Project description:Daily biological rhythms are orchestrated by a combination of the intrinsic circadian clock and external food/feeding-related signals which influence metabolism in health and disease. Understanding how and to which extent both circadian and fasting/feeding rhythms contribute to regulating daily physiology and metabolism is therefore an important ongoing effort. We generated a comprehensive proteomics dataset performing large scale time-series analysis of mouse liver obtained from feeding restricted mice over 2 full days with 16 time points in total, including as a first, a shorter post-feeding sampling-rate focus. Using our label-free absolute quantitative proteomics platform we obtained timelines with very few missing values (>99% data completeness) for over 4000 mouse liver proteoforms using 1D-LC-MS analysis of all time points and replicates (48 samples in total) allowing for robust statistical testing, as well as over 8000 mouse liver proteoforms using online 2D-LC-MS analysis of pooled replicates, providing additional depth of detection. Together our dataset provides an important resource recapitulating and extending current datasets. Our extra focus on post-feeding time points revealed a highly dynamic third metabolic period not previously observed with respect to more classic diurnal rhythmicity, providing an important addition to current knowledge.
Project description:Liver cancer is one of the most common malignant tumors worldwide, and its high aggressiveness and recurrence rate pose a major challenge. Metabolic reprogramming is one of the cancer hallmarks and allows tumor cells to adapt to drastic changes, supporting their rapid growth, survival, and proliferation under various conditions. The metabolic reprogramming of several amino acids within tumors profoundly affects the function of the immune cells. Furthermore, arginine and various other amino acids are now being widely studied as potential targets for anti-tumor therapy. However, the role of lysine metabolic reprogramming in tumors remains largely unexplored. This study aims to reveal the relationship between lysine metabolism reprogramming and the prognosis of liver cancer, the tumor immune microenvironment (TIME), and responses to immunotherapy through multi-omics analysis. Combined transcriptomic and proteomic analyses revealed a significant downregulation of lysine metabolism in tumor tissues against normal tissue adjacent to the tumor from liver cancer patients. Subsequently, we constructed a lysine metabolism score (LM score) based on nine lysine metabolic genes to stratify liver cancer patients into high and low lysine metabolism subtypes. The LM score exhibited potential in predicting both survival and immunotherapy response in liver cancer. Furthermore, significant differences were observed in prognosis, clinical staging, TIME, and immunotherapy outcomes between the subtypes with low and high lysine metabolism. In the subtype of low lysine metabolism, an immunosuppressive TIME predominated, correlating with poorer patient survival and anti-tumor immune responses. In conclusion, we uncover that disturbed lysine metabolism promotes the formation of the immunosuppressive TIME, thereby inducing immunotherapy resistance and advancing liver cancer progression. This provides an effective theoretical reference for elucidating the mechanism of immunotherapy resistance in liver cancer and seeking new therapeutic strategies.
Project description:Mitchell2013 - Liver Iron Metabolism
The model includes the core regulatory components of human liver iron metabolism.
This model is described in the article:
A computational model of liver iron metabolism.
Mitchell S, Mendes P.
PLoS Comput. Biol. 2013 Nov; 9(11): e1003299
Abstract:
Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system.
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Project description:High cholesterol diet and xenobiotic treatment induce changes in cholesterol homeostasis and drug metabolism. Mice were either 7 days on high cholesterol diet or were treated with phenobarbital. Liver samples were analyzed using Sterolgene v0 cDNA microarrays. Sterolgene microarray is a tool designed to enable focused studies of cholesterol homeostasis and drug metabolism. We show that one week of cholesterol diet down-regulates cholesterol biosynthesis and up-regulates xenobiotic metabolism (Cyp3 family). Phenobarbital treatment also up-regulates xenobiotic metabolism (Cyp2b and Cyp3a families). We can conclude that the Sterolgene series of cDNA microarrays represent novel original tool, enabling focused and cost-wise studies of cholesterol homeostasis and drug metabolism. Keywords: Treatment and diet effects
Project description:To investigate the role of Per2 in glucose metabolism in-vivo we used mice bearing a targeted gene mutation in the Per2 gene (Per2brdm) and thus unable to express a functional Per2 protein. Mice were hosted in our standard mouse facility in a 12-hour light and 12-hour dark cycle. Wt and Per2brdm mice were fed with either standard chow diet or high-fat diet for 24 weeks and analyzed for glucose homeostasis. Glucose tolerance test (GTT) and insulin tolerance test (ITT) were performed on mice food deprived for 7 hours. For pyruvate tolerance test (PTT) mice were fasted for 14 hours. Glucose levels were measured over 12 hours starvation time-courses during the light and the dark phase. Fed and fasting blood glucose and hepatic glycogen content were measured over different circadian time-points. To investigate the role of Per2 in the control of liver gene expression we performed DNA-microarray analysis of RNA preparations from liver of Per2brdm and WT mice. We extracted total RNA from liver of four different Per2brdm and WT mice. Mice from the same cohort were sacrificed after 8 hours of starvation. After normalization we performed a fold change comparison selecting probes with a p value < 0.05.
Project description:Global gene expression profiling is useful for elucidating a drug?s mechanism of action (MOA) on the liver; however, such profiling in rats is not very sensitive for predicting human druginduced liver injury, while de-differentiated monolayers of primary human hepatocytes (PHHs) do not permit chronic drug treatment. In contrast, micropatterned co-cultures (MPCCs) containing PHH colonies and 3T3-J2 fibroblasts maintain a stable liver phenotype for 4-6 weeks. Here, we used MPCCs to test the hypothesis that global gene expression patterns in stable PHHs can be used to distinguish clinical hepatotoxic drugs from their non-liver-toxic analogs and understand the MOA prior to the onset of overt hepatotoxicity. We found that MPCCs treated with the clinical hepatotoxic/non-liver-toxic pair, troglitazone/rosiglitazone, at each drug?s reported and non-toxic Cmax (maximum concentration in human plasma) level for 1, 7, and 14 days displayed a total of 12, 269, and 628 differentially expressed genes, respectively, relative to the vehicle-treated control. Troglitazone modulated >75% of transcripts across pathways such as fatty acid and drug metabolism, oxidative stress, inflammatory response, and complement/coagulation cascades. Escalating rosiglitazone?s dose to that of troglitazone?s Cmax increased modulated transcripts relative to the lower dose; however, over half the identified transcripts were still exclusively modulated by troglitazone. Lastly, other hepatotoxins (nefazodone, ibufenac, and tolcapone) also induced a greater number of differentially expressed genes in MPCCs than their non-liver-toxic analogs (buspirone, ibuprofen, and entacapone) following 7 days of treatment. In conclusion, MPCCs allow evaluation of time- and dose-dependent gene expression patterns in PHHs treated chronically with analog drugs.