Assessment of the Multifunctional Behavior of Lupin Peptide P7 and Its Metabolite Using an Integrated Strategy.
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ABSTRACT:
SUBMITTER: Lammi C
PROVIDER: S-EPMC7997369 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:P5 (LILPKHSDAD) is a hypocholesterolemic peptide from lupin protein with a multi-target activity, since it inhibits both 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCoAR) and proprotein convertase subtilisin/kexin type-9 (PCSK9). This work shows that, during epithelial transport experiments, the metabolic transformation mediated by intestinal peptidases produces two main detected peptides, ILPKHSDAD (P5-frag) and LPKHSDAD (P5-met), and that both P5 and P5-met are linearly absorbed by differentiated human intestinal Caco-2 cells. Extensive comparative structural, biochemical, and cellular characterizations of P5-met and the parent peptide P5 demonstrate that both peptides have unique characteristics and share the same mechanisms of action. In fact, they exert an intrinsically multi-target behavior being able to regulate cholesterol metabolism by modulating different pathways. The results of this study also highlight the dynamic nature of bioactive peptides that may be modulated by the biological systems they get in contact with.
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Project description:GQEQSHQDEGVIVR (T9) is a peptide originated by the tryptic digestion of lupin β-conglutin that is absorbed in human intestinal Caco-2 cells. A previous study has shown that T9 impairs the protein-protein interaction between mutant D374Y Proprotein Convertase Subtilisin/Kexin 9 (PCSK9D374Y) and the low-density lipoprotein receptor (LDLR), thus exerting a hypocholesterolemic effect. Moreover, a bioinformatic study predicting that T9 may potentially act as an inhibitor of 3-hydroxy-3-methylglutaryl CoA reductase (HMGCoAR), has suggested a complementary cholesterol-lowering activity. The present study demonstrates that T9 inhibits in vitro the HMGCoAR functionality with an IC50 value of 99.5 ± 0.56 µM. Through the inhibition of either HMGCoAR or PCSK9D374Y activities, T9 enhances the LDLR protein levels leading to an improved ability of HepG2 cells transfected with the mutant PCSK9D374Y-FLAG plasmid to uptake extracellular LDL with a final cholesterol-lowering effect. In addition, T9 modulates the PCSK9D374Y signaling pathway in transfected HepG2 cells leading to a decrease of PCSK9D374Y and HNF-1α protein levels. All these results indicate that the hypocholesterolemic effects of T9 are due to a dual mechanism of action involving either the modulation of the PCSK9D374Y or LDLR pathways. This may represent an added value from a therapeutic point of view.
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Project description:Different empirical models have been proposed in the literature to determine the fatigue strength as a function of lifetime, according to linear, parabolic, hyperbolic, exponential, and other shaped solutions. However, most of them imply a deterministic definition of the S-N field, despite the inherent scatter exhibited by the fatigue results issuing from experimental campaigns. In this work, the Bayesian theory is presented as a suitable way not only to convert deterministic into probabilistic models, but to enhance probabilistic fatigue models with the statistical distribution of the percentile curves of failure probability interpreted as their confidence bands. After a short introduction about the application of the Bayesian methodology, its advantageous implementation on an OpenSource software named OpenBUGS is presented. As a practical example, this methodology has been applied to the statistical analysis of the Maennig fatigue S-N field data using the Weibull regression model proposed by Castillo and Canteli, which allows the confidence bands of the S-N field to be determined as a function of the already available test results. Finally, a question of general interest is discussed as that concerned to the recommendable number of tests to carry out in an experimental S-N fatigue program for achieving "reliable or confident" results to be subsequently used in component design, which, generally, is not adequately and practically addressed by researchers.
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Project description:Unlike native proteins that are amenable to structural analysis at atomic resolution, unfolded proteins occupy a manifold of dynamically interconverting structures. Defining the conformations of unfolded proteins is of significant interest and importance, for folding studies and for understanding the properties of intrinsically disordered proteins. Short chain protein fragments, i.e., oligopeptides, provide an excellent test-bed in efforts to define the conformational ensemble of unfolded chains. Oligomers of alanine in particular have been extensively studied as minimalist models of the intrinsic conformational preferences of the peptide backbone. Even short alanine peptides occupy an ensemble of substates that are distinguished by small free energy differences, so that the problem of quantifying the conformational preferences of the backbone remains a fundamental challenge in protein biophysics. Here, we demonstrate an integrated computational-experimental-Bayesian approach to quantify the conformational ensembles of the model trialanine peptide in water. In this approach, peptide conformational substates are first determined objectively by clustering molecular dynamics snapshots based on both structural and dynamic information. Next, a set of spectroscopic data for each conformational substate is computed. Finally, a Bayesian statistical analysis of both experimentally measured spectroscopic data and computational results is carried out to provide a current best estimate of the substate population ensemble together with corresponding confidence intervals. This distribution of substates can be further systematically refined with additional high-quality experimental data and more accurate computational modeling. Using an experimental data set of NMR coupling constants, we have also applied this approach to characterize the conformation ensemble of trivaline in water.
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Project description:Polyphosphate (polyP), a polymer of orthophosphate (PO4 3-) of varying lengths, has been identified in all kingdoms of life. It can serve as a source of chemical bond energy (phosphoanhydride bond) that may have been used by biological systems prior to the evolution of ATP. Intracellular polyP is mainly stored as granules in specific vacuoles called acidocalcisomes, and its synthesis and accumulation appear to impact a myriad of cellular functions. It serves as a reservoir for inorganic PO4 3- and an energy source for fueling cellular metabolism, participates in maintaining adenylate and metal cation homeostasis, functions as a scaffold for sequestering cations, exhibits chaperone function, covalently binds to proteins to modify their activity, and enables normal acclimation of cells to stress conditions. PolyP also appears to have a role in symbiotic and parasitic associations, and in higher eukaryotes, low polyP levels seem to impact cancerous proliferation, apoptosis, procoagulant and proinflammatory responses and cause defects in TOR signaling. In this review, we discuss the metabolism, storage, and function of polyP in photosynthetic microbes, which mostly includes research on green algae and cyanobacteria. We focus on factors that impact polyP synthesis, specific enzymes required for its synthesis and degradation, sequestration of polyP in acidocalcisomes, its role in cellular energetics, acclimation processes, and metal homeostasis, and then transition to its potential applications for bioremediation and medical purposes.
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Project description:A new and general methodology is described for the targeted enrichment and subsequent direct mass spectrometric characterization of sample subsets bearing various chemical functionalities from highly complex mixtures of biological origin. Specifically, sample components containing a chemical moiety of interest are first selectively labeled with perfluoroalkyl groups, and the entire sample is then applied to a perfluoroalkyl-silylated porous silicon (pSi) surface. Due to the unique hydrophobic and lipophobic nature of the perfluorinated tags, unlabeled sample components are readily removed using simple surface washes, and the enriched sample fraction can then directly be analyzed by desorption/ionization on silicon mass spectrometry (DIOS-MS). Importantly, this fluorous-based enrichment methodology provides a single platform that is equally applicable to both peptide as well as small molecule focused applications. The utility of this technique is demonstrated by the enrichment and mass spectrometric analysis of both various peptide subsets from protein digests as well as amino acids from serum.
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Project description:Functional connectivity analysis of resting state blood oxygen level-dependent (BOLD) functional MRI is widely used for noninvasively studying brain functional networks. Recent findings have indicated, however, that even small (?1 mm) amounts of head movement during scanning can disproportionately bias connectivity estimates, despite various preprocessing efforts. Further complications for interregional connectivity estimation from time domain signals include the unaccounted reduction in BOLD degrees of freedom related to sensitivity losses from high subject motion. To address these issues, we describe an integrated strategy for data acquisition, denoising, and connectivity estimation. This strategy builds on our previously published technique combining data acquisition with multiecho (ME) echo planar imaging and analysis with spatial independent component analysis (ICA), called ME-ICA, which distinguishes BOLD (neuronal) and non-BOLD (artifactual) components based on linear echo-time dependence of signals-a characteristic property of BOLD T*2 signal changes. Here we show for 32 control subjects that this method provides a physically principled and nearly operator-independent way of removing complex artifacts such as motion from resting state data. We then describe a robust estimator of functional connectivity based on interregional correlation of BOLD-independent component coefficients. This estimator, called independent components regression, considerably simplifies statistical inference for functional connectivity because degrees of freedom equals the number of independent coefficients. Compared with traditional connectivity estimation methods, the proposed strategy results in fourfold improvements in signal-to-noise ratio, functional connectivity analysis with improved specificity, and valid statistical inference with nominal control of type 1 error in contrasts of connectivity between groups with different levels of subject motion.
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Project description:Viral infection is an early stage of its life cycle and represents a promising target for antiviral drug development. Here we designed and characterized three peptide inhibitors of hepatitis C virus (HCV) infection based on the structural features of the membrane-associated p7 polypeptide of HCV. The three peptides exhibited low toxicity and high stability while potently inhibiting initial HCV infection and suppressed established HCV infection at non-cytotoxic concentrations in vitro. The most efficient peptide (designated H2-3), which is derived from the H2 helical region of HCV p7 ion channel, inhibited HCV infection by inactivating both intracellular and extracellular viral particles. The H2-3 peptide inactivated free HCV with an EC50 (50% effective concentration) of 82.11 nm, which is >1000-fold lower than the CC50 (50% cytotoxic concentration) of Huh7.5.1 cells. H2-3 peptide also bound to cell membrane and protected host cells from viral infection. The peptide H2-3 did not alter the normal electrophysiological profile of the p7 ion channel or block viral release from Huh7.5.1 cells. Our work highlights a new anti-viral peptide design strategy based on ion channel, giving the possibility that ion channels are potential resources to generate antiviral peptides.
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Project description:BackgroundAt a regional or continental scale, the characterization of environmental health inequities (EHI) expresses the idea that populations are not equal in the face of pollution. It implies an analysis be conducted in order to identify and manage the areas at risk of overexposure where an increasing risk to human health is suspected. The development of methods is a prerequisite for implementing public health activities aimed at protecting populations.MethodsThis paper presents the methodological framework developed by INERIS (French National Institute for Industrial Environment and Risks) to identify a common framework for a structured and operationalized assessment of human exposure. An integrated exposure assessment approach has been developed to integrate the multiplicity of exposure pathways from various sources, through a series of models enabling the final exposure of a population to be defined.ResultsMeasured data from environmental networks reflecting the actual contamination of the environment are used to gauge the population's exposure. Sophisticated methods of spatial analysis are applied to include additional information and take benefit of spatial and inter-variable correlation to improve data representativeness and characterize the associated uncertainty. Integrated approaches bring together all the information available for assessing the source-to-human-dose continuum using a Geographic Information System, multimedia exposure and toxicokinetic model.DiscussionOne of the objectives of the integrated approach was to demonstrate the feasibility of building complex realistic exposure scenarios satisfying the needs of stakeholders and the accuracy of the modelling predictions at a fine spatial-temporal resolution. A case study is presented to provide a specific application of the proposed framework and how the results could be used to identify an overexposed population.ConclusionThis framework could be used for many purposes, such as mapping EHI, identifying vulnerable populations and providing determinants of exposure to manage and plan remedial actions and to assess the spatial relationships between health and the environment to identify factors that influence the variability of disease patterns.
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