Taste perception plays an important role in regulating food preference, eating behavior and energy homeostasis. Taste perception is modulated by a variety of factors, including gastric hormones such as ghrelin. Ghrelin can regulate growth hormone release, food intake, adiposity, and energy metabolism. Octanoylation of ghrelin by ghrelin O-acyltransferase (GOAT) is a specific post-translational modification which is essential for many biological activities of ghrelin. Ghrelin and GOAT are both widely expressed in many organs including the gustatory system. In the current study, overall metabolic profiles were assessed in wild-type (WT), ghrelin knockout (ghrelin−/−), and GOAT knockout (GOAT−/−) mice. Ghrelin−/− mice exhibited decreased food intake, increased plasma triglycerides and increased ketone bodies compared to WT mice while demonstrating WT-like body weight, fat composition and glucose control. In contrast GOAT−/− mice exhibited reduced body weight, adiposity, resting glucose and insulin levels compared to WT mice. Brief access taste behavioral tests were performed to determine taste responsivity in WT, ghrelin−/− and GOAT−/− mice. Ghrelin and GOAT null mice possessed reduced lipid taste responsivity. Furthermore, we found that salty taste responsivity was attenuated in ghrelin−/− mice, yet potentiated in GOAT−/− mice compared to WT mice. Expression of the potential lipid taste regulators Cd36 and Gpr120 were reduced in the taste buds of ghrelin and GOAT null mice, while the salt-sensitive ENaC subunit was increased in GOAT−/− mice compared with WT mice. The altered expression of Cd36, Gpr120 and ENaC may be responsible for the altered lipid and salt taste perception in ghrelin−/− and GOAT−/− mice. The data presented in the current study potentially implicates ghrelin signaling activity in the modulation of both lipid and salt taste modalities.
With the prevalence of obesity, artificial, non-nutritive sweeteners have been widely used as dietary supplements that provide sweet taste without excessive caloric load. In order to better understand the overall actions of artificial sweeteners, especially when they are chronically used, we investigated the peripheral and central nervous system effects of protracted exposure to a widely used artificial sweetener, acesulfame K (ACK). We found that extended ACK exposure (40 weeks) in normal C57BL/6J mice demonstrated a moderate and limited influence on metabolic homeostasis, including altering fasting insulin and leptin levels, pancreatic islet size and lipid levels, without affecting insulin sensitivity and bodyweight. Interestingly, impaired cognitive memory functions (evaluated by Morris Water Maze and Novel Objective Preference tests) were found in ACK-treated C57BL/6J mice, while no differences in motor function and anxiety levels were detected. The generation of an ACK-induced neurological phenotype was associated with metabolic dysregulation (glycolysis inhibition and functional ATP depletion) and neurosynaptic abnormalities (dysregulation of TrkB-mediated BDNF and Akt/Erk-mediated cell growth/survival pathway) in hippocampal neurons. Our data suggest that chronic use of ACK could affect cognitive functions, potentially via altering neuro-metabolic functions in male C57BL/6J mice.
Modulation of sensory function can help animals adjust to a changing external and internal environment. Even so, mechanisms for modulating taste sensitivity are poorly understood. Using immunohistochemical, biochemical and behavioral approaches, we found that the peptide hormone glucagon-like peptide-1 (GLP-1) and its receptor (GLP-1R) are expressed in mammalian taste buds. Furthermore, we found that GLP-1 signaling plays an important role in the modulation of taste sensitivity: GLP-1R knockout mice exhibit a dramatic reduction in sweet taste sensitivity as well as an enhanced sensitivity to umami-tasting stimuli. Together, these findings suggest a novel paracrine mechanism for the hormonal modulation of taste function in mammals.
glucagon-like peptide-1; hormone; sweet; umami; glutamate
High-dimensionality data is rapidly becoming the norm for biomedical sciences and many other analytical disciplines. Not only is the collection and processing time for such data becoming problematic, but it has become increasingly difficult to form a comprehensive appreciation of high-dimensionality data. Though data analysis methods for coping with multivariate data are well-documented in technical fields such as computer science, little effort is currently being expended to condense data vectors that exist beyond the realm of physical space into an easily interpretable and aesthetic form. To address this important need, we have developed Plurigon, a data visualization and classification tool for the integration of high-dimensionality visualization algorithms with a user-friendly, interactive graphical interface. Unlike existing data visualization methods, which are focused on an ensemble of data points, Plurigon places a strong emphasis upon the visualization of a single data point and its determining characteristics. Multivariate data vectors are represented in the form of a deformed sphere with a distinct topology of hills, valleys, plateaus, peaks, and crevices. The gestalt structure of the resultant Plurigon object generates an easily-appreciable model. User interaction with the Plurigon is extensive; zoom, rotation, axial and vector display, feature extraction, and anaglyph stereoscopy are currently supported. With Plurigon and its ability to analyze high-complexity data, we hope to see a unification of biomedical and computational sciences as well as practical applications in a wide array of scientific disciplines. Increased accessibility to the analysis of high-dimensionality data may increase the number of new discoveries and breakthroughs, ranging from drug screening to disease diagnosis to medical literature mining.
Plurigon; three dimensional; data visualization; data classification; multivariate data vectors; algorithms; systems biology; bioinformatics
Human embryonic stem cell (hESC)-derived dopaminergic (DA) neurons hold potential for treating Parkinson’s disease (PD) through cell replacement therapy. Generation of DA neurons from hESCs has been achieved by co-culture with the stromal cell line PA6, a source of stromal cell-derived inducing activity (SDIA). However, the factor(s) produced by stromal cells that constitute SDIA are largely undefined. We previously reported that medium conditioned by PA6 cells can generate functional DA neurons from NTera2 human embryonal carcinoma stem cells. Here we show that PA6-conditioned medium can induce DA neuronal differentiation in both NTera2 cells and the hESC I6 cell line. To identify the factor(s) responsible for SDIA, we used large-scale microarray analysis of gene expression combined with mass spectrometric analysis of PA6-conditioned medium (CM). The candidate factors, hepatocyte growth factor (HGF), stromal cell-derived factor-1 α (SDF1α), secreted frizzled-related protein 1 (sFRP1), and vascular endothelial growth factor D (VEGFD) were identified and their concentrations in PA6 CM were established by immunoaffinity capillary electrophoresis. Upon addition of SDF1α, sFRP1 and VEGFD to the culture medium we observed an increase in the number of cells expressing tyrosine hydroxylase (a marker for DA neurons) and beta-III tubulin (a marker for immature neurons) in both the NTera2 and I6 cell lines. These results indicate that SDF1α, sFRP1 and VEGFD are major components of SDIA, and suggest the potential use of these defined factors to elicit DA differentiation of pluripotent human stem cells for therapeutic intervention in PD.
dopaminergic neurons; neuronal differentiation; stromal cell derived inducing activity; embryonic stem cells
Accurate and reliable quantitative proteomics in cell culture has been considerably facilitated by the introduction of the stable isotope labeling by amino acids in cell culture (SILAC), combined with high resolution mass spectrometry. There are however several major sources of quantification errors that commonly occur with SILAC techniques, i.e. incomplete incorporation of isotopic amino acids, arginine-to-proline conversion, and experimental errors in final sample mixing. Dataset normalization is a widely adopted solution to such errors, however this may not completely prevent introducing incorrect expression ratios. Here we demonstrate that a label-swap replication of SILAC experiments was able to effectively correct experimental errors by averaging ratios measured in individual replicates using quantitative proteomics and phosphoproteomics of ligand treatment of neural cell cultures. Furthermore, this strategy was successfully applied to a SILAC triplet experiment, which presents a much more complicated experimental matrix, affected by both incomplete labeling and arginine-to-proline conversion. Based on our results, we suggest that SILAC experiments should be designed to incorporate label-swap replications for enhanced reliability in expression ratios.
SILAC; incomplete isotope labeling; arginine-to-proline conversion; label-swap replication; receptor
Pulsed Q collision-induced dissociation (PQD) was developed in part to facilitate detection of low-mass reporter ions using labeling reagents (e.g. iTRAQ) on LTQ platforms. It has generally been recognized that the scan speed and sensitivity of an LTQ are superior than those of an Orbitrap using the higher-energy collisional dissociation (HCD). However, the use of PQD in quantitative proteomics is limited, primarily due to the meager reproducibility of reporter ion ratios. Optimizations of PQD for iTRAQ quantification using LTQ have been reported, but a universally applicable strategy for quantifying the less abundant proteins has not been fully established. Adjustments of the AGC target, µscan, or scan speed offer only incremental improvements in reproducibility. From our experience, however, satisfactory coefficients of variation (CVs) of reporter ion ratios were difficult to achieve using the discovery-based approach. As an alternative, we implemented a target-based approach that obviates data dependency to allow repetitive data acquisitions across chromatographic peaks. Such a strategy generates enough data points for more reliable quantification. Using cAMP treatment in S49 cell lysates and this target-based approach, we were able to validate differentially expressed proteins, which were initially identified as potential candidates using the discovery-based PQD. The target-based strategy also yielded results comparable to those obtained from HCD in an Orbitrap. Our findings should aid LTQ users who desire to explore iTRAQ quantitative proteomics but have limited access to the more costly Orbitrap or other instruments.
Pulsed Q collision-induced dissociation (PQD); linear ion trap; triple quadrupole (QqQ); higher energy collisional dissociation (HCD); iTRAQ (Isobaric Tag for Relative and Absolute Quantification)
Normal aging is a complex process that affects every organ system in the body, including the taste system. Thus, we investigated the effects of the normal aging process on taste bud morphology, function, and taste responsivity in male mice at 2, 10, and 18 months of age. The 18-month-old animals demonstrated a significant reduction in taste bud size and number of taste cells per bud compared with the 2- and 10-month-old animals. The 18-month-old animals exhibited a significant reduction of protein gene product 9.5 and sonic hedgehog immunoreactivity (taste cell markers). The number of taste cells expressing the sweet taste receptor subunit, T1R3, and the sweet taste modulating hormone, glucagon-like peptide-1, were reduced in the 18-month-old mice. Concordant with taste cell alterations, the 18-month-old animals demonstrated reduced sweet taste responsivity compared with the younger animals and the other major taste modalities (salty, sour, and bitter) remained intact.
Taste buds; Aging; Sweet taste; Glucagon-like peptide-1; T1R3
Text mining is rapidly becoming an essential technique for the annotation and analysis of large biological data sets. Biomedical literature currently increases at a rate of several thousand papers per week, making automated information retrieval methods the only feasible method of managing this expanding corpus. With the increasing prevalence of open-access journals and constant growth of publicly-available repositories of biomedical literature, literature mining has become much more effective with respect to the extraction of biomedically-relevant data. In recent years, text mining of popular databases such as MEDLINE has evolved from basic term-searches to more sophisticated natural language processing techniques, indexing and retrieval methods, structural analysis and integration of literature with associated metadata. In this review, we will focus on Latent Semantic Indexing (LSI), a computational linguistics technique increasingly used for a variety of biological purposes. It is noted for its ability to consistently outperform benchmark Boolean text searches and co-occurrence models at information retrieval and its power to extract indirect relationships within a data set. LSI has been used successfully to formulate new hypotheses, generate novel connections from existing data, and validate empirical data.
latent semantic indexing; data mining; computational linguistics; molecular interactions; drug discovery
With the development of increasingly large and complex genomic and proteomic data sets, an enhancement in the complexity of available Venn diagram analytical programs is becoming increasingly important. Current freely available Venn diagram programs often fail to represent extra complexity among datasets, such as regulation pattern differences between different groups. Here we describe the development of VennPlex, a program that illustrates the often diverse numerical interactions among multiple, high-complexity datasets, using up to four data sets. VennPlex includes versatile output features, where grouped data points in specific regions can be easily exported into a spreadsheet. This program is able to facilitate the analysis of two to four gene sets and their corresponding expression values in a user-friendly manner. To demonstrate its unique experimental utility we applied VennPlex to a complex paradigm, i.e. a comparison of the effect of multiple oxygen tension environments (1–20% ambient oxygen) upon gene transcription of primary rat astrocytes. VennPlex accurately dissects complex data sets reliably into easily identifiable groups for straightforward analysis and data output. This program, which is an improvement over currently available Venn diagram programs, is able to rapidly extract important datasets that represent the variety of expression patterns available within the data sets, showing potential applications in fields like genomics, proteomics, and bioinformatics.
The microscopic image analysis of pancreatic Islet of Langerhans morphology is crucial for the investigation of diabetes and metabolic diseases. Besides the general size of the islet, the percentage and relative position of glucagon-containing alpha-, and insulin-containing beta-cells is also important for pathophysiological analyses, especially in rodents. Hence, the ability to identify, quantify and spatially locate peripheral, and “involuted” alpha-cells in the islet core is an important analytical goal. There is a dearth of software available for the automated and sophisticated positional quantification of multiple cell types in the islet core. Manual analytical methods for these analyses, while relatively accurate, can suffer from a slow throughput rate as well as user-based biases. Here we describe a newly developed pancreatic islet analytical software program, Pancreas++, which facilitates the fully automated, non-biased, and highly reproducible investigation of islet area and alpha- and beta-cell quantity as well as position within the islet for either single or large batches of fluorescent images. We demonstrate the utility and accuracy of Pancreas++ by comparing its performance to other pancreatic islet size and cell type (alpha, beta) quantification methods. Our Pancreas++ analysis was significantly faster than other methods, while still retaining low error rates and a high degree of result correlation with the manually generated reference standard.
pancreas; islets of Langerhans; alpha-cells; beta-cells; quantification; software; algorithm
The microRNA miR-519 robustly inhibits cell proliferation, in turn triggering senescence and decreasing tumor growth. However, the molecular mediators of miR-519-elicited growth inhibition are unknown. Here, we systematically investigated the influence of miR-519 on gene expression profiles leading to growth cessation in HeLa human cervical carcinoma cells. By analyzing miR-519-triggered changes in protein and mRNA expression patterns and by identifying mRNAs associated with biotinylated miR-519, we uncovered two prominent subsets of miR-519-regulated mRNAs. One subset of miR-519 target mRNAs encoded DNA maintenance proteins (including DUT1, EXO1, RPA2, and POLE4); miR-519 repressed their expression and increased DNA damage, in turn raising the levels of the cyclin-dependent kinase (cdk) inhibitor p21. The other subset of miR-519 target mRNAs encoded proteins that control intracellular calcium levels (notably, ATP2C1 and ORAI1); their downregulation by miR-519 aberrantly elevated levels of cytosolic [Ca2+] storage in HeLa cells, similarly increasing p21 levels in a manner dependent on the Ca2+-activated kinases CaMKII and GSK3β. The rises in levels of DNA damage, the Ca2+ concentration, and p21 levels stimulated an autophagic phenotype in HeLa and other human carcinoma cell lines. As a consequence, ATP levels increased, and the level of activity of the AMP-activated protein kinase (AMPK) declined, further contributing to the elevation in the abundance of p21. Our results indicate that miR-519 promotes DNA damage, alters Ca2+ homeostasis, and enhances energy production; together, these processes elevate the expression level of p21, promoting growth inhibition and cell survival.
Bioluminescence resonance energy transfer (BRET) is an improved version of earlier resonance energy transfer technologies used for the analysis of biomolecular protein interaction. BRET analysis can be applied to many transmembrane receptor classes, however the majority of the early published literature on BRET has focused on G protein-coupled receptor (GPCR) research. In contrast, there is limited scientific literature using BRET to investigate receptor tyrosine kinase (RTK) activity. This limited investigation is surprising as RTKs often employ dimerization as a key factor in their activation, as well as being important therapeutic targets in medicine, especially in the cases of cancer, diabetes, neurodegenerative, and respiratory conditions. In this review, we consider an array of studies pertinent to RTKs and other non-GPCR receptor protein–protein signaling interactions; more specifically we discuss receptor-protein interactions involved in the transmission of signaling communication. We have provided an overview of functional BRET studies associated with the RTK superfamily involving: neurotrophic receptors [e.g., tropomyosin-related kinase (Trk) and p75 neurotrophin receptor (p75NTR)]; insulinotropic receptors [e.g., insulin receptor (IR) and insulin-like growth factor receptor (IGFR)] and growth factor receptors [e.g., ErbB receptors including the EGFR, the fibroblast growth factor receptor (FGFR), the vascular endothelial growth factor receptor (VEGFR) and the c-kit and platelet-derived growth factor receptor (PDGFR)]. In addition, we review BRET-mediated studies of other tyrosine kinase-associated receptors including cytokine receptors, i.e., leptin receptor (OB-R) and the growth hormone receptor (GHR). It is clear even from the relatively sparse experimental RTK BRET evidence that there is tremendous potential for this technological application for the functional investigation of RTK biology.
receptor tyrosine kinase; RTK; protein–protein interaction; neurotrophic; insulin receptor; insulin-like growth factor receptor; epidermal growth factor receptor; cytokine receptors
Huntington's disease (HD) is a neurodegenerative disorder, which is characterized by progressive motor impairment and cognitive alterations. Changes in energy metabolism, neuroendocrine function, body weight, euglycemia, appetite function, and circadian rhythm can also occur. It is likely that the locus of these alterations is the hypothalamus. We used the HD transgenic (tg) rat model bearing 51 CAG repeats, which exhibits similar HD symptomology as HD patients to investigate hypothalamic function. We conducted detailed hypothalamic proteome analyses and also measured circulating levels of various metabolic hormones and lipids in pre-symptomatic and symptomatic animals. Our results demonstrate that there are significant alterations in HD rat hypothalamic protein expression such as glial fibrillary acidic protein (GFAP), heat shock protein-70, the oxidative damage protein glutathione peroxidase (Gpx4), glycogen synthase1 (Gys1) and the lipid synthesis enzyme acylglycerol-3-phosphate O-acyltransferase 1 (Agpat1). In addition, there are significant alterations in various circulating metabolic hormones and lipids in pre-symptomatic animals including, insulin, leptin, triglycerides and HDL, before any motor or cognitive alterations are apparent. These early metabolic and lipid alterations are likely prodromal signs of hypothalamic dysfunction. Gaining a greater understanding of the hypothalamic and metabolic alterations that occur in HD, could lead to the development of novel therapeutics for early interventional treatment of HD.
Pulsed Q collision induced dissociation (PQD) was developed to facilitate detection of low-mass reporter ions from labeling reagents (e.g. iTRAQ) in peptide quantification using an LTQ mass spectrometer (MS). Despite the large number of linear ion traps worldwide, the use and optimization of PQD for protein identification have been limited, in part due to less effective ion fragmentation relative to the collision induced dissociation (CID). PQD expands the m/z coverage of fragment ions to the lower m/z range by circumventing the typical low mass cut-off of an ion trap MS. Since database searching relies on the matching between theoretical and observed spectra, it is not clear how ion intensity and peak number might affect the outcomes of a database search. In this report, we systematically evaluated the attributes of PQD mass spectra, performed intensity optimization, and assessed the benefits of using PQD on the identification of peptides and phosphopeptides from an LTQ. Based on head-to-head comparisons between CID (higher intensity) and PQD (better m/z coverage), peptides identified using PQD generally have Xcorr scores lower than those using CID. Such score differences were considerably diminished by the use of 0.1% m-nitrobenzyl alcohol (m-NBA) in mobile phases. The ion intensities of both CID and PQD were adversely affected by increasing m/z of the precursor, with PQD more sensitive than CID. In addition to negating the 1/3 rule, PQD enhances direct bond cleavage and generates patterns of fragment ions different from those of CID, particularly for peptides with a labile functional group (e.g. phosphopeptides). The higher energy fragmentation pathway of PQD on peptide fragmentation was further compared to those of CID and the quadrupole-type activation in parallel experiments.
Pulsed Q collision induced dissociation (PQD); linear ion trap; triple quadrupole (QqQ); protein identification
The hypothalamus is an essential relay in the neural circuitry underlying energy metabolism that needs to continually adapt to changes in the energetic environment. The neuroendocrine control of food intake and energy expenditure is associated with, and likely dependent upon, hypothalamic plasticity. Severe disturbances in energy metabolism, such as those that occur in obesity, are therefore likely to be associated with disruption of hypothalamic transcriptomic plasticity. In this paper, we investigated the effects of two well-characterized antiaging interventions, caloric restriction and voluntary wheel running, in two distinct physiological paradigms, that is, diabetic (db/db) and nondiabetic wild-type (C57/Bl/6) animals to investigate the contextual sensitivity of hypothalamic transcriptomic responses. We found that, both quantitatively and qualitatively, caloric restriction and physical exercise were associated with distinct transcriptional signatures that differed significantly between diabetic and non-diabetic mice. This suggests that challenges to metabolic homeostasis regulate distinct hypothalamic gene sets in diabetic and non-diabetic animals. A greater understanding of how genetic background contributes to hypothalamic response mechanisms could pave the way for the development of more nuanced therapeutics for the treatment of metabolic disorders that occur in diverse physiological backgrounds.
Low bone mass density (BMD), a classical age-related health issue and a known health concern for fair skinned, thin, postmenopausal Caucasian women, is found to be common among individuals with developmental/intellectual disabilities (D/IDs). It is the consensus that BMD is decreased in both men and women with D/ID. Maintaining good bone health is important for this population as fractures could potentially go undetected in nonverbal individuals, leading to increased morbidity and a further loss of independence. This paper provides a comprehensive overview of bone health of adults with D/ID, their risk of fractures, and how this compares to the general aging population. We will specifically focus on the bone health of two common developmental disabilities, Down syndrome (DS) and cerebral palsy (CP), and will discuss BMD and fracture rates in these complex populations. Gaining a greater understanding of how bone health is affected in individuals with D/ID could lead to better customized treatments for these specific populations.
A strong connection between neuronal and metabolic health has been revealed in recent years. It appears that both normal and pathophysiological aging, as well as neurodegenerative disorders, are all profoundly influenced by this “neurometabolic” interface, that is, communication between the brain and metabolic organs. An important aspect of this “neurometabolic” axis that needs to be investigated involves an elucidation of molecular factors that knit these two functional signaling domains, neuronal and metabolic, together. This paper attempts to identify and discuss a potential keystone signaling factor in this “neurometabolic” axis, that is, the epidermal growth factor receptor (EGFR). The EGFR has been previously demonstrated to act as a signaling nexus for many ligand signaling modalities and cellular stressors, for example, radiation and oxidative radicals, linked to aging and degeneration. The EGFR is expressed in a wide variety of cells/tissues that pertain to the coordinated regulation of neurometabolic activity. EGFR signaling has been highlighted directly or indirectly in a spectrum of neurometabolic conditions, for example, metabolic syndrome, diabetes, Alzheimer's disease, cancer, and cardiorespiratory function. Understanding the positioning of the EGFR within the neurometabolic domain will enhance our appreciation of the ability of this receptor system to underpin highly complex physiological paradigms such as aging and neurodegeneration.
The aging process affects every tissue in the body and represents one of the most complicated and highly integrated inevitable physiological entities. The maintenance of good health during the aging process likely relies upon the coherent regulation of hormonal and neuronal communication between the central nervous system and the periphery. Evidence has demonstrated that the optimal regulation of energy usage in both these systems facilitates healthy aging. However, the proteomic effects of aging in regions of the brain vital for integrating energy balance and neuronal activity are not well understood. The hypothalamus is one of the main structures in the body responsible for sustaining an efficient interaction between energy balance and neurological activity. Therefore, a greater understanding of the effects of aging in the hypothalamus may reveal important aspects of overall organismal aging and may potentially reveal the most crucial protein factors supporting this vital signaling integration. In this study, we examined alterations in protein expression in the hypothalami of young, middle-aged, and old rats. Using novel combinatorial bioinformatics analyses, we were able to gain a better understanding of the proteomic and phenotypic changes that occur during the aging process and have potentially identified the G protein-coupled receptor/cytoskeletal-associated protein GIT2 as a vital integrator and modulator of the normal aging process.
As pharmacological data sets become increasingly large and complex, new visual analysis and filtering programs are needed to aid their appreciation. One of the most commonly used methods for visualizing biological data is the Venn diagram. Currently used Venn analysis software often presents multiple problems to biological scientists, in that only a limited number of simultaneous data sets can be analyzed. An improved appreciation of the connectivity between multiple, highly-complex datasets is crucial for the next generation of data analysis of genomic and proteomic data streams. We describe the development of VENNTURE, a program that facilitates visualization of up to six datasets in a user-friendly manner. This program includes versatile output features, where grouped data points can be easily exported into a spreadsheet. To demonstrate its unique experimental utility we applied VENNTURE to a highly complex parallel paradigm, i.e. comparison of multiple G protein-coupled receptor drug dose phosphoproteomic data, in multiple cellular physiological contexts. VENNTURE was able to reliably and simply dissect six complex data sets into easily identifiable groups for straightforward analysis and data output. Applied to complex pharmacological datasets, VENNTURE’s improved features and ease of analysis are much improved over currently available Venn diagram programs. VENNTURE enabled the delineation of highly complex patterns of dose-dependent G protein-coupled receptor activity and its dependence on physiological cellular contexts. This study highlights the potential for such a program in fields such as pharmacology, genomics, and bioinformatics.
Brain derived neurotrophic factor (BDNF) seems to be involved in regulation of synaptic plasticity and neurogenesis. BDNF plasma and serum levels have been associated with depression, Alzheimer's disease, and other psychiatric and neurodegenerative disorders. In a community sample, drawn from the Baltimore Longitudinal Study of Aging (BLSA), we examined whether BDNF plasma concentration was associated with rates of age-related change in cognitive performance (n = 429) and regional brain volume (n = 59). Plasma BDNF levels, which were significantly higher in females (p<0.05), were not associated with either concurrent cognitive performance or rates of age-related change in performance across cognitive domains (p's>0.05). Sex differences in the relationship between BDNF and the trajectories of regional brain volume changes were observed for the whole brain and frontal white matter volumes (p<0.05), whereby lower plasma BDNF was associated with steeper volume decline in females but not males. Together, our findings contribute to furthering the understanding of the relationships between plasma BDNF, structural brain integrity and cognition. Potential mechanisms mediating these relationships merit further investigation.
Optimal glucose homeostasis requires exquisitely precise adaptation of the number of insulin-secreting β-cells in the islets of Langerhans. Insulin itself positively regulates β-cell proliferation in an autocrine manner through the insulin receptor (IR) signaling pathway. It is now coming to light that cannabinoid 1 receptor (CB1R) agonism/antagonism influences insulin action in insulin-sensitive tissues. However, the cells on which the CB1Rs are expressed and their function in islets have not been firmly established. We undertook the current study to investigate if intraislet endogenous cannabinoids (ECs) regulate β-cell proliferation and if they influence insulin action.
RESEARCH DESIGN AND METHODS
We measured EC production in isolated human and mouse islets and β-cell line in response to glucose and KCl. We evaluated human and mouse islets, several β-cell lines, and CB1R-null (CB1R−/−) mice for the presence of a fully functioning EC system. We investigated if ECs influence β-cell physiology through regulating insulin action and demonstrated the therapeutic potential of manipulation of the EC system in diabetic (db/db) mice.
ECs are generated within β-cells, which also express CB1Rs that are fully functioning when activated by ligands. Genetic and pharmacologic blockade of CB1R results in enhanced IR signaling through the insulin receptor substrate 2-AKT pathway in β-cells and leads to increased β-cell proliferation and mass. CB1R antagonism in db/db mice results in reduced blood glucose and increased β-cell proliferation and mass, coupled with enhanced IR signaling in β-cells. Furthermore, CB1R activation impedes insulin-stimulated IR autophosphorylation on β-cells in a Gαi-dependent manner.
These findings provide direct evidence for a functional interaction between CB1R and IR signaling involved in the regulation of β-cell proliferation and will serve as a basis for developing new therapeutic interventions to enhance β-cell function and proliferation in diabetes.
Global profiling of phosphoproteomes has proven a great challenge due to the relatively low stoichiometry of protein phosphorylation and poor ionization efficiency in mass spectrometers. Effective, physiologically-relevant, phosphoproteome research relies on the efficient phosphopeptide enrichment from complex samples. Immobilized metal affinity chromatography and titanium dioxide chromatography (TOC) can greatly assist selective phosphopeptide enrichment. However, the complexity of resultant enriched samples is often still high, suggesting that further separation of enriched phosphopeptides is required. We have developed a pH-gradient elution technique for enhanced phosphopeptide identification in conjunction with TOC. Using this process, we have demonstrated its superiority to the traditional ‘one-pot’ strategies for differential protein identification. Our technique generated a highly specific separation of phosphopeptides by an applied pH-gradient between 9.2 and 11.3. The most efficient elution range for high-resolution phosphopeptide separation was between pH 9.2 and 9.4. High-resolution separation of multiply-phosphorylated peptides was primarily achieved using elution ranges > pH 9.4. Investigation of phosphopeptide sequences identified in each pH fraction indicated that phosphopeptides with phosphorylated residues proximal to acidic residues, including glutamic acid, aspartic acid, and other phosphorylated residues, were preferentially eluted at higher pH values.
titanium dioxide; phosphoproteomics; mass spectrometry; pH-dependent elution