Functional Genomics of Host Pathogen Interactions In Wheat Rust Pathosystem

Lovejoth

OutlineRust and their life cycle

Defense employed by host plant

Use of functional genomics in studying host pathogen interactions

Transcriptomics as a tool to study host pathogen interactions

Proteomics as a tool to study host pathogen interactions

Metabolomic approaches for studying host pathogen interactions

Wheat is one of the first cereals known to have been domesticated, and wheat's ability to self-pollinate greatly facilitated the selection of many distinct domesticated varieties.

“RUST NEVER DIES”

Two classes of pathogens based on interactions with plants

Biotrophic fungal pathogens colonize living plant tissue and obtain nutrients from living host cells, invade only a few of the cells, it can reduce the competitive abilities of the host.

Hemibiotrophs derive nutrients from a combination of feeding from living and killed host cells

Necrotrophic fungal pathogens infect and kill host tissue and extract nutrients from the dead host cells.

Powdery MildewRice Blast

“Biotrophic parasitism is evolutionarily advanced”

Rusts are plant diseases caused by pathogenic fungi of the order Pucciniales. About 7800 species are known, Phylum: Basidiomycota Class: Pucciniomycetes

Different Rust Hosts

Stem rust:Puccinia graminis f.sp. tritici

Leaf rust: Puccinia triticina

Stripe rust :Puccinia striiformis f.sp. tritici

Wheat Rust

MACROCYCLIC,MICROCYCLIC (always have an autoecious ) and DEMICYCLIC

Life Cycle

Urediospores are responsible for disease outbreak

Alternate host of stem rust of Triticum turgidium and Triticum aestivum

BarberryMeadow Rue

Alternate host of leaf rust of Triticum turgidium and Triticum aestivum

Anchusa italica Heteroecious :require two unrelated hosts to complete their life cycle,one is economic host

 Autoecious: fungus which can complete its life cycle on a single host species

.

Resistance is broadly categorized into two groupsRace-specific type, gene-for-gene resistance= Controlled by genes with major to intermediate effects, is short lived, often lasting for an average of about five years when deployed. In wheat life of effective race-specific resistance genes can be prolonged by using gene combinations. A majority of the genes follow the gene-for-gene concept (Flor, 1956)

Race-nonspecific type (Polygenic, horizontal, general, minor, partial, slow rusting and residual). When present alone, APR genes do not confer adequate resistance especially under high disease pressure; however, combinations of 4–5 such genes usually result in ‘‘near-immunity’’ or a high level of resistance.

(Singh et al.,2010)

(Dodds et al.,2010)

In the coevolution of host-microbe interactions, pathogens acquired the ability to deliver effector proteins to the plant cell to suppress PTI, allowing pathogen growth and disease. In response to the delivery of pathogen effector proteins, plants acquired surveillance proteins (R proteins) to monitor the presence of the pathogen effector proteins

Immune Responses in Plants

Additional modulation of the defense response is brought about by the effects of a third signal transduction cascade triggered by ethylene (ET) produced upon attack .Each of the signal-transduction pathways acts to activate a distinct set of defense genes (Koornneef et al., 2008)

Different Pathways Triggered on Pathogen Invasion

VEGETATIVE STORAGE PROTEIN 1 (VSP1) by JA, GLUTAREDOXIN 480 (GRX480) by SA, and PATHOGENESIS-RELATED 3/CHITINASE B (PR-3/ChiB) and PR-4/HEVEIN-LIKE (HEL) by ET. PLANT DEFENSIN 1.2 (PDF1.2) by ET and JA, or PR-1 and GLUTATHION-S-TRANSFERASE 1 (GST1) by ET and SA.

(Verk et al.,2010)

Genes Triggered by Different Pathways

Lincoln Taiz and Eduardo Zeiger

Plant Response To Fungal Pathogen

(Jun Liu et al.,2008)

Cross Talk Between ETI and PTI

Susceptible wheat leaves (Mingxian 169) inoculated with Puccinia striiformis on the fifth day after inoculation. Haustoria developed normally, mitochondria and nuclei normal ,no adverse effect on host cell after haustoria formation ,haustorial protoplasm was well preserved and organelles were regularly arranged, EHM continuous and undulated.

COMPATIBLE INTERACTIONS(Susceptibility)

(Ma et al.,2009)

INCOMPATIBLE INTERACTIONS(Resistance)

(Ma et al.,2009)

Intercellular hypha with vacuolated mitochondria ,hyphal wall thickening and deeply stained ,haustorial mother cell vacuolated with deeply stained lipid material ,haustorium surrounded by callose and necrotized, extrahaustorial membrane wrinkled, extrahaustorial matrix thickened with electron-dense material deposited, host organelles disintegrated.

Cytological differences in compatible and incompatible interactions

(Ma et al.,2009)

ResistanceSusceptibility

LIMITATIONS OF TRADITIONAL BREEDING IN STUDYING HOSTPATHOGEN INTERACTIONS

Most genes confer race-specific resistance in a gene-for-gene manner. 1900 - 1955 various major resistance genes were discovered.

Erosion of race specific resistance genes, or their combinations

Wheat varieties relying on race-specific resistance lose effectivenesswithin a few years.

Adult plant resistance is believed to be more durable but it is more difficult to evaluate, the multigenic nature of APR impedes the use of MAS efforts

Adaptability of fungi due to enormous genetic fluidity, result in genomic rearrangements and mutations, migration and adaptation of the fungus to the diverse climatic conditions where wheat is grown

(Bakkeren et al.,2012)

Need of studying HPI? To reveal Signal transduction events and expression of disease resistance. Understand molecular basis and evolution of R gene specificity. Evolution of plant disease resistance to specific pathogen. Cloning and characterization of plant R genes. Identification of novel and stable plant resistance genes

How functional genomics can help?

Identification of fungal virulence genes or host response elements, which can be useful for breeding programmes.

Large-scale genomic projects will reveal many PAMPs

(Bakkeren et al.,2012)

Wheat (Triticum aestivum L.) with a large genome (16000 Mb) High proportion ( 80%) of repetitive sequences. ∼

Development of thousands of molecular markers (including RFLPs, SSRs, AFLPs, SNPs, and DArT markers), construction of molecular genetic and physical maps

Development of more than 1 million ESTs

Development of BAC/BIBAC resources for individual chromosomes

Functional genomics approaches like TILLING, RNAi, and epigenetics have been utilized successfully, and a number of genes/QTL have been cloned. The first genes to be isolated from wheat by map-based cloning included three resistance genes, including leaf rust Lr21; and Lr10; and powdery mildew Pm3b

Organellar genomes including chloroplast and mitochondrial genomes have been fully sequenced

(Gupta.,2009)

Wheat Genomics

Puccinia graminis f. sp. tritici genomic

89-Mb genome ofby Sanger whole-genome shotgun strategy.

No evidence for whole-genome duplication or large-scale dispersed segmental duplications ,the expanded size results from a massive proliferation of transposable elements.

Predicted 17,773 protein coding genes.

Genomic features related to their obligate biotrophic lifestyle include expanded lineage-specific gene families, a large repertoire of effector-like small secreted proteins, impaired nitrogen and sulfur assimilation pathways, and expanded families of amino acid and oligopeptide membrane transporters.

(Sebastein et al.,2011)

Development and application of global (genome-wide or system-wide) experimental approaches to assess gene function by making use of the information and reagents provided by structural genomics. (Hieter and Boguski 1997)

Functional genomics includes a systematic analysis of mRNA and protein expression, exploration of gene product interactions and their influence on different phenotypical traits to define gene functions.

Functional Genomics

Gene expression profiling can be divided into three categories:

1 PCR: RT-PCR (qualitative and quantitative),DDRT-PCR

2) Sequencing based :cDNA sequencing (full-length cDNAs, subtracted cDNAs, normalized cDNA libraries), SAGE, Massive parallel signature sequencing ,454 and Solexa.

3) Hybridization based: Northern blots, Macroarrays, DNA microarrays, Oligonucleotide microarrays, Differential display, cDNA-AFLP

Deep sequencing and whole genome tilling arrays are becoming increasing important

(Yunbi Xu.,2010)

Transcriptomics

Interaction Transcriptome

(Peer M. Schenk et al.,2012)

Interaction transcriptomics reveals key plant and microbial genes that play important roles during these and other as yet unknown interactions. Challenges include How to discriminate pathogen from host ESTs. Similarity searches to genome/cDNA sequences.

Results in high-copy-number mRNAs being overrepresented whereas low-copy-number mRNAs are missed. NGS do not require cDNA molecules to be cloned before sequencing ,however, they provide much shorter reads than are obtained by Sanger sequencing. Ultra-high-throughput sequencing will yield reads from more mRNAs, complete transcripts will have to be assembled from many short reads.

cDNA Sequencing

Arrays

For cereal rust fungi, microarrays (Duplessis et al., 2011) and EST/cDNA arrays have yielded information on genes expressed during infection. The use of the Wheat GeneChip® technique is often conditioned by known gene sequences. with limited ESTs unspecific to different wheat materials

Transcriptome analysis of the wheat–Puccinia striiformis f. sp. tritici interaction

(Coram et al.,2008)

To identify transcripts associated with the Yr5 –mediated incompatible interaction and theYr5 -compatible interaction, the Wheat GeneChip was used to profile the changes occurring inwheat isolines gene after inoculation with Pst. Gene Chip represents over 55 000 wheat transcripts from all chromosomes and ancestral genomes. The temporal pattern of transcript accumulation showed a peak at 24 h after infection that may reflect haustorial penetration by Pst at 16 h.. Annotation revealed that the presence of Yr5 resulted in a rapid and amplified resistance response involving signalling pathways and defence-related transcripts ,protein kinase signalling , reactive oxygen species. To facilitate the map-based cloning of Yr5 , the GeneChip data was explored for the development of genetic markers that were linked to Yr5

Differential Display rtPCR

(Peng Liang.,2002)

Differential-display reverse transcription PCR (DDRT-PCR) is a PCR-based method that allows extensive analysis of gene expression ,identification of virulence factors, genes involved in cell death, and signaling genes

(Osman et al.,2008)

Genes associated with resistance to wheat yellow rust disease identified bydifferential display analysis

Differential display reverse transcriptase-PCR method (DDRT-PCR) was used on two of the yellow rust differential lines of wheat, infected with the virulent and the avirulent Puccinia striiformis f. sp. tritici races together with appropriate control inoculations.

Total of 90 primer combinations were used in DDRT-PCR reactions were generated with different time points ,60 differentially expressed bands were identified and excised from sequencing gels. Among them, 50 could be reamplified and 39 of these 50 were randomly selected to be cloned and sequenced.

(Osman et al.,2008)

Genes detected may have roles in ubiquitinylation, programmed cell death , putative antifungal activities, disease resistance responses, pathogenesis related responses, plant disease resistance like genes, pathogen related genes, and a gene with putative antifungal activity. Genes involved in ubiquitin mediated protein degradation are regulated in wheat in response to yellow rust incompatible pathogen infection and suggest that ubiquitinylation and protein degradation are significant regulatory mechanisms in wheat yellow rust disease resistance

(Osman et al.,2008)

Resistance mechanism may be diverging in these two plants

( Gadgil et al .,2002)

Subtractive Suppression Hybridization

(SSH) is a PCR-based cDNA subtraction method, and it selectively amplify target cDNA fragments (differentially expressed) and simultaneously suppress nontarget DNA amplification.

A (SSH) cDNA library was constructed from Pst infected seedling leaves of an immune resistant germplasm Shaanmai 139. A total of 84 ESTs were obtained. BlastX searches identified 45 of the Unigenes as similar to those encoding proteins of known function and eight of unknown function. Blast EST analysis of these Pst-induced genes showed that they were mostly homologous to genes that are induced by cold. The genes of known function include those with potential biological roles in signal transmission, energy and metabolism, transcription regulation, phenyl propanoid pathway, and defense response.

(Hong et al .,2011)

Gene Expression in Wheat Induced by Inoculation with Puccinia striiformis West

Senescence-associated genes, omega-6 fatty acid desaturase, and acyl-CoA synthetase showed bimodial pattern suggesting complex patterns of defense related gene expression in multi resistance gene cultivars.

(Hong et al .,2011)

Serial Analysis Of Gene Expression

The basis of the technique is that these 12 bp sequences, despite their shortness, are sufficient to enable the gene that codes for the mRNA to be identified. SAGE sampling is based on sequencing mRNA output, not on hybridization of mRNA output to probes, so transcription levels are measured more quantitatively than by microarray.

LongSAGE, RL-SAGE and SuperSAGE

Magnaporthe grisea (blast)-infected rice leaves, gene expression profiles of both the rice host and blast fungus was simultaneously monitored ,revealing that the hydrophobin gene is the most actively transcribed M. grisea gene in blast-infected rice leaves.

SuperSAGE has been applied to study gene expression changes elicitor-treated Nicotiana benthamiana, a ‘‘nonmodel’’ organism for which no DNAdatabase is available. SuperSAGE allowed rapid identification of genes up- or down-regulated by the elicitor.

(Matsumura.,2003)

(Matsumura et al.,2003)

cDNA AFLP

cDNA AFLP does not require prior sequence information and is universal for any organisms or interactions, and is, therefore, a powerful tool for identifying novel genes in non-model organisms like wheat

Differential gene expression in incompatible interaction between wheat and stripe rust fungus revealed by cDNA-AFLP and comparison to compatible interaction

52,992 transcript derived fragments (TDFs) were generated with 64 primer pairs and 2,437 of them displayed altered expression patterns after inoculation , 1,787 up-regulated and 650 down-regulated. 161 TDFs were shared by both interactions, 94 were expressed specifically in the incompatible interaction .A large group (17.6%) of these genes shared high homology with genes involved in metabolism and photosynthesis; 13.8% to genes with functions related to disease defense and signal transduction; and those in the remaining groups (12.9%) to genes involved in transcription, transport processes, protein metabolism, and cell structure.

(Wang et al.,2010)

Roche/454 FLX: 2004Illumina Solexa Genome Analyzer: 2006Applied Biosystems SOLiDTM System: 2007

NGS PLATFORMS

Illumina Genome Analyzer

Originally developed by Solexa, now subsidiary of Illumina.

Commercially available in 2006 It produces 8-12 million reads per sample

of 36 bp length = 10 GB/week. Run takes 3 days for 7 samples. Low error rate, mostly base changes, few

indels

The DNA sample of interest is sheared to appropriate size (average ~800bp) using a compressed air device known as a nebulizer. The flow cell surface is coated with single stranded oligonucleotides that correspond to the sequences of the adapters, repeated denaturation and extension results in localized amplification of single molecules in millions of unique locations across the flow cell surface. This process occurs "cluster station“.

A flow cell containing millions of unique clusters is now loaded into the 1G sequencer for automated cycles of extension and imaging.

Differences in Platforms

(Tremblay.,2011)

A mRNA-Seq strategy using the Illumina platform in susceptible Glycine max during Infection with soyabean rust.

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

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 ,15 million sequences corresponding to soybean genes were obtained. 42% of the genes were down regulated 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 and signal transduction.

(Tremblay.,2011)

(Tremblay.,2011)

mRNA abundance are not always mirrored by corresponding protein levels .An mRNA produced in abundance may be degraded rapidly or translated inefficiently

Many transcripts give rise to more than one proteins

Global study of the protein content of a cell

Why Proteomics to Study Host Pathogen Interactions

Isotope Coded Affinity Tags (ICAT) the isotopes are in affinity tags, Stable isotope labeling by amino acidsin cell culture (SILAC) uses stable isotopes to label amino acids , Isotope tagged relative and absolutequantitation (iTRAQ) peptides derived from each sample are derivatized with amine-specific isobaric tags. Partial proteome of cereal rust fungi has been generated a, focusing on isolated haustoria from Pt-infected wheat (Song et al., 2011).Over 260 proteins were identified .Among the proteins are many predicted pathogenicity and virulence factors. (B.F. Quirino et al.,2010)

Proteomics Work Flow

Protein phosphorylation is a pivotal process during plant–pathogen interactions

Proteins undergo phosphorylation /cleaved or degraded

(B.F. Quirino et al.,2010)

Analysis of the wheat and Puccinia triticina (leaf rust) proteomes during a susceptible host-pathogen interaction

Susceptible line of wheat was infected with a virulent race of leaf rust and compared to mock-inoculated wheat using 2-DE (with IEF pH 4–8) and MS. Up-regulated protein spots were excised and analyzed by MALDI-TOF MS/MS, followed by cross-species protein identification. Where possible MS/MS spectra were matched to homologous proteins in the NCBI database or to fungal ESTs encoding putative proteins. Searching was done using the MASCOT search engine. Of these 7 are host proteins, 22 are fungal proteins of known or hypothetical function and 3 are unknown proteins of putative fungal origin.

Distribution of proteins excised from gels and analyzed by MS/MS, by putative origin. Peptides from 32 proteins were analyzed.

(Rampitsch et al. ,2006)

Wheat leaves mock inoculated with oil (9DPI).

Wheat leaves inoculated with a virulent race of leaf rust (9 DPI)

(Rampitsch et al., 2006)

eIF5a and EF1b :in a rice-virus interactionEIF1b :in maize as a result of fungal attack Alpha 4 subunit of the 20S proteasome

Wheat Proteins and Fungal Proteins

Involved in the control of protein turnover

Metabolic enzymes (e.g., carbohydrate kinase); structural proteins (alpha-tubulin and ribosomal protein); heat shock proteins, ascorbate peroxidase

Degrades unwanted proteins

(Rampitsch et al. ,2006)

Involved in protecting the fungus from general stress generated by host defense mechanisms, combating a plant-induced oxidative burst

Genomics helping Proteomics

Pathosystems genomes of both interaction partners have been fully sequenced:M. oryzae/rice, R. solanacearum/Arabidopsis, X. oryzae pv. oryzae/rice, P. syringae pv. oryzae/rice, X. campestris pv. campestris/Arabidopsis, P. syringae pv. tomato/ Arabidopsis and X. fastidiosa/grapevine.

Proteomics studies with these pathosystems protein identification assignment of a particular protein to plant or pathogen origin.understanding of different aspects of plant–pathogen interactions

(B.F. Quirino et al.,2010)

Metabolomics comprehensive identification and quantification of all low molecular weight metabolites and their relationships in a biological sample at a specific time point.Metabolomics provides information about the ultimate biochemical outcome of changes in the genome, transcriptome and proteome.

METABOLOMICS

Mass spectrometry utilizes the retention time for a given metabolite, as well as the mass/charge ratio (MS), often in conjunction with the mass/charge ratio of fragmentation daughter ions in tandem MS (MS/MS), for identification. NMR exploits structurally dependent changes in the magnetic resonance of suitable nuclei for metabolite identification. Light spectroscopic approaches do not perform as well for the individual identification of metabolites but represent a more cost-effective approach to determining changes in the overall metabolic profile or fingerprint of a sample.

(Kafsack et al.,2010)

Steps and Techniques Involved In Metabolomics

Investigation of interactions in the pathosystem potato (Solanum tuberosum L.) and Rhizoctonia solani using metabolomics

(Konstantinos A. Aliferis & Suha Jabaji.,2012)

The level of metabolites is determined by the properties and concentration of enzymes. Thus, the level of metabolites represents the molecular phenotype of a cell or organism in response to the genetic or environmental factors. Detected compounds includes a large number of primary and secondary metabolites belonging to amino, fatty and carboxylic acids, carbohydrates, and terpenoid and steroidal glycoalkaloids

Potato sprout metabolome ,selected sub-networks of amino acids and glycoalkaloids .Nodes represent metabolites, enzymes or chemical reactions. Changes in more than 300 identified potato sprout metabolites in response to pathogen attack was detected .Discovery of biomarkers that could be exploited in plant breeding, and applications in biotechnology and/or crop protection

(Konstantinos A. Aliferis & Suha Jabaji.,2012)

Challenges in metabolome studies during plant pathogen interaction

Presence of fungal-derived metabolites

Lack of unifying principles such as genetic code

Solutions

Use of fungal metabolite profiles.

The construction of comprehensive metabolite databases for fungal metabolites.

(Konstantinos A. Aliferis a & Suha Jabaji.,2012)

ConclusionUnderstanding the developmental and physiological adaptations of pathogens that allow them to invade plants, colonize tissues, and subvert plant metabolism is considerable challenge.

It is important to develop an understanding of the molecular basis of pathogen recognition by plants, which underlies the evolution of disease resistance. Combining information derived from metabolomics, proteomics and transcriptomics will help to understanding of the wheat rust pathosystem.