Plasma protein binding in uremia: Extraction and characterization of an inhibitor

Plasma protein binding in uremia: Extraction and characterization of an inhibitor. The impairment of binding of drugs and other substances to serum albumin in patients with uremia can be restored to normal or near normal levels by adsorption with charcoal or synthetic polymers at pH 3. We used a nonionic poly-styrene-divinylbenzene copolymer to treat uremic plasma at pH 3. We observed a marked improvement of binding. Subsequent elution of this resin with ethanol produced a substance that, when dried and recombined with normal plasma, caused dose-dependent impairment of phenytoin and tryptophan binding. Restoration of normal binding affinity occurred after retreatment of this abnormalized plasma with resin at pH 3. Plasma and pleural fluid exudate from patients with uremia yielded, after extraction by the above technique, an inhibitor(s) of phenytoin binding in amounts averaging five times that extracted from equal volumes of normal plasma. This inhibitor (Ix) is water soluble, heat stable, and dialyzable across cellophane membranes. Unlike fatty acids, which can also interfere with binding, Ix partitions primarily in the water phase in solvent partition studies but undergoes a sharp transition in the pH 4 to 5 range, suggesting the presence of a carboxyl group. These findings lend further support to the hypothesis that a retained ligand(s) is responsible for impaired plasma binding associated with uremia and suggests a role for organic acids known to accumulate in renal failure.Liaison aux protéines plasmatiques dans l'urémie: Extraction et caractérisation d'un inhibiteur. L'altération de la liaison de drogues et d'autres substances à l'albumine sérique au cours de l'urémie peut être complètement ou presque complètement supprimée par l'adsorption sur du charbon ou des polymères synthétiques à pH 3. Nous avons utilisé un co-polymère non ionique polystyrènedivinylbenzène pour traiter le plasma urémique à pH 3 et observé une amélioration importante de la liaison. L'élution ultérieure de cette résine par l'éthanol produit une substance qui, lorsqu'elle est séchée et recombinée avec du plasma normal, détermine une altération dose dépendante de la liaison de la diphényl-hydantoïne et du tryptophane. La récupération d'une affinité de liaison normale a été obtenue après un nouveau traitement du plasma par la résine à pH 3. Le plasma et le liquide pleural de malades urémiques a donné, après extraction par la technique ci-dessus, un inhibiteur(s) de la liaison de la phénylhydantoïne en quantité cinq fois plus grande que celle extraite devolumes identiques de plasma normal. Cet inhibiteur (Ix) est soluble dans l'eau, thermostable et dialysable à travers des membranes de cellophane. A la différence des acides gras, qui peuvent aussi interférer avec la liaison, Ix passe dans la phase aqueuse au cours des études de partition dans des solvants, mais subit une transition brusque dans la gamme de pH 4 à 5, ce qui suggère la présence d'un groupe carboxyle. Ces constatations apportent des arguments supplémentaires à l'hypothèse selon laquelle un ligand (ou des ligands) retenus au cours de l'urémie est responsable de l'altération de la liaison plasmatique et suggère un rôle des acides organiques dont l'accumulation est connue dans l'insuffisance rénale

Coalescence Efficiency of Surface Modified PBT Meltblown Nonwovens in the Separation of Water from Diesel Fuel Containing Surfactants

Removal of water from diesel fuel is essential to ensure efficient operation of High-Pressure Common-Rail (HPCR) fuel injection systems used in modern diesel engines. The presence of surfactants in modern fuels (including biofuels) can create conditions in which the interfacial tension between water and fuel is reduced, leading to coalescing media being “disarmed” and less effective in separation. To elucidate this phenomenon in industrially available depth coalescing media, the coalescence efficiency of surface-modified poly (butylene terephthalate) (PBT) meltblown nonwoven fabrics possessing a wide range of wetting behaviours was studied. Tuning of the wettability was accomplished by alkaline hydrolysis of the medium. Using reference grade diesel fuel with and without added surfactants, the coalescence efficiency and quality factor were studied by means of a purpose-built coalescence test rig. The coalescence efficiency was found to depend on both the fuel composition and the wettability of the treated PBT and the optimum efficiency for each test fuel required a difference in wettability of the PBT. For reference grade diesel, increasing the wettability to ‘intermediate’ level improved coalescence efficiency, but the quality factor can be negatively affected by water droplet retention within the medium. The reduced quality factor associated with hydrophilic media was even more pronounced in fuels containing surfactants due to increased pressure drop and re-emulsification of the fuel in water. These findings highlight the practical challenges that exist in engineering a “universal” coalescing medium suitable for removing water from diesel fuels containing surfactants, based solely on the modulation of fibre wettability and hydrophilicity

Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in the present work for the successful visualization of the machine-part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure

Non-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolution

Models of protein evolution currently come in two flavors: generalist and specialist. Generalist models (e.g. PAM, JTT, WAG) adopt a one-size-fits-all approach, where a single model is estimated from a number of different protein alignments. Specialist models (e.g. mtREV, rtREV, HIVbetween) can be estimated when a large quantity of data are available for a single organism or gene, and are intended for use on that organism or gene only. Unsurprisingly, specialist models outperform generalist models, but in most instances there simply are not enough data available to estimate them. We propose a method for estimating alignment-specific models of protein evolution in which the complexity of the model is adapted to suit the richness of the data. Our method uses non-negative matrix factorization (NNMF) to learn a set of basis matrices from a general dataset containing a large number of alignments of different proteins, thus capturing the dimensions of important variation. It then learns a set of weights that are specific to the organism or gene of interest and for which only a smaller dataset is available. Thus the alignment-specific model is obtained as a weighted sum of the basis matrices. Having been constrained to vary along only as many dimensions as the data justify, the model has far fewer parameters than would be required to estimate a specialist model. We show that our NNMF procedure produces models that outperform existing methods on all but one of 50 test alignments. The basis matrices we obtain confirm the expectation that amino acid properties tend to be conserved, and allow us to quantify, on specific alignments, how the strength of conservation varies across different properties. We also apply our new models to phylogeny inference and show that the resulting phylogenies are different from, and have improved likelihood over, those inferred under standard models

HIV-Specific Probabilistic Models of Protein Evolution

Comparative sequence analyses, including such fundamental bioinformatics techniques as similarity searching, sequence alignment and phylogenetic inference, have become a mainstay for researchers studying type 1 Human Immunodeficiency Virus (HIV-1) genome structure and evolution. Implicit in comparative analyses is an underlying model of evolution, and the chosen model can significantly affect the results. In general, evolutionary models describe the probabilities of replacing one amino acid character with another over a period of time. Most widely used evolutionary models for protein sequences have been derived from curated alignments of hundreds of proteins, usually based on mammalian genomes. It is unclear to what extent these empirical models are generalizable to a very different organism, such as HIV-1–the most extensively sequenced organism in existence. We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution. Our procedure pools the information from multiple sequence alignments, and provided software implementation can be run efficiently in parallel on a computer cluster. We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches. Our models had a consistently superior fit relative to the best existing models and to parameter-rich data-driven models when benchmarked on independent HIV-1 alignments, demonstrating evolutionary biases in amino-acid substitution that are unique to HIV, and that are not captured by the existing models. The scoring matrices derived from the models showed a marked difference from common amino-acid scoring matrices. The use of an appropriate evolutionary model recovered a known viral transmission history, whereas a poorly chosen model introduced phylogenetic error. We argue that our model derivation procedure is immediately applicable to other organisms with extensive sequence data available, such as Hepatitis C and Influenza A viruses

A Naturally Occurring Polymorphism at Drosophila melanogaster Lim3 Locus, a Homolog of Human LHX3/4, Affects Lim3 Transcription and Fly Lifespan

Lim3 encodes an RNA polymerase II transcription factor with a key role in neuron specification. It was also identified as a candidate gene that affects lifespan. These pleiotropic effects indicate the fundamental significance of the potential interplay between neural development and lifespan control. The goal of this study was to analyze the causal relationships between Lim3 structural variations, and gene expression and lifespan changes, and to provide insights into regulatory pathways controlling lifespan. Fifty substitution lines containing second chromosomes from a Drosophila natural population were used to analyze the association between lifespan and sequence variation in the 5′-regulatory region, and first exon and intron of Lim3A, in which we discovered multiple transcription start sites (TSS). The core and proximal promoter organization for Lim3A and a previously unknown mRNA named Lim3C were described. A haplotype of two markers in the Lim3A regulatory region was significantly associated with variation in lifespan. We propose that polymorphisms in the regulatory region affect gene transcription, and consequently lifespan. Indeed, five polymorphic markers located within 380 to 680 bp of the Lim3A major TSS, including two markers associated with lifespan variation, were significantly associated with the level of Lim3A transcript, as evaluated by real time RT-PCR in embryos, adult heads, and testes. A naturally occurring polymorphism caused a six-fold change in gene transcription and a 25% change in lifespan. Markers associated with long lifespan and intermediate Lim3A transcription were present in the population at high frequencies. We hypothesize that polymorphic markers associated with Lim3A expression are located within the binding sites for proteins that regulate gene function, and provide general rather than tissue-specific regulation of transcription, and that intermediate levels of Lim3A expression confer a selective advantage and longer lifespan

Hyperactive S6K1 Mediates Oxidative Stress and Endothelial Dysfunction in Aging: Inhibition by Resveratrol

Mammalian target of rapamycin (mTOR)/S6K1 signalling emerges as a critical regulator of aging. Yet, a role of mTOR/S6K1 in aging-associated vascular endothelial dysfunction remains unknown. In this study, we investigated the role of S6K1 in aging-associated endothelial dysfunction and effects of the polyphenol resveratrol on S6K1 in aging endothelial cells. We show here that senescent endothelial cells displayed higher S6K1 activity, increased superoxide production and decreased bioactive nitric oxide (NO) levels than young endothelial cells, which is contributed by eNOS uncoupling. Silencing S6K1 in senescent cells reduced superoxide generation and enhanced NO production. Conversely, over-expression of a constitutively active S6K1 mutant in young endothelial cells mimicked endothelial dysfunction of the senescent cells through eNOS uncoupling and induced premature cellular senescence. Like the mTOR/S6K1 inhibitor rapamycin, resveratrol inhibited S6K1 signalling, resulting in decreased superoxide generation and enhanced NO levels in the senescent cells. Consistent with the data from cultured cells, an enhanced S6K1 activity, increased superoxide generation, and decreased bioactive NO levels associated with eNOS uncoupling were also detected in aortas of old WKY rats (aged 20–24 months) as compared to the young animals (1–3 months). Treatment of aortas of old rats with resveratrol or rapamycin inhibited S6K1 activity, oxidative stress, and improved endothelial NO production. Our data demonstrate a causal role of the hyperactive S6K1 in eNOS uncoupling leading to endothelial dysfunction and vascular aging. Resveratrol improves endothelial function in aging, at least in part, through inhibition of S6K1. Targeting S6K1 may thus represent a novel therapeutic approach for aging-associated vascular disease

Pro-autophagic signal induction by bacterial pore-forming toxins

Pore-forming toxins (PFT) comprise a large, structurally heterogeneous group of bacterial protein toxins. Nucleated target cells mount complex responses which allow them to survive moderate membrane damage by PFT. Autophagy has recently been implicated in responses to various PFT, but how this process is triggered is not known, and the significance of the phenomenon is not understood. Here, we show that S. aureus α-toxin, Vibrio cholerae cytolysin, streptolysin O and E. coli haemolysin activate two pathways leading to autophagy. The first pathway is triggered via AMP-activated protein kinase (AMPK). AMPK is a major energy sensor which induces autophagy by inhibiting the target of rapamycin complex 1 (TORC1) in response to a drop of the cellular ATP/AMP-ratio, as is also observed in response to membrane perforation. The second pathway is activated by the conserved eIF2α-kinase GCN2, which causes global translational arrest and promotes autophagy in response to starvation. The latter could be accounted for by impaired amino acid transport into target cells. Notably, PKR, an eIF2α-kinase which has been implicated in autophagy induction during viral infection, was also activated upon membrane perforation, and evidence was obtained that phosphorylation of eIF2α is required for the accumulation of autophagosomes in α-toxin-treated cells. Treatment with 3-methyl-adenine inhibited autophagy and disrupted the ability of cells to recover from sublethal attack by S. aureus α-toxin. We propose that PFT induce pro-autophagic signals through membrane perforation–dependent nutrient and energy depletion, and that an important function of autophagy in this context is to maintain metabolic homoeostasis

CodonTest: Modeling Amino Acid Substitution Preferences in Coding Sequences

Codon models of evolution have facilitated the interpretation of selective forces operating on genomes. These models, however, assume a single rate of non-synonymous substitution irrespective of the nature of amino acids being exchanged. Recent developments have shown that models which allow for amino acid pairs to have independent rates of substitution offer improved fit over single rate models. However, these approaches have been limited by the necessity for large alignments in their estimation. An alternative approach is to assume that substitution rates between amino acid pairs can be subdivided into rate classes, dependent on the information content of the alignment. However, given the combinatorially large number of such models, an efficient model search strategy is needed. Here we develop a Genetic Algorithm (GA) method for the estimation of such models. A GA is used to assign amino acid substitution pairs to a series of rate classes, where is estimated from the alignment. Other parameters of the phylogenetic Markov model, including substitution rates, character frequencies and branch lengths are estimated using standard maximum likelihood optimization procedures. We apply the GA to empirical alignments and show improved model fit over existing models of codon evolution. Our results suggest that current models are poor approximations of protein evolution and thus gene and organism specific multi-rate models that incorporate amino acid substitution biases are preferred. We further anticipate that the clustering of amino acid substitution rates into classes will be biologically informative, such that genes with similar functions exhibit similar clustering, and hence this clustering will be useful for the evolutionary fingerprinting of genes