Insoluble glycogen, a metabolizable internal adsorbent, decreases the lethality of endotoxin shock in rats

Insoluble glycogen is an enzymatically modified form of naturally occurring soluble glycogen with a great adsorbing capacity. It can be metabolized by phagocytes to glucose. In this study we used insoluble glycogen intravenously in the experimental endotoxin shock of rats. Wistar male rats were sensitized to endotoxin by Pb acetate. The survival of rats were compared in groups of animals endotoxin shock treated and non-treated with insoluble glycogen. Furthermore, we have determined in vitro the binding capacity of insoluble glycogen for endotoxin, tumour necrosis factor alpha, interleukin-1 and secretable phospholipase A2. Use of 10 mg/kg dose of insoluble glycogen could completely prevent the lethality of shock induced by LD50 quantity of endotoxin in rats. All animals treated survived. Insoluble glycogen is a form of ‘metabolizable internal adsorbents’. It can potentially be used for treatment of septic shock

Helix compactness and stability: Electron structure calculations of conformer dependent thermodynamic functions

Structure, stability, cooperativity and molecular packing of two major backbone forms: 310-helix and β-strand are investigated. Long models HCO-(Xxx)n-NH2 Xxx = Gly and (l-)Ala, n ⩽ 34, are studied at two levels of theory including the effect of dispersion forces. Structure and folding preferences are established, the length modulated cooperativity and side-chain determined fold compactness is quantified. By monitoring ΔG°β→α rather than the electronic energy, ΔEβ→α, it appears that Ala is a much better helix forming residue than Gly. The achiral Gly forms a more compact 310-helix than any chiral amino acid residue probed here for l-Ala

Epistasis for Growth Rate and Total Metabolic Flux in Yeast

Studies of interactions between gene deletions repeatedly show that the effect of epistasis on the growth of yeast cells is roughly null or barely positive. These observations relate generally to the pace of growth, its costs in terms of required metabolites and energy are unknown. We measured the maximum rate at which yeast cultures grow and amounts of glucose they consume per synthesized biomass for strains with none, single, or double gene deletions. Because all strains were maintained under a fermentative mode of growth and thus shared a common pattern of metabolic processes, we used the rate of glucose uptake as a proxy for the total flux of metabolites and energy. In the tested sample, the double deletions showed null or slightly positive epistasis both for the mean growth and mean flux. This concordance is explained by the fact that average efficiency of converting glucose into biomass was nearly constant, that is, it did not change with the strength of growth effect. Individual changes in the efficiency caused by gene deletions did have a genetic basis as they were consistent over several environments and transmitted between single and double deletion strains indicating that the efficiency of growth, although independent of its rate, was appreciably heritable. Together, our results suggest that data on the rate of growth can be used as a proxy for the rate of total metabolism when the goal is to find strong individual interactions or estimate the mean epistatic effect. However, it may be necessary to assay both growth and flux in order to detect smaller individual effects of epistasis

Posterior Association Networks and Functional Modules Inferred from Rich Phenotypes of Gene Perturbations

Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental and computational framework to efficiently address this challenge by limiting the ‘search space’ for important genetic interactions. We propose to integrate rich phenotypes of multiple single gene perturbations to robustly predict functional modules, which can subsequently be subjected to further experimental investigations such as combinatorial gene silencing. We present posterior association networks (PANs) to predict functional interactions between genes estimated using a Bayesian mixture modelling approach. The major advantage of this approach over conventional hypothesis tests is that prior knowledge can be incorporated to enhance predictive power. We demonstrate in a simulation study and on biological data, that integrating complementary information greatly improves prediction accuracy. To search for significant modules, we perform hierarchical clustering with multiscale bootstrap resampling. We demonstrate the power of the proposed methodologies in applications to Ewing's sarcoma and human adult stem cells using publicly available and custom generated data, respectively. In the former application, we identify a gene module including many confirmed and highly promising therapeutic targets. Genes in the module are also significantly overrepresented in signalling pathways that are known to be critical for proliferation of Ewing's sarcoma cells. In the latter application, we predict a functional network of chromatin factors controlling epidermal stem cell fate. Further examinations using ChIP-seq, ChIP-qPCR and RT-qPCR reveal that the basis of their genetic interactions may arise from transcriptional cross regulation. A Bioconductor package implementing PAN is freely available online at http://bioconductor.org/packages/release/bioc/html/PANR.html

Step-wise evolution of complex chemical defenses in millipedes: a phylogenomic approach

With fossil representatives from the Silurian capable of respiring atmospheric oxygen, millipedes are among the oldest terrestrial animals, and likely the first to acquire diverse and complex chemical defenses against predators. Exploring the origin of complex adaptive traits is critical for understanding the evolution of Earth’s biological complexity, and chemical defense evolution serves as an ideal study system. The classic explanation for the evolution of complexity is by gradual increase from simple to complex, passing through intermediate “stepping stone� states. Here we present the first phylogenetic-based study of the evolution of complex chemical defenses in millipedes by generating the largest genomic-based phylogenetic dataset ever assembled for the group. Our phylogenomic results demonstrate that chemical complexity shows a clear pattern of escalation through time. New pathways are added in a stepwise pattern, leading to greater chemical complexity, independently in a number of derived lineages. This complexity gradually increased through time, leading to the advent of three distantly related chemically complex evolutionary lineages, each uniquely characteristic of each of the respective millipede groups