FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2794737913> ?p ?o ?g. }

• W2794737913 endingPage "268" @default.
• W2794737913 startingPage "238" @default.
• W2794737913 abstract "•Body-wide multi-organ transcriptome datasets encompassing diverse disease models•Skin is a disease-sensor organ, and FGF23 mediates a bone-skin cross talk in diseases•Diverse putative inter-organ cross talk selectively associates with diseases•A cross-species map illustrating the mouse-human relationships Virtually all diseases affect multiple organs. However, our knowledge of the body-wide effects remains limited. Here, we report the body-wide transcriptome landscape across 13–23 organs of mouse models of myocardial infarction, diabetes, kidney diseases, cancer, and pre-mature aging. Using such datasets, we find (1) differential gene expression in diverse organs across all models; (2) skin as a disease-sensor organ represented by disease-specific activities of putative gene-expression network; (3) a bone-skin cross talk mediated by a bone-derived hormone, FGF23, in response to dysregulated phosphate homeostasis, a known risk-factor for kidney diseases; (4) candidates for the signature activities of many more putative inter-organ cross talk for diseases; and (5) a cross-species map illustrating organ-to-organ and model-to-disease relationships between human and mouse. These findings demonstrate the usefulness and the potential of such body-wide datasets encompassing mouse models of diverse disease types as a resource in biological and medical sciences. Furthermore, the findings described herein could be exploited for designing disease diagnosis and treatment. Virtually all diseases affect multiple organs. However, our knowledge of the body-wide effects remains limited. Here, we report the body-wide transcriptome landscape across 13–23 organs of mouse models of myocardial infarction, diabetes, kidney diseases, cancer, and pre-mature aging. Using such datasets, we find (1) differential gene expression in diverse organs across all models; (2) skin as a disease-sensor organ represented by disease-specific activities of putative gene-expression network; (3) a bone-skin cross talk mediated by a bone-derived hormone, FGF23, in response to dysregulated phosphate homeostasis, a known risk-factor for kidney diseases; (4) candidates for the signature activities of many more putative inter-organ cross talk for diseases; and (5) a cross-species map illustrating organ-to-organ and model-to-disease relationships between human and mouse. These findings demonstrate the usefulness and the potential of such body-wide datasets encompassing mouse models of diverse disease types as a resource in biological and medical sciences. Furthermore, the findings described herein could be exploited for designing disease diagnosis and treatment. Diseases are conventionally studied in the context of changes and responses that occur in only one or a few selected organs. For example, in studying heart diseases, the heart is the main focus point. In some cases, a few other organs (e.g., kidney, lung, brain) are included in the studies, as they are known to interact with the heart via circulating hormones and other systemic factors (McGrath et al., 2005McGrath M.F. de Bold M.L. de Bold A.J. The endocrine function of the heart.Trends Endocrinol. Metab. 2005; 16: 469-477Abstract Full Text Full Text PDF PubMed Scopus (209) Google Scholar). In investigating kidney diseases, multiple organs (e.g., bone, heart, brain, liver) are often studied on the basis of the inter-organ communication (Hu et al., 2013Hu M.C. Shiizaki K. Kuro-o M. Moe O.W. Fibroblast growth factor 23 and Klotho: physiology and pathophysiology of an endocrine network of mineral metabolism.Annu. Rev. Physiol. 2013; 75: 503-533Crossref PubMed Scopus (409) Google Scholar, Ix and Sharma, 2010Ix J.H. Sharma K. Mechanisms linking obesity, chronic kidney disease, and fatty liver disease: the roles of fetuin-A, adiponectin, and AMPK.J. Am. Soc. Nephrol. 2010; 21: 406-412Crossref PubMed Scopus (264) Google Scholar, Kuro-o, 2010Kuro-o M. A potential link between phosphate and aging–lessons from Klotho-deficient mice.Mech. Ageing Dev. 2010; 131: 270-275Crossref PubMed Scopus (132) Google Scholar, Vervloet et al., 2014aVervloet M.G. Adema A.Y. Larsson T.E. Massy Z.A. The role of klotho on vascular calcification and endothelial function in chronic kidney disease.Semin. Nephrol. 2014; 34: 578-585Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar). Tumorigenesis in a specific organ imposes critical impacts on metabolic organs, eventually causing the whole-body-level condition known as cachexia (Droujinine and Perrimon, 2016Droujinine I.A. Perrimon N. Interorgan communication pathways in physiology: focus on Drosophila.Annu. Rev. Genet. 2016; 50: 539-570Crossref PubMed Scopus (111) Google Scholar). Even in this case, only a few selected organs are the subjects of the studies. Currently, we know very little about exactly to what extent diverse organs in the body are influenced in a specific disease condition. In an effort to understand the whole-body level biology of the human and non-human model organisms, the multi-organ omics databases have been established and made available to the public. Especially, the Genotype-Tissue Expression (GTEx) (GTEx Consortium, 2013GTEx ConsortiumThe Genotype-tissue expression (GTEx) project.Nat. Genet. 2013; 45: 580-585Crossref PubMed Scopus (4320) Google Scholar) and The Human Protein Atlas (Uhlen et al., 2015Uhlen M. Fagerberg L. Hallstrom B.M. Lindskog C. Oksvold P. Mardinoglu A. Sivertsson A. Kampf C. Sjostedt E. Asplund A. et al.Proteomics. Tissue-based map of the human proteome.Science. 2015; 347: 1260419Crossref PubMed Scopus (7270) Google Scholar) publish comprehensive transcriptome and transcriptome/proteome datasets of multiple organs of healthy human subjects, respectively. Most recently, an international research project referred to as Human Cell Atlas (Regev et al., 2017Regev A. Teichmann S.A. Lander E.S. Amit I. Benoist C. Birney E. Bodenmiller B. Campbell P.J. Carninci P. Clatworthy M. et al.Science Forum: the human cell Atlas.Elife. 2017; 6https://doi.org/10.7554/eLife.27041Crossref Scopus (978) Google Scholar) has been initiated and is aiming to have the complete whole-body map of the human at the individual cell level. A similar approach has already generated transcriptome datasets of approximately 6,000 individual cells in Drosophila (Karaiskos et al., 2017Karaiskos N. Wahle P. Alles J. Boltengagen A. Ayoub S. Kipar C. Kocks C. Rajewsky N. Zinzen R.P. The Drosophila embryo at single-cell transcriptome resolution.Science. 2017; 358: 194-199Crossref PubMed Scopus (207) Google Scholar), a commonly used model organism in biology and medicine. Recently, we reported comprehensive whole-body transcriptome datasets of normal and several mutant zebrafish (Takada et al., 2017Takada N. Omae M. Sagawa F. Chi N.C. Endo S. Kozawa S. Sato T.N. Re-evaluating the functional landscape of the cardiovascular system during development.Biol. Open. 2017; 6: 1756-1770Crossref PubMed Scopus (6) Google Scholar), a vertebrate model frequently used for the study of biology and medicine. Although such comprehensive multi-organ datasets of healthy subjects and developmental models are being generated, comparably comprehensive multi-organ and multi-disease datasets remain limited. The Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) has provided transcriptome datasets of several organs derived from human subjects with coronary artery diseases (CADs) (Franzen et al., 2016Franzen O. Ermel R. Cohain A. Akers N.K. Di Narzo A. Talukdar H.A. Foroughi-Asl H. Giambartolomei C. Fullard J.F. Sukhavasi K. et al.Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases.Science. 2016; 353: 827-830Crossref PubMed Scopus (154) Google Scholar). However, diverse organs are yet to be represented. Furthermore, the comparable datasets for other types of diseases are not available, thus disease-to-disease comparisons remain a challenge. Hence, what is missing is the dataset representing the body-wide diverse organs and multiple diseases. The availability of such datasets allows for the evaluations of molecular changes that occur in diverse organs in each disease model. Such datasets also make it possible to perform direct organ-to-organ comparisons among various disease models and also inter-organ comparisons within the specific model. Such analyses can be effectively used to deduce the body-wide inter-organ communication network. Furthermore, their utility could extend to the identification of disease-specific and/or organ-specific molecular signatures that could serve as biomarkers for diseases and/or molecular targets for therapeutics. Moreover, such body-wide multi-disease model datasets could be used to make organ-to-organ comparisons with human datasets to characterize similarities and dissimilarities between the mouse and human transcriptome landscapes. In this report, we provide such datasets. We describe comprehensive transcriptome datasets of 13–23 organs from diverse disease models: myocardial infarction, diabetes, kidney diseases, brain tumor, and pre-mature aging. The data are generated from one to three stages representing early to late phases of the progression of each disease condition. For kidney diseases, we provide datasets from multiple different models, each representing overlapping but distinct risk factors for the disease. The analyses of such datasets reveal organ-to-organ similarities and dissimilarities among different disease models. They also show common and distinct features of the transcriptome landscape among distinct organs within each model. These analyses identified the skin as one of the unexpected organs that appears to sense disease-associated pathophysiological condition(s). Experimental validations found 25 genes in the skin that are differentially expressed in the kidney-disease models. We also show that their expression is differentially modulated by a bone-derived systemic factor, FGF23, suggesting a bone-skin interaction in kidney disease or related conditions. More global body-wide network analyses across multiple organs in each model identify candidates for inter-organ cross talk underlying disease-associated pathophysiological changes. The utility of our mouse model datasets is also illustrated by showing the organ-to-organ differences in the degree of similarity in the genome-wide gene expression patterns between human and each mouse model. The comparison of the mouse datasets to an orthologous human disease dataset provides an insight into the degree of the relatedness of the mouse model to human disease. We discuss the utility of such rich body-wide datasets across multiple disease models for the study of disease and also the relevance of the findings to human biology and clinical applications. Comprehensive transcriptome data were generated from mouse models of diverse human diseases and disease-related conditions (Figure 1). We chose seven relatively well-established and widely used mouse models and generated transcriptome datasets from one to three pathophysiological stages for each model (Figure 1). Models for myocardial infarction (MI) (Murakoshi et al., 2013Murakoshi M. Saiki K. Urayama K. Sato T.N. An anthelmintic drug, pyrvinium pamoate, thwarts fibrosis and ameliorates myocardial contractile dysfunction in a mouse model of myocardial infarction.PLoS One. 2013; 8: e79374Crossref PubMed Scopus (28) Google Scholar) (Figure 1A), streptozotocin (STZ)-induced diabetes (Graham et al., 2011Graham M.L. Janecek J.L. Kittredge J.A. Hering B.J. Schuurman H.J. The streptozotocin-induced diabetic nude mouse model: differences between animals from different sources.Comp. Med. 2011; 61: 356-360PubMed Google Scholar, Portha et al., 1989Portha B. Blondel O. Serradas P. McEvoy R. Giroix M.H. Kergoat M. Bailbe D. The rat models of non-insulin dependent diabetes induced by neonatal streptozotocin.Diabete Metab. 1989; 15: 61-75PubMed Google Scholar) (Figure 1B), kidney diseases and related conditions including chronic kidney disease (CKD), chronic kidney disease mineral and bone disorder (CKD-MBD) (Hu et al., 2013Hu M.C. Shiizaki K. Kuro-o M. Moe O.W. Fibroblast growth factor 23 and Klotho: physiology and pathophysiology of an endocrine network of mineral metabolism.Annu. Rev. Physiol. 2013; 75: 503-533Crossref PubMed Scopus (409) Google Scholar, John et al., 2011John G.B. Cheng C.Y. Kuro-o M. Role of Klotho in aging, phosphate metabolism, and CKD.Am. J. Kidney Dis. 2011; 58: 127-134Abstract Full Text Full Text PDF PubMed Scopus (140) Google Scholar, Kuro-o, 2013Kuro-o M. A phosphate-centric paradigm for pathophysiology and therapy of chronic kidney disease.Kidney Int. Suppl. (2011). 2013; 3: 420-426Abstract Full Text Full Text PDF PubMed Scopus (82) Google Scholar, Kuro-o, 2017Kuro-o M. The FGF23 and Klotho system beyond mineral metabolism.Clin. Exp. Nephrol. 2017; 21: 64-69Crossref PubMed Scopus (41) Google Scholar, Kuro-o and Moe, 2017Kuro-o M. Moe O.W. FGF23-alphaKlotho as a paradigm for a kidney-bone network.Bone. 2017; 100: 4-18Crossref PubMed Scopus (57) Google Scholar, Kuro-o, 2010Kuro-o M. A potential link between phosphate and aging–lessons from Klotho-deficient mice.Mech. Ageing Dev. 2010; 131: 270-275Crossref PubMed Scopus (132) Google Scholar, Miller et al., 2010Miller R.P. Tadagavadi R.K. Ramesh G. Reeves W.B. Mechanisms of cisplatin nephrotoxicity.Toxins (Basel). 2010; 2: 2490-2518Crossref PubMed Scopus (1074) Google Scholar, Stubbs et al., 2007Stubbs J.R. Liu S. Tang W. Zhou J. Wang Y. Yao X. Quarles L.D. Role of hyperphosphatemia and 1,25-dihydroxyvitamin D in vascular calcification and mortality in fibroblastic growth factor 23 null mice.J. Am. Soc. Nephrol. 2007; 18: 2116-2124Crossref PubMed Scopus (240) Google Scholar, Tani et al., 2017Tani T. Orimo H. Shimizu A. Tsuruoka S. Development of a novel chronic kidney disease mouse model to evaluate the progression of hyperphosphatemia and associated mineral bone disease.Sci. Rep. 2017; 7: 2233Crossref PubMed Scopus (26) Google Scholar, Watanabe et al., 2017Watanabe R. Fujita N. Sato Y. Kobayashi T. Morita M. Oike T. Miyamoto K. Kuro-o O.M. Michigami T. Fukumoto S. et al.Enpp1 is an anti-aging factor that regulates Klotho under phosphate overload conditions.Sci. Rep. 2017; 7: 7786Crossref PubMed Scopus (21) Google Scholar) (Figure 1C), cancer (Kiaris et al., 2000Kiaris H. Schally A.V. Nagy A. Sun B. Szepeshazi K. Halmos G. Regression of U-87 MG human glioblastomas in nude mice after treatment with a cytotoxic somatostatin analog AN-238.Clin. Cancer Res. 2000; 6: 709-717PubMed Google Scholar) (Figure 1D), pre-mature aging (Butterfield and Poon, 2005Butterfield D.A. Poon H.F. The senescence-accelerated prone mouse (SAMP8): a model of age-related cognitive decline with relevance to alterations of the gene expression and protein abnormalities in Alzheimer's disease.Exp. Gerontol. 2005; 40: 774-783Crossref PubMed Scopus (265) Google Scholar) (Figure 1E), and kidney injury induced by cisplatin (Megyesi et al., 1998Megyesi J. Safirstein R.L. Price P.M. Induction of p21WAF1/CIP1/SDI1 in kidney tubule cells affects the course of cisplatin-induced acute renal failure.J. Clin. Invest. 1998; 101: 777-782Crossref PubMed Scopus (246) Google Scholar) (Figure 1F) were generated (see Transparent Methods) and validated (Figures S1–S5, Table S1. Raw Data of Ejection Fraction, Related to Figure 1, Table S2. Raw Data of Blood Glucose in STZ Model, Related to Figure 1, Table S3. Raw Data of Inorganic Phosphorus in Plasma for CKD Models and HPi4w Model, Related to Figure 1, Table S4. Raw Data of Creatinine in Plasma for CKD Models and HPi4w Model, Related to Figure 1, Table S5. Raw Data of ELISA for FGF23 in Plasma for CKD Models and HPi4w Model, Related to Figure 1, Table S6. Raw Data of qRT-PCR for GBM Model, Related to Figure 1, Table S7. Raw Data of Step-through Test for SAMP8 Mouse, Related to Figure 1, Table S8. Raw Data of Inorganic Phosphorus in Plasma for Phosphorus Overload Models, Related to Figure 1, Table S9. Raw Data of ELISA for FGF23 in Plasma for Phosphorus Overload Models in WT and FGF23-Deficient Mice, Related to Figure 1). From each model and control mice (WT and WT + Saline) (see Transparent Methods), 13–23 organs were harvested and processed for RNA sequence (RNAseq) analyses (see Transparent Methods). The overall quality of the sequence data was demonstrated by the virtually identical gene expression patterns between the control datasets (WT and WT + Saline) and the publicly available wild-type (WT) mouse datasets (Pervouchine et al., 2015Pervouchine D.D. Djebali S. Breschi A. Davis C.A. Barja P.P. Dobin A. Tanzer A. Lagarde J. Zaleski C. See L.H. et al.Enhanced transcriptome maps from multiple mouse tissues reveal evolutionary constraint in gene expression.Nat. Commun. 2015; 6: 5903Crossref PubMed Scopus (54) Google Scholar) (Figure S6). A subtle difference could be attributed to strain, age, sex, or housing-condition differences (see Transparent Methods). The validity of the datasets was further confirmed by the regulated expressions of the known disease marker genes (Bosworth and de Boer, 2013Bosworth C. de Boer I.H. Impaired vitamin D metabolism in CKD.Semin. Nephrol. 2013; 33: 158-168Abstract Full Text Full Text PDF PubMed Scopus (61) Google Scholar, Cheng et al., 2015Cheng Y. Kang H. Shen J. Hao H. Liu J. Guo Y. Mu Y. Han W. Beta-cell regeneration from vimentin+/MafB+ cells after STZ-induced extreme beta-cell ablation.Sci. Rep. 2015; 5: 11703Crossref PubMed Scopus (20) Google Scholar, Hiratsuka et al., 2006Hiratsuka S. Watanabe A. Aburatani H. Maru Y. Tumour-mediated upregulation of chemoattractants and recruitment of myeloid cells predetermines lung metastasis.Nat. Cell Biol. 2006; 8: 1369-1375Crossref PubMed Scopus (820) Google Scholar, Jarve et al., 2017Jarve A. Muhlstedt S. Qadri F. Nickl B. Schulz H. Hubner N. Ozcelik C. Bader M. Adverse left ventricular remodeling by glycoprotein nonmetastatic melanoma protein B in myocardial infarction.FASEB J. 2017; 31: 556-568Crossref PubMed Scopus (12) Google Scholar, Pabla et al., 2015Pabla N. Gibson A.A. Buege M. Ong S.S. Li L. Hu S. Du G. Sprowl J.A. Vasilyeva A. Janke L.J. et al.Mitigation of acute kidney injury by cell-cycle inhibitors that suppress both CDK4/6 and OCT2 functions.Proc. Natl. Acad. Sci. USA. 2015; 112: 5231-5236Crossref PubMed Scopus (66) Google Scholar, Port et al., 2011Port J.D. Walker L.A. Polk J. Nunley K. Buttrick P.M. Sucharov C.C. Temporal expression of miRNAs and mRNAs in a mouse model of myocardial infarction.Physiol. Genomics. 2011; 43: 1087-1095Crossref PubMed Scopus (41) Google Scholar, Ramesh and Reeves, 2002Ramesh G. Reeves W.B. TNF-alpha mediates chemokine and cytokine expression and renal injury in cisplatin nephrotoxicity.J. Clin. Invest. 2002; 110: 835-842Crossref PubMed Scopus (712) Google Scholar, Reed et al., 2011Reed A.L. Tanaka A. Sorescu D. Liu H. Jeong E.M. Sturdy M. Walp E.R. Dudley Jr., S.C. Sutliff R.L. Diastolic dysfunction is associated with cardiac fibrosis in the senescence-accelerated mouse.Am. J. Physiol. Heart Circ. Physiol. 2011; 301: H824-H831Crossref PubMed Scopus (78) Google Scholar, Sehl et al., 2000Sehl P.D. Tai J.T. Hillan K.J. Brown L.A. Goddard A. Yang R. Jin H. Lowe D.G. Application of cDNA microarrays in determining molecular phenotype in cardiac growth, development, and response to injury.Circulation. 2000; 101: 1990-1999Crossref PubMed Scopus (113) Google Scholar, Suyama et al., 2012Suyama T. Okada S. Ishijima T. Iida K. Abe K. Nakai Y. High phosphorus diet-induced changes in NaPi-IIb phosphate transporter expression in the rat kidney: DNA microarray analysis.PLoS One. 2012; 7: e29483Crossref PubMed Scopus (27) Google Scholar, Tonne et al., 2013Tonne J.M. Sakuma T. Deeds M.C. Munoz-Gomez M. Barry M.A. Kudva Y.C. Ikeda Y. Global gene expression profiling of pancreatic islets in mice during streptozotocin-induced beta-cell damage and pancreatic Glp-1 gene therapy.Dis. Model. Mech. 2013; 6: 1236-1245Crossref PubMed Scopus (49) Google Scholar, Wada et al., 2016Wada N. Yamanaka S. Shibato J. Rakwal R. Hirako S. Iizuka Y. Kim H. Matsumoto A. Kimura A. Takenoya F. et al.Behavioral and omics analyses study on potential involvement of dipeptide balenine through supplementation in diet of senescence-accelerated mouse prone 8.Genom. Data. 2016; 10: 38-50Crossref PubMed Scopus (12) Google Scholar) (Figure 2). To evaluate how broadly the gene expressions are altered across various organs, we compared both model and sham control with WT control datasets independently using DESeq2 analysis. For convenience, we used 2-fold change as differentially expressed genes (i.e., |log2(fold-change)| > 1) with p < 0.0001 (Wald test and adjusted by Benjamini-Hochberg method). The number of such differentially expressed genes specifically for one organ in each model, but not in the corresponding sham control, was counted. In addition, the number of the differentially expressed genes in multiple organs for all combinations of the organs in each model, but not in the corresponding sham control, was also counted. The results of such organ-to-organ comparisons for each model are shown in Figure 3. It is important to note that the organs analyzed for each model overlap but are not identical; thus the model-to-model comparison should not be performed using these graphs. Hence, for the model-to-model comparison for each organ, the number of differentially expressed genes in each organ isolated from each model was analyzed (Figure 4). The number of differentially expressed genes specifically in one model or sham control, but not in the other models or sham controls, and also in multiple combinations of the models/sham controls, but not in other combinations was counted. The results of such model-to-model comparisons for each organ are shown in Figure 4.Figure 3Organ-to-Organ Comparisons of the Differentially Expressed Genes for Each ModelShow full captionThe color or the colors (the vertical alignment) at the bottom indicate the organ or the combination of the organs, respectively. The differentially expressed genes in each organ/organ combination of each model were identified by comparison with the corresponding dataset of WT. The number (in log2) of the differentially expressed genes in each organ/organ combination of each disease model, but not in that of the corresponding sham control, was counted and is shown as bar graph for each model. The number (in log2) of the differentially expressed genes in each organ/organ combination of the cisplatin model, as compared with the corresponding dataset of WT, was counted and is shown as bar graph. It is important to note that the organs analyzed for each model overlap but are not identical (see Figure 1). The organs that are not represented in each model are indicated by blue-color x axis line. Thus the comparison is valid only among the organs that are represented in each organ. The corresponding organ-to-organ comparisons of the GO terms are shown in Figure S7.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 4Model-to-Model Comparisons of the Gene Expression for Each OrganShow full captionThe color or the colors (the vertical alignment) at the bottom indicate the model or the model combinations, respectively. The number (in log2) of the differentially expressed genes (as compared with the corresponding WT dataset) in each model/model combination of each organ was counted and is shown as bar graph for each model. Only organs that are common to all models are analyzed and shown. The model-to-model comparisons of the GO terms are shown in Figure S8.View Large Image Figure ViewerDownload Hi-res image Download (PPT) The color or the colors (the vertical alignment) at the bottom indicate the organ or the combination of the organs, respectively. The differentially expressed genes in each organ/organ combination of each model were identified by comparison with the corresponding dataset of WT. The number (in log2) of the differentially expressed genes in each organ/organ combination of each disease model, but not in that of the corresponding sham control, was counted and is shown as bar graph for each model. The number (in log2) of the differentially expressed genes in each organ/organ combination of the cisplatin model, as compared with the corresponding dataset of WT, was counted and is shown as bar graph. It is important to note that the organs analyzed for each model overlap but are not identical (see Figure 1). The organs that are not represented in each model are indicated by blue-color x axis line. Thus the comparison is valid only among the organs that are represented in each organ. The corresponding organ-to-organ comparisons of the GO terms are shown in Figure S7. The color or the colors (the vertical alignment) at the bottom indicate the model or the model combinations, respectively. The number (in log2) of the differentially expressed genes (as compared with the corresponding WT dataset) in each model/model combination of each organ was counted and is shown as bar graph for each model. Only organs that are common to all models are analyzed and shown. The model-to-model comparisons of the GO terms are shown in Figure S8. As expected, the organ(s) that is(are) conventionally known to show pathophysiological responses in each model show a large number of differentially expressed genes. In the MI models, the clusters of taller bars in the heart and the combinations including the heart are obvious (Figure 3). A closer examination found 1,302, 1,891, and 1,178 differentially expressed genes in the heart or the organ combinations, including the heart of E-MI, M-MI, and L-MI, respectively, but not in the corresponding sham-control models (Tables S10 and S11). Furthermore, according to the ranking in the number of differentially expressed genes in the heart, the M-MI model is the first with 629 genes (Figure 4 and Table S12). The second is E−/M-MI models with 261 genes (i.e., 261 genes are differentially expressed in both E− and M-MI models) (Figure 4 and Table S12). The third (253 genes), fourth (133 genes), fifth (105 genes), and sixth (102 genes) were E-MI model, M-/L-MI models, E−/M-/L-MI models, and L-MI model, respectively (Figure 4 and Table S12). The genes that are also regulated in the corresponding sham controls were excluded in this ranking (Figure 4 and Table S12). In the kidney disease models, we found 499, 631, and 1,124 differentially expressed genes in the kidney or the organ combinations, including the kidney of E-CKD, M-CKD, and L-CKD, respectively, but not in the corresponding sham-control models (Figure 3, Tables S10 and S11). In the cisplatin-treated model, the kidney shows 1,482 differentially expressed genes (Figure 3, Tables S10 and S11). However, the overall landscape of the organ-to-organ and the model-to-model comparisons was more complex than anticipated. It appears that the differential gene expression patterns extend to more diverse organ types than conventionally assumed (Figure 3). Although, in each model, the number of differentially expressed genes varies from organ to organ and also depends on the organ combinations, the differentially expressed genes are broadly distributed across organs and organ combinations (Figure 3). The organ-to-organ comparisons of Gene Ontology (GO) terms of the differentially expressed genes also show the overlapping and distinct distribution pattern for each organ (Figure S7 and Table S13). In the model-to-model comparisons (Figure 4), the differentially expressed genes in each organ can be detected both in a specific model and also are shared among multiple organs, as indicated by the broadly distributed patterns of the bars in each graph (Figure 4). A closer examination also identifies the lack of differentially expressed genes in a certain model(s) and/or a model combination(s), as indicated by the lack of the bars (Figure 4). A common distribution pattern can also be found among the organs, suggesting the possibility that some organs respond more robustly than the others (Figure 4). The model-to-model comparisons of GO terms of the differentially expressed genes in each organ also show both the model/model-combination-specific GO terms and those shared by multiple models/model combinations (Figure S8 and Table S13). We show that differential gene expression can be found broadly across diverse organs (Figure 3). In particular, the skin is one of the most robustly affected organs (Figure 3, Tables S10 and S11). In each model, but not in the corresponding sham control, a large number of genes are differentially expressed in the skin: 337 (E-MI), 378 (M-MI), 248 (L-MI), 477 (E-CKD), 294 (M-CKD), 707 (L-CKD), 361 (E-STZ), 349 (M-STZ), 974 (E-SAMP8), 3,731 (M-SAMP8), 526 (L-SAMP8), 381 (E-GBM), 267 (M-GBM), 79 (HPi4w), 2,011 (Cisplatin) genes (Figure 3, Tables S10 and S11). The model-specific differentially expressed skin genes were also found: 8 (E-MI), 10 (M-MI), 2 (L-MI), 13 (E-CKD), 6 (M-CKD), 28 (L-CKD), 21 (E-STZ), 38 (M-STZ), 110 (E-SAMP8), 2,160 (M-SAMP8), 33 (L-SAMP8), 5 (E-GBM), 25 (M-GBM), 6 (HPi4w), 536 (Cisplatin) genes (Figure 4 and Table S12). Next, we applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify signature gene expression network activities of the skin that distinguish one pathophysiological condition from the other (Figure 5 and see Transparent Methods). For each model, we found a set of modules consisting of unique GO terms that show relative" @default.
• W2794737913 created "2018-04-06" @default.
• W2794737913 creator A5009952646 @default.
• W2794737913 creator A5010204881 @default.
• W2794737913 creator A5020236389 @default.
• W2794737913 creator A5024825569 @default.
• W2794737913 creator A5026792692 @default.
• W2794737913 creator A5048985971 @default.
• W2794737913 creator A5050528992 @default.
• W2794737913 creator A5052311523 @default.
• W2794737913 creator A5059401513 @default.
• W2794737913 creator A5067236227 @default.
• W2794737913 creator A5075306366 @default.
• W2794737913 creator A5088543566 @default.
• W2794737913 creator A5089499419 @default.
• W2794737913 creator A5090201226 @default.
• W2794737913 date "2018-04-01" @default.
• W2794737913 modified "2023-10-10" @default.
• W2794737913 title "The Body-wide Transcriptome Landscape of Disease Models" @default.
• W2794737913 cites W1608625663 @default.
• W2794737913 cites W1968332563 @default.
• W2794737913 cites W1968933022 @default.
• W2794737913 cites W1970204106 @default.
• W2794737913 cites W1989131858 @default.
• W2794737913 cites W1994246870 @default.
• W2794737913 cites W2004426288 @default.
• W2794737913 cites W2008659365 @default.
• W2794737913 cites W2011206988 @default.
• W2794737913 cites W2012034410 @default.
• W2794737913 cites W2013658897 @default.
• W2794737913 cites W2016318035 @default.
• W2794737913 cites W2018838463 @default.
• W2794737913 cites W2019316722 @default.
• W2794737913 cites W2025170983 @default.
• W2794737913 cites W2030156126 @default.
• W2794737913 cites W2033601733 @default.
• W2794737913 cites W2038831107 @default.
• W2794737913 cites W2043958868 @default.
• W2794737913 cites W2047073502 @default.
• W2794737913 cites W2070341655 @default.
• W2794737913 cites W2073811662 @default.
• W2794737913 cites W2087418023 @default.
• W2794737913 cites W2087836641 @default.
• W2794737913 cites W2104612852 @default.
• W2794737913 cites W2110023205 @default.
• W2794737913 cites W2112189821 @default.
• W2794737913 cites W2114360920 @default.
• W2794737913 cites W2133574882 @default.
• W2794737913 cites W2150341551 @default.
• W2794737913 cites W2159511438 @default.
• W2794737913 cites W2161630557 @default.
• W2794737913 cites W2165056208 @default.
• W2794737913 cites W2166442426 @default.
• W2794737913 cites W2167670747 @default.
• W2794737913 cites W2168893761 @default.
• W2794737913 cites W2214871873 @default.
• W2794737913 cites W2276937735 @default.
• W2794737913 cites W2343742479 @default.
• W2794737913 cites W2508591721 @default.
• W2794737913 cites W2510111922 @default.
• W2794737913 cites W2519882242 @default.
• W2794737913 cites W2545841884 @default.
• W2794737913 cites W2551959240 @default.
• W2794737913 cites W2553963191 @default.
• W2794737913 cites W2605624585 @default.
• W2794737913 cites W2616194894 @default.
• W2794737913 cites W2743267052 @default.
• W2794737913 cites W2763353370 @default.
• W2794737913 cites W2763746713 @default.
• W2794737913 cites W2783547662 @default.
• W2794737913 cites W4213108508 @default.
• W2794737913 cites W4231793812 @default.
• W2794737913 cites W4240717991 @default.
• W2794737913 cites W4245958271 @default.
• W2794737913 cites W4251170353 @default.
• W2794737913 cites W796386759 @default.
• W2794737913 doi "https://doi.org/10.1016/j.isci.2018.03.014" @default.
• W2794737913 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6135982" @default.
• W2794737913 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30428375" @default.
• W2794737913 hasPublicationYear "2018" @default.
• W2794737913 type Work @default.
• W2794737913 sameAs 2794737913 @default.
• W2794737913 citedByCount "17" @default.
• W2794737913 countsByYear W27947379132019 @default.
• W2794737913 countsByYear W27947379132020 @default.
• W2794737913 countsByYear W27947379132021 @default.
• W2794737913 countsByYear W27947379132022 @default.
• W2794737913 countsByYear W27947379132023 @default.
• W2794737913 crossrefType "journal-article" @default.
• W2794737913 hasAuthorship W2794737913A5009952646 @default.
• W2794737913 hasAuthorship W2794737913A5010204881 @default.
• W2794737913 hasAuthorship W2794737913A5020236389 @default.
• W2794737913 hasAuthorship W2794737913A5024825569 @default.
• W2794737913 hasAuthorship W2794737913A5026792692 @default.
• W2794737913 hasAuthorship W2794737913A5048985971 @default.
• W2794737913 hasAuthorship W2794737913A5050528992 @default.
• W2794737913 hasAuthorship W2794737913A5052311523 @default.
• W2794737913 hasAuthorship W2794737913A5059401513 @default.

• next