Microtubule Associated Proteins in Plants and the Processes They Manage

Microtubule associated proteins （MAPs） are proteins that physically bind to microtubules in eukaryotes. MAPs play important roles in regulating the polymerization and organization of microtubules and in using the ensuing microtubule arrays to carry out a variety of cellular functions. In plants, MAPs manage the construction, repositioning, and dismantling of four distinct microtubule arrays throughout the cell cycle. Three of these arrays, the cortical array, the preprophase band, and the phragmoplast, are prominent to plants and are responsible for facilitating cell wall deposition and modification, transducing signals, demarcating the plane of cell division, and forming the new cell plate during cytokinesis. This review highlights important aspects of how MAPs in plants establish and maintain microtubule arrays as well as regulate cell growth, cell division, and cellular responses to the environment.     

The microarray analysis for gene expression in haploids and diploids derived from twin-seedling rice

In this study, microarray technique was employed to analyze the gene expression at the RNA level between haploids and corresponding diploids derived from a rice twin-seedling line SARII-628. Differ- ent degrees of expression variations were observed in the plant after haploidization. The main results are as follows: (1) after haploidization, the ratio of the sensitive loci was 2.47% of the total loci designed on chip. Those loci were randomly distributed on the 12 pairs of rice chromosomes and the activated loci were more than the silenced ones. (2) Gene clusters on chromosome were observed for 33 se- quences. (3) GoPipe function classification for 575 sensitive loci revealed an involvement in the bio- logical process, cell component and molecular function. (4) RT-PCR generally validated the result from microarray with a coincidence rate of 83.78%. And for the randomly-selected activated or silenced loci in chip analysis, the coincidence rate was up to 91.86%.     

Histone modifications dictate specific biological readouts

The basic unit of chromatin is the nucleosomal core particle, containing 147 bp of DNA that wraps twice around an octamer of core histones. The core histones bear a highly dynamic N-terminal amino acid tail around 20-35 residues in length and rich in basic amino acids. These tails extending from the surface of nucleosome play an important role in folding of nucleosomal arrays into higher order chromatin structure, which plays an important role in eukaryotic gene regulation. The amino terminal tails protruding from the nuclesomes get modified by the addition of small groups such as methyl, acetyl and phosphoryl groups. In this review, we focus on these complex modi- fication patterns and their biological functions. Moreover, these modifications seem to be part of a complex scheme where distinct histone modifications act in a sequential manner or in combination to form a ＂histone code＂ read by other proteins to control the structure and/or function of the chromatin fiber. Errors in this histone code may be involved in many human diseases especially cancer, the nature of which could be therapeutically exploited. Increasing evidence suggests that many proteins bear multiple, distinct modifications, and the ability of one modification to antagonize or synergize the deposition of another can have significant biological consequences.     

Grasshopper （Orthoptera： Acrididae） biodiversity and grassland ecosystems

Interesting results may arise by combining studies on the structure and function of ecosystems with that of biodiversity for certain species. Grasshopper biodiversity is the result of the evolution of grassland ecosystems; however, it also impacts on the structure and the function of those ecosystems. We consider there to be a close relationship between the health of grassland ecosystems and grasshopper biodiversity. The main problems involved in this relationship are likely to include： （i） grasshopper biodiversity and its spatial pattern; （ii） the effect of grasshopper biodiversity on the ecological processes of grassland ecosystems; （iii） the biodiversity threshold of grasshopper population explosions; （iv） the relationship between grasshopper biodiversity and the natural and human factors that affect grassland ecosystems; and （v） grasshopper biodiversity and the health of grassland ecosystems. The solutions to these problems may provide sound bases for controlling disasters caused by grasshoppers and managing grassland ecosystems in the west of China. In this paper, we introduced two concepts for grasshopper biodiversity, that is, ＂spatial pattern＂ and ＂biodiversity threshold＂. It is helpful to understand the action of the spatial pattern of grasshopper biodiversity on the ecological processes of grassland ecosystems and the effect of this spatial pattern on the health of those ecosystems, owing to the fact that, in the west of China, grasslands are vast and grasshoppers are widely distributed. Moreover, we inferred that the change in the level of component richness at each type of grasshopper biodiversity can make an impact on grassland ecosystems, and therefore, there is likely to be a threshold to grasshopper biodiversity for the stability and the sustainability of those ecosystems.     

A Comparative Study of the Microbial Communities Between the Mineral Surface and the Bioleaching Solution Using the Microarray Method

In order to explore the bioleaching mechanism and improve the bioleaching efficiency,the micro-bial community in the bioleaching solution was compared with that on the surface of minerals based on the microarray analysis.Meanwhile,the elements composition in the bioleaching solution was analyzed using the ICP-AES method.Results showed that there was a high concentration of S and Cu in the leaching solution which up to 2 380 mg/L and 1 378 mg/L,respectively,after continuously bioleaching of copper-ore concen-trate for 30 days by a mixed culture associated with 12 species of bioleaching microorganisms.Based on the data of microarray,the total of cell number in the surface of minerals was far higher than that in the bi-oleaching solution.Furthermore,the dominant communities on the surface of minerals,such as Acidithiobacillus ferrooxidans,Acidithiobacillus thiooxidans and Acidithiobacillus caldus,were similar to that in the bioleaching solution.However,the relative level of some bacteria,such as Sulfobacillus aci-dophilus and Sulfobacillus thermosulfidooxidans,showed great discrepancy with lower presence in the bi-oleaching solution with respect to the mineral surface.     

Analyses of Sexual Reproductive Success in Transgenic and/or Mutant Plants

The pistil, the female reproductive organ of plants, is a key player in the success of sexual plant reproduction. Ultimately, the production of fruits and seeds depends on the proper pistil development and function. Therefore, the identification and characterization of pistil expressed genes is essential for a better understanding and manipulation of the plant reproduction process. For studying the function of pistil expressed genes, transgenic and/or mutant plants for the genes of interest are used. The present article provides a review of methods already exploited to analyze sexual reproductive success. We intend to supply useful information and to guide future experiments in the study of genes affecting pistil development and function.     

An ensemble method for gene discovery based on DNA microarray data

DNA microarrays are now able to measure the expressions of thousands of genes simultaneously. These measurements or gene profiling provides a snapshot?of life that maps to a cross section of ge-netic activities in a four-dimension space of time and the biological entity. Although recent microarray ex-periments[1, 2] hold the promise of the innovative tech-nology to cast new insights onto discovery of secrets of life, development of powerful and efficient analysis strategies for microarray dat…     

Microarray data quality control improves the detection of differentially expressed genes

Microarrays have become a routine tool for biomedical research. Data quality assessment is an essential part of the analysis, but it is still not easy to perform objectively or in an automated manner, and as a result it is often neglected. Here, we compared two strategies of array-level quality control using five publicly available microarray experiments: outlier removal and array weights. We also compared them against no outlier removal and random array removal. We find that removing outlier arrays can improve the signal-to-noise ratio and thus strengthen the power of detecting differentially expressed genes. Using array weights is similarly effective, but its applicability is more limited. The quality metrics presented here are implemented in the Bioconductor package arrayQualityMetrics.     

Power enhancement via multivariate outlier testing with gene expression arrays

Motivation: As the use of microarrays in human studies continuesto increase, stringent quality assurance is necessary to ensureaccurate experimental interpretation. We present a formal approachfor microarray quality assessment that is based on dimensionreduction of established measures of signal and noise componentsof expression followed by parametric multivariate outlier testing. Results: We applied our approach to several data resources.First, as a negative control, we found that the Affymetrix andIllumina contributions to MAQC data were free from outliersat a nominal outlier flagging rate of =0.01. Second, we createda tunable framework for artificially corrupting intensity datafrom the Affymetrix Latin Square spike-in experiment to allowinvestigation of sensitivity and specificity of quality assurance(QA) criteria. Third, we applied the procedure to 507 Affymetrixmicroarray GeneChips processed with RNA from human peripheralblood samples. We show that exclusion of arrays by this approachsubstantially increases inferential power, or the ability todetect differential expression, in large clinical studies. Availability: http://bioconductor.org/packages/2.3/bioc/html/arrayMvout.htmland http://bioconductor.org/packages/2.3/bioc/html/affyContam.htmlaffyContam (credentials: readonly/readonly) Contact: aasare{at}immunetolerance.org; stvjc{at}channing.harvard.edu The authors wish it to be known that, in their opinion, thefirst two authors should be regarded as joint First Authors. Associate Editor: Trey Ideker     

MDQC: a new quality assessment method for microarrays based on quality control reports

MOTIVATION: The process of producing microarray data involves multiple steps, some of which may suffer from technical problems and seriously damage the quality of the data. Thus, it is essential to identify those arrays with low quality. This article addresses two questions: (1) how to assess the quality of a microarray dataset using the measures provided in quality control (QC) reports; (2) how to identify possible sources of the quality problems. RESULTS: We propose a novel multivariate approach to evaluate the quality of an array that examines the 'Mahalanobis distance' of its quality attributes from those of other arrays. Thus, we call it Mahalanobis Distance Quality Control (MDQC) and examine different approaches of this method. MDQC flags problematic arrays based on the idea of outlier detection, i.e. it flags those arrays whose quality attributes jointly depart from those of the bulk of the data. Using two case studies, we show that a multivariate analysis gives substantially richer information than analyzing each parameter of the QC report in isolation. Moreover, once the QC report is produced, our quality assessment method is computationally inexpensive and the results can be easily visualized and interpreted. Finally, we show that computing these distances on subsets of the quality measures in the report may increase the method's ability to detect unusual arrays and helps to identify possible reasons of the quality problems. AVAILABILITY: The library to implement MDQC will soon be available from Bioconductor.     

An Integrated Approach for Identifying Wrongly Labelled Samples When Performing Classification in Microarray Data

Background

Using hybrid approach for gene selection and classification is common as results obtained are generally better than performing the two tasks independently. Yet, for some microarray datasets, both classification accuracy and stability of gene sets obtained still have rooms for improvement. This may be due to the presence of samples with wrong class labels (i.e. outliers). Outlier detection algorithms proposed so far are either not suitable for microarray data, or only solve the outlier detection problem on their own.

Results

We tackle the outlier detection problem based on a previously proposed Multiple-Filter-Multiple-Wrapper (MFMW) model, which was demonstrated to yield promising results when compared to other hybrid approaches (Leung and Hung, 2010). To incorporate outlier detection and overcome limitations of the existing MFMW model, three new features are introduced in our proposed MFMW-outlier approach: 1) an unbiased external Leave-One-Out Cross-Validation framework is developed to replace internal cross-validation in the previous MFMW model; 2) wrongly labeled samples are identified within the MFMW-outlier model; and 3) a stable set of genes is selected using an L1-norm SVM that removes any redundant genes present. Six binary-class microarray datasets were tested. Comparing with outlier detection studies on the same datasets, MFMW-outlier could detect all the outliers found in the original paper (for which the data was provided for analysis), and the genes selected after outlier removal were proven to have biological relevance. We also compared MFMW-outlier with PRAPIV (Zhang et al., 2006) based on same synthetic datasets. MFMW-outlier gave better average precision and recall values on three different settings. Lastly, artificially flipped microarray datasets were created by removing our detected outliers and flipping some of the remaining samples'' labels. Almost all the ‘wrong’ (artificially flipped) samples were detected, suggesting that MFMW-outlier was sufficiently powerful to detect outliers in high-dimensional microarray datasets.     

ArrayOU: A Web Application for Microarray Data Analysis and Visualization

A web-based microarray data analysis tool, ArrayOU (freely available at www.bioinformatics.plantbio.ohiou.edu.), has been developed at the Ohio University Genomics Facility for the research and education community to analyze Agilent microarray data. Agilent''s microarray pipeline has gained in popularity as a result of its ease of use and low cost of customized arrays. The current version of the ArrayOU pipeline allows users to visualize, analyze, and annotate microarray data from commercially available and customized Agilent expression arrays and is extendable for further implementations.     

Advances in the development of human protein microarrays

Introduction: High-content protein microarrays in principle enable the functional interrogation of the human proteome in a broad range of applications, including biomarker discovery, profiling of immune responses, identification of enzyme substrates, and quantifying protein-small molecule, protein-protein and protein-DNA/RNA interactions. As with other microarrays, the underlying proteomic platforms are under active technological development and a range of different protein microarrays are now commercially available. However, deciphering the differences between these platforms to identify the most suitable protein microarray for the specific research question is not always straightforward.

Areas covered: This review provides an overview of the technological basis, applications and limitations of some of the most commonly used full-length, recombinant protein and protein fragment microarray platforms, including ProtoArray Human Protein Microarrays, HuProt Human Proteome Microarrays, Human Protein Atlas Protein Fragment Arrays, Nucleic Acid Programmable Arrays and Immunome Protein Arrays.

Expert commentary: The choice of appropriate protein microarray platform depends on the specific biological application in hand, with both more focused, lower density and higher density arrays having distinct advantages. Full-length protein arrays offer advantages in biomarker discovery profiling applications, although care is required in ensuring that the protein production and array fabrication methodology is compatible with the required downstream functionality.     

Pleiotropic benefit of monomeric and oligomeric flavanols on vascular health--a randomized controlled clinical pilot study

Background

Cardiovascular diseases are expanding to a major social-economic burden in the Western World and undermine man''s deep desire for healthy ageing. Epidemiological studies suggest that flavanol-rich foods (e.g. grapes, wine, chocolate) sustain cardiovascular health. For an evidenced-based application, however, sound clinical data on their efficacy are strongly demanded.

Methods

In a double-blind, randomized, placebo-controlled intervention study we supplemented 28 male smokers with 200 mg per day of monomeric and oligomeric flavanols (MOF) from grape seeds. At baseline, after 4 and 8 weeks we measured macro- and microvascular function and a cluster of systemic biomarkers for major pathological processes occurring in the vasculature: disturbances in lipid metabolism and cellular redox balance, and activation of inflammatory cells and platelets.

Results

In the MOF group serum total cholesterol and LDL decreased significantly (P≤0.05) by 5% (n = 11) and 7% (n = 9), respectively in volunteers with elevated baseline levels. Additionally, after 8 weeks the ratio of glutathione to glutathione disulphide in erythrocytes rose from baseline by 22% (n = 15, P<0.05) in MOF supplemented subjects. We also observed that MOF supplementation exerts anti-inflammatory effects in blood towards ex vivo added bacterial endotoxin and significantly reduces expression of inflammatory genes in leukocytes. Conversely, alterations in macro- and microvascular function, platelet aggregation, plasma levels of nitric oxide surrogates, endothelin-1, C-reactive protein, fibrinogen, prostaglandin F2alpha, plasma antioxidant capacity and gene expression levels of antioxidant defense enzymes did not reach statistical significance after 8 weeks MOF supplementation. However, integrating all measured effects into a global, so-called vascular health index revealed a significant improvement of overall vascular health by MOF compared to placebo (P≤0.05).

Conclusion

Our integrative multi-biomarker approach unveiled the pleiotropic vascular health benefit of an 8 weeks supplementation with 200 mg/d MOF in humans.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00742287","term_id":"NCT00742287"}}NCT00742287     

The Methodology Used to Measure Differential Gene Expression Affects the Outcome

Confirmation of gene expression by a second methodology is critical in order to detect false-positive findings associated with microarrays. However, the impact of methodology upon the measurement of gene expression has not been rigorously evaluated. In the current study, we compared differential gene expression between PC3 and PC3-M human prostate cancer cell lines using three separate methods: microarray, quantitative RT/PCR (qRT/PCR), and Northern blotting. The PC3 to PC3-M ratio of gene expression was determined for each of 24 different genes evaluated, by each of the three methods. Comparison of gene expression ratios between Northern and microarray, Northern and qRT/PCR, and microarray and qRT/PCR, gave correlation coefficients (r) of 0.72, 0.39, and 0.63, respectively. In each instance, one to two outlier genes were apparent. Their exclusion from analysis gave r values of 0.79, 0.72, and 0.83, respectively. These findings demonstrate that the assessment of differential gene expression is dependent upon the methodology used in each situation where outcome between different methodologies was compared, the presence of a relatively limited number of outlier genes precludes high overall correlation between the methods. Validation of gene expression by different methods should be performed whenever possible.     

DigOut: viewing differential expression genes as outliers

With regards to well-replicated two-conditional microarray datasets, the selection of differentially expressed (DE) genes is a well-studied computational topic, but for multi-conditional microarray datasets with limited or no replication, the same task is not properly addressed by previous studies. This paper adopts multivariate outlier analysis to analyze replication-lacking multi-conditional microarray datasets, finding that it performs significantly better than the widely used limit fold change (LFC) model in a simulated comparative experiment. Compared with the LFC model, the multivariate outlier analysis also demonstrates improved stability against sample variations in a series of manipulated real expression datasets. The reanalysis of a real non-replicated multi-conditional expression dataset series leads to satisfactory results. In conclusion, a multivariate outlier analysis algorithm, like DigOut, is particularly useful for selecting DE genes from non-replicated multi-conditional gene expression dataset.