Isofuranodiene, a natural sesquiterpene isolated from wild celery (Smyrnium olusatrum L.), protects rats against acute ischemic stroke

The myrrh-like furanosesquiterpene isofuranodiene (IFD) is the main constituent of wild celery (Smyrnium olusatrum L., Apiaceae), an overlooked vegetable that was cultivated during the Roman Empire. In the present study, we investigated the protective effects of IFD pre-treatment against oxidative stress and inflammatory response in an animal model of ischemic stroke. IFD was isolated by the crystallization of Smyrnium olusatrum essential oil, and its structure and purity were confirmed by NMR and HPLC analyses. Acute pre-treatment of IFD (10 mg/kg i.p.) significantly reduced the levels of the inflammatory cytokines IL-1β and TNF-α, the expression of pNF-κB/NF-κB, and the lipid peroxidation indicator MDA. Finally, IFD boosted a faster recovery and better scores in grid-walking and modified neurological severity scores (mNSS) tests. Taken together, these findings indicate IFD as a promising lead compound for the discovery of new treatments of brain ischemia

Three-dimensionally Ordered Macroporous Structure Enabled Nanothermite Membrane of Mn2O3/Al

Mn2O3 has been selected to realize nanothermite membrane for the first time in the literature. Mn2O3/Al nanothermite has been synthesized by magnetron sputtering a layer of Al film onto three-dimensionally ordered macroporous (3DOM) Mn2O3 skeleton. The energy release is significantly enhanced owing to the unusual 3DOM structure, which ensures Al and Mn2O3 to integrate compactly in nanoscale and greatly increase effective contact area. The morphology and DSC curve of the nanothermite membrane have been investigated at various aluminizing times. At the optimized aluminizing time of 30 min, energy release reaches a maximum of 2.09 kJ∙g−1, where the Al layer thickness plays a decisive role in the total energy release. This method possesses advantages of high compatibility with MEMS and can be applied to other nanothermite systems easily, which will make great contribution to little-known nanothermite research

Ecological distribution and population physiology defined by proteomics in a natural microbial community

Community proteomics applied to natural microbial biofilms resolves how the physiology of different populations from a model ecosystem change with measured environmental factors in situ.The initial colonists, Leptospirillum Group II bacteria, persist throughout ecological succession and dominate all communities, a pattern that resembles community assembly patterns in some macroecological systems.Interspecies interactions, and not abiotic environmental factors, demonstrate the strongest correlation to physiological changes of Leptospirillum Group II.Environmental niches of subdominant populations seem to be determined by combinations of specific sets of abiotic environmental factors

Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Timm W, Scherbart A, Boecker S, Kohlbacher O, Nattkemper TW. Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics. BMC Bioinformatics. 2008;9(1):443.Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e. g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification. Results: In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as string-based features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted features. Based on these features, two advanced machine learning methods (support vector regression and local linear maps) are shown to yield good results for this problem (Pearson correlation of 0.68 in a ten-fold cross validation). Conclusion: The techniques presented here are a useful first step going beyond the binary prediction of proteotypic peptides towards a more quantitative prediction of peak intensities. These predictions in turn will turn out to be beneficial for mass spectrometry-based quantitative proteomics

A structure filter for the Eukaryotic Linear Motif Resource

<p>Abstract</p> <p>Background</p> <p>Many proteins are highly modular, being assembled from globular domains and segments of natively disordered polypeptides. Linear motifs, short sequence modules functioning independently of protein tertiary structure, are most abundant in natively disordered polypeptides but are also found in accessible parts of globular domains, such as exposed loops. The prediction of novel occurrences of known linear motifs attempts the difficult task of distinguishing functional matches from stochastically occurring non-functional matches. Although functionality can only be confirmed experimentally, confidence in a putative motif is increased if a motif exhibits attributes associated with functional instances such as occurrence in the correct taxonomic range, cellular compartment, conservation in homologues and accessibility to interacting partners. Several tools now use these attributes to classify putative motifs based on confidence of functionality.</p> <p>Results</p> <p>Current methods assessing motif accessibility do not consider much of the information available, either predicting accessibility from primary sequence or regarding any motif occurring in a globular region as low confidence. We present a method considering accessibility and secondary structural context derived from experimentally solved protein structures to rectify this situation. Putatively functional motif occurrences are mapped onto a representative domain, given that a high quality reference SCOP domain structure is available for the protein itself or a close relative. Candidate motifs can then be scored for solvent-accessibility and secondary structure context. The scores are calibrated on a benchmark set of experimentally verified motif instances compared with a set of random matches. A combined score yields 3-fold enrichment for functional motifs assigned to high confidence classifications and 2.5-fold enrichment for random motifs assigned to low confidence classifications. The structure filter is implemented as a pipeline with both a graphical interface via the ELM resource <url>http://elm.eu.org/</url> and through a Web Service protocol.</p> <p>Conclusion</p> <p>New occurrences of known linear motifs require experimental validation as the bioinformatics tools currently have limited reliability. The ELM structure filter will aid users assessing candidate motifs presenting in globular structural regions. Most importantly, it will help users to decide whether to expend their valuable time and resources on experimental testing of interesting motif candidates.</p

Acute Lead Exposure Increases Arterial Pressure: Role of the Renin-Angiotensin System

Background: Chronic lead exposure causes hypertension and cardiovascular disease. Our purpose was to evaluate the effects of acute exposure to lead on arterial pressure and elucidate the early mechanisms involved in the development of lead-induced hypertension. Methodology/Principal Findings: Wistar rats were treated with lead acetate (i.v. bolus dose of 320 μg/Kg), and systolic arterial pressure, diastolic arterial pressure and heart rate were measured during 120 min. An increase in arterial pressure was found, and potential roles of the renin-angiotensin system, Na+,K+-ATPase and the autonomic reflexes in this change in the increase of arterial pressure found were evaluated. In anesthetized rats, lead exposure: 1) produced blood lead levels of 37±1.7 μg/dL, which is below the reference blood concentration (60 μg/dL); 2) increased systolic arterial pressure (Ct: 109±3 mmHg vs Pb: 120±4 mmHg); 3) increased ACE activity (27% compared to Ct) and Na+,K+-ATPase activity (125% compared to Ct); and 4) did not change the protein expression of the α1-subunit of Na+,K+-ATPase, AT1 and AT2. Pre-treatment with an AT1 receptor blocker (losartan, 10 mg/Kg) or an ACE inhibitor (enalapril, 5 mg/Kg) blocked the lead-induced increase of arterial pressure. However, a ganglionic blockade (hexamethonium, 20 mg/Kg) did not prevent lead's hypertensive effect. Conclusion: Acute exposure to lead below the reference blood concentration increases systolic arterial pressure by increasing angiotensin II levels due to ACE activation. These findings offer further evidence that acute exposure to lead can trigger early mechanisms of hypertension development and might be an environmental risk factor for cardiovascular diseaseThis study was supported by grants from CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico)/FAPES (Fundação de Amparo à Pesquisa do Espírito Santo)/FUNCITEC (Fundação de Ciência e Tecnologia)(39767531/07), Brazil and from MCINN (Ministerio de Ciencia e Innovación) (SAF 2009- 07201) and ISCIII (Instituto de Salud Carlos III) (Red RECAVA- Red Temática de Investigación en Enfermedades Cardiovasculares del Instituto de Salud Carlos III, RD06/0014/0011), Spai

Employing the Metabolic “Branch Point Effect” to Generate an All-or-None, Digital-like Response in Enzymatic Outputs and Enzyme-Based Sensors

Here, we demonstrate a strategy to convert the graded Michaelis−Menten response typical of unregulated enzymes into a sharp, effectively all-or-none response. We do so using an approach analogous to the “branch point effect”, a mechanism observed in naturally occurring metabolic networks in which two or more enzymes compete for the same substrate. As a model system, we used the enzymatic reaction of glucose oxidase (GOx) and coupled it to a second, nonsignaling reaction catalyzed by the higher affinity enzyme hexokinase (HK) such that, at low substrate concentrations, the second enzyme outcompetes the first, turning off the latter’s response. Above an arbitrarily selected “threshold” substrate concentration, the nonsignaling HK enzyme saturates leading to a “sudden” activation of the first signaling GOx enzyme and a far steeper dose−response curve than that observed for simple Michaelis−Menten kinetics. Using the well-known GOx-based amperometric glucose sensor to validate our strategy, we have steepen the normally graded response of this enzymatic sensor into a discrete yes/no output similar to that of a multimeric cooperative enzyme with a Hill coefficient above 13. We have also shown that, by controlling the HK reaction we can precisely tune the threshold target concentration at which we observe the enzyme output. Finally, we demonstrate the utility of this strategy for achieving effective noise attenuation in enzyme logic gates. In addition to supporting the development of biosensors with digital-like output, we envisage that the use of all-or-none enzymatic responses will also improve our ability to engineer efficient enzyme-based catalysis reactions in synthetic biology applications