The PPARα agonist fenofibrate suppresses B-cell lymphoma in mice by modulating lipid metabolism

Obesity is associated with an increased risk for malignant lymphoma development. We used Bcr/Abl transformed B cells to determine the impact of aggressive lymphoma formation on systemic lipid mobilization and turnover. In wild-type mice, tumor size significantly correlated with depletion of white adipose tissues (WAT), resulting in increased serum free fatty acid (FFA) concentrations which promote B-cell proliferation in vitro. Moreover, B-cell tumor development induced hepatic lipid accumulation due to enhanced hepatic fatty acid (FA) uptake and impaired FA oxidation. Serum triglyceride, FFA, phospholipid and cholesterol levels were significantly elevated. Consistently, serum VLDL/LDL-cholesterol and apolipoprotein B levels were drastically increased. These findings suggest that B-cell tumors trigger systemic lipid mobilization from WAT to the liver and increase VLDL/LDL release from the liver to promote tumor growth. Further support for this concept stems from experiments where we used the peroxisome proliferator-activated receptor α (PPARα) agonist and lipid-lowering drug fenofibrate that significantly suppressed tumor growth independent of angiogenesis and inflammation. In addition to WAT depletion, fenofibrate further stimulated FFA uptake by the liver and restored hepatic FA oxidation capacity, thereby accelerating the clearance of lipids released from WAT. Furthermore, fenofibrate blocked hepatic lipid release induced by the tumors. In contrast, lipid utilization in the tumor tissue itself was not increased by fenofibrate which correlates with extremely low expression levels of PPARα in B-cells. Our data show that fenofibrate associated effects on hepatic lipid metabolism and deprivation of serum lipids are capable to suppress B-cell lymphoma growth which may direct novel treatment strategies. This article is part of a Special Issue entitled Lipid Metabolism in Cancer.     

DNMTs are required for delayed genome instability caused by radiation

The ability of ionizing radiation to initiate genomic instability has been harnessed in the clinic where the localized delivery of controlled doses of radiation is used to induce cell death in tumor cells. Though very effective as a therapy, tumor relapse can occur in vivo and its appearance has been attributed to the radio-resistance of cells with stem cell-like features. The molecular mechanisms underlying these phenomena are unclear but there is evidence suggesting an inverse correlation between radiation-induced genomic instability and global hypomethylation. To further investigate the relationship between DNA hypomethylation, radiosensitivity and genomic stability in stem-like cells we have studied mouse embryonic stem cells containing differing levels of DNA methylation due to the presence or absence of DNA methyltransferases. Unexpectedly, we found that global levels of methylation do not determine radiosensitivity. In particular, radiation-induced delayed genomic instability was observed at the Hprt gene locus only in wild-type cells. Furthermore, absence of Dnmt1 resulted in a 10-fold increase in de novo Hprt mutation rate, which was unaltered by radiation. Our data indicate that functional DNMTs are required for radiation-induced genomic instability, and that individual DNMTs play distinct roles in genome stability. We propose that DNMTS may contribute to the acquirement of radio-resistance in stem-like cells.     

Brain signal predictions from multi-scale networks using a linearized framework

Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (≳ 10⁶ neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials (‘spikes’) or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin.Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for.The work also constitutes a major advance in computational efficiency of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework.     

Bioefficacy of plant essential oils against pulse beetles Callosobruchus spp. (Coleoptera: Bruchidae) in pigeon pea seeds with particular reference to Clausena pentaphylla (Roxb.) DC.

Thirty essential oils from higher plants of Gorakhpur Division (India) were evaluated at 0.36?μl/ml against two pulse beetles, Callosobruchus chinensis L. and C. maculatus F., causing infestation of pigeon pea seeds during storage. Clausena pentaphylla oil was more effective and exhibited absolute repellency against both the insects followed by Ocimum canum, Salvia plebeia and Zingiber zerumbet oils. Among these four oils, C. pentaphylla oil was most toxic and showed 100% mortality of both the insects at 10-μl dosage and 24-h exposure (LD₅₀?=?2.7?μl for C. chinensis & 2.4?μl for C. maculatus). Physical factors, viz. temperature, storage and autoclaving, did not cause any adverse effect on the toxicity of Clausena oil. During in vivo investigation, the oil protected 1?kg of pigeon pea seeds completely without reducing weight loss and seed damage up to 6?months when stored in gunny bags and glass containers. The oil was standardised by determining its various physicochemical properties. Thus, C. pentaphylla oil can be judiciously exploited as herbal insecticide against pulse beetles of pigeon pea seeds during storage.     

Quantitative Genetic Analysis of Retinal Degeneration in the Blind Cavefish Astyanax mexicanus

The retina is the light-sensitive tissue of the eye that facilitates vision. Mutations within genes affecting eye development and retinal function cause a host of degenerative visual diseases, including retinitis pigmentosa and anophthalmia/microphthalmia. The characin fish Astyanax mexicanus includes both eyed (surface fish) and eyeless (cavefish) morphs that initially develop eyes with normal retina; however, early in development, the eyes of cavefish degenerate. Since both surface and cave morphs are members of the same species, they serve as excellent evolutionary mutant models with which to identify genes causing retinal degeneration. In this study, we crossed the eyed and eyeless forms of A. mexicanus and quantified the thickness of individual retinal layers among 115 F₂ hybrid progeny. We used next generation sequencing (RAD-seq) and microsatellite mapping to construct a dense genetic map of the Astyanax genome, scan for quantitative trait loci (QTL) affecting retinal thickness, and identify candidate genes within these QTL regions. The map we constructed for Astyanax includes nearly 700 markers assembled into 25 linkage groups. Based on our scans with this map, we identified four QTL, one each associated with the thickness of the ganglion, inner nuclear, outer plexiform, and outer nuclear layers of the retina. For all but one QTL, cavefish alleles resulted in a clear reduction in the thickness of the affected layer. Comparative mapping of genetic markers within each QTL revealed that each QTL corresponds to an approximately 35 Mb region of the zebrafish genome. Within each region, we identified several candidate genes associated with the function of each affected retinal layer. Our study is the first to examine Astyanax retinal degeneration in the context of QTL mapping. The regions we identify serve as a starting point for future studies on the genetics of retinal degeneration and eye disease using the evolutionary mutant model Astyanax.     

Detecting protein complexes based on a combination of topological and biological properties in protein-protein interaction network

Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexes from raw protein protein interactions (PPIs) is an important area of research. Earlier work has been limited mostly to yeast. Such protein complex identification methods, when applied to large human PPIs often give poor performance. We introduce a novel method called CSC to detect protein complexes. The method is evaluated in terms of positive predictive value, sensitivity and accuracy using the datasets of the model organism, yeast and humans. CSC outperforms several other competing algorithms for both organisms. Further, we present a framework to establish the usefulness of CSC in analyzing the influence of a given disease gene in a complex topologically as well as biologically considering eight major association factors.