867 resultados para fructan metabolism
Resumo:
The energy demands of the brain are high: they account for at least 20% of the body's energy consumption. Evolutionary studies indicate that the emergence of higher cognitive functions in humans is associated with an increased glucose utilization and expression of energy metabolism genes. Functional brain imaging techniques such as fMRI and PET, which are widely used in human neuroscience studies, detect signals that monitor energy delivery and use in register with neuronal activity. Recent technological advances in metabolic studies with cellular resolution have afforded decisive insights into the understanding of the cellular and molecular bases of the coupling between neuronal activity and energy metabolism and point at a key role of neuron-astrocyte metabolic interactions. This article reviews some of the most salient features emerging from recent studies and aims at providing an integration of brain energy metabolism across resolution scales.
Resumo:
Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.
Resumo:
Pheochromocytoma (PHEO) and paraganglioma (PGL) are catecholamine-producing neuroendocrine tumors that arise respectively inside or outside the adrenal medulla. Several reports have shown that adrenal glucocorticoids (GC) play an important regulatory role on the genes encoding the main enzymes involved in catecholamine (CAT) synthesis i.e. tyrosine hydroxylase (TH), dopamine β-hydroxylase (DBH) and phenylethanolamine N-methyltransferase (PNMT). To assess the influence of tumor location on CAT metabolism, 66 tissue samples (53 PHEO, 13 PGL) and 73 plasma samples (50 PHEO, 23 PGL) were studied. Western blot and qPCR were performed for TH, DBH and PNMT expression. We found a significantly lower intra-tumoral concentration of CAT and metanephrines (MNs) in PGL along with a downregulation of TH and PNMT at both mRNA and protein level compared with PHEO. However, when PHEO were partitioned into noradrenergic (NorAd) and mixed tumors based on an intra-tumoral CAT ratio (NE/E >90%), PGL and NorAd PHEO sustained similar TH, DBH and PNMT gene and protein expression. CAT concentration and composition were also similar between NorAd PHEO and PGL, excluding the use of CAT or MNs to discriminate between PGL and PHEO on the basis of biochemical tests. We observed an increase of TH mRNA concentration without correlation with TH protein expression in primary cell culture of PHEO and PGL incubated with dexamethasone during 24 hours; no changes were monitored for PNMT and DBH at both mRNA and protein level in PHEO and PGL. Altogether, these results indicate that long term CAT synthesis is not driven by the close environment where the tumor develops and suggest that GC alone is not sufficient to regulate CAT synthesis pathway in PHEO/PGL.
Resumo:
Allylnitrile, cis-crotononitrile, and 3,3 -iminodipropionitrile are known to cause vestibular toxicity in rodents, and evidence is available indicating that cis-2-pentenenitrile shares this effect. We evaluated nineteen nitriles for vestibular toxicity in wild type (129S1) and CYP2E1-null mice, including all the above, several neurotoxic nitriles, and structurally similar nitriles. A new acute toxicity test protocol was developed to facilitate evaluation of the vestibular toxicity by a specific behavioral test battery at doses up to sub-lethal levels while using a limited number of animals. A mean number of 8.5±0.3 animals per nitrile, strain and sex was necessary to obtain evidence of vestibular toxicity and optionally an estimation of the lethal dose. For several but not all nitriles, lethal doses significantly increased in CYP2E1-null mice. The protocol revealed the vestibular toxicity of five nitriles, including previously identified ototoxic compounds and one nitrile (trans-crotononitrile) known to have a different profile of neurotoxic effects in the rat. In all five cases, both sexes were affected and no decrease in susceptibility was apparent in CYP2E1-null mice respect to 129S1 mice. Fourteen nitriles caused no vestibular toxicity, including six nitriles tested in CYP2E1-null mice at doses significantly larger than the maximal doses that can be tested in wild type animals. We conclude that only a subset of low molecular weight nitriles is toxic to the vestibular system, that species-dependent differences exist in this vestibular toxicity, and that CYP2E1-mediated metabolism is not involved in this effect of nitriles although it has a role in the acute lethality of some of these compounds
Resumo:
Pancreatic ductal adenocarcinoma (PDAC) is expected to become the second leading cause of cancer death by 2030. Current therapeutic options are limited, warranting an urgent need to explore innovative treatment strategies. Due to specific microenvironment constraints including an extensive desmoplastic stroma reaction, PDAC faces major metabolic challenges, principally hypoxia and nutrient deprivation. Their connection with oncogenic alterations such as KRAS mutations has brought metabolic reprogramming to the forefront of PDAC therapeutic research. The Warburg effect, glutamine addiction, and autophagy stand as the most important adaptive metabolic mechanisms of cancer cells themselves, however metabolic reprogramming is also an important feature of the tumor microenvironment, having a major impact on epigenetic reprogramming and tumor cell interactions with its complex stroma. We present a comprehensive overview of the main metabolic adaptations contributing to PDAC development and progression. A review of current and future therapies targeting this range of metabolic pathways is provided.
Resumo:
The lldPRD operon of Escherichia coli, involved in L-lactate metabolism, is induced by growth in this compound. We experimentally identified that this system is transcribed from a single promoter with an initiation site located 110 nucleotides upstream of the ATG start codon. On the basis of computational data, it had been proposed that LldR and its homologue PdhR act as regulators of the lldPRD operon. Nevertheless, no experimental data on the function of these regulators have been reported so far. Here we show that induction of an lldP-lacZ fusion by L-lactate is lost in an lldR mutant, indicating the role of LldR in this induction. Expression analysis of this construct in a pdhR mutant ruled out the participation of PdhR in the control of lldPRD. Gel shift experiments showed that LldR binds to two operator sites, O1 (positions 105 to 89) and O2 (positions 22 to 38), with O1 being filled at a lower concentration of LldR. L-Lactate induced a conformational change in LldR that did not modify its DNA binding activity. Mutations in O1 and O2 enhanced the basal transcriptional level. However, only mutations in O1 abolished induction by L-lactate. Mutants with a change in helical phasing between O1 and O2 behaved like O2 mutants. These results were consistent with the hypothesis that LldR has a dual role, acting as a repressor or an activator of lldPRD. We propose that in the absence of L-lactate, LldR binds to both O1 and O2, probably leading to DNA looping and the repression of transcription. Binding of L-lactate to LldR promotes a conformational change that may disrupt the DNA loop, allowing the formation of the transcription open complex.
Resumo:
Stereochemical factors are known to play a significant role in the metabolism of drugs and other xenobiotics. Following Prelog's lead, types of metabolic stereoselectivity can be categorized as (i) substrate stereoselectivity (the differential metabolism of two or more stereoisomeric substrates) and (ii) product stereoselectivity (the differential formation of two or more stereoisomeric metabolites from a single substrate). Combinations of the two categories exist as (iii) substrate-product stereoselectivities, meaning that product stereoselectivity itself is substrate stereoselective. Here, published examples of metabolic stereoselectivities are examined in the light of these concepts. In parallel, a graphical scheme is presented with a view to facilitate learning and help researchers to solve classification problems.