Parsa Hosseini

Analysis of Gene expression in soybean (Glycine max) roots in response to the root knot nematode Meloidogyne incognita using microarrays and KEGG pathways

BackgroundRoot-knot nematodes are sedentary endoparasites that can infect more than 3000 plant species. Root-knot nematodes cause an estimated

Syncytium gene expression in Glycine max([PI 88788]) roots undergoing a resistant reaction to the parasitic nematode Heterodera glycines.

100 billion annual loss worldwide. For successful establishment of the root-knot nematode in its host plant, it causes dramatic morphological and physiological changes in plant cells. The expression of some plant genes is altered by the nematode as it establishes its feeding site.ResultsWe examined the expression of soybean (Glycine max) genes in galls formed in roots by the root-knot nematode, Meloidogyne incognita, 12 days and 10 weeks after infection to understand the effects of infection of roots by M. incognita. Gene expression was monitored using the Affymetrix Soybean GeneChip containing 37,500 G. max probe sets. Gene expression patterns were integrated with biochemical pathways from the Kyoto Encyclopedia of Genes and Genomes using PAICE software. Genes encoding enzymes involved in carbohydrate and cell wall metabolism, cell cycle control and plant defense were altered.ConclusionsA number of different soybean genes were identified that were differentially expressed which provided insights into the interaction between M. incognita and soybean and into the formation and maintenance of giant cells. Some of these genes may be candidates for broadening plants resistance to root-knot nematode through over-expression or silencing and require further examination.

Population-specific gene expression in the plant pathogenic nematode Heterodera glycines exists prior to infection and during the onset of a resistant or susceptible reaction in the roots of the Glycine max genotype Peking.

The plant parasitic nematode, Heterodera glycines is the major pathogen of Glycine max (soybean). H. glycines accomplish parasitism by creating a nurse cell known as the syncytium from which it feeds. The syncytium undergoes two developmental phases. The first is a parasitism phase where feeding sites are selected, initiating the development of the syncytium. During this earlier phase (1-4 days post infection), syncytia undergoing resistant and susceptible reactions appear the same. The second phase is when the resistance response becomes evident (between 4 and 6dpi) and is completed by 9dpi. Analysis of the resistant reaction of G. max genotype PI 88788 (G. max([PI 88788])) to H. glycines population NL1-RHg/HG-type 7 (H. glycines([NL1-RHg/HG-type 7])) is accomplished by laser microdissection of syncytia at 3, 6 and 9dpi. Comparative analyses are made to pericycle and their neighboring cells isolated from mock-inoculated roots. These analyses reveal induced levels of the jasmonic acid biosynthesis and 13-lipoxygenase pathways. Direct comparative analyses were also made of syncytia at 6 days post infection to those at 3dpi (base line). The comparative analyses were done to identify localized gene expression that characterizes the resistance phase of the resistant reaction. The most highly induced pathways include components of jasmonic acid biosynthesis, 13-lipoxygenase pathway, S-adenosyl methionine pathway, phenylpropanoid biosynthesis, suberin biosynthesis, adenosylmethionine biosynthesis, ethylene biosynthesis from methionine, flavonoid biosynthesis and the methionine salvage pathway. In comparative analyses of 9dpi to 6dpi (base line), these pathways, along with coumarin biosynthesis, cellulose biosynthesis and homogalacturonan degradation are induced. The experiments presented here strongly implicate the jasmonic acid defense pathway as a factor involved in the localized resistant reaction of G. max([PI 88788]) to H. glycines([NL1-RHg/HG-type 7]).

An efficient annotation and gene-expression derivation tool for Illumina Solexa datasets

BackgroundA single Glycine max (soybean) genotype (Peking) reacts differently to two different populations of Heterodera glycines (soybean cyst nematode) within the first twelve hours of infection during resistant (R) and susceptible (S) reactions. This suggested that H. glycines has population-specific gene expression signatures. A microarray analysis of 7539 probe sets representing 7431 transcripts on the Affymetrix® soybean GeneChip® were used to identify population-specific gene expression signatures in pre-infective second stage larva (pi-L2) prior to their infection of Peking. Other analyses focused on the i nfective L2 at 12h ours post infection (i-L212h), and the i nfective sedentary stages at 3d ays post infection (i-L23d) and 8d ays post infection (i-L2/L38d).ResultsDifferential expression and false discovery rate (FDR) analyses comparing populations of pi-L2 (i.e., incompatible population, NL1-RHg to compatible population, TN8) identified 71 genes that were induced in NL1-RHg as compared to TN8. These genes included putative gland protein G23G12, putative esophageal gland protein Hgg-20 and arginine kinase. The comparative analysis of pi-L2 identified 44 genes that were suppressed in NL1-RHg as compared to TN8. These genes included a different Hgg-20 gene, an EXPB1 protein and a cuticular collagen. By 12 h, there were 7 induced genes and 0 suppressed genes in NL1-RHg. By 3d, there were 9 induced and 10 suppressed genes in NL1-RHg. Substantial changes in gene expression became evident subsequently. At 8d there were 13 induced genes in NL1-RHg. This included putative gland protein G20E03, ubiquitin extension protein, putative gland protein G30C02 and β-1,4 endoglucanase. However, 1668 genes were found to be suppressed in NL1-RHg. These genes included steroid alpha reductase, serine proteinase and a collagen protein.ConclusionThese analyses identify a genetic expression signature for these two populations both prior to and subsequently as they undergo an R or S reaction. The identification of genes like steroid alpha reductase and serine proteinase that are involved in feeding and nutritional uptake as being highly suppressed during the R response at 8d may indicate genes that the plant is targeting. The analyses also identified numerous putative parasitism genes that are differentially expressed. The 1668 genes that are suppressed in NL1-RHg, and hence induced in TN8 may represent genes that are important during the parasitic stages of H. glycines development. The potential for different arrays of putative parasitism genes to be expressed in different nematode populations may indicate how H. glycines evolve mechanisms to overcome resistance.

Gene Expression in Leaves of Susceptible Glycine max during Infection with Phakopsora pachyrhizi Using Next Generation Sequencing

BackgroundThe data produced by an Illumina flow cell with all eight lanes occupied, produces well over a terabyte worth of images with gigabytes of reads following sequence alignment. The ability to translate such reads into meaningful annotation is therefore of great concern and importance. Very easily, one can get flooded with such a great volume of textual, unannotated data irrespective of read quality or size. CASAVA, a optional analysis tool for Illumina sequencing experiments, enables the ability to understand INDEL detection, SNP information, and allele calling. To not only extract from such analysis, a measure of gene expression in the form of tag-counts, but furthermore to annotate such reads is therefore of significant value.FindingsWe developed TASE (Tag counting and Analysis of Solexa Experiments), a rapid tag-counting and annotation software tool specifically designed for Illumina CASAVA sequencing datasets. Developed in Java and deployed using jTDS JDBC driver and a SQL Server backend, TASE provides an extremely fast means of calculating gene expression through tag-counts while annotating sequenced reads with the genes presumed function, from any given CASAVA-build. Such a build is generated for both DNA and RNA sequencing. Analysis is broken into two distinct components: DNA sequence or read concatenation, followed by tag-counting and annotation. The end result produces output containing the homology-based functional annotation and respective gene expression measure signifying how many times sequenced reads were found within the genomic ranges of functional annotations.ConclusionsTASE is a powerful tool to facilitate the process of annotating a given Illumina Solexa sequencing dataset. Our results indicate that both homology-based annotation and tag-count analysis are achieved in very efficient times, providing researchers to delve deep in a given CASAVA-build and maximize information extraction from a sequencing dataset. TASE is specially designed to translate sequence data in a CASAVA-build into functional annotations while producing corresponding gene expression measurements. Achieving such analysis is executed in an ultrafast and highly efficient manner, whether the analysis be a single-read or paired-end sequencing experiment. TASE is a user-friendly and freely available application, allowing rapid analysis and annotation of any given Illumina Solexa sequencing dataset with ease.

Regulatory interplay between soybean root and soybean cyst nematode during a resistant and susceptible reaction

Soybean rust is caused by the obligate biotrophic fungus Phakopsora pachyrhizi, an exotic pathogen causing important yield losses in soybean production. We used an mRNA-Seq strategy to analyze the expression pattern of soybean genes and better understand molecular events occurring in soybean following the infection. cDNA libraries were constructed from RNA isolated from whole infected soybean leaves 10 days after inoculation with P. pachyrhizi and sequenced using an Illumina platform to identify soybean genes that are affected by pathogen growth. We obtained 15 million sequences corresponding to soybean genes. Forty-two percent of the genes were downregulated including genes encoding proteins involved in amino acid metabolism, carbohydrate metabolism, and transport facilitation; 31% were upregulated including genes encoding proteins involved in lipid metabolism, glycan biosynthesis, and signal transduction. Candidate host genes identified in this study will be manipulated to assay their potential to control soybean rust disease.

Using an ensemble of statistical metrics to quantify large sets of plant transcription factor binding sites.

BackgroundPlant–parasitic nematodes (PPNs) are obligate parasites that feed on the roots of living host plants. Often, these nematodes can lay hundreds of eggs, each capable of surviving without a host for as long as 12 years. When it comes to wreaking havoc on agricultural yield, few nematodes can compare to the soybean cyst nematode (SCN). Quantifying soybean (Glycine max) transcription factor binding sites (TFBSs) during a late–stage SCN resistant and susceptible reaction can shed light onto the systematic interplay between host and pathogen, thereby elucidating underlying cis–regulatory mechanisms.ResultsWe sequenced the soybean root transcriptome at 6 and 8 days upon independent inoculation with a virulent and avirulent SCN population. Genes such as β–1,4 glucanase, chalcone synthase, superoxide dismutase and various heat shock proteins (HSPs) exhibited reaction–specific expression profiles. Several likely defense–response genes candidates were also identified which are believed to confer SCN resistance. To explore magnitude of TFBS representation during SCN pathogenesis, a multivariate statistical software identified 46 over–represented TFBSs which capture soybean regulatory dynamics across both reactions.ConclusionsOur results reveal a set of soybean TFBSs which are over–represented solely throughout a resistant and susceptible SCN reaction. This set furthers our understanding of soybean cis–regulatory dynamics by providing reaction–specific levels of over–representation at 6 and 8 days after inoculation (dai) with SCN.

MAPT and PAICE: Tools for time series and single time point transcriptionist visualization and knowledge discovery

BackgroundFrom initial seed germination through reproduction, plants continuously reprogram their transcriptional repertoire to facilitate growth and development. This dynamic is mediated by a diverse but inextricably-linked catalog of regulatory proteins called transcription factors (TFs). Statistically quantifying TF binding site (TFBS) abundance in promoters of differentially expressed genes can be used to identify binding site patterns in promoters that are closely related to stress-response. Output from today’s transcriptomic assays necessitates statistically-oriented software to handle large promoter-sequence sets in a computationally tractable fashion.ResultsWe present Marina, an open-source software for identifying over-represented TFBSs from amongst large sets of promoter sequences, using an ensemble of 7 statistical metrics and binding-site profiles. Through software comparison, we show that Marina can identify considerably more over-represented plant TFBSs compared to a popular software alternative.ConclusionsMarina was used to identify over-represented TFBSs in a two time-point RNA-Seq study exploring the transcriptomic interplay between soybean (Glycine max) and soybean rust (Phakopsora pachyrhizi). Marina identified numerous abundant TFBSs recognized by transcription factors that are associated with defense-response such as WRKY, HY5 and MYB2. Comparing results from Marina to that of a popular software alternative suggests that regardless of the number of promoter-sequences, Marina is able to identify significantly more over-represented TFBSs.

A gene expression analysis of syncytia laser microdissected from the roots of the Glycine max (soybean) genotype PI 548402 (Peking) undergoing a resistant reaction after infection by Heterodera glycines (soybean cyst nematode).

With the advent of next-generation sequencing, -omics fields such as transcriptomics have experienced increases in data throughput on the order of magnitudes. In terms of analyzing and visually representing these huge datasets, an intuitive and computationally tractable approach is to map quantified transcript expression onto biochemical pathways while employing datamining and visualization principles to accelerate knowledge discovery. We present two cross-platform tools: MAPT (Mapping and Analysis of Pathways through Time) and PAICE (Pathway Analysis and Integrated Coloring of Experiments), an easy to use analysis suite to facilitate time series and single time point transcriptomics analysis. In unison, MAPT and PAICE serve as a visual workbench for transcriptomics knowledge discovery, data-mining and functional annotation. Both PAICE and MAPT are two distinct but yet inextricably linked tools. The former is specifically designed to map EC accessions onto KEGG pathways while handling multiple gene copies, detection-call analysis, as well as UN/annotated EC accessions lacking quantifiable expression. The latter tool integrates PAICE datasets to drive visualization, annotation, and data-mining. Availability The database is available for free at http://sourceforge.net/projects/paice/http://sourceforge.net/projects/mapt/