The minimal α‐crystallin domain of Mj Hsp16.5 is functional at non‐heat‐shock conditions

The small heat shock protein (sHSP) from Methanococcus jannaschii (Mj Hsp16.5) forms a monodisperse 24mer and each of its monomer contains two flexible N‐ and C‐terminals and a rigid α‐crystallin domain with an extruding β‐strand exchange loop. The minimal α‐crystallin domain with a β‐sandwich fold is conserved in sHSP family, while the presence of the β‐strand exchange loop is divergent. The function of the β‐strand exchange loop and the minimal α‐crystallin domain of Mj Hsp16.5 need further study. In the present study, we constructed two fragment‐deletion mutants of Mj Hsp16.5, one with both the N‐ and C‐terminals deleted (ΔNΔC) and the other with a further deletion of the β‐strand exchange loop (ΔNΔLΔC). ΔNΔC existed as a dimer in solution. In contrast, the minimal α‐crystallin domain ΔNΔLΔC became polydisperse in solution and exhibited more efficient chaperone‐like activities to prevent amorphous aggregation of insulin B chain and fibril formation of the amyloidogenic peptide dansyl‐SSTSAA‐W than the mutant ΔNΔC and the wild type did. The hydrophobic probe binding experiments indicated that ΔNΔLΔC exposed much more hydrophobic surface than ΔNΔC. Our study also demonstrated that Mj Hsp16.5 used different mechanisms for protecting different substrates. Though Mj Hsp16.5 formed stable complexes with substrates when preventing thermal aggregation, no complexes were detected when preventing aggregation under non‐heat‐shock conditions. Proteins 2014; 82:1156–1167. © 2013 Wiley Periodicals, Inc.     

人工髋关节置换术在320例股骨头缺血性坏死中的治疗体会

目的：探讨人工髋关节置换术在治疗股骨头缺血性坏死（ANFH）中的临床疗效。方法：选择2007年2月-2011年2月我院收治的320例（340髋）股骨头缺血性坏死患者,均采用人工髋关节置换术对患者进行治疗,其中160例（172髋）患者应用骨水泥型假体进行治疗,另外160例（168）患者采用非骨水泥型假体进行治疗。采用Harris评分对患者手术前后的髋关节功能情况进行评价,并比较骨水泥治疗组和非骨水泥治疗组的临床疗效。结果：患者均获得随访,随访时间为3～18个月。全部患者手术后的Harris评分明显高于手术前,差异有统计学意义（P〈0．05）。骨水泥治疗组和非骨水泥治疗组在术后出血量、术后Harris评分及住院时间方面的差异无统计学意义（P〉0．05）,但非骨水泥治疗组的并发症发生率明显低于骨水泥治疗组（P〈0．05）。结论：采用人工髋关节置换术治疗ANFH疗效显著,能明显改善患者的生活质量,骨水泥型假体与非骨水泥型假体的治疗效果相当,应根据患者的具体情况进行合理的选择。     

Cryptobiosis--a peculiar state of biological organization

David Keilin (Proc. Roy. Soc. Lond. B, 150, 1959, 149–191) coined the term ‘cryptobiosis’ (hidden life) and defined it as ‘the state of an organism when it shows no visible signs of life and when its metabolic activity becomes hardly measurable, or comes reversibly to a standstill.’ I consider selected aspects of the 300 year history of research on this unusual state of biological organization. Cryptobiosis is peculiar in the sense that organisms capable of achieving it exhibit characteristics that differ dramatically from those of living ones, yet they are not dead either, so one may propose that cryptobiosis is a unique state of biological organization. I focus chiefly on animal anhydrobiosis, achieved by the reversible loss of almost all the organism's water. The adaptive biochemical and biophysical mechanisms allowing this to take place involve the participation of large concentrations of polyhydroxy compounds, chiefly the disaccharides trehalose or sucrose. Stress (heat shock) proteins might also be involved, although the details are poorly understood and seem to be organism-specific. Whether the removal of molecular oxygen (anoxybiosis) results in the reversible cessation of metabolism in adapted organisms is considered, with the result being ‘yes and no’, depending on how one defines metabolism. Basic research on cryptobiosis has resulted in unpredicted applications that are of substantial benefit to the human condition and a few of these are described briefly.     

Various strategies for obtaining artificial hemoproteins: From “hemoabzymes” to “hemozymes”

The design of artificial hemoproteins that could lead to new biocatalysts for selective oxidation reactions of organic compounds presents a huge interest especially in pharmacology, both for a better understanding of the metabolic profile of drugs and for the synthesis of enantiomerically pure molecules that could be involved in the design of drugs.The present results show that the so-called “host-guest strategy” that involves the non-covalent incorporation of anionic water-soluble iron-porphyrins into xylanase A from Streptomyces lividans, a low cost protein, leads to such an artificial hemoprotein that is able to perform the stereoselective oxidation of sulfides.     

A cultured human neural network operates a robotic actuator

The development of bio-electronic prostheses, hybrid human-electronics devices and bionic robots has been the aim of many researchers. Although neurophysiologic processes have been widely investigated and bio-electronics has developed rapidly, the dynamics of a biological neuronal network that receive sensory inputs, store and control information is not yet understood. Toward this end, we have taken an interdisciplinary approach to study the learning and response of biological neural networks to complex stimulation patterns. This paper describes the design, execution, and results of several experiments performed in order to investigate the behavior of complex interconnected structures found in biological neural networks. The experimental design consisted of biological human neurons stimulated by parallel signal patterns intended to simulate complex perceptions. The response patterns were analyzed with an innovative artificial neural network (ANN), called ITSOM (Inductive Tracing Self Organizing Map). This system allowed us to decode the complex neural responses from a mixture of different stimulations and learned memory patterns inherent in the cell colonies. In the experiment described in this work, neurons derived from human neural stem cells were connected to a robotic actuator through the ANN analyzer to demonstrate our ability to produce useful control from simulated perceptions stimulating the cells. Preliminary results showed that in vitro human neuron colonies can learn to reply selectively to different stimulation patterns and that response signals can effectively be decoded to operate a minirobot. Lastly the fascinating performance of the hybrid system is evaluated quantitatively and potential future work is discussed.     

Modelling evolutionary cell behaviour using neural networks: Application to tumour growth

In this paper, we present a modelling framework for cellular evolution that is based on the notion that a cell’s behaviour is driven by interactions with other cells and its immediate environment. We equip each cell with a phenotype that determines its behaviour and implement a decision mechanism to allow evolution of this phenotype. This decision mechanism is modelled using feed-forward neural networks, which have been suggested as suitable models of cell signalling pathways. The environmental variables are presented as inputs to the network and result in a response that corresponds to the phenotype of the cell. The response of the network is determined by the network parameters, which are subject to mutations when the cells divide. This approach is versatile as there are no restrictions on what the input or output nodes represent, they can be chosen to represent any environmental variables and behaviours that are of importance to the cell population under consideration. This framework was implemented in an individual-based model of solid tumour growth in order to investigate the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. Our results show that the oxygen concentration affects the tumour at the morphological level, but more importantly has a direct impact on the evolutionary dynamics. When the supply of oxygen is limited we observe a faster divergence away from the initial genotype, a higher population diversity and faster evolution towards aggressive phenotypes. The implementation of this framework suggests that this approach is well suited for modelling systems where evolution plays an important role and where a changing environment exerts selection pressure on the evolving population.     

Efficient Reconstruction of Metabolic Pathways by Bidirectional Chemical Search

One of the main challenges in systems biology is the establishment of the metabolome: a catalogue of the metabolites and biochemical reactions present in a specific organism. Current knowledge of biochemical pathways as stored in public databases such as KEGG, is based on carefully curated genomic evidence for the presence of specific metabolites and enzymes that activate particular biochemical reactions. In this paper, we present an efficient method to build a substantial portion of the artificial chemistry defined by the metabolites and biochemical reactions in a given metabolic pathway, which is based on bidirectional chemical search. Computational results on the pathways stored in KEGG reveal novel biochemical pathways. This work has been partially supported by the Spanish CICYT project TIN2004-07925-C03-01 GRAMMARS, by Spanish DGI projects MTM2006-07773 COMGRIO and MTM2006-15038-C02-01, and by EU project INTAS IT 04-77-7178.     

Efficient double-stranded DNA cleavage by artificial zinc-finger nucleases composed of one zinc-finger protein and a single-chain FokI dimer

Zinc-finger–FokI nucleases (ZFNs) are useful for manipulating genomic DNA, but two ZFNs are required to cleave one site of double-stranded DNA (dsDNA), which limits the choice of targets. To refine ZFN technology, we constructed artificial zinc-finger nucleases containing an artificial zinc-finger protein (AZP) and a single-chain FokI dimer with nine different peptide linkers between two FokI molecules (designated AZP–scFokI). DNA cleavage assays revealed that the AZP–scFokI variant possessing the longest peptide linker cleaved dsDNA with equal or greater reactivity than the corresponding AZP–FokI dimer. The DNA cleavage pattern of AZP–scFokI suggests that the enhanced dsDNA cleavage was due to increased formation of FokI dimer in AZP–scFokI. Furthermore, we demonstrated that AZP–scFokI site-specifically cleaved its target DNA due to the AZP moiety discriminating one base pair difference. Thus, a single AZP–scFokI molecule is able to cleave dsDNA efficiently and site-specifically, and enhances the usefulness of the ZFN approach.     

γ-turn types prediction in proteins using the two-stage hybrid neural discriminant model

Due to the slightly success of protein secondary structure prediction using the various algorithmic and non-algorithmic techniques, similar techniques have been developed for predicting γ-turns in proteins by Kaur and Raghava [2003. A neural-network based method for prediction of γ-turns in proteins from multiple sequence alignment. Protein Sci. 12, 923-929]. However, the major limitation of previous methods was inability in predicting γ-turn types. In a recent investigation we introduced a sequence based predictor model for predicting γ-turn types in proteins [Jahandideh, S., Sabet Sarvestani, A., Abdolmaleki, P., Jahandideh, M., Barfeie, M, 2007a. γ-turn types prediction in proteins using the support vector machines. J. Theor. Biol. 249, 785-790]. In the present work, in order to analyze the effect of sequence and structure in the formation of γ-turn types and predicting γ-turn types in proteins, we applied novel hybrid neural discriminant modeling procedure. As the result, this study clarified the efficiency of using the statistical model preprocessors in determining the effective parameters. Moreover, the optimal structure of neural network can be simplified by a preprocessor in the first stage of hybrid approach, thereby reducing the needed time for neural network training procedure in the second stage and the probability of overfitting occurrence decreased and a high precision and reliability obtained in this way.