diabetes</subject><subject>Diabetes mellitus (insulin dependent)</subject><subject>Diabetic kidney disease</subject><subject>Diabetic Nephropathies - metabolism</subject><subject>Fatty Acids, Omega-3 - metabolism</subject><subject>Kidney diseases</subject><subject>Kidney Tubules, Proximal - metabolism</subject><subject>Kidneys</subject><subject>Life Sciences</subject><subject>lipid metabolism</subject><subject>Male</subject><subject>Mass spectroscopy</subject><subject>Metabolic Networks and Pathways</subject><subject>Metabolites</subject><subject>Metabolome</subject><subject>Metabolomics</subject><subject>Mice</subject><subject>Mice, Inbred C57BL</subject><subject>Mitochondria</subject><subject>Mitochondrial Membranes - metabolism</subject><subject>Molecular Medicine</subject><subject>Morbidity</subject><subject>Original Article</subject><subject>Polyunsaturated fatty acids</subject><subject>Proximal tubules</subject><subject>Pseudouridine - metabolism</subject><subject>renal proximal tubule</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>Spatial distribution</subject><subject>Spectrometry, Mass, Electrospray Ionization</subject><subject>Urine</subject><issn>1573-3882</issn><issn>1573-3890</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</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><recordid>eNp1kc-O0zAQxi0EYpfCA3BBFpwDdpzEzgWpKgUq7QqkLmfLfyZbrxK72A6CB-C9cZWlwIHTWDPf_OazPoSeU_KaEsLfJEoZ6SpSk4p2jFfiAbqkLWcVEz15eH6L-gI9SemOkKbpOXmMLhjt67btmkv08912v6uu9zusvMXX25v1_vN6s8XOW2dUhoRHd3QWK-1DnNTosiu9k1aNGSJYPLkczCF4G50a8QSTjsoDNmE6Bg8-p8LC1ikN2RkcwRfVMYbvrtBwnvU8QnqKHg1qTPDsvq7Ql_fbm83H6urTh91mfVWZYjxXnNSt0EKxxmoNZKg1MZz1Boaea2V6ywWDmltDO6uGWhmt7CD6ptO6F8AMW6G3C_c46wmsKfaiGuUxFjPxhwzKyX8n3h3kbfgmOSOUNawAXi6AkLKTybgM5mCC92CypJxywbsienV_JYavM6Qs78Icy7-TrE-QljUlmBWii8rEkFKE4WyDEnmKVy7xyhKvPMUrRdl58bf_88bvPIugXgSpjPwtxD-n_0_9BTSms0c</recordid><startdate>20200110</startdate><enddate>20200110</enddate><creator>Zhang, Guanshi</creator><creator>Zhang, Jialing</creator><creator>DeHoog, Rachel J.</creator><creator>Pennathur, Subramaniam</creator><creator>Anderton, Christopher R.</creator><creator>Venkatachalam, Manjeri A.</creator><creator>Alexandrov, Theodore</creator><creator>Eberlin, Livia S.</creator><creator>Sharma, Kumar</creator><general>Springer US</general><general>Springer Nature B.V</general><general>Springer</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>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>M7P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>OIOZB</scope><scope>OTOTI</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-7550-8525</orcidid><orcidid>https://orcid.org/0000000275508525</orcidid></search><sort><creationdate>20200110</creationdate><title>DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules</title><author>Zhang, Guanshi ; Zhang, Jialing ; DeHoog, Rachel J. ; Pennathur, Subramaniam ; Anderton, Christopher R. ; Venkatachalam, Manjeri A. ; Alexandrov, Theodore ; Eberlin, Livia S. ; Sharma, Kumar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c497t-70258b8a34dbbe0f2b0c739cef97bac9d783e27dc16daf2acbadf8946bb98e3c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Animal models</topic><topic>Animals</topic><topic>BASIC BIOLOGICAL SCIENCES</topic><topic>Biochemistry</topic><topic>Bioinformatics</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Cardiolipins - metabolism</topic><topic>Cell Biology</topic><topic>Computational Biology</topic><topic>DESI-MSI</topic><topic>Developmental Biology</topic><topic>Diabetes</topic><topic>Diabetes mellitus (insulin dependent)</topic><topic>Diabetic kidney disease</topic><topic>Diabetic Nephropathies - metabolism</topic><topic>Fatty Acids, Omega-3 - metabolism</topic><topic>Kidney diseases</topic><topic>Kidney Tubules, Proximal - metabolism</topic><topic>Kidneys</topic><topic>Life Sciences</topic><topic>lipid metabolism</topic><topic>Male</topic><topic>Mass spectroscopy</topic><topic>Metabolic Networks and Pathways</topic><topic>Metabolites</topic><topic>Metabolome</topic><topic>Metabolomics</topic><topic>Mice</topic><topic>Mice, Inbred C57BL</topic><topic>Mitochondria</topic><topic>Mitochondrial Membranes - metabolism</topic><topic>Molecular Medicine</topic><topic>Morbidity</topic><topic>Original Article</topic><topic>Polyunsaturated fatty acids</topic><topic>Proximal tubules</topic><topic>Pseudouridine - metabolism</topic><topic>renal proximal tubule</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>Spatial distribution</topic><topic>Spectrometry, Mass, Electrospray Ionization</topic><topic>Urine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Guanshi</creatorcontrib><creatorcontrib>Zhang, Jialing</creatorcontrib><creatorcontrib>DeHoog, Rachel J.</creatorcontrib><creatorcontrib>Pennathur, Subramaniam</creatorcontrib><creatorcontrib>Anderton, Christopher R.</creatorcontrib><creatorcontrib>Venkatachalam, Manjeri A.</creatorcontrib><creatorcontrib>Alexandrov, Theodore</creatorcontrib><creatorcontrib>Eberlin, Livia S.</creatorcontrib><creatorcontrib>Sharma, Kumar</creatorcontrib><creatorcontrib>Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)</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>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 Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</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>Biological Science 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>OSTI.GOV - Hybrid</collection><collection>OSTI.GOV</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Metabolomics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Guanshi</au><au>Zhang, Jialing</au><au>DeHoog, Rachel J.</au><au>Pennathur, Subramaniam</au><au>Anderton, Christopher R.</au><au>Venkatachalam, Manjeri A.</au><au>Alexandrov, Theodore</au><au>Eberlin, Livia S.</au><au>Sharma, Kumar</au><aucorp>Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules</atitle><jtitle>Metabolomics</jtitle><stitle>Metabolomics</stitle><addtitle>Metabolomics</addtitle><date>2020-01-10</date><risdate>2020</risdate><volume>16</volume><issue>1</issue><spage>11</spage><epage>11</epage><pages>11-11</pages><artnum>11</artnum><issn>1573-3882</issn><eissn>1573-3890</eissn><abstract>Introduction
Diabetic kidney disease (DKD) is the most prevalent complication in diabetic patients, which contributes to high morbidity and mortality. Urine and plasma metabolomics studies have been demonstrated to provide valuable insights for DKD. However, limited information on spatial distributions of metabolites in kidney tissues have been reported.
Objectives
In this work, we employed an ambient desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) coupled to a novel bioinformatics platform (METASPACE) to characterize the metabolome in a mouse model of DKD.
Methods
DESI-MSI was performed for spatial untargeted metabolomics analysis in kidneys of mouse models (F1 C57BL/6J-Ins2Akita male mice at 17 weeks of age) of type 1 diabetes (T1D, n = 5) and heathy controls (n = 6).
Results
Multivariate analyses (i.e., PCA and PLS-DA (a 2000 permutation test:
P
< 0.001)) showed clearly separated clusters for the two groups of mice on the basis of 878 measured
m
/
z
’s in kidney cortical tissues. Specifically, mice with T1D had increased relative abundances of pseudouridine, accumulation of free polyunsaturated fatty acids (PUFAs), and decreased relative abundances of cardiolipins in cortical proximal tubules when compared with healthy controls.
Conclusion
Results from the current study support potential key roles of pseudouridine and cardiolipins for maintaining normal RNA structure and normal mitochondrial function, respectively, in cortical proximal tubules with DKD. DESI-MSI technology coupled with METASPACE could serve as powerful new tools to provide insight on fundamental pathways in DKD.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>31925564</pmid><doi>10.1007/s11306-020-1637-8</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-7550-8525</orcidid><orcidid>https://orcid.org/0000000275508525</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Animal models Animals BASIC BIOLOGICAL SCIENCES Biochemistry Bioinformatics Biomedical and Life Sciences Biomedicine Cardiolipins - metabolism Cell Biology Computational Biology DESI-MSI Developmental Biology Diabetes Diabetes mellitus (insulin dependent) Diabetic kidney disease Diabetic Nephropathies - metabolism Fatty Acids, Omega-3 - metabolism Kidney diseases Kidney Tubules, Proximal - metabolism Kidneys Life Sciences lipid metabolism Male Mass spectroscopy Metabolic Networks and Pathways Metabolites Metabolome Metabolomics Mice Mice, Inbred C57BL Mitochondria Mitochondrial Membranes - metabolism Molecular Medicine Morbidity Original Article Polyunsaturated fatty acids Proximal tubules Pseudouridine - metabolism renal proximal tubule Ribonucleic acid RNA Spatial distribution Spectrometry, Mass, Electrospray Ionization Urine |
| title | DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules |
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