Identification, Selection, and Enrichment of Cardiomyocyte Precursors

The large-scale production of cardiomyocytes is a key step in the development of cell therapy and tissue engineering to treat cardiovascular diseases, particularly those caused by ischemia. the main objective of this study was to establish a procedure for the efficient production of cardiomyocytes by reprogramming mesenchymal stem cells from adipose tissue. First, lentiviral vectors expressing neoR and GFP under the control of promoters expressed specifically during cardiomyogenesis were constructed to monitor cell reprogramming into precardiomyocytes and to select cells for amplification and characterization. Cellular reprogramming was performed using 5'-azacytidine followed by electroporation with plasmid pOKS2a, which expressed Oct4, Sox2, and Klf4. Under these conditions, GFP expression began only after transfection with pOKS2a, and less than 0.015% of cells were GFP(+). These GFP(+) cells were selected for G418 resistance to find molecular markers of cardiomyocytes by RT-PCR and immunocytochemistry. Both genetic and protein markers of cardiomyocytes were present in the selected cells, with some variations among them. Cell doubling time did not change after selection. Together, these results indicate that enrichment with vectors expressing GFP and neoR under cardiomyocyte-specific promoters can produce large numbers of cardiomyocyte precursors (CMPs), which can then be differentiated terminally for cell therapy and tissue engineering.

The gut microbiota as a contributing factor to antipsychotic-induced weight gain and metabolic dysfunction

Schizophrenia represents one of the world’s most devastating illnesses due to its often lifelong course and debilitating nature. The treatment of schizophrenia has vastly improved over recent decades with the discovery of several antipsychotic compounds; however these drugs are not without adverse effects that must be addressed to maximize their therapeutic value. Newer, atypical, antipsychotics are associated with a compilation of serious metabolic side effects including weight gain, insulin resistance, fat deposition, glucose dysregulation and ensuing co-morbidities such as type II diabetes mellitus. The mechanisms underlying these side effects remain to be fully elucidated and adequate interventions are lacking. Further understanding of the factors that contribute these side effects is therefore required in order to develop effective adjunctive therapies and to potentially design antipsychotic drugs in the future with reduced impact on the metabolic health of patients. We investigated if the gut microbiota represented a novel mechanism contributing to the metabolic dysfunction associated with atypical antipsychotics. The gut microbiota comprises the bacteria that exist symbiotically within the gastrointestinal tract, and has been shown in recent years to be involved in several aspects of energy balance and metabolism. We have demonstrated that administration of certain antipsychotics in the rat results in an altered microbiota profile and, moreover, that the microbiota is required for the full scale of metabolic dysfunction to occur. We have further shown that specific antibiotics can attenuate certain aspects of olanzapine and risperidone–induced metabolic dysfunction, in particular fat deposition and adipose tissue inflammation. Mechanisms underlying this novel link appear to involve energy utilization via expression of lipogenic genes as well as reduced inflammatory tone. Taken together, these data indicate that the gut microbiota is an important factor involved in the myriad of metabolic complications associated with antipsychotic therapy. Furthermore, these data support the future investigation of microbial-based therapeutics for not only antipsychotic-induced weight gain but also for tackling the global obesity epidemic.

An examination of the construction of meanings for obesity in Ireland

Obesity has been defined as a consequence of energy imbalance, where energy intake exceeds energy expenditure and results in a build-up of adipose tissue. However, this scientific definition masks the complicated social meanings associated with the condition. This research investigated the construction of meaning around obesity at various levels of inquiry to inform how obesity is portrayed and understood in Ireland. A multi-paradigmatic approach was adopted, drawing on theory and methods from psychology and sociology and an analytical framework combining the Common Sense Model and framing theory was employed. In order to examine the exo-level meanings of obesity, content analysis was performed on two media data sets (n=479, n=346) and a thematic analysis was also performed on the multiple newspaper sample (n=346). At the micro-level, obesity discourses were investigated via the thematic analysis of comments sampled from an online message board. Finally, an online survey assessed individual-level beliefs and understandings of obesity. The media analysis revealed that individual blame for obesity was pervasive and the behavioural frame was dominant. A significant increase in attention to obesity over time was observed, manifestations of weight stigma were common, and there was an emotive discourse of blame directed towards the parents of obese children. The micro-level analysis provided insight into the weight-based stigma in society and a clear set of negative ‘default’ judgements accompanied the obese label. The survey analysis confirmed that the behavioural frame was the dominant means of understanding obesity. One of the strengths of this thesis is the link created between framing and the Common Sense Model in the development of an analytical framework for application in the examination of health/illness representations. This approach helped to ascertain the extent of the pervasive biomedical and individual blame discourse on obesity, which establishes the basis for the stigmatisation of obese persons.

The impact of whey protein isolate on energy balance regulation

Using C57BL/6J mice fed whey protein isolate (WPI) enriched high fat (HFD) or low-fat diets (LFD), this study tested the hypothesis that WPI directly impacts on adiposity by influencing lipid metabolism. WPI suppressed HFD-induced body fat and increased lean mass at 8 weeks of dietary challenge despite elevated plasma triacylglycerol (TAG) levels, suggesting reduced TAG storage. WPI reduced HFD-associated hypothalamic leptin and insulin receptor (IR) mRNA expression, and prevented HFD-associated reductions in adipose tissue IR and glucose transporter 4 expression. These effects were largely absent at 21 weeks of HFD feeding, however WPI increased lean mass and cause a trend towards decreased fat mass, with notable increased Lactobacillus and decreased Clostridium gut bacterial species. Increasing the protein to carbohydrate ratio enhanced the above effects, and shifted the gut microbiota composition away from the HFD group. Seven weeks of WPI intake with a LFD decreased insulin signalling gene expression in the adipose tissue in association with an increased fat accumulation. WPI reduced intestinal weight and length, suggesting a potential functional relationship between WPI, gastro-intestinal morphology and insulin related signalling in the adipose. Extending the study to 15 weeks, did not affect adipose fat weight, but decreased energy intake, weight gain and intestinal length. The functionality of protein sensing lysophosphatidic acid receptor 5 (LPA5) in 3T3-L1 pre-adipocytes was assessed. Over-expression of the receptor in 3T3-L1 pre-adipocytes provided a growth advantage to the cells and suppressed cellular differentiation into mature fat cells. In conclusion, the data demonstrates WPI impacts on adiposity by influencing lipid metabolism in a temporal manner, resulting possibly due to changes in lean mass, hypothalamic and adipose gene expression, gut microbiota and gastrointestinal morphology. The data also showed LPA5 is a novel candidate in regulating of preadipocyte growth and differentiation, and may mediate dietary protein effects on adipose tissue.

Prolactin receptor signaling is essential for perinatal brown adipocyte function: a role for insulin-like growth factor-2.

BACKGROUND: The lactogenic hormones prolactin (PRL) and placental lactogens (PL) play central roles in reproduction and mammary development. Their actions are mediated via binding to PRL receptor (PRLR), highly expressed in brown adipose tissue (BAT), yet their impact on adipocyte function and metabolism remains unclear. METHODOLOGY/PRINCIPAL FINDINGS: PRLR knockout (KO) newborn mice were phenotypically characterized in terms of thermoregulation and their BAT differentiation assayed for gene expression studies. Derived brown preadipocyte cell lines were established to evaluate the molecular mechanisms involved in PRL signaling on BAT function. Here, we report that newborn mice lacking PRLR have hypotrophic BAT depots that express low levels of adipocyte nuclear receptor PPARgamma2, its coactivator PGC-1alpha, uncoupling protein 1 (UCP1) and the beta3 adrenoceptor, reducing mouse viability during cold challenge. Immortalized PRLR KO preadipocytes fail to undergo differentiation into mature adipocytes, a defect reversed by reintroduction of PRLR. That the effects of the lactogens in BAT are at least partly mediated by Insulin-like Growth Factor-2 (IGF-2) is supported by: i) a striking reduction in BAT IGF-2 expression in PRLR KO mice and in PRLR-deficient preadipocytes; ii) induction of cellular IGF-2 expression by PRL through JAK2/STAT5 pathway activation; and iii) reversal of defective differentiation in PRLR KO cells by exogenous IGF-2. CONCLUSIONS: Our findings demonstrate that the lactogens act in concert with IGF-2 to control brown adipocyte differentiation and growth. Given the prominent role of brown adipose tissue during the perinatal period, our results identified prolactin receptor signaling as a major player and a potential therapeutic target in protecting newborn mammals against hypothermia.

Role of T cells in malnutrition and obesity.

Nutritional status is critically important for immune cell function. While obesity is characterized by inflammation that promotes metabolic syndrome including cardiovascular disease and insulin resistance, malnutrition can result in immune cell defects and increased risk of mortality from infectious diseases. T cells play an important role in the immune adaptation to both obesity and malnutrition. T cells in obesity have been shown to have an early and critical role in inducing inflammation, accompanying the accumulation of inflammatory macrophages in obese adipose tissue, which are known to promote insulin resistance. How T cells are recruited to adipose tissue and activated in obesity is a topic of considerable interest. Conversely, T cell number is decreased in malnourished individuals, and T cells in the setting of malnutrition have decreased effector function and proliferative capacity. The adipokine leptin, which is secreted in proportion to adipocyte mass, may have a key role in mediating adipocyte-T cell interactions in both obesity and malnutrition, and has been shown to promote effector T cell function and metabolism while inhibiting regulatory T cell proliferation. Additionally, key molecular signals are involved in T cell metabolic adaptation during nutrient stress; among them, the metabolic regulator AMP kinase and the mammalian target of rapamycin have critical roles in regulating T cell number, function, and metabolism. In summary, understanding how T cell number and function are altered in obesity and malnutrition will lead to better understanding of and treatment for diseases where nutritional status determines clinical outcome.

Micro-Anatomical Quantitative Imaging Towards Enabling Automated Diagnosis of Thick Tissues at the Point of Care

Histopathology is the clinical standard for tissue diagnosis. However, histopathology has several limitations including that it requires tissue processing, which can take 30 minutes or more, and requires a highly trained pathologist to diagnose the tissue. Additionally, the diagnosis is qualitative, and the lack of quantitation leads to possible observer-specific diagnosis. Taken together, it is difficult to diagnose tissue at the point of care using histopathology.

Several clinical situations could benefit from more rapid and automated histological processing, which could reduce the time and the number of steps required between obtaining a fresh tissue specimen and rendering a diagnosis. For example, there is need for rapid detection of residual cancer on the surface of tumor resection specimens during excisional surgeries, which is known as intraoperative tumor margin assessment. Additionally, rapid assessment of biopsy specimens at the point-of-care could enable clinicians to confirm that a suspicious lesion is successfully sampled, thus preventing an unnecessary repeat biopsy procedure. Rapid and low cost histological processing could also be potentially useful in settings lacking the human resources and equipment necessary to perform standard histologic assessment. Lastly, automated interpretation of tissue samples could potentially reduce inter-observer error, particularly in the diagnosis of borderline lesions.

To address these needs, high quality microscopic images of the tissue must be obtained in rapid timeframes, in order for a pathologic assessment to be useful for guiding the intervention. Optical microscopy is a powerful technique to obtain high-resolution images of tissue morphology in real-time at the point of care, without the need for tissue processing. In particular, a number of groups have combined fluorescence microscopy with vital fluorescent stains to visualize micro-anatomical features of thick (i.e. unsectioned or unprocessed) tissue. However, robust methods for segmentation and quantitative analysis of heterogeneous images are essential to enable automated diagnosis. Thus, the goal of this work was to obtain high resolution imaging of tissue morphology through employing fluorescence microscopy and vital fluorescent stains and to develop a quantitative strategy to segment and quantify tissue features in heterogeneous images, such as nuclei and the surrounding stroma, which will enable automated diagnosis of thick tissues.

To achieve these goals, three specific aims were proposed. The first aim was to develop an image processing method that can differentiate nuclei from background tissue heterogeneity and enable automated diagnosis of thick tissue at the point of care. A computational technique called sparse component analysis (SCA) was adapted to isolate features of interest, such as nuclei, from the background. SCA has been used previously in the image processing community for image compression, enhancement, and restoration, but has never been applied to separate distinct tissue types in a heterogeneous image. In combination with a high resolution fluorescence microendoscope (HRME) and a contrast agent acriflavine, the utility of this technique was demonstrated through imaging preclinical sarcoma tumor margins. Acriflavine localizes to the nuclei of cells where it reversibly associates with RNA and DNA. Additionally, acriflavine shows some affinity for collagen and muscle. SCA was adapted to isolate acriflavine positive features or APFs (which correspond to RNA and DNA) from background tissue heterogeneity. The circle transform (CT) was applied to the SCA output to quantify the size and density of overlapping APFs. The sensitivity of the SCA+CT approach to variations in APF size, density and background heterogeneity was demonstrated through simulations. Specifically, SCA+CT achieved the lowest errors for higher contrast ratios and larger APF sizes. When applied to tissue images of excised sarcoma margins, SCA+CT correctly isolated APFs and showed consistently increased density in tumor and tumor + muscle images compared to images containing muscle. Next, variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82% and 75%. The utility of this approach was further tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78% and 82%. The results indicate that SCA+CT can accurately delineate APFs in heterogeneous tissue, which is essential to enable automated and rapid surveillance of tissue pathology.

Two primary challenges were identified in the work in aim 1. First, while SCA can be used to isolate features, such as APFs, from heterogeneous images, its performance is limited by the contrast between APFs and the background. Second, while it is feasible to create mosaics by scanning a sarcoma tumor bed in a mouse, which is on the order of 3-7 mm in any one dimension, it is not feasible to evaluate an entire human surgical margin. Thus, improvements to the microscopic imaging system were made to (1) improve image contrast through rejecting out-of-focus background fluorescence and to (2) increase the field of view (FOV) while maintaining the sub-cellular resolution needed for delineation of nuclei. To address these challenges, a technique called structured illumination microscopy (SIM) was employed in which the entire FOV is illuminated with a defined spatial pattern rather than scanning a focal spot, such as in confocal microscopy.

Thus, the second aim was to improve image contrast and increase the FOV through employing wide-field, non-contact structured illumination microscopy and optimize the segmentation algorithm for new imaging modality. Both image contrast and FOV were increased through the development of a wide-field fluorescence SIM system. Clear improvement in image contrast was seen in structured illumination images compared to uniform illumination images. Additionally, the FOV is over 13X larger than the fluorescence microendoscope used in aim 1. Initial segmentation results of SIM images revealed that SCA is unable to segment large numbers of APFs in the tumor images. Because the FOV of the SIM system is over 13X larger than the FOV of the fluorescence microendoscope, dense collections of APFs commonly seen in tumor images could no longer be sparsely represented, and the fundamental sparsity assumption associated with SCA was no longer met. Thus, an algorithm called maximally stable extremal regions (MSER) was investigated as an alternative approach for APF segmentation in SIM images. MSER was able to accurately segment large numbers of APFs in SIM images of tumor tissue. In addition to optimizing MSER for SIM image segmentation, an optimal frequency of the illumination pattern used in SIM was carefully selected because the image signal to noise ratio (SNR) is dependent on the grid frequency. A grid frequency of 31.7 mm-1 led to the highest SNR and lowest percent error associated with MSER segmentation.

Once MSER was optimized for SIM image segmentation and the optimal grid frequency was selected, a quantitative model was developed to diagnose mouse sarcoma tumor margins that were imaged ex vivo with SIM. Tumor margins were stained with acridine orange (AO) in aim 2 because AO was found to stain the sarcoma tissue more brightly than acriflavine. Both acriflavine and AO are intravital dyes, which have been shown to stain nuclei, skeletal muscle, and collagenous stroma. A tissue-type classification model was developed to differentiate localized regions (75x75 µm) of tumor from skeletal muscle and adipose tissue based on the MSER segmentation output. Specifically, a logistic regression model was used to classify each localized region. The logistic regression model yielded an output in terms of probability (0-100%) that tumor was located within each 75x75 µm region. The model performance was tested using a receiver operator characteristic (ROC) curve analysis that revealed 77% sensitivity and 81% specificity. For margin classification, the whole margin image was divided into localized regions and this tissue-type classification model was applied. In a subset of 6 margins (3 negative, 3 positive), it was shown that with a tumor probability threshold of 50%, 8% of all regions from negative margins exceeded this threshold, while over 17% of all regions exceeded the threshold in the positive margins. Thus, 8% of regions in negative margins were considered false positives. These false positive regions are likely due to the high density of APFs present in normal tissues, which clearly demonstrates a challenge in implementing this automatic algorithm based on AO staining alone.

Thus, the third aim was to improve the specificity of the diagnostic model through leveraging other sources of contrast. Modifications were made to the SIM system to enable fluorescence imaging at a variety of wavelengths. Specifically, the SIM system was modified to enabling imaging of red fluorescent protein (RFP) expressing sarcomas, which were used to delineate the location of tumor cells within each image. Initial analysis of AO stained panels confirmed that there was room for improvement in tumor detection, particularly in regards to false positive regions that were negative for RFP. One approach for improving the specificity of the diagnostic model was to investigate using a fluorophore that was more specific to staining tumor. Specifically, tetracycline was selected because it appeared to specifically stain freshly excised tumor tissue in a matter of minutes, and was non-toxic and stable in solution. Results indicated that tetracycline staining has promise for increasing the specificity of tumor detection in SIM images of a preclinical sarcoma model and further investigation is warranted.

In conclusion, this work presents the development of a combination of tools that is capable of automated segmentation and quantification of micro-anatomical images of thick tissue. When compared to the fluorescence microendoscope, wide-field multispectral fluorescence SIM imaging provided improved image contrast, a larger FOV with comparable resolution, and the ability to image a variety of fluorophores. MSER was an appropriate and rapid approach to segment dense collections of APFs from wide-field SIM images. Variables that reflect the morphology of the tissue, such as the density, size, and shape of nuclei and nucleoli, can be used to automatically diagnose SIM images. The clinical utility of SIM imaging and MSER segmentation to detect microscopic residual disease has been demonstrated by imaging excised preclinical sarcoma margins. Ultimately, this work demonstrates that fluorescence imaging of tissue micro-anatomy combined with a specialized algorithm for delineation and quantification of features is a means for rapid, non-destructive and automated detection of microscopic disease, which could improve cancer management in a variety of clinical scenarios.

Structured Illumination Microscopy and a Quantitative Image Analysis for the Detection of Positive Margins in a Pre-Clinical Genetically Engineered Mouse Model of Sarcoma.

Intraoperative assessment of surgical margins is critical to ensuring residual tumor does not remain in a patient. Previously, we developed a fluorescence structured illumination microscope (SIM) system with a single-shot field of view (FOV) of 2.1 × 1.6 mm (3.4 mm2) and sub-cellular resolution (4.4 μm). The goal of this study was to test the utility of this technology for the detection of residual disease in a genetically engineered mouse model of sarcoma. Primary soft tissue sarcomas were generated in the hindlimb and after the tumor was surgically removed, the relevant margin was stained with acridine orange (AO), a vital stain that brightly stains cell nuclei and fibrous tissues. The tissues were imaged with the SIM system with the primary goal of visualizing fluorescent features from tumor nuclei. Given the heterogeneity of the background tissue (presence of adipose tissue and muscle), an algorithm known as maximally stable extremal regions (MSER) was optimized and applied to the images to specifically segment nuclear features. A logistic regression model was used to classify a tissue site as positive or negative by calculating area fraction and shape of the segmented features that were present and the resulting receiver operator curve (ROC) was generated by varying the probability threshold. Based on the ROC curves, the model was able to classify tumor and normal tissue with 77% sensitivity and 81% specificity (Youden's index). For an unbiased measure of the model performance, it was applied to a separate validation dataset that resulted in 73% sensitivity and 80% specificity. When this approach was applied to representative whole margins, for a tumor probability threshold of 50%, only 1.2% of all regions from the negative margin exceeded this threshold, while over 14.8% of all regions from the positive margin exceeded this threshold.

Thiazolidinediones: effects on insulin resistance and the cardiovascular system.

Abstract
Thiazolidinediones (TZDs) have been used for the treatment of hyperglycaemia in type 2 diabetes for the past 10 years. They may delay the development of type 2 diabetes in individuals at high risk of developing the condition, and have been shown to have potentially beneficial effects on cardiovascular risk factors. TZDs act as agonists of peroxisome proliferator-activated receptor-gamma (PPAR-gamma) primarily in adipose tissue. PPAR-gamma receptor activation by TZDs improves insulin sensitivity by promoting fatty acid uptake into adipose tissue, increasing production of adiponectin and reducing levels of inflammatory mediators such as tumour necrosis factor-alpha (TNF-alpha), plasminogen activator inhibitor-1(PAI-1) and interleukin-6 (IL-6). Clinically, TZDs have been shown to reduce measures of atherosclerosis such as carotid intima-media thickness (CIMT). However, in spite of beneficial effects on markers of cardiovascular risk, TZDs have not been definitively shown to reduce cardiovascular events in patients, and the safety of rosiglitazone in this respect has recently been called into question. Dual PPAR-alpha/gamma agonists may offer superior treatment of insulin resistance and cardioprotection, but their safety has not yet been assured

Significance of peroxisome proliferator-activated receptors in the cardiovascular system in health and disease.

Peroxisome proliferator-activated receptors (PPARs) are ligand-activated nuclear transcription factors that belong to the nuclear receptor superfamily. Three isoforms of PPAR have been identified, alpha, delta and gamma, which play distinct roles in the regulation of key metabolic processes, such as glucose and lipid redistribution. PPARalpha is expressed predominantly in the liver, kidney and heart, and is primarily involved in fatty acid oxidation. PPARgamma is mainly associated with adipose tissue, where it controls adipocyte differentiation and insulin sensitivity. PPARdelta is abundantly and ubiquitously expressed, but as yet its function has not been clearly defined. Activators of PPARalpha (fibrates) and gamma (thiazolidinediones) have been used clinically for a number of years in the treatment of hyperlipidaemia and to improve insulin sensitivity in diabetes. More recently, PPAR activation has been found to confer additional benefits on endothelial function, inflammation and thrombosis, suggesting that PPAR agonists may be good candidates for the treatment of cardiovascular disease. In this regard, it has been demonstrated that PPAR activators are capable of reducing blood pressure and attenuating the development of atherosclerosis and cardiac hypertrophy. This review will provide a detailed discussion of the current understanding of basic PPAR physiology, with particular reference to the cardiovascular system. It will also examine the evidence supporting the involvement of the different PPAR isoforms in cardiovascular disease and discuss the current and potential future clinical applications of PPAR activators.

PIM-1 kinase expression in adipocytic neoplasms: diagnostic and biological implications

The differential diagnosis of soft tissue tumours poses a considerable challenge for pathologists, especially adipocytic tumours, as these may show considerable overlap in clinical presentation and morphological features with many other mesenchymal neoplasms. Hence, a specific and reliable marker that identifies adipocytic differentiation is much sought. We investigated the immunohistochemical expression of PIM-1 kinase in 35 samples of soft tissue tumours using tissue microarray technology and 49 full sections of adipocytic (n = 26) and non-adipocytic tumours (n = 23). Benign and malignant adipocytic tumours showed strong expression of PIM-1 while the non-adipocytic tumours were either negative or showed only weak staining for the protein. In myxoid liposarcomas, PIM-1 showed a distinct, unique vacuolar staining pattern, clearly outlining fine cytoplasmic lipid vacuoles. By contrast, non-adipocytic myxoid tumours (myxoma, chordoma and myxoid chondrosarcoma) did not show this vacuolar pattern of PIM-1 staining, although vacuolated cells were present on H&E. This differential expression was confirmed at a gene expression level in selected cases. Our results indicate that the expression of PIM-1 in adipose tissue may be a useful marker of adipocytic differentiation, in particular if the staining is both of high intensity and present in a unique, vacuolar pattern.

An immunohistochemical study of the distribution of biotin in tissues of pigs and chickens

An optimised indirect peroxidase-anti-peroxidase immunohistochemical technique was used to detect endogenous biotin in frozen tissue sections from biotin-supplemented and biotin-depleted pigs and chickens. A monoclonal anti-biotin antibody was used as primary antibody in this technique. Immunoreactive biotin was detected in many tissues of both species including liver, kidney, pancreas, adipose tissue, adrenal gland, testis, brain, choroid plexus, cardiac and skeletal muscle, epithelium of the respiratory and digestive systems, skin and lymphoid tissues. The specificity of immunostaining for biotin was confirmed by the finding of reduced staining intensities in tissues of biotin-depleted animals compared to those of biotin-supplemented animals. The results of this study suggest that biotin has metabolic functions in a wider range of tissues than previously known. They also indicate that endogenous tissue biotin should be considered as a source of false positive staining when immunohistochemical or histochemical techniques which use avidin or streptavidin reagents or anti-biotin antibodies as components of the detection system, are applied to tissue sections.

Transcriptional regulation of a brown adipocyte-specific gene, UCP1, by KLF11 and KLF15

Several growth factors and transcription factors have been reported to play important roles in brown adipocyte differentiation and modulation of thermogenic gene expression, especially the expression of UCP1. In this study, we focused on KLF11 and KLF15, which were expressed highly in brown adipose tissue. Our data demonstrated that KLF11 and KLF15 interacted directly with the UCP1 promoter using GC-box and GT-boxes, respectively. Co-transfection of KLF11 and KLF15 in the mesenchymal stem cell line muBM3.1 during brown adipocyte differentiation enhanced the expression level of UCP1. KLF11, but not KLF15, was essential for UCP1 expression during brown adipocyte differentiation of muBM3.1.

Recent advances in thermoregulation

Thermoregulation is the maintenance of a relatively constant core body temperature. Humans normally maintain a body temperature at 37°C, and maintenance of this relatively high temperature is critical to human survival. This concept is so important that control of thermoregulation is often the principal example cited when teaching physiological homeostasis. A basic understanding of the processes underpinning temperature regulation is necessary for all undergraduate students studying biology and biology-related disciplines, and a thorough understanding is necessary for those students in clinical training. Our aim in this review is to broadly present the thermoregulatory process taking into account current advances in this area. First, we summarize the basic concepts of thermoregulation and subsequently assess the physiological responses to heat and cold stress, including vasodilation and vasoconstriction, sweating, nonshivering thermogenesis, piloerection, shivering, and altered behavior. Current research is presented concerning the body's detection of thermal challenge, peripheral and central thermoregulatory control mechanisms, including brown adipose tissue in adult humans and temperature transduction by the relatively recently discovered transient receptor potential channels. Finally, we present an updated understanding of the neuroanatomic circuitry supporting thermoregulation.

The E3 ubiquitin ligase Pellino3 protects against obesity-induced inflammation and insulin resistance

Diet-induced obesity can induce low-level inflammation and insulin resistance. Interleukin-1β (IL-1β) is one of the key proinflammatory cytokines that contributes to the generation of insulin resistance and diabetes, but the mechanisms that regulate obesity-driven inflammation are ill defined. Here we found reduced expression of the E3 ubiquitin ligase Pellino3 in human abdominal adipose tissue from obese subjects and in adipose tissue of mice fed a high-fat diet and showing signs of insulin resistance. Pellino3-deficient mice demonstrated exacerbated high-fat-diet-induced inflammation, IL-1β expression, and insulin resistance. Mechanistically, Pellino3 negatively regulated TNF receptor associated 6 (TRAF6)-mediated ubiquitination and stabilization of hypoxia-inducible factor 1α (HIF1α), resulting in reduced HIF1α-induced expression of IL-1β. Our studies identify a regulatory mechanism controlling diet-induced insulin resistance by highlighting a critical role for Pellino3 in regulating IL-1β expression with implications for diseases like type 2 diabetes.