Phenotypic and genetic patterns of resistance to the pathogen Phakopsora pachyrhizi in populations of Glycine canescens

Summary Phenotypic patterns of resistance to nine races of the pathogen Phakopsora pachyrhizi (soybean rust) in two natural populations of Glycine canescens were determined. In both populations there was considerable variability both within and between different host lines in their resistance or susceptibility to the nine different pathogen races. The genetic basis of these patterns of resistance was analyzed through an extensive series of crosses. In both host populations resistance was conditioned by single dominant genes with major phenotypic effects. One, two or three such genes were present in each host line. Using the principles of the gene-for-gene hypothesis, knowledge about the number of resistance genes present in each host line and by cross comparison of the phenotypic patterns of disease resistance detected in each line, estimates were made of the number of resistance genes or alleles present in each population of G. canescens. The two populations contained a minimum of 10 and 12 resistance genes. The relevance of these results to agriculture is discussed briefly.     

Expressed sequence tag analysis of the soybean rust pathogen Phakopsora pachyrhizi

Soybean rust is caused by the obligate fungal pathogen Phakopsora pachyrhizi Sydow. A unidirectional cDNA library was constructed using mRNA isolated from germinating P. pachyrhizi urediniospores to identify genes expressed at this physiological stage. Single pass sequence analysis of 908 clones revealed 488 unique expressed sequence tags (ESTs, unigenes) of which 107 appeared as multiple copies. BLASTX analysis identified 189 unigenes with significant similarities (Evalue<10(-5)) to sequences deposited in the NCBI non-redundant protein database. A search against the NCBI dbEST using the BLASTN algorithm revealed 32 ESTs with high or moderate similarities to plant and fungal sequences. Using the Expressed Gene Anatomy Classification, 31.7% of these ESTs were involved in primary metabolism, 14.3% in gene/protein expression, 7.4% in cell structure and growth, 6.9% in cell division, 4.8% in cell signaling/cell communication, and 4.8% in cell/organism defense. Approximately 29.6% of the identities were to hypothetical proteins and proteins with unknown function.     

Identifying differentially expressed genes in cDNA microarray experiments.

A major goal of microarray experiments is to determine which genes are differentially expressed between samples. Differential expression has been assessed by taking ratios of expression levels of different samples at a spot on the array and flagging spots (genes) where the magnitude of the fold difference exceeds some threshold. More recent work has attempted to incorporate the fact that the variability of these ratios is not constant. Most methods are variants of Student's t-test. These variants standardize the ratios by dividing by an estimate of the standard deviation of that ratio; spots with large standardized values are flagged. Estimating these standard deviations requires replication of the measurements, either within a slide or between slides, or the use of a model describing what the standard deviation should be. Starting from considerations of the kinetics driving microarray hybridization, we derive models for the intensity of a replicated spot, when replication is performed within and between arrays. Replication within slides leads to a beta-binomial model, and replication between slides leads to a gamma-Poisson model. These models predict how the variance of a log ratio changes with the total intensity of the signal at the spot, independent of the identity of the gene. Ratios for genes with a small amount of total signal are highly variable, whereas ratios for genes with a large amount of total signal are fairly stable. Log ratios are scaled by the standard deviations given by these functions, giving model-based versions of Studentization. An example is given.     

Mycoparasitism of Phakopsora pachyrhizi,the soybean rust pathogen,by Simplicillium lanosoniveum

Rust of soybean, caused by Phakopsora pachyrhizi, was first reported in the southeastern US in 2004 where it quickly became established. Yield losses ranged from 35% to more than 80%. The mycophilic fungus Simplicillium lanosoniveum was previously shown to colonize rust pustules on soybean leaves and prevent germination of urediniospores. The number of pustules on soybean leaves also was significantly reduced when S. lanosoniveum colonized leaves. This study examined the antagonistic interactions between P. pachyrhizi and S. lanosoniveum using confocal, transmission (TEM) and scanning electron microscopy (SEM) in order to determine if S. lanosoniveum was mycoparasitic. Co-inoculated detached soybean leaves were examined using TEM and SEM to examine changes in urediniospores colonized by S. lanosoniveum. An isolate of S. lanosoniveum, previously shown to be an antagonist of P. pachyrhizi, was transformed with the green fluorescent protein (GFP) gene and used to document the infection process using confocal microscopy. S. lanosoniveum colonized pustules and coiled around urediniospores before infection. The mycoparasite penetrated urediniospores through germ pores within 24 h after inoculation, at which time organelles showed signs of degradation. By 2 days after inoculation, there was extensive colonization of urediniospores by hyphae of S. lanosoniveum. Hyphae of the mycoparasite erupted from the colonized urediniospores at 7 days after inoculation, and there was extensive sporulation on the surface of urediniospores. Over 90% of urediniospores were colonized within 5 days after inoculation with the mycoparasite. We showed in previous studies that S. lanosoniveum is an antagonist of P. pachyrhizi as well as an effective biocontrol agent, but we were unable to document its mode of action. We now present microscopic evidence that S. lanosoniveum is a mycoparasite.     

Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering

A Bayesian model-based clustering approach is proposed for identifying differentially expressed genes in meta-analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from non-differentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration-driven discovery rate (IDR; Choi et al., 2003) can be easily computed. The model-based approach is also compared with commonly used permutation methods, and it is shown that the model-based approach is superior to the permutation methods when there are excessive under-expressed genes compared to over-expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets.     

Identifying differentially expressed genes using false discovery rate controlling procedures

MOTIVATION: DNA microarrays have recently been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The probability that a false identification (type I error) is committed can increase sharply when the number of tested genes gets large. Correlation between the test statistics attributed to gene co-regulation and dependency in the measurement errors of the gene expression levels further complicates the problem. In this paper we address this very large multiplicity problem by adopting the false discovery rate (FDR) controlling approach. In order to address the dependency problem, we present three resampling-based FDR controlling procedures, that account for the test statistics distribution, and compare their performance to that of the na?ve application of the linear step-up procedure in Benjamini and Hochberg (1995). The procedures are studied using simulated microarray data, and their performance is examined relative to their ease of implementation. RESULTS: Comparative simulation analysis shows that all four FDR controlling procedures control the FDR at the desired level, and retain substantially more power then the family-wise error rate controlling procedures. In terms of power, using resampling of the marginal distribution of each test statistics substantially improves the performance over the na?ve one. The highest power is achieved, at the expense of a more sophisticated algorithm, by the resampling-based procedures that resample the joint distribution of the test statistics and estimate the level of FDR control. AVAILABILITY: An R program that adjusts p-values using FDR controlling procedures is freely available over the Internet at www.math.tau.ac.il/~ybenja.     

Identifying differentially expressed genes from microarray experiments via statistic synthesis

MOTIVATION: A common objective of microarray experiments is the detection of differential gene expression between samples obtained under different conditions. The task of identifying differentially expressed genes consists of two aspects: ranking and selection. Numerous statistics have been proposed to rank genes in order of evidence for differential expression. However, no one statistic is universally optimal and there is seldom any basis or guidance that can direct toward a particular statistic of choice. RESULTS: Our new approach, which addresses both ranking and selection of differentially expressed genes, integrates differing statistics via a distance synthesis scheme. Using a set of (Affymetrix) spike-in datasets, in which differentially expressed genes are known, we demonstrate that our method compares favorably with the best individual statistics, while achieving robustness properties lacked by the individual statistics. We further evaluate performance on one other microarray study.     

Leaf gas exchange and chlorophyll a fluorescence in soybean leaves infected by Phakopsora pachyrhizi

Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, is one of the most important foliar diseases affecting soybean production worldwide. This study aimed to investigate the photosynthetic performance (leaf gas exchange, chlorophyll (Chl) a fluorescence images and photosynthetic pigment pools) of soybean plants sprayed with Acibenzolar‐S‐Methyl (ASM) and the fungicide epoxiconazole + pyraclostrobin (Epo+Pyr) and further inoculated with P. pachyrhizi. The ASR symptoms progressed much faster on the leaves of plants from the control treatment (water spray) in comparison with the ASM and Epo+Pyr treatments. In general, the values for the leaf gas exchange parameters net carbon assimilation rate (A), stomatal conductance to water vapour (g_s), internal CO₂ concentration (C_i) and transpiration rate (E) increased for the infected plants sprayed with ASM or Epo+Pyr in comparison with plants from the control treatment. The values for the initial fluorescence (F_o), maximal fluorescence (F_m), maximal photosystem II quantum efficiency (F_v/F_m), effective photosystem II quantum yield (Y(II)) and quantum yield of regulated energy dissipation (Y(NPQ)) were consistently higher for the ASM and Epo+Pyr treatments in comparison with the control treatment at advanced stages of fungal infection. By contrast, the values for quantum yield of non‐regulated energy dissipation (Y(NO) were significantly lower for the ASM and Epo+Pyr treatments. The concentrations of total Chl a+b and carotenoids significantly increased for infected plants sprayed with ASM and Epo+Pyr in comparison with plants from the control treatment. The results of this study demonstrated that the spray of soybean plants with either ASM or Epo+Pyr contributed to reduce the negative effect of ASR on the photosynthesis of soybean plants.     

Identifying differentially expressed genes in cancer patients using a non-parameter Ising model

Identification of genes and pathways involved in diseases and physiological conditions is a major task in systems biology. In this study, we developed a novel non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also proposed a simulated annealing algorithm to find the optimal configuration of the Ising model. The Ising model was applied to two breast cancer microarray data sets. The results showed that more cancer-related DE sub-networks and genes were identified by the Ising model than those by the Markov random field model. Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model.     

Biphasic gene expression changes elicited by Phakopsora pachyrhizi in soybean correlate with fungal penetration and haustoria formation

Inoculation of soybean (Glycine max) plants with Phakopsora pachyrhizi, the causal organism of Asian soybean rust, elicits a biphasic response characterized by a burst of differential gene expression in the first 12 h. A quiescent period occurs from 24 to 48 h after inoculation, in which P. pachyrhizi continues to develop but does not elicit strong host responses, followed by a second phase of intense gene expression. To correlate soybean responses with P. pachyrhizi growth and development, we inoculated the soybean cultivar Ankur (accession PI462312), which carries the Rpp3 resistance gene, with avirulent and virulent isolates of P. pachyrhizi. The avirulent isolate Hawaii 94-1 elicits hypersensitive cell death that limits fungal growth on Ankur and results in an incompatible response, while the virulent isolate Taiwan 80-2 grows extensively, sporulates profusely, and produces a compatible reaction. Inoculated leaves were collected over a 288-h time course for microarray analysis of soybean gene expression and microscopic analysis of P. pachyrhizi growth and development. The first burst in gene expression correlated with appressorium formation and penetration of epidermal cells, while the second burst of gene expression changes followed the onset of haustoria formation in both compatible and incompatible interactions. The proliferation of haustoria coincided with the inhibition of P. pachyrhizi growth in the incompatible interaction or the beginning of accelerated growth in the compatible interaction. The temporal relationships between P. pachyrhizi growth and host responses provide an important context in which to view interacting gene networks that mediate the outcomes of their interactions.     

Phakopsora pachyrhizi, the causal agent of Asian soybean rust

The plant pathogenic basidiomycete fungi Phakopsora pachyrhizi and Phakopsora meibomiae cause rust disease in soybean plants. Phakopsora pachyrhizi originated in Asia–Australia, whereas the less aggressive P. meibomiae originated in Latin America. In the New World, P. pachyrhizi was first reported in the 1990s to have spread to Hawaii and, since 2001, it has been found in South America. In 2004, the pathogen entered continental USA. This review provides detailed information on the taxonomy and molecular biology of the pathogen, and summarizes strategies to combat the threat of this devastating disease.
Taxonomy: Phakopsora pachyrhizi Syd. & P. Syd; uredial anamorph: Malupa sojae (syn. Uredo sojae ); Domain Eukaryota; Kingdom Fungi; Phylum Basidiomycota; Order Uredinales; Class Urediniomycetes; Family Phakopsoraceae; Genus Phakopsora ( http://www.indexfungorum.org ). The nomenclature of rust spores and spore-producing structures used within this review follows Agrios GN (2005) Plant Pathology , 5th edn. London: Elsevier/Academic Press.
Host range: In the field, P. pachyrhizi infects leaf tissue from a broad range (at least 31 species in 17 genera) of leguminous plants. Infection of an additional 60 species in other genera has been achieved under laboratory conditions.
Disease symptoms: At the beginning of the disease, small, tan-coloured lesions, restricted by leaf veins, can be observed on infected soybean leaves. Lesions enlarge and, 5–8 days after initial infection, rust pustules (uredia, syn. uredinia) become visible. Uredia develop more frequently in lesions on the lower surface of the leaf than on the upper surface. The uredia open with a round ostiole through which uredospores are released.     

Prediction of the in planta Phakopsora pachyrhizi secretome and potential effector families

Asian soybean rust (ASR), caused by the obligate biotrophic fungus Phakopsora pachyrhizi, can cause losses greater than 80%. Despite its economic importance, there is no soybean cultivar with durable ASR resistance. In addition, the P. pachyrhizi genome is not yet available. However, the availability of other rust genomes, as well as the development of sample enrichment strategies and bioinformatics tools, has improved our knowledge of the ASR secretome and its potential effectors. In this context, we used a combination of laser capture microdissection (LCM), RNAseq and a bioinformatics pipeline to identify a total of 36 350 P. pachyrhizi contigs expressed in planta and a predicted secretome of 851 proteins. Some of the predicted secreted proteins had characteristics of candidate effectors: small size, cysteine rich, do not contain PFAM domains (except those associated with pathogenicity) and strongly expressed in planta. A comparative analysis of the predicted secreted proteins present in Pucciniales species identified new members of soybean rust and new Pucciniales‐ or P. pachyrhizi‐specific families (tribes). Members of some families were strongly up‐regulated during early infection, starting with initial infection through haustorium formation. Effector candidates selected from two of these families were able to suppress immunity in transient assays, and were localized in the plant cytoplasm and nuclei. These experiments support our bioinformatics predictions and show that these families contain members that have functions consistent with P. pachyrhizi effectors.     

Identifying differentially expressed genes in human acute leukemia and mouse brain microarray datasets utilizing QTModel

One of the essential issues in microarray data analysis is to identify differentially expressed genes (DEGs) under different experimental treatments. In this article, a statistical procedure was proposed to identify the DEGs for gene expression data with or without missing observations from microarray experiment with one- or two-treatment factors. An F statistic based on Henderson method III was constructed to test the significance of differential expression for each gene under different treatment(s) levels. The cutoff P value was adjusted to control the experimental-wise false discovery rate. A human acute leukemia dataset corrected from 38 leukemia patients was reanalyzed by the proposed method. In comparison to the results from significant analysis of microarray (SAM) and microarray analysis of variance (MAANOVA), it was indicated that the proposed method has similar performance with MAANOVA for data with one-treatment factor, but MAANOVA cannot directly handle missing data. In addition, a mouse brain dataset collected from six brain regions of two inbred strains (two-treatment factors) was reanalyzed to identify genes with distinct regional-specific expression patterns. The results showed that the proposed method could identify more distinct regional-specific expression patterns than the previous analysis of the same dataset. Moreover, a computer program was developed and incorporated in the software QTModel, which is freely available at .