Xiangqin Cui

Microarray data analysis: from disarray to consolidation and consensus

In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to a weekly deluge of papers that describe purportedly novel algorithms for analysing changes in gene expression. Although the many procedures that are available might be bewildering to biologists who wish to apply them, statistical geneticists are recognizing commonalities among the different methods. Many are special cases of more general models, and points of consensus are emerging about the general approaches that warrant use and elaboration.

Statistical tests for differential expression in cDNA microarray experiments

Extracting biological information from microarray data requires appropriate statistical methods. The simplest statistical method for detecting differential expression is the t test, which can be used to compare two conditions when there is replication of samples. With more than two conditions, analysis of variance (ANOVA) can be used, and the mixed ANOVA model is a general and powerful approach for microarray experiments with multiple factors and/or several sources of variation.

Repeatability of published microarray gene expression analyses

Given the complexity of microarray-based gene expression studies, guidelines encourage transparent design and public data availability. Several journals require public data deposition and several public databases exist. However, not all data are publicly available, and even when available, it is unknown whether the published results are reproducible by independent scientists. Here we evaluated the replication of data analyses in 18 articles on microarray-based gene expression profiling published in Nature Genetics in 2005–2006. One table or figure from each article was independently evaluated by two teams of analysts. We reproduced two analyses in principle and six partially or with some discrepancies; ten could not be reproduced. The main reason for failure to reproduce was data unavailability, and discrepancies were mostly due to incomplete data annotation or specification of data processing and analysis. Repeatability of published microarray studies is apparently limited. More strict publication rules enforcing public data availability and explicit description of data processing and analysis should be considered.

The rf2 Nuclear Restorer Gene of Male-Sterile T-Cytoplasm Maize

The T cytoplasm of maize serves as a model for the nuclear restoration of cytoplasmic male sterility. The rf2 gene, one of two nuclear genes required for fertility restoration in male-sterile T-cytoplasm (cmsT) maize, was cloned. The protein predicted by the rf2 sequence is a putative aldehyde dehydrogenase, which suggests several mechanisms that might explain Rf2-mediated fertility restoration in cmsT maize. Aldehyde dehydrogenase may be involved in the detoxification of acetaldehyde produced by ethanolic fermentation during pollen development, may play a role in energy metabolism, or may interact with URF13, the mitochondrial protein associated with male sterility in cmsT maize.

MAANOVA: A Software Package for the Analysis of Spotted cDNA Microarray Experiments

We describe a software package called MAANOVA (MicroArray ANalysis Of VAriance). MAANOVA is a collection of functions for statistical analysis of gene expression data from two-color cDNA microarray experiments. It is available in both the Matlab and R programming environments and can be run on any platform that supports these packages. MAANOVA allows the user to assess data quality, apply data transformations, estimate relative gene expression from designed experiments with ANOVA models, evaluate and interpret ANOVA models, formally test for differential expression of genes and estimate false-discovery rates, produce graphical summaries of expression patterns, and perform cluster analysis with bootstrapping. The development of MAANOVA was motivated by the need to analyze microarray data that arise from sophisticated designed experiments. MAANOVA provides specialized functions for microarray analysis in an open-ended format within flexible computing environments. MAANOVA functions can be used alone or in co mbination with other functions for the rigorous statistical analysis of microarray data.

Mitochondrial Aldehyde Dehydrogenase Activity Is Required for Male Fertility in Maize

Some plant cytoplasms express novel mitochondrial genes that cause male sterility. Nuclear genes that disrupt the accumulation of the corresponding mitochondrial gene products can restore fertility to such plants. The Texas (T) cytoplasm mitochondrial genome of maize expresses a novel protein, URF13, which is necessary for T cytoplasm–induced male sterility. Working in concert, functional alleles of two nuclear genes, rf1 and rf2, can restore fertility to T cytoplasm plants. Rf1 alleles, but not Rf2 alleles, reduce the accumulation of URF13. Hence, Rf2 differs from typical nuclear restorers in that it does not alter the accumulation of the mitochondrial protein necessary for T cytoplasm–induced male sterility. This study established that the rf2 gene encodes a soluble protein that accumulates in the mitochondrial matrix. Three independent lines of evidence establish that the RF2 protein is an aldehyde dehydrogenase (ALDH). The finding that T cytoplasm plants that are homozygous for the rf2-R213 allele are male sterile but accumulate normal amounts of RF2 protein that lacks normal mitochondrial (mt) ALDH activity provides strong evidence that rf2-encoded mtALDH activity is required to restore male fertility to T cytoplasm maize. Detailed genetic analyses have established that the rf2 gene also is required for anther development in normal cytoplasm maize. Hence, it appears that the rf2 gene was recruited recently to function as a nuclear restorer. ALDHs typically have very broad substrate specificities. Indeed, the RF2 protein is capable of oxidizing at least three aldehydes. Hence, the specific metabolic pathway(s) within which the rf2-encoded mtALDH acts remains to be discovered.

Transformations for cDNA Microarray Data

Two channel microarray data often contain systematic variations that can be minimized by data transformation prior to further analysis. The most commonly observed effects are revealed by viewing scatter plots of the logarithm of the ratio by the average logarithmic intensity of the two color channels (RI plots). In this paper we present a general model for signal intensity data with multiple error sources. We demonstrate how these sources of error influence the shape of an RI plot. We then compare some currently available transformation strategies in terms of their mechanism and performance on both simulated and real microarray data. A linlog transformation is proposed to stabilize the variance of the log ratios. We also propose a regional smoothing method to remove variation in log ratios due to spatial heterogeneity on the microarray surface. The discussed transformations represent an important initial step in microarray data analysis for both ratio-based and ANOVA methods.

Design and validation issues in RNA-seq experiments

The next-generation sequencing technologies are being rapidly applied in biological research. Tens of millions of short sequences generated in a single experiment provide us enormous information on genome composition, genetic variants, gene expression levels and protein binding sites depending on the applications. Various methods are being developed for analyzing the data generated by these technologies. However, the relevant experimental design issues have rarely been discussed. In this review, we use RNA-seq as an example to bring this topic into focus and to discuss experimental design and validation issues pertaining to next-generation sequencing in the quantification of transcripts.

Arabidopsis extra large G-protein 2 (XLG2) interacts with the Gβ subunit of heterotrimeric G protein and functions in disease resistance.

Heterotrimeric GTP-binding proteins, which consist of Galpha, Gbeta, and Ggamma subunits, play important roles in transducing extracellular signals perceived by cell surface receptors into intracellular physiological responses. In addition to a single prototypical Galpha protein (GPA1), Arabidopsis has three unique Galpha-like proteins, known as XLG1, XLG2, and XLG3, that have been found to be localized in nuclei, although their functions and mode of action remain largely unknown. Through a transcriptomic analysis, we found that XLG2 and XLG3 were rapidly induced by infection with the bacterial pathogen Pseudomonas syringae, whereas the XLG1 transcript level was not affected by pathogen infection. A reverse genetic screen revealed that the xlg2 loss-of-function mutation causes enhanced susceptibility to P. syringae. Transcriptome profiling revealed that the xlg2 mutation affects pathogen-triggered induction of a small set of defense-related genes. However, xlg1 and xlg3 mutants showed no difference from wild-type plants in resistance to P. syringae. In addition, the xlg2 xlg3 double mutant and the xlg1 xlg2 xlg3 triple mutant were not significantly different from the xlg2 single mutant in the disease resistance phenotype, suggesting that the roles of XLG1 and XLG3 in defense, if any, are less significant than for XLG2. Constitutive overexpression of XLG2 leads to the accumulation of abnormal transcripts from multiple defense-related genes. Through co-immunoprecipitation assays, XLG2 was found to interact with AGB1, the sole Gbeta subunit in Arabidopsis, which has previously been found to be a positive regulator in resistance to necrotrophic fungal pathogens. However, no significant difference was found between three xlg single mutants, the xlg2 xlg3 double mutant, the xlg triple mutant, and wild-type plants in resistance to the necrotrophic fungal pathogens Botrytis cinerea or Alternaria brassicicola. These results suggest that XLG2 and AGB1 are components of a G-protein complex different from the prototypical heterotrimeric G-protein and may have distinct functions in modulating defense responses.

Innate Transcriptional Networks Activated in Bladder in Response to Uropathogenic Escherichia coli Drive Diverse Biological Pathways and Rapid Synthesis of IL-10 for Defense against Bacterial Urinary Tract Infection

Early transcriptional activation events that occur in bladder immediately following bacterial urinary tract infection (UTI) are not well defined. In this study, we describe the whole bladder transcriptome of uropathogenic Escherichia coli (UPEC) cystitis in mice using genome-wide expression profiling to define the transcriptome of innate immune activation stemming from UPEC colonization of the bladder. Bladder RNA from female C57BL/6 mice, analyzed using 1.0 ST-Affymetrix microarrays, revealed extensive activation of diverse sets of innate immune response genes, including those that encode multiple IL-family members, receptors, metabolic regulators, MAPK activators, and lymphocyte signaling molecules. These were among 1564 genes differentially regulated at 2 h postinfection, highlighting a rapid and broad innate immune response to bladder colonization. Integrative systems-level analyses using InnateDB (http://www.innatedb.com) bioinformatics and ingenuity pathway analysis identified multiple distinct biological pathways in the bladder transcriptome with extensive involvement of lymphocyte signaling, cell cycle alterations, cytoskeletal, and metabolic changes. A key regulator of IL activity identified in the transcriptome was IL-10, which was analyzed functionally to reveal marked exacerbation of cystitis in IL-10–deficient mice. Studies of clinical UTI revealed significantly elevated urinary IL-10 in patients with UPEC cystitis, indicating a role for IL-10 in the innate response to human UTI. The whole bladder transcriptome presented in this work provides new insight into the diversity of innate factors that determine UTI on a genome-wide scale and will be valuable for further data mining. Identification of protective roles for other elements in the transcriptome will provide critical new insight into the complex cascade of events that underpin UTI.