A recombineering-based gene tagging system for Arabidopsis

One of the most information-rich aspects of gene functional studies is characterization of gene expression profiles at cellular resolution, and subcellular localization of the corresponding proteins. These studies require visualization of the endogenous gene products using specific antibodies, or, more commonly, generation of whole-gene translational fusions with a reporter gene such as a fluorescent protein. To facilitate the generation of such translational fusions and to ensure that all cis-regulatory sequences are included, we have used a bacterial homologous recombination system (recombineering) to insert fluorescent protein tags into genes of interest harbored by transformation-competent bacterial artificial chromosomes (TACs). This approach has several advantages compared to other classical strategies. First, the researcher does not have to guess what the regulatory sequences of a gene are, as tens of thousands of base pairs flanking the gene of interest can be included in the construct. Second, because the genes of interest are not amplified by PCR, there are practically no limits to the size of a gene that can be tagged. Third, there are no restrictions on the location in which the fluorescent protein can be inserted, as the position is determined by sequence homology with the recombination primers. Finally, all of the required strains and TAC clones are publically available, and the experimental procedures described here are simple and robust. Thus, we suggest that recombineering-based gene tagging should be the gold standard for gene expression studies in Arabidopsis.     

三种植被冠层二向反射分布函数模型的比较

随着定量遥感技术的发展, 描述森林冠层二向反射分布函数(BRDF)的机理模型越来越多。该研究采用3种植被冠层BRDF模型——DART模型、4SCALE模型和MGEOSAIL模型, 模拟了不同郁闭度样地在红光、近红外波段各个观测角度下的场景反射率, 并比较分析了不同BRDF模型的适用性和局限性。结果表明: MGEOSAIL模型只适于模拟样地郁闭度较小、林木个体较大条件下的场景反射率, 且热点效果不十分明显; DART模型和4SCALE模型适于任何郁闭度条件下的场景反射率的模拟, 并且精度较高; 4SCALE模型模拟的场景反射率介于DART模型模拟的1次散射与5次散射之间。这3种模型在模拟近红外波段的场景反射率时, 均存在“碗边”效应。     

RFLP tagging of a gene for aroma in rice

Summary We report here the identification of a DNA marker closely linked to a gene for aroma in rice. The DNA marker was identified by testing 126 mapped rice genomic, cDNA, and oat cDNA, clones as hybridization probes against Southern blots, consisting of DNA from a pair of nearly isogenic lines (NILs) with or without the aroma gene. Chromosomal segments introgressed from the donor genome were distinguished by RFLPs between the NILs. Linkage association of the clone with the gene was verified using an F₃ segregating for aroma. Cosegregation of the scented phenotype and donor-derived allele indicated the presence of linkage between the DNA marker and the gene. RFLP analysis showed that the gene is linked to a single-copy DNA clone, RG28, on chromosome 8, at a distance of 4.5 cM. The availability of a linked DNA marker may facilitate early selection for the aroma gene in rice breeding programs.     

A comparison of two cell regulatory models entailing high dimensional attractors representing phenotype

Two models for mammalian cell regulation that invoke the concept of cellular phenotype represented by high dimensional dynamic attractor states are compared. In one model the attractors are derived from an experimentally determined genetic regulatory network (GRN) for the cell type. As the state space architecture within which the attractors are embedded is determined by the binding sites on proteins and the recognition sites on DNA the attractors can be described as “hard-wired” in the genome through the genomic DNA sequence. In the second model attractors arising from the interactions between active gene products (mainly proteins) and independent of the genomic sequence, are descended from a pre-cellular state from which life originated. As this model is based on the cell as an open system the attractor acts as the interface between the cell and its environment. Environmental sources of stress can serve to trigger attractor and therefore phenotypic, transitions without entailing genotypic sequence changes.It is asserted that the evidence from cell and molecular biological research and logic, favours the second model. If correct there are important implications for understanding how environmental factors impact on evolution and may be implicated in hereditary and somatic disease.     

Activation tagging in plants: a tool for gene discovery

A significant limitation of classical loss-of-function screens designed to dissect genetic pathways is that they rarely uncover genes that function redundantly, are compensated by alternative metabolic or regulatory circuits, or which have an additional role in early embryo or gametophyte development. Activation T-DNA tagging is one approach that has emerged in plants to help circumvent these potential problems. This technique utilises a T-DNA sequence that contains four tandem copies of the cauliflower mosaic virus (CaMV) 35S enhancer sequence. This element enhances the expression of neighbouring genes either side of the randomly integrated T-DNA tag, resulting in gain-of-function phenotypes. Activation tagging has identified a number of genes fundamental to plant development, metabolism and disease resistance in Arabidopsis. This review provides selected examples of these discoveries to highlight the utility of this technology. The recent development of activation tagging strategies for other model plant systems and the construction of new more sophisticated vectors for the generation of conditional alleles are also discussed. These recent advances have significantly expanded the horizons for gain-of-function genetics in plants.     

Integrating heterogeneous gene expression data for gene regulatory network modelling

Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors' knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.     

BCC-NER: bidirectional,contextual clues named entity tagger for gene/protein mention recognition

Tagging biomedical entities such as gene, protein, cell, and cell-line is the first step and an important pre-requisite in biomedical literature mining. In this paper, we describe our hybrid named entity tagging approach namely BCC-NER (bidirectional, contextual clues named entity tagger for gene/protein mention recognition). BCC-NER is deployed with three modules. The first module is for text processing which includes basic NLP pre-processing, feature extraction, and feature selection. The second module is for training and model building with bidirectional conditional random fields (CRF) to parse the text in both directions (forward and backward) and integrate the backward and forward trained models using margin-infused relaxed algorithm (MIRA). The third and final module is for post-processing to achieve a better performance, which includes surrounding text features, parenthesis mismatching, and two-tier abbreviation algorithm. The evaluation results on BioCreative II GM test corpus of BCC-NER achieve a precision of 89.95, recall of 84.15 and overall F-score of 86.95, which is higher than the other currently available open source taggers.     

Improved techniques for endogenous epitope tagging and gene deletion in Toxoplasma gondii

Toxoplasma gondii is an excellent model organism for studies on the biology of the Apicomplexa due to its ease of in vitro cultivation and genetic manipulation. Large-scale reverse genetic studies in T. gondii have, however, been difficult due to the low frequency of homologous recombination. Efforts to ensure homologous recombination have necessitated engineering long flanking regions in the targeting construct. This requirement makes it difficult to engineer chromosomally targeted epitope tags or gene knock out constructs only by restriction enzyme mediated cloning steps. To address this issue we employed multisite Gateway® recombination techniques to generate chromosomal gene manipulation targeting constructs. Incorporation of 1.5 to 2.0 kb flanking homologous sequences in PCR generated targeting constructs resulted in 90% homologous recombination events in wild type T. gondii (RH strain) as determined by epitope tagging and target gene deletion experiments. Furthermore, we report that split marker constructs were equally efficient for targeted gene disruptions using the T. gondii UPRT gene locus as a test case. The methods described in this paper represent an improved strategy for efficient epitope tagging and gene disruptions in T. gondii.     

Integrating human behavior and snake ecology with agent-based models to predict snakebite in high risk landscapes

Snakebite causes more than 1.8 million envenoming cases annually and is a major cause of death in the tropics especially for poor farmers. While both social and ecological factors influence the chance encounter between snakes and people, the spatio-temporal processes underlying snakebites remain poorly explored. Previous research has focused on statistical correlates between snakebites and ecological, sociological, or environmental factors, but the human and snake behavioral patterns that drive the spatio-temporal process have not yet been integrated into a single model. Here we use a bottom-up simulation approach using agent-based modelling (ABM) parameterized with datasets from Sri Lanka, a snakebite hotspot, to characterise the mechanisms of snakebite and identify risk factors. Spatio-temporal dynamics of snakebite risks are examined through the model incorporating six snake species and three farmer types (rice, tea, and rubber). We find that snakebites are mainly climatically driven, but the risks also depend on farmer types due to working schedules as well as species present in landscapes. Snake species are differentiated by both distribution and by habitat preference, and farmers are differentiated by working patterns that are climatically driven, and the combination of these factors leads to unique encounter rates for different landcover types as well as locations. Validation using epidemiological studies demonstrated that our model can explain observed patterns, including temporal patterns of snakebite incidence, and relative contribution of bites by each snake species. Our predictions can be used to generate hypotheses and inform future studies and decision makers. Additionally, our model is transferable to other locations with high snakebite burden as well.     

Adaptive models for gene networks

Biological systems are often treated as time-invariant by computational models that use fixed parameter values. In this study, we demonstrate that the behavior of the p53-MDM2 gene network in individual cells can be tracked using adaptive filtering algorithms and the resulting time-variant models can approximate experimental measurements more accurately than time-invariant models. Adaptive models with time-variant parameters can help reduce modeling complexity and can more realistically represent biological systems.     

Bsr-REMI: An improved method for gene tagging using a new vector inDictyostelium

Using a plasmid pBsr2 which carries a blasticidin S-resistant gene, we have improved the method of REMI (restriction enzyme-mediated integration) provided for insertional mutagenesis inDictyostelium discoideum (bsr-REMI). To confirm usefulness of thebsr-REMI, transformation efficiency, copy number of integrated DNA, and randomness of integration into genome were examined.     

Rich probabilistic models for gene expression

Clustering is commonly used for analyzing gene expression data. Despite their successes, clustering methods suffer from a number of limitations. First, these methods reveal similarities that exist over all of the measurements, while obscuring relationships that exist over only a subset of the data. Second, clustering methods cannot readily incorporate additional types of information, such as clinical data or known attributes of genes. To circumvent these shortcomings, we propose the use of a single coherent probabilistic model, that encompasses much of the rich structure in the genomic expression data, while incorporating additional information such as experiment type, putative binding sites, or functional information. We show how this model can be learned from the data, allowing us to discover patterns in the data and dependencies between the gene expression patterns and additional attributes. The learned model reveals context-specific relationships, that exist only over a subset of the experiments in the dataset. We demonstrate the power of our approach on synthetic data and on two real-world gene expression data sets for yeast. For example, we demonstrate a novel functionality that falls naturally out of our framework: predicting the "cluster" of the array resulting from a gene mutation based only on the gene's expression pattern in the context of other mutations.     

Integrating seasonal climate prediction and agricultural models for insights into agricultural practice

Interest in integrating crop simulation models with dynamic seasonal climate forecast models is expanding in response to a perceived opportunity to add value to seasonal climate forecasts for agriculture. Integrated modelling may help to address some obstacles to effective agricultural use of climate information. First, modelling can address the mismatch between farmers' needs and available operational forecasts. Probabilistic crop yield forecasts are directly relevant to farmers' livelihood decisions and, at a different scale, to early warning and market applications. Second, credible ex ante evidence of livelihood benefits, using integrated climate-crop-economic modelling in a value-of-information framework, may assist in the challenge of obtaining institutional, financial and political support; and inform targeting for greatest benefit. Third, integrated modelling can reduce the risk and learning time associated with adaptation and adoption, and related uncertainty on the part of advisors and advocates. It can provide insights to advisors, and enhance site-specific interpretation of recommendations when driven by spatial data. Model-based 'discussion support systems' contribute to learning and farmer-researcher dialogue. Integrated climate-crop modelling may play a genuine, but limited role in efforts to support climate risk management in agriculture, but only if they are used appropriately, with understanding of their capabilities and limitations, and with cautious evaluation of model predictions and of the insights that arises from model-based decision analysis.     

Integrating gene expression and GO classification for PCA by preclustering

Background  

Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per group can then be used to identify interesting GO categories in relation to the experimental settings. However, the expression profiles present in GO classes are often heterogeneous, i.e., there are several different expression profiles within one class. As a result, important experimental findings can be obscured because the summarizing profile does not seem to be of interest. We propose to tackle this problem by finding homogeneous subclasses within GO categories: preclustering.     

R-ISSR as a new tool for genomic fingerprinting, mapping, and gene tagging

In the present study we propose and test the concept of R-ISSR, a new tool for genomic fingerprinting, mapping, and gene tagging. The concept is based on the fact that primers for inter-simple sequence repeat (ISSR) and random-amplified polymorphic DNA (RAPD) analysis elicit different genomic information, and the combined use of these 2 kinds of primers in the same polymerase chain reaction (PCR) reactions might reveal new genomic loci that could not be detected with either technique alone. The feasibility of this tool was first electronically simulated with sequence analysis software andArabidopsis chromosome sequence. Next, different combinations of ISSR and RAPD primers were applied in real PCR reactions to detect new genomic loci in 2 maize lines (Q319 and 1145). Sequencing gels were used to separate PCR products and showed good resolving ability in comparison with agarose gels. RAPD primers could be successfully used with ISSR primers for the detection of new genomic loci and applied in a new way for genomic mapping, fingerprinting, and gene tagging.     

Development of Ac and Ds transposon tagging lines for gene isolation in diploid potato

For the development of an efficient transposon tagging strategy it is important to generate populations of plants containing unique independent transposon insertions that will mutate genes of interest. To develop such a transposon system in diploid potato the behavior of the autonomous maize transposable element Ac and the mobile Ds element was studied. A GBSS (Waxy) excision assay developed for Ac was used to monitor excision in somatic starch-forming tissue like tubers and pollen. Excision of Ac results in production of amylose starch that stains blue with iodine. The frequency and patterns of blue staining starch granules on tuber slices enabled the identification of transformants with different Ac activity. After excision the GBSS complementation was usually not complete, probably due to the segment of DNA flanking Ac that is left behind in the GBSS gene. Molecular and phenotypic analyses of 40 primary transformants classified into 4 phenotypic classes revealed reproducible patterns. A very high percentage (32.5%) of the primary transformants clearly showed early excision in the first transformed cell as displayed both by the analysis of the GBSS excision marker gene as well as DNA blot analyses. Genotypes useful for tagging strategies were used for crosses and the frequency of independent germinal transpositions was assessed. In crosses to Ds genotypes, excision of Ds was revealed that correlated to the activity of the Ac genotype. A line displaying Ac amplification to multiple copies conferred a high frequency of independent Ds transpositions. The genotypes described here are useful in somatic insertion mutagenesis aimed at the isolation of tagged mutations in diploid potato.     

Technical advance: a high throughput system for transposon tagging and promoter trapping in tomato

We describe new tools for functional analysis of the tomato genome based on insertional mutagenesis with the maize Ac/Ds transposable elements in the background of the miniature cultivar Micro-Tom. 2932 F3 families, in which Ds elements transposed and were stabilized, were screened for phenotypic mutations. Out of 10 families that had a clear mutant phenotype, only one mutant was Ds-tagged. In addition, we developed promoter trapping using the firefly luciferase reporter gene and enhancer trapping, using beta-glucuronidase (GUS). We show that luciferase can be used as a non-invasive reporter to identify, isolate and regenerate somatic sectors, to study the time course of mutant expression, and to identify inducible genes. Out of 108 families screened for luciferase activity 55% showed expression in the flower, 11% in the fruit and 4% in seedlings, suggesting a high rate of Ds insertion into genes. Preferential insertion into genes was supported by the analysis of Ds flanking sequences: 28 out of 50 sequenced Ds insertion sites were similar to known genes or to ESTs. In summary, the 2932 lines described here contain 2-3 Ds inserts per line, representing a collection of approximately 7500 Ds insertions. This collection has potential for use in high-throughput functional analysis of genes and promoter isolation in tomato.