Synthesis of the mating factor of Saccharomyces cerevisiae and its truncated peptides : the structure-activity relationship.

The mating factor of Saccharomyces cerevisiae, Trp-His-Trp-Leu-Gln-Leu-Lys-Pro-Gly-Gln-Pro-Met-Tyr, has been synthesized to confirm the structure of the natural mating factor. The tridecapeptide has the same biological activity as the natural mating factor. From the studies on the biological activity of its truncated peptide synthesized the minimum sequence of the peptide require for the mating factor was deduced to be as His-Trp-Leu-Gln-Leu-Lys-Pro-Gly-Gln.     

Epizootiology of baculoviruses

Analysis of published data demonstrated that the process of baculovirus epizootic depends on a set of various factors. The efficiency of the host-parasite interaction depends on the virus biological activity, multiplicity of infection, biological properties and physiological state of the insect, properties of the food plants, and environmental factors. Based on the data obtained by highly sensitive methods of molecular biology, this review summarizes the discussion of the source of viral infection in baculovirus epizootics. These data shed light on the mechanisms underlying the development of epizootic, a relatively underexplored natural phenomenon.     

A user-friendly biological workstation

Learning methods developed by artificial intelligence research teams are very efficient for biological sequences analysis but they need running on large computers accessed by terminals. These computers are interfaced with standard displays involving long and unpleasant alphanumerical data handling. The "biological work station" is a personal computer with a color graphic screen providing a user-friendly interface for the artificial intelligence learning programs running on large computers. It provides to biologist a graphical convenient tool for sequence analysis built with efficient man-machine communication methods such as multiwindows, icons and mouse selection. It allows the biologist to edit and display sequences in an efficient and natural way, showing off directly on color pictures the data and the results of learning programs.     

湿地生物连通性影响因素及测度方法研究进展

湿地是地球上最多样化和最具生产力的生态系统之一,但近年来由于人类活动的频繁干扰,自然环境以前所未有的速度变化,两者的协同作用导致湿地呈现破碎化趋势。湿地的退化、转化使得湿地生物连通性减弱。良好的湿地生物连通性有利于湿地生态系统服务功能的维持、生物多样性的保护以及生物对气候变化适应水平的提高。因此理解、评估和保护生物连通性对于湿地生态系统而言至关重要。综述了湿地生物连通性的定义、总结了生物连通发生与变化的四个相互作用因子,从结构连通性、功能连通性两个测定角度,论述动物、植物和微生物三类研究对象生物连通性的测度方法,最后从湿地生物连通的时空尺度,连通与物质循环的耦合关系,湿地生物连通性评估分析模型改进角度提出了未来的研究方向,从而确保湿地生物连通网络的完整有效。可为湿地生物多样性、湿地保护和管理提供新的思路。     

The use of evolutionary patterns in protein annotation

With genomic data skyrocketing, their biological interpretation remains a serious challenge. Diverse computational methods address this problem by pointing to the existence of recurrent patterns among sequence, structure, and function. These patterns emerge naturally from evolutionary variation, natural selection, and divergence--the defining features of biological systems--and they identify molecular events and shapes that underlie specificity of function and allosteric communication. Here we review these methods, and the patterns they identify in case studies and in proteome-wide applications, to infer and rationally redesign function.     

Multivariate statistical approach to the temporal and spatial patterns of selected bioindicators observed in the North Sea during the years 1995–1997

A comprehensive database, containing biological and chemical information, collected in the framework of the bilateral interdisciplinary MARS project (”biological indicators of natural and man-made changes in marine and coastal waters”) during the years 1995–1997 in the coastal environment of the North Sea, was subjected to a multivariate statistical evaluation. The MARS project was designated to combine a variety of approaches and to develop a set of methods for the employment of biological indicators in pollution monitoring and environmental quality assessment. In total, nine ship cruises to four coastal sampling sites were conducted; 765 fish and 384 mussel samples were analysed for biological and chemical parameters. Additional information on the chemical background at the sampling sites was derived from sediment samples, collected at each of the four sampling sites. Based on the available chemical data in sediments and black mussel (Mytilus edulis) a pollution gradient between the selected sites, was established. The chemical body burden of flounder (Platichthys flesus) from these sites, though, did not reflect this gradient equally clear. In contrast, the biological information derived from measurements in fish samples displayed significant a regional as well as a temporal pattern. A multivariate bioindicator data matrix was evaluated employing a factor analysis model to identify relations between selected biological indicators, and to improve the understanding of a regional and temporal component in the parameter response. In a second approach, applying the k-means algorithm on the data matrix, two significantly different clusters of samples, characterised by the current health status of the fish, were extracted. Using this classification a temporal, and in the second order, a less pronounced spatial effect was evident. In particular, during July 1996, a clear sign of deteriorating environmental conditions was extracted from the biological data matrix. Received: 20 June 1999 / Received in revised form: 8 November 1999 / Accepted: 8 November 1999     

定量评价天敌控害功能的生态能学方法

由于昆虫的能量完全来自于寄主,因此昆虫摄入的能量相当于食物的被取食消耗量。生态能量学方法的基本原理就是捕食性天敌完全依靠捕食猎物(害虫)而获取能量。显然,捕食性天敌摄入的能量就相当于为猎物(害虫)的被捕食消耗量,也即是捕食性天敌摄入量等于猎物(害虫)的被捕食消耗量。由此,可通过研究捕食性天敌和害虫种群的能量动态,定量分析捕食性天敌对害虫的控制作用。本文详细论述了生态能量学方法的基本原理、测算方法,并以棉田捕食性瓢虫类捕食作用为例,介绍了该方法的应用,为定量评价天敌的控害作用提供了一种新方法。     

天然活性多肽的研究进展

综述了近年来天然来源的活性多肽的研究近况,主要侧重直链肽的研究近况,包括人,动物,植物来源多肽的提取分离方法,氨基酸组成以及生物活性等方面的研究进展。     

Putting the Biological Species Concept to the Test: Using Mating Networks to Delimit Species

Although interfertility is the key criterion upon which Mayr’s biological species concept is based, it has never been applied directly to delimit species under natural conditions. Our study fills this gap. We used the interfertility criterion to delimit two closely related oak species in a forest stand by analyzing the network of natural mating events between individuals. The results reveal two groups of interfertile individuals connected by only few mating events. These two groups were largely congruent with those determined using other criteria (morphological similarity, genotypic similarity and individual relatedness). Our study, therefore, shows that the analysis of mating networks is an effective method to delimit species based on the interfertility criterion, provided that adequate network data can be assembled. Our study also shows that although species boundaries are highly congruent across methods of species delimitation, they are not exactly the same. Most of the differences stem from assignment of individuals to an intermediate category. The discrepancies between methods may reflect a biological reality. Indeed, the interfertility criterion is an environment-dependant criterion as species abundances typically affect rates of hybridization under natural conditions. Thus, the methods of species delimitation based on the interfertility criterion are expected to give results slightly different from those based on environment-independent criteria (such as the genotypic similarity criteria). However, whatever the criterion chosen, the challenge we face when delimiting species is to summarize continuous but non-uniform variations in biological diversity. The grade of membership model that we use in this study appears as an appropriate tool.     

Statistical methods for panel data from a semi-Markov process, with application to HPV

Continuous-time, multistate processes can be used to represent a variety of biological processes in the public health sciences; yet the analysis of such processes is complex when they are observed only at a limited number of time points. Inference methods for such panel data have been developed for time homogeneous Markov models, but there has been little research done for other classes of processes. We develop likelihood-based methods for panel data from a semi-Markov process, where transition intensities depend on the duration of time in the current state. The proposed methods account for possible misclassification of states. To illustrate the methods, we investigate a three- and a four-state models in detail and apply the results to model the natural history of oncogenic genital human papillomavirus infections in women.     

Utilizing logical relationships in genomic data to decipher cellular processes

The wealth of available genomic data has spawned a corresponding interest in computational methods that can impart biological meaning and context to these experiments. Traditional computational methods have drawn relationships between pairs of proteins or genes based on notions of equality or similarity between their patterns of occurrence or behavior. For example, two genes displaying similar variation in expression, over a number of experiments, may be predicted to be functionally related. We have introduced a natural extension of these approaches, instead identifying logical relationships involving triplets of proteins. Triplets provide for various discrete kinds of logic relationships, leading to detailed inferences about biological associations. For instance, a protein C might be encoded within an organism if, and only if, two other proteins A and B are also both encoded within the organism, thus suggesting that gene C is functionally related to genes A and B. The method has been applied fruitfully to both phylogenetic and microarray expression data, and has been used to associate logical combinations of protein activity with disease state phenotypes, revealing previously unknown ternary relationships among proteins, and illustrating the inherent complexities that arise in biological data.     

Geostatistics and spatial analysis in biological anthropology

A variety of methods have been used to make evolutionary inferences based on the spatial distribution of biological data, including reconstructing population history and detection of the geographic pattern of natural selection. This article provides an examination of geostatistical analysis, a method used widely in geology but which has not often been applied in biological anthropology. Geostatistical analysis begins with the examination of a variogram, a plot showing the relationship between a biological distance measure and the geographic distance between data points and which provides information on the extent and pattern of spatial correlation. The results of variogram analysis are used for interpolating values of unknown data points in order to construct a contour map, a process known as kriging. The methods of geostatistical analysis and discussion of potential problems are applied to a large data set of anthropometric measures for 197 populations in Ireland. The geostatistical analysis reveals two major sources of spatial variation. One pattern, seen for overall body and craniofacial size, shows an east-west cline most likely reflecting the combined effects of past population dispersal and settlement. The second pattern is seen for craniofacial height and shows an isolation by distance pattern reflecting rapid spatial changes in the midlands region of Ireland, perhaps attributable to the genetic impact of the Vikings. The correspondence of these results with other analyses of these data and the additional insights generated from variogram analysis and kriging illustrate the potential utility of geostatistical analysis in biological anthropology.     

CorrSieve: software for summarizing and evaluating Structure output

The clustering software Structure has been used extensively to infer population structure in natural populations from multilocus genotype data. Determining meaningful values of K, the assumed number of subpopulations is one of the primary challenges of making biological inferences from Structure data. The package CorrSieve summarizes Structure output and performs a number of tests, including both previously reported methods and novel ones, to help determine meaningful values of K.     

Comparison of two chemical cleavage methods for preparation of a truncated form of recombinant human insulin-like growth factor I from a secreted fusion protein

We have produced a naturally occurring variant of human insulin-like growth factor I, truncated by three amino acids at the amino terminus. The polypeptide is obtained as a fusion protein in Escherichia coli. The fusion partner is a synthetic IgG-binding peptide. During fermentation the fusion protein is secreted into the medium, and is purified on IgG--Sepharose prior to cleavage. Two different genes for the fusion protein were used, allowing chemical cleavage at either a tryptophan linker or a methionine linker between the fusion partner and the growth factor, using N-chlorosuccinimide (NCS) or cyanogen bromide (CNBr) respectively. A partial CNBr cleavage yielded the native peptide, whereas the NCS cleavage yielded a product in which the single methionine had been oxidized to the sulfoxide. The forms from both cleavage methods exhibited biological activity and were characterized after purification to homogeneity. Both cleavage methods gave products having correct N- and C-terminal ends. The purified product had a biological activity equal to that of corresponding material from natural sources, 15 000 U/mg. Modified forms of truncated IGF-I were also identified, purified and characterized. Modifications such as proteolysis and misincorporation of norleucine for methionine occurred during biosynthesis, while oxidation of methionine took place during both fermentation and chemical cleavage.     

Using control genes to correct for unwanted variation in microarray data

Microarray expression studies suffer from the problem of batch effects and other unwanted variation. Many methods have been proposed to adjust microarray data to mitigate the problems of unwanted variation. Several of these methods rely on factor analysis to infer the unwanted variation from the data. A central problem with this approach is the difficulty in discerning the unwanted variation from the biological variation that is of interest to the researcher. We present a new method, intended for use in differential expression studies, that attempts to overcome this problem by restricting the factor analysis to negative control genes. Negative control genes are genes known a priori not to be differentially expressed with respect to the biological factor of interest. Variation in the expression levels of these genes can therefore be assumed to be unwanted variation. We name this method "Remove Unwanted Variation, 2-step" (RUV-2). We discuss various techniques for assessing the performance of an adjustment method and compare the performance of RUV-2 with that of other commonly used adjustment methods such as Combat and Surrogate Variable Analysis (SVA). We present several example studies, each concerning genes differentially expressed with respect to gender in the brain and find that RUV-2 performs as well or better than other methods. Finally, we discuss the possibility of adapting RUV-2 for use in studies not concerned with differential expression and conclude that there may be promise but substantial challenges remain.     

Probing metal-protein interactions using a de novo design approach

De novo design of metalloproteins provides a valuable tool for understanding the structural constraints and functional attributes of natural biological systems using first principles. This review focuses on recent research aimed primarily at probing the subtle interactions between metals and proteins in designed systems. Considerable attention has focussed on redefining novel design methods used in mimicking natural hemeproteins, mononuclear and dinuclear metallopeptides and functional biological electron-transfer proteins. The present results indicate that the field of metalloprotein design is contributing significantly to the understanding of metals in biology.     

The state of commercial augmentative biological control: plenty of natural enemies,but a frustrating lack of uptake

Augmentative biological control concerns the periodical release of natural enemies. In commercial augmentative biological control, natural enemies are mass-reared in biofactories for release in large numbers to obtain an immediate control of pests. The history of commercial mass production of natural enemies spans a period of roughly 120 years. It has been a successful, environmentally and economically sound alternative for chemical pest control in crops like fruit orchards, maize, cotton, sugar cane, soybean, vineyards and greenhouses. Currently, augmentative biological control is in a critical phase, even though during the past decades it has moved from a cottage industry to professional production. Many efficient species of natural enemies have been discovered and 230 are commercially available today. The industry developed quality control guidelines, mass production, shipment and release methods as well as adequate guidance for farmers. However, augmentative biological control is applied on a frustratingly small acreage. Trends in research and application are reviewed, causes explaining the limited uptake are discussed and ways to increase application of augmentative biological control are explored.