Energy Metabolism in Uncoupling Protein 3 Gene Knockout Mice

Uncoupling protein 3 (UCP3) is a member of the mitochondrial anion carrier superfamily. Based upon its high homology with UCP1 and its restricted tissue distribution to skeletal muscle and brown adipose tissue, UCP3 has been suggested to play important roles in regulating energy expenditure, body weight, and thermoregulation. Other postulated roles for UCP3 include regulation of fatty acid metabolism, adaptive responses to acute exercise and starvation, and prevention of reactive oxygen species (ROS) formation. To address these questions, we have generated mice lacking UCP3 (UCP3 knockout (KO) mice). Here, we provide evidence that skeletal muscle mitochondria lacking UCP3 are more coupled (i.e. increased state 3/state 4 ratio), indicating that UCP3 has uncoupling activity. In addition, production of ROS is increased in mitochondria lacking UCP3. This study demonstrates that UCP3 has uncoupling activity and that its absence may lead to increased production of ROS. Despite these effects on mitochondrial function, UCP3 does not seem to be required for body weight regulation, exercise tolerance, fatty acid oxidation, or cold-induced thermogenesis. The absence of such phenotypes in UCP3 KO mice could not be attributed to up-regulation of other UCP mRNAs. However, alternative compensatory mechanisms cannot be excluded. The consequence of increased mitochondrial coupling in UCP3 KO mice on metabolism and the possible role of yet unidentified compensatory mechanisms, remains to be determined

Energy Metabolism in Uncoupling Protein 3 Gene Knockout Mice

Uncoupling protein 3 (UCP3) is a member of the mitochondrial anion carrier superfamily. Based upon its high homology with UCP1 and its restricted tissue distribution to skeletal muscle and brown adipose tissue, UCP3 has been suggested to play important roles in regulating energy expenditure, body weight, and thermoregulation. Other postulated roles for UCP3 include regulation of fatty acid metabolism, adaptive responses to acute exercise and starvation, and prevention of reactive oxygen species (ROS) formation. To address these questions, we have generated mice lacking UCP3 (UCP3 knockout (KO) mice). Here, we provide evidence that skeletal muscle mitochondria lacking UCP3 are more coupled (i.e. increased state 3/state 4 ratio), indicating that UCP3 has uncoupling activity. In addition, production of ROS is increased in mitochondria lacking UCP3. This study demonstrates that UCP3 has uncoupling activity and that its absence may lead to increased production of ROS. Despite these effects on mitochondrial function, UCP3 does not seem to be required for body weight regulation, exercise tolerance, fatty acid oxidation, or cold-induced thermogenesis. The absence of such phenotypes in UCP3 KO mice could not be attributed to up-regulation of other UCP mRNAs. However, alternative compensatory mechanisms cannot be excluded. The consequence of increased mitochondrial coupling in UCP3 KO mice on metabolism and the possible role of yet unidentified compensatory mechanisms, remains to be determined

A Comparative Study of Different Methodologies for Fault Diagnosis in Multivariate Quality Control

Different methodologies for fault diagnosis in multivariate quality control have been proposed in recent years. These methods work in the space of the original measured variables and have performed reasonably well when there is a reduced number of mildly correlated quality and/or process variables with a well-conditioned covariance matrix. These approaches have been introduced by emphasizing their positive or negative virtues, generally on an individual basis, so it is not clear for the practitioner the best method to be used. This paper provides a comprehensive study of the performance of diverse methodological approaches when tested on a large number of distinct simulated scenarios. Our primary aim is to highlight key weaknesses and strengths in these methods as well as clarifying their relationships and the requirements for their implementation in practice.Vidal Puig, S.; Ferrer, A. (2014). A Comparative Study of Different Methodologies for Fault Diagnosis in Multivariate Quality Control. Communications in Statistics - Simulation and Computation. 43(5):986-1005. doi:10.1080/03610918.2012.720745S9861005435Arteaga, F., & Ferrer, A. (2010). How to simulate normal data sets with the desired correlation structure. Chemometrics and Intelligent Laboratory Systems, 101(1), 38-42. doi:10.1016/j.chemolab.2009.12.003Doganaksoy, N., Faltin, F. W., & Tucker, W. T. (1991). Identification of out of control quality characteristics in a multivariate manufacturing environment. Communications in Statistics - Theory and Methods, 20(9), 2775-2790. doi:10.1080/03610929108830667Fuchs, C., & Benjamini, Y. (1994). Multivariate Profile Charts for Statistical Process Control. Technometrics, 36(2), 182-195. doi:10.1080/00401706.1994.10485765Hawkins, D. M. (1991). Multivariate Quality Control Based on Regression-Adiusted Variables. Technometrics, 33(1), 61-75. doi:10.1080/00401706.1991.10484770Editorial Board. (2007). Computational Statistics & Data Analysis, 51(8), iii-v. doi:10.1016/s0167-9473(07)00125-9Hayter, A. J., & Tsui, K.-L. (1994). Identification and Quantification in Multivariate Quality Control Problems. Journal of Quality Technology, 26(3), 197-208. doi:10.1080/00224065.1994.11979526HOCHBERG, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75(4), 800-802. doi:10.1093/biomet/75.4.800HOMMEL, G. (1988). A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika, 75(2), 383-386. doi:10.1093/biomet/75.2.383Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28(4), 409-428. doi:10.1080/00224065.1996.11979699Li, J., Jin, J., & Shi, J. (2008). Causation-BasedT2Decomposition for Multivariate Process Monitoring and Diagnosis. Journal of Quality Technology, 40(1), 46-58. doi:10.1080/00224065.2008.11917712Mason, R. L., Tracy, N. D., & Young, J. C. (1995). Decomposition ofT2 for Multivariate Control Chart Interpretation. Journal of Quality Technology, 27(2), 99-108. doi:10.1080/00224065.1995.11979573Mason, R. L., Tracy, N. D., & Young, J. C. (1997). A Practical Approach for Interpreting Multivariate T2 Control Chart Signals. Journal of Quality Technology, 29(4), 396-406. doi:10.1080/00224065.1997.11979791Murphy, B. J. (1987). Selecting Out of Control Variables With the T 2 Multivariate Quality Control Procedure. The Statistician, 36(5), 571. doi:10.2307/2348668Rencher, A. C. (1993). The Contribution of Individual Variables to Hotelling’s T 2 , Wilks’ Λ, and R 2. Biometrics, 49(2), 479. doi:10.2307/2532560Roy, J. (1958). Step-Down Procedure in Multivariate Analysis. The Annals of Mathematical Statistics, 29(4), 1177-1187. doi:10.1214/aoms/1177706449Runger, G. C., Alt, F. B., & Montgomery, D. C. (1996). Contributors to a multivariate statistical process control chart signal. Communications in Statistics - Theory and Methods, 25(10), 2203-2213. doi:10.1080/03610929608831832Sankoh, A. J., Huque, M. F., & Dubey, S. D. (1997). Some comments on frequently used multiple endpoint adjustment methods in clinical trials. Statistics in Medicine, 16(22), 2529-2542. doi:10.1002/(sici)1097-0258(19971130)16:223.0.co;2-jTukey, J. W., Ciminera, J. L., & Heyse, J. F. (1985). Testing the Statistical Certainty of a Response to Increasing Doses of a Drug. Biometrics, 41(1), 295. doi:10.2307/253066

Lipid zonation and phospholipid remodeling in nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) can progress from simple steatosis (i.e., nonalcoholic fatty liver [NAFL]) to nonalcoholic steatohepatitis (NASH), cirrhosis, and cancer. Currently, the driver for this progression is not fully understood; in particular, it is not known how NAFLD and its early progression affects the distribution of lipids in the liver, producing lipotoxicity and inflammation. In this study, we used dietary and genetic mouse models of NAFL and NASH and translated the results to humans by correlating the spatial distribution of lipids in liver tissue with disease progression using advanced mass spectrometry imaging technology. We identified several lipids with distinct zonal distributions in control and NAFL samples and observed partial to complete loss of lipid zonation in NASH. In addition, we found increased hepatic expression of genes associated with remodeling the phospholipid membrane, release of arachidonic acid (AA) from the membrane, and production of eicosanoid species that promote inflammation and cell injury. The results of our immunohistochemistry analyses suggest that the zonal location of remodeling enzyme LPCAT2 plays a role in the change in spatial distribution for AA-containing lipids. This results in a cycle of AA-enrichment in pericentral hepatocytes, membrane release of AA, and generation of proinflammatory eicosanoids and may account for increased oxidative damage in pericentral regions in NASH. Conclusion: NAFLD is associated not only with lipid enrichment, but also with zonal changes of specific lipids and their associated metabolic pathways. This may play a role in the heterogeneous development of NAFLD. (Hepatology 2017;65:1165-1180)

Lipocalin prostaglandin D synthase and PPARγ2 coordinate to regulate carbohydrate and lipid metabolism in vivo

Mice lacking Peroxisome Proliferator-Activated Receptor γ2 (PPARγ2) have unexpectedly normal glucose tolerance and mild insulin resistance. Mice lacking PPARγ2 were found to have elevated levels of Lipocalin prostaglandin D synthase (L-PGDS) expression in BAT and subcutaneous white adipose tissue (WAT). To determine if induction of L-PGDS was compensating for a lack of PPARγ2, we crossed L-PGDS KO mice to PPARγ2 KO mice to generate Double Knock Out mice (DKO). Using DKO mice we demonstrated a requirement of L-PGDS for maintenance of subcutaneous WAT (scWAT) function. In scWAT, DKO mice had reduced expression of thermogenic genes, the de novo lipogenic program and the lipases ATGL and HSL. Despite the reduction in markers of lipolysis in scWAT, DKO mice had a normal metabolic rate and elevated serum FFA levels compared to L-PGDS KO alone. Analysis of intra-abdominal white adipose tissue (epididymal WAT) showed elevated expression of mRNA and protein markers of lipolysis in DKO mice, suggesting that DKO mice may become more reliant on intra-abdominal WAT to supply lipid for oxidation. This switch in depot utilisation from subcutaneous to epididymal white adipose tissue was associated with a worsening of whole organism metabolic function, with DKO mice being glucose intolerant, and having elevated serum triglyceride levels compared to any other genotype. Overall, L-PGDS and PPARγ2 coordinate to regulate carbohydrate and lipid metabolism

Defective extracellular matrix remodeling in brown adipose tissue is associated with fibro-inflammation and reduced diet-induced thermogenesis.

The relevance of extracellular matrix (ECM) remodeling is reported in white adipose tissue (AT) and obesity-related dysfunctions, but little is known about the importance of ECM remodeling in brown AT (BAT) function. Here, we show that a time course of high-fat diet (HFD) feeding progressively impairs diet-induced thermogenesis concomitantly with the development of fibro-inflammation in BAT. Higher markers of fibro-inflammation are associated with lower cold-induced BAT activity in humans. Similarly, when mice are housed at thermoneutrality, inactivated BAT features fibro-inflammation. We validate the pathophysiological relevance of BAT ECM remodeling in response to temperature challenges and HFD using a model of a primary defect in the collagen turnover mediated by partial ablation of the Pepd prolidase. Pepd-heterozygous mice display exacerbated dysfunction and BAT fibro-inflammation at thermoneutrality and in HFD. Our findings show the relevance of ECM remodeling in BAT activation and provide a mechanism for BAT dysfunction in obesity

mTORC1 in AGRP neurons integrates exteroceptive and interoceptive food-related cues in the modulation of adaptive energy expenditure in mice

Energy dissipation through interscapular brown adipose tissue (iBAT) thermogenesis is an important contributor to adaptive energy expenditure. However, it remains unresolved how acute and chronic changes in energy availability are detected by the brain to adjust iBAT activity and maintain energy homeostasis. Here, we provide evidence that AGRP inhibitory tone to iBAT represents an energy-sparing circuit that integrates environmental food cues and internal signals of energy availability. We establish a role for the nutrient-sensing mTORC1 signaling pathway within AGRP neurons in the detection of environmental food cues and internal signals of energy availability, and in the bi-directional control of iBAT thermogenesis during nutrient deficiency and excess. Collectively, our findings provide insights into how mTORC1 signaling within AGRP neurons surveys energy availability to engage iBAT thermogenesis, and identify AGRP neurons as a neuronal substrate for the coordination of energy intake and adaptive expenditure under varying physiological and environmental contexts.This work was supported by the Medical Research Council New Blood Fellowship [MR/M501736/1] to CB, a NIDDK K99/R00 award to CB, the Medical Research Council Metabolic Disease Unit programme grant, the Disease Model Core facilities, and the Wellcome Trust Cambridge Mouse Biochemistry Laboratory. Animal procedures were performed under Tony Coll’s home office PPL 80/2497 and Toni Vidal-Puig PPL 80/2484

Uncoupling proteins, dietary fat and the metabolic syndrome

There has been intense interest in defining the functions of UCP2 and UCP3 during the nine years since the cloning of these UCP1 homologues. Current data suggest that both UCP2 and UCP3 proteins share some features with UCP1, such as the ability to reduce mitochondrial membrane potential, but they also have distinctly different physiological roles. Human genetic studies consistently demonstrate the effect of UCP2 alleles on type-2 diabetes. Less clear is whether UCP2 alleles influence body weight or body mass index (BMI) with many studies showing a positive effect while others do not. There is strong evidence that both UCP2 and UCP3 protect against mitochondrial oxidative damage by reducing the production of reactive oxygen species. The evidence that UCP2 protein is a negative regulator of insulin secretion by pancreatic β-cells is also strong: increased UCP2 decreases glucose stimulated insulin secretion ultimately leading to β-cell dysfunction. UCP2 is also neuroprotective, reducing oxidative stress in neurons. UCP3 may also transport fatty acids out of mitochondria thereby protecting the mitochondria from fatty acid anions or peroxides. Current data suggest that UCP2 plays a role in the metabolic syndrome through down-regulation of insulin secretion and development of type-2 diabetes. However, UCP2 may protect against atherosclerosis through reduction of oxidative stress and both UCP2 and UCP3 may protect against obesity. Thus, these UCP1 homologues may both contribute to and protect from the markers of the metabolic syndrome

Latent Structures based-Multivariate Statistical Process Control: a paradigm shift

The basic fundamentals of statistical process control (SPC) were proposed by Walter Shewhart for data-starved production environments typical in the 1920s and 1930s. In the 21st century, the traditional scarcity of data has given way to a data-rich environment typical of highly automated and computerized modern processes. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio, multistage and multiway structures, and missing values. Conventional univariate and multivariate SPC techniques are not suitable in these environments. This article discusses the paradigm shift to which those working in the quality improvement field should pay keen attention. We advocate the use of latent structure based multivariate statistical process control methods as efficient quality improvement tools in these massive data contexts. This is a strategic issue for industrial success in the tremendously competitive global market.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02.Ferrer, A. (2014). Latent Structures based-Multivariate Statistical Process Control: a paradigm shift. Quality Engineering. 26(1):72-91. https://doi.org/10.1080/08982112.2013.846093S7291261Aparisi, F., Jabaioyes, J., & Carrion, A. (1999). Statistical properties of the lsi multivariate control chart. Communications in Statistics - Theory and Methods, 28(11), 2671-2686. doi:10.1080/03610929908832445Arteaga, F., & Ferrer, A. (2002). Dealing with missing data in MSPC: several methods, different interpretations, some examples. Journal of Chemometrics, 16(8-10), 408-418. doi:10.1002/cem.750Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: an overview. 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Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: theoretical aspects. Journal of Chemometrics, 26(7), 361-373. doi:10.1002/cem.2440Duchesne, C., Liu, J. J., & MacGregor, J. F. (2012). Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems, 117, 116-128. doi:10.1016/j.chemolab.2012.04.003Efron, B., & Gong, G. (1983). A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation. The American Statistician, 37(1), 36-48. doi:10.1080/00031305.1983.10483087Ferrer, A. (2007). Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process. Quality Engineering, 19(4), 311-325. doi:10.1080/08982110701621304Fuchs, C. (1998). Multivariate Quality Control. doi:10.1201/9781482273731Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. 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IEEE Control Systems, 22(5), 10-25. doi:10.1109/mcs.2002.1035214Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4), 213-246. doi:10.1002/acs.859Kourti, T. (2006). Process Analytical Technology Beyond Real-Time Analyzers: The Role of Multivariate Analysis. Critical Reviews in Analytical Chemistry, 36(3-4), 257-278. doi:10.1080/10408340600969957Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28(4), 409-428. doi:10.1080/00224065.1996.11979699Liu, R. Y. (1995). Control Charts for Multivariate Processes. Journal of the American Statistical Association, 90(432), 1380-1387. doi:10.1080/01621459.1995.10476643Liu, R. Y., Singh, K., & Teng*, J. H. (2004). DDMA-charts: Nonparametric multivariate moving average control charts based on data depth. 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(2007). Integration of colour and textural information in multivariate image analysis: defect detection and classification issues. Journal of Chemometrics, 21(1-2), 10-23. doi:10.1002/cem.1026Bisgaard, S., Doganaksoy, N., Fisher, N., Gunter, B., Hahn, G., Keller-McNulty, S., … Wu, C. F. J. (2008). The Future of Industrial Statistics: A Panel Discussion. Technometrics, 50(2), 103-127. doi:10.1198/004017008000000136Stoumbos, Z. G., Reynolds, M. R., Ryan, T. P., & Woodall, W. H. (2000). The State of Statistical Process Control as We Proceed into the 21st Century. Journal of the American Statistical Association, 95(451), 992-998. doi:10.1080/01621459.2000.10474292Tracy, N. D., Young, J. C., & Mason, R. L. (1992). Multivariate Control Charts for Individual Observations. Journal of Quality Technology, 24(2), 88-95. doi:10.1080/00224065.1992.12015232Wierda, S. J. (1994). Multivariate statistical process control—recent results and directions for future research. 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Digital Imaging for Online Monitoring and Control of Industrial Snack Food Processes. Industrial & Engineering Chemistry Research, 42(13), 3036-3044. doi:10.1021/ie020941

PPARgamma activity in subcutaneous abdominal fat tissue and fat mass gain during short-term overfeeding

Objective: As the peroxisome proliferator-activated receptor (PPAR) plays a central role in fat mass regulation, we investigated whether initial subcutaneous PPAR activity is related to fat mass generation during overfeeding. Subjects: Fourteen healthy female subjects (age 254 years, BMI 22.12.3 kg/m2). Design and measurements: Subjects were overfed with a diet supplying 50% more energy than baseline energy requirements for 14 days. Fasting blood samples were analyzed for leptin, insulin and glucose. Fasting subcutaneous abdominal fat biopsies were obtained for analysis of PPAR1, PPAR2, aP2 and UCP2 mRNAs. Results: Initial PPAR1 and 2, aP2 and UCP2 mRNAs were not related to fat gain (P>0.12). However, PPAR1, PPAR2 and aP2 mRNA changes were positively related to changes in plasma leptin (