植物EXO70基因家族的研究进展

胞吐是存在于所有真核生物的一种极其重要的细胞活动,直接参与了激素和神经信号的分泌、细胞生长、细胞极性的建立,细胞分裂和细胞壁的形成等多项生理过程。在胞吐过程中,高尔基后转运膜泡与靶膜的识别是由进化上高度保守的胞泌复合体（exocyst）介导的。该复合体由8个蛋白亚基构成,其中EXO70是组成胞泌复合体功能的关键亚基,可与小G蛋白和膜脂互作,参与复合体在靶膜组装。目前,对植物胞泌复合体功能的了解非常有限,已有证据显示其广泛参与了细胞生长,细胞壁形成、细胞分裂等多种生物学过程。与酵母和动物相比,植物胞泌复合体的一个显著特征是：EXO70在高等植物基因组中存在多个同源基因,其具体生物学功能尚不清楚。本文综述胞泌复合体的研究进展,重点讨论植物EXO70的多基因家族,推测不同的EXO70可能参与了组织细胞或运载底物特异的膜泡转运过程。     

The multiple mixed lymphocyte reaction: variables important in the test as a measure of lymphocyte competence in man.

In order to utilize the mixed lymphocyte reaction (MLR) as an assay of T-lymphocyte competence, pools of target lymphocytes obtained from different individuals are used to increase the magnitude and decrease the variation of the in vitro response. We evaluated variations in MLR response due to variations in target cell populations. Response increased with an increased target/responder cell ratio. Peak response occurred with a target/responder cell ratio of between 1:1 and 1:4. Response to a pool of lymphocytes from different individuals increased as the number of individuals contributing to the pool increased. Peak stimulation occurred with three to four different donors to the target cell pool. Stimulation produced by pooled target cells resulted in a higher mean index of stimulation and decreased variation of response as compared to stimulation produced by target cells from individual donors. Stimulation produced by pooled target cells was approximately equal to the sum of the stimulation produced by each of the target cell populations acting alone. These findings indicate a practical method of modifying the MLR as a test of T-lymphocyte function.     

A Network of (Autonomic) Clock Outputs

The circadian clock in the suprachiasmatic nuclei (SCN) is composed of thousands of oscillator neurons, each of which is dependent on the cell‐autonomous action of a defined set of circadian clock genes. A major question is still how these individual oscillators are organized into a biological clock producing a coherent output that is able to time all the different daily changes in behavior and physiology. We investigated which anatomical connections and neurotransmitters are used by the biological clock to control the daily release pattern of a number of hormones. The picture that emerged shows projections contacting target neurons in the medial hypothalamus surrounding the SCN. The activity of these pre‐autonomic and neuro‐endocrine target neurons is controlled by differentially timed waves of, among others, vasopressin, GABA, and glutamate release from SCN terminals. Together our data indicate that, with regard to the timing of their main release period within the light‐dark (LD) cycle, at least 4 subpopulations of SCN neurons should be discerned. The different subgroups do not necessarily follow the phenotypic differences among SCN neurons. Thus, different subgroups can be found within neuron populations containing the same neurotransmitter. Remarkably, a similar distinction of 4 differentially timed subpopulations of SCN neurons was recently also discovered in experiments determining the temporal patterns of rhythmicity in individual SCN neurons by way of the electrophysiology or clock gene expression. Moreover, the specialization of the SCN may go as far as a single body structure; i.e., the SCN seems to contain neurons that specifically target the liver, pineal, and adrenal.     

A network of (autonomic) clock outputs

The circadian clock in the suprachiasmatic nuclei (SCN) is composed of thousands of oscillator neurons, each of which is dependent on the cell-autonomous action of a defined set of circadian clock genes. A major question is still how these individual oscillators are organized into a biological clock producing a coherent output that is able to time all the different daily changes in behavior and physiology. We investigated which anatomical connections and neurotransmitters are used by the biological clock to control the daily release pattern of a number of hormones. The picture that emerged shows projections contacting target neurons in the medial hypothalamus surrounding the SCN. The activity of these pre-autonomic and neuro-endocrine target neurons is controlled by differentially timed waves of, among others, vasopressin, GABA, and glutamate release from SCN terminals. Together our data indicate that, with regard to the timing of their main release period within the light-dark (LD) cycle, at least 4 subpopulations of SCN neurons should be discerned. The different subgroups do not necessarily follow the phenotypic differences among SCN neurons. Thus, different subgroups can be found within neuron populations containing the same neurotransmitter. Remarkably, a similar distinction of 4 differentially timed subpopulations of SCN neurons was recently also discovered in experiments determining the temporal patterns of rhythmicity in individual SCN neurons by way of the electrophysiology or clock gene expression. Moreover, the specialization of the SCN may go as far as a single body structure; i.e., the SCN seems to contain neurons that specifically target the liver, pineal, and adrenal.     

PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data

Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http://dragon.bio.purdue.edu/pfp/.     

From gene to phenotype in Drosophila and other organisms

The growing number of cloned eukaryotic genes lacking a defined or proven biological function poses a major challenge in 'reverse genetics'. A method is described here that permits efficient screening for new lesions in, or close to, genes corresponding to cloned DNA sequences of interest. The technique involves transposon mutagenesis, followed by screening of DNA isolated from a population of mutagenised individuals (or their progeny) for evidence that the population contains at least one individual in which transposon insertion has occurred at the target locus. Detection of rare individuals within the population is facilitated by the use of the polymerase chain reaction (PCR). Once recognised, specific individuals (or their progeny) are isolated from the population by a process of sib-selection. In cases where insertion of the transposon has occurred close to, but not within, the target locus, secondary events involving imprecise excision of the transposon will nonetheless allow the isolation of mutant individuals. Though the method was developed specifically for the transposon-mutagenesis of Drosophila, extensions to other organisms and to other mutagenic strategies are feasible and some of the possibilities are discussed.     

Affinity purification-mass spectrometry. Powerful tools for the characterization of protein complexes.

Multi-protein complexes are emerging as important entities of biological activity inside cells that serve to create functional diversity by contextual combination of gene products and, at the same time, organize the large number of different proteins into functional units. Many a time, when studying protein complexes rather than individual proteins, the biological insight gained has been fundamental, particularly in cases in which proteins with no previous functional annotation could be placed into a functional context derived from their 'molecular environment'. In this minireview, we summarize the current state of the art for the retrieval of multiprotein complexes by affinity purification and their analysis by mass spectrometry. The advances in technology made over the past few years now enable the study of protein complexes on a proteomic scale and it can be anticipated that the knowledge gathered from such projects will fuel drug target discovery and validation pipelines and that the technology is also going to prove valuable in the emerging field of systems biology.     

MicroRNAs as genetic sculptors: Fishing for clues

microRNAs (miRNAs) encode small RNA molecules of ～22nts in length that regulate the deadenylation, translation, and decay of their target mRNAs. The identification of miRNAs in plants and animals has uncovered a new layer of gene regulation with important implications for development, cellular homeostasis and disease. Because each miRNA is predicted to regulate several hundred genes, a major challenge in the field remains to elucidate the precise roles for each miRNA and to understand the physiological relevance of individual miRNA–target interactions in vivo. Despite the wide variety of biological contexts where miRNAs function, a common theme emerges, whereby miRNAs shape gene expression within both spatial and temporal dimensions by removing messages from previous cellular states as well as modulating the levels of actively transcribed genes. This review will focus on the role that the teleost Danio rerio (zebrafish) has played in shaping our understanding of miRNA function in vertebrates.     

Negative feedback may suppress variation to improve collective foraging performance

Social insect colonies use negative as well as positive feedback signals to regulate foraging behaviour. In ants and bees individual foragers have been observed to use negative pheromones or mechano-auditory signals to indicate that forage sources are not ideal, for example being unrewarded, crowded, or dangerous. Here we propose an additional function for negative feedback signals during foraging, variance reduction. We show that while on average populations will converge to desired distributions over forage patches both with and without negative feedback signals, in small populations negative feedback reduces variation around the target distribution compared to the use of positive feedback alone. Our results are independent of the nature of the target distribution, providing it can be achieved by foragers collecting only local information. Since robustness is a key aim for biological systems, and deviation from target foraging distributions may be costly, we argue that this could be a further important and hitherto overlooked reason that negative feedback signals are used by foraging social insects.     

On the number of experiments required to find the causal structure of complex systems

The need to capture the complexity of biological systems in a simpler formalism is the underlying impetus of biological sciences. Understanding the function of many biological complex systems, such as genetic networks or molecular signalling pathways, requires precise identification of the interactions between their individual components. A number of questions in the study of complex systems are then important-in particular, what can be inferred about the interactions in a complex system from an arbitrary set of experiments, and, what is the minimum number of experiments required to characterize the system? This paper shows that the problem of finding the minimal causal structure of a system based on a set of observations is computationally intractable for even moderately sized systems (it is NP-hard), but a reasonable approximation can be found in a relatively short (polynomial) time. Next, it is shown that the number of experiments required to characterize a complex system grows exponentially with the upper bound on the number of immediate upstream influences of each element, but only logarithmically with the number of elements in the system. This makes it possible to study biological systems with extremely large number of interacting elements and relatively sparse interconnections, such as gene regulatory and cell signalling networks. Finally, the construction of a randomized experimental sequence which achieves this bound is discussed.     

Bayesian hierarchical error model for analysis of gene expression data

MOTIVATION: Analysis of genome-wide microarray data requires the estimation of a large number of genetic parameters for individual genes and their interaction expression patterns under multiple biological conditions. The sources of microarray error variability comprises various biological and experimental factors, such as biological and individual replication, sample preparation, hybridization and image processing. Moreover, the same gene often shows quite heterogeneous error variability under different biological and experimental conditions, which must be estimated separately for evaluating the statistical significance of differential expression patterns. Widely used linear modeling approaches are limited because they do not allow simultaneous modeling and inference on the large number of these genetic parameters and heterogeneous error components on different genes, different biological and experimental conditions, and varying intensity ranges in microarray data. RESULTS: We propose a Bayesian hierarchical error model (HEM) to overcome the above restrictions. HEM accounts for heterogeneous error variability in an oligonucleotide microarray experiment. The error variability is decomposed into two components (experimental and biological errors) when both biological and experimental replicates are available. Our HEM inference is based on Markov chain Monte Carlo to estimate a large number of parameters from a single-likelihood function for all genes. An F-like summary statistic is proposed to identify differentially expressed genes under multiple conditions based on the HEM estimation. The performance of HEM and its F-like statistic was examined with simulated data and two published microarray datasets-primate brain data and mouse B-cell development data. HEM was also compared with ANOVA using simulated data. AVAILABILITY: The software for the HEM is available from the authors upon request.     

A Network of (Autonomic) Clock Outputs

The circadian clock in the suprachiasmatic nuclei (SCN) is composed of thousands of oscillator neurons, each dependent on the cell‐autonomous action of a defined set of circadian clock genes. A major question is still how these individual oscillators are organized into a biological clock that produces a coherent output capable of timing all the different daily changes in behavior and physiology. We investigated which anatomical connections and neurotransmitters are used by the biological clock to control the daily release pattern of a number of hormones. The picture that emerged shows projections contacting target neurons in the medial hypothalamus surrounding the SCN. The activity of these pre‐autonomic and neuro‐endocrine target neurons is controlled by differentially timed waves of vasopressin, GABA, and glutamate release from SCN terminals, among other factors. Together our data indicate that, with regard to the timing of their main release period within the LD cycle, at least four subpopulations of SCN neurons should be discernible. The different subgroups do not necessarily follow the phenotypic differences among SCN neurons. Thus, different subgroups can be found within neuron populations containing the same neurotransmitter. Remarkably, a similar distinction of four differentially timed subpopulations of SCN neurons was recently also discovered in experiments determining the temporal patterns of rhythmicity in individual SCN neurons by way of the electrophysiology or clock gene expression. Moreover, the specialization of the SCN may go as far as a single body structure, i.e., the SCN seems to contain neurons that specifically target the liver, pineal gland, and adrenal gland.     

Reliability of Computation in the Cerebellum

The mossy fiber-granule cell-parallel fiber-Purkinje cell system of the cerebellar cortex is investigated from the viewpoint of reliability of computation. It is shown that the effects of variability in the inputs to a Purkinje cell can be reduced by having a large number of parallel fibers whose activities are statistically independent. The mossy fiber-granule cell relay is shown to be capable of performing the required function of transforming the activity in a small number of mossy fibers into activity in a much larger number of parallel fibers, while ensuring that there is little correlation between the activities of individual parallel fibers. The effects of variability in the outputs of Purkinje cells may be reduced by redundancy and convergence schemes, as evidenced by the geometrical pattern of parallel fibers and Purkinje cells and the convergence of these cells onto their target neurons.     

Isolation and characterization of new homing endonuclease specificities at individual target site positions

Homing endonucleases are highly specific DNA endonucleases, encoded within mobile introns or inteins, that induce targeted recombination, double-strand repair and gene conversion of their cognate target sites. Due to their biological function and high level of target specificity, these enzymes are under intense investigation as tools for gene targeting. These studies require that naturally occurring enzymes be redesigned to recognize novel target sites. Here, we report studies in which the homodimeric LAGLIDADG homing endonuclease I-CreI is altered at individual side-chains corresponding to contact points to distinct base-pairs in its target site. The resulting enzyme constructs drive specific elimination of selected DNA targets in vivo and display shifted specificities of DNA binding and cleavage in vitro. Crystal structures of two of these constructs demonstrate that substitution of individual side-chain/DNA contact patterns can occur with almost no structural deformation or rearrangement of the surrounding complex, facilitating an isolated, modular redesign strategy for homing endonuclease activity and specificity.     

Applications of congenic strains in the mouse

The sequencing of the human and the mouse genomes has shown that the chromosomes of these two species contain approximately 30,000 genes. The biological systems that can be studied in an individual or in a tissue result from complex interactions within this multitude of genes. Before describing these interactions, it is necessary to understand the function of each gene. In the mouse, congenic strains are developed to introduce a chromosomal segment in a given inbred genetic background. One can then compare the biological effects of different alleles at the same locus in the same genetic background or the effect of a given allele in different genetic backgrounds. One can also introduce into different congenic strains with the same genetic background genes which control a complex genetic trait, then combine these genes by appropriate crosses to study their interactions. Although the chromosomal segment transferred into a congenic strain usually contains up to several hundreds of genes, molecular markers can be used to reduce this number as well as the number of crosses required for the development of congenic strains.