Supplementary Table 2: DNP-MRS Probes Used to Measure Metabolic Flux
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Aspartate Aminotransferase (AST, GOT), Human Liver
BioVision 05/18 For research use only Aspartate Aminotransferase (AST, GOT), Human Liver CATALOG NO: P1299-100 1 units RELATED PRODUCTS: ALTERNATE NAMES: Aspartate Transaminase, Glutamate Oxaloacetate, AST, GOT, Aspartate Aminotransferase (AST) (Mouse) ELISA Kit (Cat. No. E4320) sGOT, AspAT, ASAT, serum glutamic oxaloacetic transaminase, AAT Aspartate Aminotransferase (AST) (Human) ELISA Kit (Cat. No. E4319) Aspartate Aminotransferase (AST) (Rat) ELISA Kit (Cat. No. E4321) SOURCE: Human Liver Aspartate Aminotransferase (AST or SGOT) Activity Colorimetric Assay Kit (Cat. PURITY: Purified No. K753) Anti-GOT2 Antibody (cat. No. A1273) MOL. WEIGHT: ~92 kDa Anti-GOT1 Antibody (Cat. No. A1272) FORM: Lyophilized GOT2, human recombinant (Cat. No. 7809) GOT1, human recombinant (Cat. No. 7808) STORAGE CONDITIONS: Store at -20°C. Avoid repeated freezing and thawing cycles. BIOLOGICAL ACTIVITY: ≥ 1 U/mg (Dimension® Clinical Chemistry System) UNIT DEFINITATION: One unit will catalyze the transamination of one micromole of L- aspartate to alpha-ketoglutarate forming L-glutamate and oxaloacetate per minute at 37°C and pH 7.8.Measured at 340 nm as one equimolar amount of NAD produced by a coupled reaction. RECONSTITUTION: > 1 mg/mL in tris buffered saline, 1% BSA, pH 8.0. AST/SGOT is found in many tissues throughout the body, including DESCRIPTION: the liver, heart, muscles, kidney, and brain. If any of these organs or tissues is affected by disease or injury, AST is released into the bloodstream. This means that AST isn't as specific an indicator of liver damage as ALT (also known as alanine aminotransferase, another type of enzyme found almost entirely in the liver). The ratio of AST and ALT levels are commonly used as biomarkers for liver health. -
Supplemental Figure 1. Vimentin
Double mutant specific genes Transcript gene_assignment Gene Symbol RefSeq FDR Fold- FDR Fold- FDR Fold- ID (single vs. Change (double Change (double Change wt) (single vs. wt) (double vs. single) (double vs. wt) vs. wt) vs. single) 10485013 BC085239 // 1110051M20Rik // RIKEN cDNA 1110051M20 gene // 2 E1 // 228356 /// NM 1110051M20Ri BC085239 0.164013 -1.38517 0.0345128 -2.24228 0.154535 -1.61877 k 10358717 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 /// BC 1700025G04Rik NM_197990 0.142593 -1.37878 0.0212926 -3.13385 0.093068 -2.27291 10358713 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 1700025G04Rik NM_197990 0.0655213 -1.71563 0.0222468 -2.32498 0.166843 -1.35517 10481312 NM_027283 // 1700026L06Rik // RIKEN cDNA 1700026L06 gene // 2 A3 // 69987 /// EN 1700026L06Rik NM_027283 0.0503754 -1.46385 0.0140999 -2.19537 0.0825609 -1.49972 10351465 BC150846 // 1700084C01Rik // RIKEN cDNA 1700084C01 gene // 1 H3 // 78465 /// NM_ 1700084C01Rik BC150846 0.107391 -1.5916 0.0385418 -2.05801 0.295457 -1.29305 10569654 AK007416 // 1810010D01Rik // RIKEN cDNA 1810010D01 gene // 7 F5 // 381935 /// XR 1810010D01Rik AK007416 0.145576 1.69432 0.0476957 2.51662 0.288571 1.48533 10508883 NM_001083916 // 1810019J16Rik // RIKEN cDNA 1810019J16 gene // 4 D2.3 // 69073 / 1810019J16Rik NM_001083916 0.0533206 1.57139 0.0145433 2.56417 0.0836674 1.63179 10585282 ENSMUST00000050829 // 2010007H06Rik // RIKEN cDNA 2010007H06 gene // --- // 6984 2010007H06Rik ENSMUST00000050829 0.129914 -1.71998 0.0434862 -2.51672 -
Ornithine Aminotransferase, an Important Glutamate-Metabolizing Enzyme at the Crossroads of Multiple Metabolic Pathways
biology Review Ornithine Aminotransferase, an Important Glutamate-Metabolizing Enzyme at the Crossroads of Multiple Metabolic Pathways Antonin Ginguay 1,2, Luc Cynober 1,2,*, Emmanuel Curis 3,4,5,6 and Ioannis Nicolis 3,7 1 Clinical Chemistry, Cochin Hospital, GH HUPC, AP-HP, 75014 Paris, France; [email protected] 2 Laboratory of Biological Nutrition, EA 4466 PRETRAM, Faculté de Pharmacie, Université Paris Descartes, 75006 Paris, France 3 Laboratoire de biomathématiques, plateau iB2, Faculté de Pharmacie, Université Paris Descartes, 75006 Paris, France; [email protected] (E.C.); [email protected] (I.N.) 4 UMR 1144, INSERM, Université Paris Descartes, 75006 Paris, France 5 UMR 1144, Université Paris Descartes, 75006 Paris, France 6 Service de biostatistiques et d’informatique médicales, hôpital Saint-Louis, Assistance publique-hôpitaux de Paris, 75010 Paris, France 7 EA 4064 “Épidémiologie environnementale: Impact sanitaire des pollutions”, Faculté de Pharmacie, Université Paris Descartes, 75006 Paris, France * Correspondence: [email protected]; Tel.: +33-158-411-599 Academic Editors: Arthur J.L. Cooper and Thomas M. Jeitner Received: 26 October 2016; Accepted: 24 February 2017; Published: 6 March 2017 Abstract: Ornithine δ-aminotransferase (OAT, E.C. 2.6.1.13) catalyzes the transfer of the δ-amino group from ornithine (Orn) to α-ketoglutarate (aKG), yielding glutamate-5-semialdehyde and glutamate (Glu), and vice versa. In mammals, OAT is a mitochondrial enzyme, mainly located in the liver, intestine, brain, and kidney. In general, OAT serves to form glutamate from ornithine, with the notable exception of the intestine, where citrulline (Cit) or arginine (Arg) are end products. -
Multiomics Integration Elucidates Metabolic Modulators of Drug
bioRxiv preprint doi: https://doi.org/10.1101/2021.01.07.425721; this version posted January 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Multiomics Integration Elucidates Metabolic Modulators of Drug Resistance in Lymphoma Choueiry, Fouad1*, Singh, Satishkumar2,3*, Sun, Xiaowei1, Zhang, Shiqi1, Sircar, Anuvrat2,3 ,Hart, Amber2, Alinari, Lapo2,3, Narendranath Epperla2,3, Baiocchi, Robert2,3, Zhu, Jiangjiang1,# and Sehgal, Lalit2,3# 1 Department of Human Sciences, The Ohio State University, Columbus, OH 43210; 2 Division of Hematology, Department of Internal Medicine, The Ohio State, 3 James Comprehensive Cancer Center, The Ohio State University Columbus, OH 43210. *Equal contribution, #Corresponding authors Abstract Background Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma (NHL). B-cell NHLs rely on Bruton’s tyrosine kinase (BTK) mediated B-cell receptor signaling for survival and disease progression. However, they are often resistant to BTK inhibitors or soon acquire resistance after drug exposure resulting in the drug tolerant form. The drug tolerant clones proliferate faster, have increased metabolic activity, and shift to oxidative phosphorylation; however, how this metabolic programming occurs in the drug resistant tumor is poorly understood. Methods In this study, we explored for the first time the metabolic regulators of ibrutinib-resistant activated B-cell (ABC) DLBCL using a ‘multi-omics’ analysis that integrated metabolomics (using high-resolution mass spectrometry) and transcriptomic (gene expression analysis). Overlay of the unbiased statistical analyses, genetic perturbation and pharmaceutical inhibition, were further used to identify the key players that contribute to the metabolic reprograming of the drug resistant clone. -
Supplementary Materials
1 Supplementary Materials: Supplemental Figure 1. Gene expression profiles of kidneys in the Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice. (A) A heat map of microarray data show the genes that significantly changed up to 2 fold compared between Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice (N=4 mice per group; p<0.05). Data show in log2 (sample/wild-type). 2 Supplemental Figure 2. Sting signaling is essential for immuno-phenotypes of the Fcgr2b-/-lupus mice. (A-C) Flow cytometry analysis of splenocytes isolated from wild-type, Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice at the age of 6-7 months (N= 13-14 per group). Data shown in the percentage of (A) CD4+ ICOS+ cells, (B) B220+ I-Ab+ cells and (C) CD138+ cells. Data show as mean ± SEM (*p < 0.05, **p<0.01 and ***p<0.001). 3 Supplemental Figure 3. Phenotypes of Sting activated dendritic cells. (A) Representative of western blot analysis from immunoprecipitation with Sting of Fcgr2b-/- mice (N= 4). The band was shown in STING protein of activated BMDC with DMXAA at 0, 3 and 6 hr. and phosphorylation of STING at Ser357. (B) Mass spectra of phosphorylation of STING at Ser357 of activated BMDC from Fcgr2b-/- mice after stimulated with DMXAA for 3 hour and followed by immunoprecipitation with STING. (C) Sting-activated BMDC were co-cultured with LYN inhibitor PP2 and analyzed by flow cytometry, which showed the mean fluorescence intensity (MFI) of IAb expressing DC (N = 3 mice per group). 4 Supplemental Table 1. Lists of up and down of regulated proteins Accession No. -
Supporting Information
Supporting Information Edgar et al. 10.1073/pnas.1601895113 SI Methods (Actimetrics), and recordings were analyzed using LumiCycle Mice. Sample size was determined using the resource equation: Data Analysis software (Actimetrics). E (degrees of freedom in ANOVA) = (total number of exper- – Cell Cycle Analysis of Confluent Cell Monolayers. NIH 3T3, primary imental animals) (number of experimental groups), with −/− sample size adhering to the condition 10 < E < 20. For com- WT, and Bmal1 fibroblasts were sequentially transduced − − parison of MuHV-4 and HSV-1 infection in WT vs. Bmal1 / with lentiviral fluorescent ubiquitin-based cell cycle indicators mice at ZT7 (Fig. 2), the investigator did not know the genotype (FUCCI) mCherry::Cdt1 and amCyan::Geminin reporters (32). of the animals when conducting infections, bioluminescence Dual reporter-positive cells were selected by FACS (Influx Cell imaging, and quantification. For bioluminescence imaging, Sorter; BD Biosciences) and seeded onto 35-mm dishes for mice were injected intraperitoneally with endotoxin-free lucif- subsequent analysis. To confirm that expression of mCherry:: Cdt1 and amCyan::Geminin correspond to G1 (2n DNA con- erin (Promega E6552) using 2 mg total per mouse. Following < ≤ anesthesia with isofluorane, they were scanned with an IVIS tent) and S/G2 (2 n 4 DNA content) cell cycle phases, Lumina (Caliper Life Sciences), 15 min after luciferin admin- respectively, cells were stained with DNA dye DRAQ5 (abcam) and analyzed by flow cytometry (LSR-Fortessa; BD Biosci- istration. Signal intensity was quantified using Living Image ences). To examine dynamics of replicative activity under ex- software (Caliper Life Sciences), obtaining maximum radiance perimental confluent conditions, synchronized FUCCI reporter for designated regions of interest (photons per second per − − − monolayers were observed by time-lapse live cell imaging over square centimeter per Steradian: photons·s 1·cm 2·sr 1), relative 3 d (Nikon Eclipse Ti-E inverted epifluorescent microscope). -
Glutamine Supports Pancreatic Cancer Growth Through a Kras- Regulated Metabolic Pathway
Glutamine supports pancreatic cancer growth through a Kras- regulated metabolic pathway The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Son, J., C. A. Lyssiotis, H. Ying, X. Wang, S. Hua, M. Ligorio, R. M. Perera, et al. 2013. “Glutamine supports pancreatic cancer growth through a Kras-regulated metabolic pathway.” Nature 496 (7443): 101-105. doi:10.1038/nature12040. http://dx.doi.org/10.1038/ nature12040. Published Version doi:10.1038/nature12040 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:11878814 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA NIH Public Access Author Manuscript Nature. Author manuscript; available in PMC 2013 October 04. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: Nature. 2013 April 4; 496(7443): 101–105. doi:10.1038/nature12040. Glutamine supports pancreatic cancer growth through a Kras- regulated metabolic pathway Jaekyoung Son1,#, Costas A. Lyssiotis2,3,11,#, Haoqiang Ying4, Xiaoxu Wang1, Sujun Hua4, Matteo Ligorio8, Rushika M. Perera5, Cristina R. Ferrone8, Edouard Mullarky2,3,11, Ng Shyh- Chang2,9, Ya’an Kang10, Jason B. Fleming10, Nabeel Bardeesy5, John M. Asara3,6, Marcia C. Haigis7, Ronald A. DePinho4, Lewis C. Cantley2,3,11,*, and Alec -
IL21R Expressing CD14+CD16+ Monocytes Expand in Multiple
Plasma Cell Disorders SUPPLEMENTARY APPENDIX IL21R expressing CD14 +CD16 + monocytes expand in multiple myeloma patients leading to increased osteoclasts Marina Bolzoni, 1 Domenica Ronchetti, 2,3 Paola Storti, 1,4 Gaetano Donofrio, 5 Valentina Marchica, 1,4 Federica Costa, 1 Luca Agnelli, 2,3 Denise Toscani, 1 Rosanna Vescovini, 1 Katia Todoerti, 6 Sabrina Bonomini, 7 Gabriella Sammarelli, 1,7 Andrea Vecchi, 8 Daniela Guasco, 1 Fabrizio Accardi, 1,7 Benedetta Dalla Palma, 1,7 Barbara Gamberi, 9 Carlo Ferrari, 8 Antonino Neri, 2,3 Franco Aversa 1,4,7 and Nicola Giuliani 1,4,7 1Myeloma Unit, Dept. of Medicine and Surgery, University of Parma; 2Dept. of Oncology and Hemato-Oncology, University of Milan; 3Hematology Unit, “Fondazione IRCCS Ca’ Granda”, Ospedale Maggiore Policlinico, Milan; 4CoreLab, University Hospital of Parma; 5Dept. of Medical-Veterinary Science, University of Parma; 6Laboratory of Pre-clinical and Translational Research, IRCCS-CROB, Referral Cancer Center of Basilicata, Rionero in Vulture; 7Hematology and BMT Center, University Hospital of Parma; 8Infectious Disease Unit, University Hospital of Parma and 9“Dip. Oncologico e Tecnologie Avanzate”, IRCCS Arcispedale Santa Maria Nuova, Reggio Emilia, Italy ©2017 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol. 2016.153841 Received: August 5, 2016. Accepted: December 23, 2016. Pre-published: January 5, 2017. Correspondence: [email protected] SUPPLEMENTAL METHODS Immunophenotype of BM CD14+ in patients with monoclonal gammopathies. Briefly, 100 μl of total BM aspirate was incubated in the dark with anti-human HLA-DR-PE (clone L243; BD), anti-human CD14-PerCP-Cy 5.5, anti-human CD16-PE-Cy7 (clone B73.1; BD) and anti-human CD45-APC-H 7 (clone 2D1; BD) for 20 min. -
CDH12 Cadherin 12, Type 2 N-Cadherin 2 RPL5 Ribosomal
5 6 6 5 . 4 2 1 1 1 2 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 A A A A A A A A A A A A A A A A A A A A C C C C C C C C C C C C C C C C C C C C R R R R R R R R R R R R R R R R R R R R B , B B B B B B B B B B B B B B B B B B B , 9 , , , , 4 , , 3 0 , , , , , , , , 6 2 , , 5 , 0 8 6 4 , 7 5 7 0 2 8 9 1 3 3 3 1 1 7 5 0 4 1 4 0 7 1 0 2 0 6 7 8 0 2 5 7 8 0 3 8 5 4 9 0 1 0 8 8 3 5 6 7 4 7 9 5 2 1 1 8 2 2 1 7 9 6 2 1 7 1 1 0 4 5 3 5 8 9 1 0 0 4 2 5 0 8 1 4 1 6 9 0 0 6 3 6 9 1 0 9 0 3 8 1 3 5 6 3 6 0 4 2 6 1 0 1 2 1 9 9 7 9 5 7 1 5 8 9 8 8 2 1 9 9 1 1 1 9 6 9 8 9 7 8 4 5 8 8 6 4 8 1 1 2 8 6 2 7 9 8 3 5 4 3 2 1 7 9 5 3 1 3 2 1 2 9 5 1 1 1 1 1 1 5 9 5 3 2 6 3 4 1 3 1 1 4 1 4 1 7 1 3 4 3 2 7 6 4 2 7 2 1 2 1 5 1 6 3 5 6 1 3 6 4 7 1 6 5 1 1 4 1 6 1 7 6 4 7 e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m -
Supplementary Materials
Supplementary Materials COMPARATIVE ANALYSIS OF THE TRANSCRIPTOME, PROTEOME AND miRNA PROFILE OF KUPFFER CELLS AND MONOCYTES Andrey Elchaninov1,3*, Anastasiya Lokhonina1,3, Maria Nikitina2, Polina Vishnyakova1,3, Andrey Makarov1, Irina Arutyunyan1, Anastasiya Poltavets1, Evgeniya Kananykhina2, Sergey Kovalchuk4, Evgeny Karpulevich5,6, Galina Bolshakova2, Gennady Sukhikh1, Timur Fatkhudinov2,3 1 Laboratory of Regenerative Medicine, National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia 2 Laboratory of Growth and Development, Scientific Research Institute of Human Morphology, Moscow, Russia 3 Histology Department, Medical Institute, Peoples' Friendship University of Russia, Moscow, Russia 4 Laboratory of Bioinformatic methods for Combinatorial Chemistry and Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia 5 Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russia 6 Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia Figure S1. Flow cytometry analysis of unsorted blood sample. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S2. Flow cytometry analysis of unsorted liver stromal cells. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S3. MiRNAs expression analysis in monocytes and Kupffer cells. Full-length of heatmaps are presented. -
Cyclin D1 Signaling Pathway in ER-Negative Breast Cancer Song Gao1, Anqi Ge3, Shouping Xu1, Zilong You1, Shipeng Ning1, Yashuang Zhao3* and Da Pang1,2*
Gao et al. Journal of Experimental & Clinical Cancer Research (2017) 36:179 DOI 10.1186/s13046-017-0648-4 RESEARCH Open Access PSAT1 is regulated by ATF4 and enhances cell proliferation via the GSK3β/β-catenin/ cyclin D1 signaling pathway in ER-negative breast cancer Song Gao1, Anqi Ge3, Shouping Xu1, Zilong You1, Shipeng Ning1, Yashuang Zhao3* and Da Pang1,2* Abstract Background: A growing amount of evidence has indicated that PSAT1 is an oncogene that plays an important role in cancer progression and metastasis. In this study, we explored the expression and function of PSAT1 in estrogen receptor (ER)-negative breast cancer. Method: The expression level of PSAT1 in breast cancer tissues and cells was analyzed using real-time-PCR (RT-PCR) , TCGA datasets or immunohistochemistry (IHC). The overall survival of patients with ER-negative breast cancer stratified by the PSAT1 expression levels was evaluated using Kaplan-Meier analysis. The function of PSAT1 was analyzed using a series of in vitro assays. Moreover, a nude mouse model was used to evaluate the function of PSAT1 in vivo. qRT-PCR and western blot assays were used to evaluate gene and protein expression, respectively, in the indicated cells. In addition, we demonstrated that PSAT1 was activated by ATF4 by chromatin immunoprecipitation (ChIP) assays. Results: mRNA expression of PSAT1 was up-regulated in ER-negative breast cancer. A tissue microarray that included 297 specimens of ER-negative breast cancer was subjected to an immunohistochemistry assay, which demonstrated that PSAT1 was overexpressed and predicted a poor clinical outcome of patients with this disease. -
AMPK Signaling Regulates Expression of Urea Cycle Enzymes in Response to Changes in Dietary Protein Intake
bioRxiv preprint doi: https://doi.org/10.1101/439380; this version posted October 10, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. AMPK signaling regulates expression of urea cycle enzymes in response to changes in dietary protein intake Sandra Kirsch Heibel1Y, Peter J McGuire2Y, Nantaporn Haskins1, Himani Datta Majumdar1, Sree Rayavarapu1‡, Kanneboyina Nagaraju3, Yetrib Hathout3, Kristy Brown1, Mendel Tuchman1, Ljubica Caldovic1*, 1 Center for Genetic Medicine Research/Children's National Medical Center, Washington, DC, USA 2 National Human Genome Research Institute/National Institutes for Health, Bethesda, MD, USA 3 Department of Pharmaceutical Sciences/Binghamton University, Binghamton NY, USA YThese authors contributed equally to this work. ‡Current address: Division of Clinical Review, Office of Bioequivalence, Office of Generic Drugs, Center for Drug Evaluation and Research/Food and Drug Administration, Silver Spring, MD, USA * [email protected] Abstract Abundance of urea cycle enzymes in the liver is regulated by the dietary protein intake. Although urea cycle enzyme levels rise in response to a high protein diet, signaling networks that sense dietary protein intake and trigger changes in expression of urea cycle genes have not been identified. The aim of this study was to identify signaling pathway(s) that respond to changes in protein intake and regulate expression of urea cycle genes in mice and human hepatocytes. Mice were adapted to either control or high (HP) protein diets followed by isolation of liver protein and mRNA and integrated analysis of the proteomic and transcriptome profiles.