[3H]GLYCOGEN HYDROLYSIS IN BRAIN SLICES: RESPONSES TO NEUROTRANSMITTERS AND MODULATION OF NORADRENALINE RECEPTORS

Abstract— Different agents have been investigated for their effects on [C³H]glycogen synthesized in mouse cortical slices. Of these noradrenaline, serotonin and histamine induced clear concentration-dependent glycogenolysis.
[C³H]Glycogen hydrolysis induced by noradrenaline appears to be mediated by beta-adrenergic receptors because it is completely prevented by timolol, while phentolamine is ineffective. It seems to involve cyclic AMP because it is potentiated in the presence of isobutylmethylxanthine; in addition dibutyryl cyclic AMP (but not dibutyryl cyclic GMP) promotes glycogenolysis.
Lower concentrations of noradrenaline were necessary for [C³H]glycogen hydrolysis (EC₅₀= 0.5μM) than for stimulation of cyclic AMP accumulation (EC₅₀= 8μM).
After subchronic reserpine treatment the concentration-response curve to noradrenaline was significantly shifted to the left (EC₅₀= 0.09 ± 0.02 μM as compared with 0.49 ± 0.08 μM in saline-pretreated mice) without modifications of either the basal [C³H]glycogen level, maximal glycogenolytic effect, or the dibutyryl cAMP-induced glycogenolytic response.
In addition to noradrenaline, clear concentration-dependent [³H]glycogen hydrolysis was observed in the presence of histamine or serotonin. In contrast to the partial [³H]glycogen hydrolysis elicited by these biogenic amines, depolarization of the slices by 50 mM K⁺ provoked a nearly total [C³H)glycogen hydrolysis.     

Autophagy modulator plays a part in UV protection

Ultraviolet (UV)-induced DNA damage is a major risk factor for skin cancers including melanoma. UVRAG, originally identified to complement UV sensitivity in xeroderma pigmentosum (XP), has since been implicated in modulating macroautophagy/autophagy, in coordinating different intracellular trafficking pathways, and in maintaining chromosomal stability. Intriguingly, our recent study has demonstrated that UVRAG plays an essential role in protecting cells from UV-induced DNA damage by activating the nucleotide excision repair (NER) pathway. Since NER is the major mechanism by which cells maintain DNA integrity against UV insult, the inactivation of UVRAG seen in some melanoma may impart these cells with an ability to accumulate high-load UV mutagenesis, leading to cancer progression. Thus, this property of UVRAG has untapped potential to be of fundamental importance in understanding the genetics and pathogenesis of human skin cancer.     

Diagnosis of natural killer cell lymphoma by cytology and flow cytometric immunophenotyping. A case report

BACKGROUND: Natural killer (NK) cell lymphoma is a rare type of non-Hodgkin's lymphoma. It classically presents in the nasal region in Asian patients. There are few reports of its cytologic features. We describe a case that we diagnosed by fine needle aspiration (FNA) biopsy using flow cytometry immunophenotyping and cytomorphology. CASE: A 55-year-old, Chinese man presented with symptoms consistent with nasal obstruction. At examination, a polypoid lesion extending from the nose to the back of the throat was found. An intraoral FNA biopsy was performed. Representative smears were obtained and the remainder of the material sent for flow cytometry. A diagnosis of NK cell lymphoma was made. The patient was given chemotherapy and radiotherapy, with complete resolution of the lesion. Recurrence was noted on follow up seven months later. Pieces of tissue were taken for histology and flow cytometry and showed recurrent NK cell lymphoma. The lesion was again successfully treated by chemotherapy followed by radiotherapy. CONCLUSION: In the correct setting, a definitive diagnosis of non-Hodgkin's lymphoma can be made by FNA biopsy. This case of NK cell lymphoma was diagnosed by FNA biopsy using cytomorphology, flow cytometry immunophenotyping and clinical correlation.     

Development and applications of in-gel CNBr/tryptic digestion combined with mass spectrometry for the analysis of membrane proteins

Hydrophobic membrane proteins often have complex functions and are thus of great interest. However, their analysis presents a challenge because they are not readily soluble in polar solvents and often undergo aggregation. We present a sequential CNBr and trypsin in-gel digestion method combined with mass spectrometry for membrane protein analysis. CNBr selectively cleaves methionine residues. But due to the low number of methionines in proteins, CNBr cleavage produces a small number of large peptide fragments with MWs typically >2000, which are difficult to extract from gel pieces. To produce a larger number of smaller peptides than that obtained by using CNBr alone, we demonstrate that trypsin can be used to further digest the sample in gel. The use of n-octyl glucoside (n-OG) to enhance the digestion efficiency and peptide recovery was also studied. We demonstrate that the sensitivity of this membrane protein identification method is in the tens of picomole regime, which is compatible to the Coomassie staining gel-spot visualization method, and is more sensitive than other techniques reported in the literature. This CNBr/trypsin in-gel digestion method is also found to be very reproducible and has been successfully applied for the analysis of complex protein mixtures extracted from biological samples. The results are presented from a study of the analysis of bacteriorhodopsin, nitrate reductase 1 gamma chain, and a complex protein mixture extracted from the endoplasmic recticulum membrane of mouse liver.     

Is Demography Destiny? Application of Machine Learning Techniques to Accurately Predict Population Health Outcomes from a Minimal Demographic Dataset

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.     

Acceleration of Age-Associated Methylation Patterns in HIV-1-Infected Adults

Patients with treated HIV-1-infection experience earlier occurrence of aging-associated diseases, raising speculation that HIV-1-infection, or antiretroviral treatment, may accelerate aging. We recently described an age-related co-methylation module comprised of hundreds of CpGs; however, it is unknown whether aging and HIV-1-infection exert negative health effects through similar, or disparate, mechanisms. We investigated whether HIV-1-infection would induce age-associated methylation changes. We evaluated DNA methylation levels at >450,000 CpG sites in peripheral blood mononuclear cells (PBMC) of young (20-35) and older (36-56) adults in two separate groups of participants. Each age group for each data set consisted of 12 HIV-1-infected and 12 age-matched HIV-1-uninfected samples for a total of 96 samples. The effects of age and HIV-1 infection on methylation at each CpG revealed a strong correlation of 0.49, p<1 x10^-200 and 0.47, p<1x10^-200. Weighted gene correlation network analysis (WGCNA) identified 17 co-methylation modules; module 3 (ME3) was significantly correlated with age (cor=0.70) and HIV-1 status (cor=0.31). Older HIV-1⁺ individuals had a greater number of hypermethylated CpGs across ME3 (p=0.015). In a multivariate model, ME3 was significantly associated with age and HIV status (Data set 1: β_age= 0.007088, p=2.08 x 10^-9; β_HIV= 0.099574, p=0.0011; Data set 2: β_age= 0.008762, p=1.27x 10^-5; β_HIV= 0.128649, p= 0.0001). Using this model, we estimate that HIV-1 infection accelerates age-related methylation by approximately 13.7 years in data set 1 and 14.7 years in data set 2. The genes related to CpGs in ME3 are enriched for polycomb group target genes known to be involved in cell renewal and aging. The overlap between ME3 and an aging methylation module found in solid tissues is also highly significant (Fisher-exact p=5.6 x 10^-6, odds ratio=1.91). These data demonstrate that HIV-1 infection is associated with methylation patterns that are similar to age-associated patterns and suggest that general aging and HIV-1 related aging work through some common cellular and molecular mechanisms. These results are an important first step for finding potential therapeutic targets and novel clinical approaches to mitigate the detrimental effects of both HIV-1-infection and aging.