Ethanol fermentation in a magnetically fluidized bed reactor with immobilized Saccharomyces cerevisiae in magnetic particles

Ethanol fermentation by immobilized Saccharomyces cerevisiae cells in magnetic particles was successfully carried out in a magnetically stabilized fluidized bed reactor (MSFBR). These immobilized magnetic particles solidified in a 2 % CaCl(2) solution were stable and had high ethanol fermentation activity. The performance of ethanol fermentation of glucose in the MSFBR was affected by initial particle loading rate, feed sugar concentration and dilution rate. The ethanol theoretical yield, productivity and concentration reached 95.3%, 26.7 g/L h and 66 g/L, respectively, at a particle loading rate of 41% and a feed dilution rate of 0.4 h(-1) with a glucose concentration of 150 g/L when the magnetic field intensity was kept in the range of 85-120 Oe. In order to use this developed MSFBR system for ethanol production from cheap raw materials, cane molasses was used as the main fermentation substrate for continuous ethanol fermentation with the immobilized S. cerevisiae cells in the reactor system. Molasses gave comparative ethanol productivity in comparison with glucose in the MSFBR, and the higher ethanol production was observed in the MSFBR than in a fluidized bed reactor (FBR) without a magnetic field.     

Miscellaneous terpenoid constituents of Abies nephrolepis and their moderate cytotoxic activities

Three monoterpenoids and two triterpenoids were isolated from Abiesnephrolepis together with 53 known terpenoids. The structures of the compounds were established by 1D and 2D NMR spectroscopy. The absolute configuration of 3-hydroxycamphane-2-carboxylic acid was established as (1S,2R,3S,4R) by Cu-Kα X-ray crystallography. All 58 isolates were tested for cytotoxic activity against four tumor cells viz. A549 (human lung adenocarcinoma), Colo205 (colon adenocarcinoma), QGY-7703 (human hepatoma) and THP-1 (human monocytic leukemia). α-Cadinol exhibited the best effects on A549, Colo205 and QGY-7703 with IC₅₀ values of 8.6, 8.1 and 4.6 μg/mL, respectively.     

Detecting temporal protein complexes from dynamic protein-protein interaction networks

Background

Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.

Results

In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.

Conclusions

Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-335) contains supplementary material, which is available to authorized users.     

Lanthanide Salts of Heteropoly Molybdotungstosilicate LnHSiMo10W2O40·xH2O (Ln?=?Pr, Nd, Sm, Gd, Tb, Dy, Yb) Binding to Bovine Serum Albumin: A Fluorescence Quenching Study

In the present work, the interaction between a series of novel lanthanide salts of heteropoly molybdotungstosilicate LnHSiMo(10)W(2)O(40)·xH(2)O (LnW(2); Ln = Pr (x = 23), Nd (x = 24), Sm (x = 26), Gd (x = 20), Tb (x = 23), Dy (x = 21), Yb (x = 25)), and bovine serum albumin (BSA) was investigated by spectroscopic approach at different temperatures under imitated physiological conditions. In the mechanism discussion, it was proved that the fluorescence quenching of BSA by LnW(2) is a result of the formation of LnW(2)-BSA complex. Binding affinity between LnW(2) and BSA was determined using Scatchard equation and the modified Stern-Volmer equation, and the corresponding electronic structure-affinity relationship were discussed. The results of thermodynamic parameters ?G, ?H, ?S at different temperatures indicate that the electrostatic interactions play a major role in LnW(2)-BSA binding process. Moreover, the enthalpy change (?H) and entropy change (?S) were in accordance with the "enthalpy-entropy compensation" equation obtained from this and previous work. Furthermore, the distance r between donor (BSA) and acceptor (LnW(2)) was obtained according to fluorescence resonance energy transfer.     

Structural and micromechanical characterization of type I collagen gels

In this paper we report a study where we use a novel optical tweezers technique to measure the local viscoelastic properties of type I collagen solutions spanning the sol-to-gel transition. We use phase contrast optical microscopy to reveal dense and sparse regions of the rigid fibril networks, and find that the spatial variations in the mechanical properties of the collagen gels closely follow the structural properties. Within the dense phase of the connected network in the gel samples, there are regions that exhibit drastically different viscoelastic properties. Within the sparse regions of the gel samples, no evidence of elasticity is found. In type I collagen gels, we find a high degree of structural inhomogeneity. The inhomogeneity in the structural properties of collagen gels and the corresponding viscoelastic properties provide benchmark measurements for the behavior of desirable biological materials, or tissue equivalents.     

Regulation of RasGRP1 function in T cell development and activation by its unique tail domain

The Ras-guanyl nucleotide exchange factor RasGRP1 plays a critical role in T cell receptor-mediated Erk activation. Previous studies have emphasized the importance of RasGRP1 in the positive selection of thymocytes, activation of T cells, and control of autoimmunity. RasGRP1 consists of a number of well-characterized domains, which it shares with its other family members; however, RasGRP1 also contains an ~200 residue-long tail domain, the function of which is unknown. To elucidate the physiological role of this domain, we generated knock-in mice expressing RasGRP1 without the tail domain. Further analysis of these knock-in mice showed that thymocytes lacking the tail domain of RasGRP1 underwent aberrant thymic selection and, following TCR stimulation, were unable to activate Erk. Furthermore, the deletion of the tail domain led to enhanced CD4(+) T cell expansion in aged mice, as well as the production of autoantibodies. Mechanistically, the tail-deleted form of RasGRP1 was not able to traffic to the cell membrane following stimulation, indicating a potential reason for its inability to activate Erk. While the DAG-binding C1 domain of RasGRP1 has long been recognized as an important factor mediating Erk activation, we have revealed the physiological relevance of the tail domain in RasGRP1 function and control of Erk signaling.     

Cloning and characterization of a gene encoding HMG-CoA reductase from Ganoderma lucidum and its functional identification in yeast

A gene encoding 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGR) was isolated from a triterpene-producing fungus, Ganoderma lucidum (Reishi or Lingzhi). This report provides the complete nucleotide sequence of the full-length cDNA encoding HMGR and its genomic DNA sequence. The cDNA of the HMGR (GenBank Accession no., EU263989) was found to contain an open reading frame (ORF) of 3,681 bp encoding a 1,226-amino-acid polypeptide, whereas the HMGR genomic DNA sequence (GenBank Accession no., EU263990) consisted of 4,262 bp and contained seven exons and six introns. The deduced amino acid sequence of G. lucidum HMGR showed significant homology to the known HMGRs from Ustilago maydis and Cryptococcus neoformans, and contained four conserved domains. Gene expression analysis showed that the expression level was relatively low in mycelia incubated for 10, 12, and 14 d, and reached the highest level in the primordia. Functional complementation of Gl-HMGR in a HMGR-deficient mutant yeast strain indicated that the cloned cDNA encoded a HMG-CoA reductase.     

A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks

Background

The accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data collected by high-throughput experimental techniques are known to be quite noisy. It is hard to achieve reliable prediction results by simply applying computational methods on PPI data. Behind protein interactions, there are protein domains that interact with each other. Therefore, based on domain-protein associations, the joint analysis of PPIs and domain-domain interactions (DDI) has the potential to obtain better performance in protein complex detection. As traditional computational methods are designed to detect protein complexes from a single PPI network, it is necessary to design a new algorithm that could effectively utilize the information inherent in multiple heterogeneous networks.

Results

In this paper, we introduce a novel multi-network clustering algorithm to detect protein complexes from multiple heterogeneous networks. Unlike existing protein complex identification algorithms that focus on the analysis of a single PPI network, our model can jointly exploit the information inherent in PPI and DDI data to achieve more reliable prediction results. Extensive experiment results on real-world data sets demonstrate that our method can predict protein complexes more accurately than other state-of-the-art protein complex identification algorithms.

Conclusions

In this work, we demonstrate that the joint analysis of PPI network and DDI network can help to improve the accuracy of protein complex detection.

     

Monte Carlo simulation for single RNA unfolding by force

Using polymer elastic theory and known RNA free energies, we construct a Monte Carlo algorithm to simulate the single RNA folding and unfolding by mechanical force on the secondary structure level. For the constant force ensemble, we simulate the force-extension curves of the P5ab, P5abc deltaA, and P5abc molecules in equilibrium. For the constant extension ensemble, we focus on the mechanical behaviors of the RNA P5ab molecule, which include the unfolding force dependence on the pulling speed, the force-hysteresis phenomenon, and the coincidence of stretching-relaxing force-curves in thermal equilibrium. We particularly simulate the time traces of the end-to-end distance of the P5ab under the constant force in equilibrium, which also have been recorded in the recent experiment. The reaction rate constants for the folding and unfolding are calculated. Our results show that the agreement between the simulation and the experimental measurements is satisfactory.