Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles

Abstract The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic‐induced injury. Xenobiotic‐induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introd...
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Gespeichert in:

Autor*in:	Kenichi Shimada [verfasserIn] Timothy J Mitchison [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	automated diagnosis body weight loss data mining ferroptosis toxicogenomics

Übergeordnetes Werk:	In: Molecular Systems Biology - Wiley, 2005, 15(2019), 2, Seite n/a-n/a
Übergeordnetes Werk:	volume:15 ; year:2019 ; number:2 ; pages:n/a-n/a

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.15252/msb.20188636

Katalog-ID:	DOAJ058303626

Internformat


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520			\|a Abstract The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic‐induced injury. Xenobiotic‐induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine‐learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin‐induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole‐body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.
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allfields	10.15252/msb.20188636 doi (DE-627)DOAJ058303626 (DE-599)DOAJf16e758e89464f388371d435d364d4f1 DE-627 ger DE-627 rakwb eng QH301-705.5 R5-920 Kenichi Shimada verfasserin aut Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic‐induced injury. Xenobiotic‐induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine‐learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin‐induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole‐body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease. automated diagnosis body weight loss data mining ferroptosis toxicogenomics Biology (General) Medicine (General) Timothy J Mitchison verfasserin aut In Molecular Systems Biology Wiley, 2005 15(2019), 2, Seite n/a-n/a (DE-627)490536905 (DE-600)2193510-5 17444292 nnns volume:15 year:2019 number:2 pages:n/a-n/a https://doi.org/10.15252/msb.20188636 kostenfrei https://doaj.org/article/f16e758e89464f388371d435d364d4f1 kostenfrei https://doi.org/10.15252/msb.20188636 kostenfrei https://doaj.org/toc/1744-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2019 2 n/a-n/a
spelling	10.15252/msb.20188636 doi (DE-627)DOAJ058303626 (DE-599)DOAJf16e758e89464f388371d435d364d4f1 DE-627 ger DE-627 rakwb eng QH301-705.5 R5-920 Kenichi Shimada verfasserin aut Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic‐induced injury. Xenobiotic‐induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine‐learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin‐induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole‐body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease. automated diagnosis body weight loss data mining ferroptosis toxicogenomics Biology (General) Medicine (General) Timothy J Mitchison verfasserin aut In Molecular Systems Biology Wiley, 2005 15(2019), 2, Seite n/a-n/a (DE-627)490536905 (DE-600)2193510-5 17444292 nnns volume:15 year:2019 number:2 pages:n/a-n/a https://doi.org/10.15252/msb.20188636 kostenfrei https://doaj.org/article/f16e758e89464f388371d435d364d4f1 kostenfrei https://doi.org/10.15252/msb.20188636 kostenfrei https://doaj.org/toc/1744-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2019 2 n/a-n/a
allfields_unstemmed	10.15252/msb.20188636 doi (DE-627)DOAJ058303626 (DE-599)DOAJf16e758e89464f388371d435d364d4f1 DE-627 ger DE-627 rakwb eng QH301-705.5 R5-920 Kenichi Shimada verfasserin aut Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic‐induced injury. Xenobiotic‐induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine‐learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin‐induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole‐body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease. automated diagnosis body weight loss data mining ferroptosis toxicogenomics Biology (General) Medicine (General) Timothy J Mitchison verfasserin aut In Molecular Systems Biology Wiley, 2005 15(2019), 2, Seite n/a-n/a (DE-627)490536905 (DE-600)2193510-5 17444292 nnns volume:15 year:2019 number:2 pages:n/a-n/a https://doi.org/10.15252/msb.20188636 kostenfrei https://doaj.org/article/f16e758e89464f388371d435d364d4f1 kostenfrei https://doi.org/10.15252/msb.20188636 kostenfrei https://doaj.org/toc/1744-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2019 2 n/a-n/a
allfieldsGer	10.15252/msb.20188636 doi (DE-627)DOAJ058303626 (DE-599)DOAJf16e758e89464f388371d435d364d4f1 DE-627 ger DE-627 rakwb eng QH301-705.5 R5-920 Kenichi Shimada verfasserin aut Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic‐induced injury. Xenobiotic‐induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine‐learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin‐induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole‐body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease. automated diagnosis body weight loss data mining ferroptosis toxicogenomics Biology (General) Medicine (General) Timothy J Mitchison verfasserin aut In Molecular Systems Biology Wiley, 2005 15(2019), 2, Seite n/a-n/a (DE-627)490536905 (DE-600)2193510-5 17444292 nnns volume:15 year:2019 number:2 pages:n/a-n/a https://doi.org/10.15252/msb.20188636 kostenfrei https://doaj.org/article/f16e758e89464f388371d435d364d4f1 kostenfrei https://doi.org/10.15252/msb.20188636 kostenfrei https://doaj.org/toc/1744-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2019 2 n/a-n/a
allfieldsSound	10.15252/msb.20188636 doi (DE-627)DOAJ058303626 (DE-599)DOAJf16e758e89464f388371d435d364d4f1 DE-627 ger DE-627 rakwb eng QH301-705.5 R5-920 Kenichi Shimada verfasserin aut Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic‐induced injury. Xenobiotic‐induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine‐learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin‐induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole‐body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease. automated diagnosis body weight loss data mining ferroptosis toxicogenomics Biology (General) Medicine (General) Timothy J Mitchison verfasserin aut In Molecular Systems Biology Wiley, 2005 15(2019), 2, Seite n/a-n/a (DE-627)490536905 (DE-600)2193510-5 17444292 nnns volume:15 year:2019 number:2 pages:n/a-n/a https://doi.org/10.15252/msb.20188636 kostenfrei https://doaj.org/article/f16e758e89464f388371d435d364d4f1 kostenfrei https://doi.org/10.15252/msb.20188636 kostenfrei https://doaj.org/toc/1744-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2019 2 n/a-n/a
language	English
source	In Molecular Systems Biology 15(2019), 2, Seite n/a-n/a volume:15 year:2019 number:2 pages:n/a-n/a
sourceStr	In Molecular Systems Biology 15(2019), 2, Seite n/a-n/a volume:15 year:2019 number:2 pages:n/a-n/a
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	automated diagnosis body weight loss data mining ferroptosis toxicogenomics Biology (General) Medicine (General)
isfreeaccess_bool	true
container_title	Molecular Systems Biology
authorswithroles_txt_mv	Kenichi Shimada @@aut@@ Timothy J Mitchison @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	490536905
id	DOAJ058303626
language_de	englisch
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callnumber-first	Q - Science
author	Kenichi Shimada
spellingShingle	Kenichi Shimada misc QH301-705.5 misc R5-920 misc automated diagnosis misc body weight loss misc data mining misc ferroptosis misc toxicogenomics misc Biology (General) misc Medicine (General) Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles
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topic_title	QH301-705.5 R5-920 Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles automated diagnosis body weight loss data mining ferroptosis toxicogenomics
topic	misc QH301-705.5 misc R5-920 misc automated diagnosis misc body weight loss misc data mining misc ferroptosis misc toxicogenomics misc Biology (General) misc Medicine (General)
topic_unstemmed	misc QH301-705.5 misc R5-920 misc automated diagnosis misc body weight loss misc data mining misc ferroptosis misc toxicogenomics misc Biology (General) misc Medicine (General)
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title	Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles
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title_full	Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles
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title_sort	unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles
callnumber	QH301-705.5
title_auth	Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles
abstract	Abstract The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic‐induced injury. Xenobiotic‐induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine‐learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin‐induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole‐body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.
abstractGer	Abstract The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic‐induced injury. Xenobiotic‐induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine‐learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin‐induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole‐body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.
abstract_unstemmed	Abstract The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic‐induced injury. Xenobiotic‐induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine‐learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin‐induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole‐body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.
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title_short	Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles
url	https://doi.org/10.15252/msb.20188636 https://doaj.org/article/f16e758e89464f388371d435d364d4f1 https://doaj.org/toc/1744-4292
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up_date	2024-07-03T17:12:19.243Z
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Unsupervised identification of disease states from high‐dimensional physiological and histopathological profiles

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