Applications of Transcriptomics in Plant-Virus Interactions

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Applications of Transcriptomics in Plant-Virus Interactions

The symptoms associated with the viral infection reflect extensive global changes in host transcription that lead to alterations in cellular homeostasis and developmental processes. A variety of techniques ranging from in situ hybridization with individual genes to global profiling of host mRNA transcripts using oligonucleotide or cDNA microarrays and sequencing-based approaches have been used to identify the host gene-expression changes that occur as a result of virus infection.

In situ hybridization performed on virus-infected embryonic tissues and cotyledons provided exquisite spatial resolution of the relationship between virus infection and the expression of a selected set of host genes. This technique has not been utilized to examine gene expression on a genome-wide scale. The most popular technology, DNA microarray, has been used for large-scale gene expression studies of virus-host interactions in the model dicot hosts Arabidopsis and Nicotiana benthamiana (Whitham et al., 2006). Gene expression patterns at whole-genome level or a set of genes in many plant species have been studied using microarrays for addressing a wide range of biological problems (Rensink and Buell, 2005; Garg et al., 2010; Sharma et al., 2012).

The sequencing-based approaches include sequencing of expressed sequence tags, serial analysis of gene expression, massively parallel signature sequencing methods and whole transcriptome sequencing using next generation sequencing (NGS) technologies (ribonucleic acid sequencing (RNA-Seq)). Among the sequencing-based approaches, RNA-Seq provides a better alternative over microarray for gene expression studies in terms of robustness, resolution, and reproducibility (’t Hoen et al., 2008; Marioni et al., 2008; Ozsolak and Milos, 2011). In addition, the RNA-Seq approach was shown to have relatively little variation between technical replicates for identifying differentially expressed genes (Marioni et al., 2008).

The gene expression studies using transcriptomic approaches reveal functional groups of genes whose expression profiles are altered in response to viral infection, and roles of viral RNA and protein components in eliciting changes in host gene expression. The common responses that a broad range of viruses alter plant gene expression have been divided into two major categories of cellular stress and developmental defects. This division is convenient for studying individual responses, the associated gene expression changes and the viral factors that contribute to the responses. However, these categories may not necessarily be exclusive. It has been suggested that the stress-like responses are characterized by the induction of heat shock proteins (HSP) and defense-like responses by the induction of pathogenesis related (PR) genes and other genes associated with plant disease defense. Apart from the plant stress or defense responses, genes that may have direct connections to the developmental defects (i.e., the disease symptoms) were observed from viral infection. Indeed viruses interfere with host gene expression and affect plant growth and development possibly by disturbing or by interfering in signaling pathways involving phytohormones and regulatory small RNAs (Whitham et al., 2006).

Plant-virus interactions and Proteomics

One of the most explored areas of research based on global-scale analysis of proteins which leads to direct understanding of function and regulation of genes is proteomics. Proteomics is the analysis of the complete functional protein complement of the genome under defined conditions. Comprehensive identification of proteins, their isoforms, as well as their prevalence in each tissue, characterizing the biochemical and cellular functions of each protein and the analysis of protein regulation and its relation to other regulatory networks are the applications of proteomic approaches (Wu et al., 2010).

Quantitative proteome analysis combines high resolution two dimensional gel electrophoresis (2DE) or gel-free liquid chromatography (LC) protein separation with mass spectrometric (MS) or tandem MS (MS/MS) identification of selected proteins (HumpherySmith et al., 1997; Celis and Gromov, 1999; Ong and Pandey, 2001; Garfin, 2003; Carrette et al., 2006; Penque, 2009; Lopez, 2007).

The rapid and global analysis of the protein expression in an organism is facilitated by the combination of conventional 2DE with the advanced mass spectrometry. In order to get high resolution in 2DE, proteins have to be completely denatured, disaggregated, reduced and solubilized to disrupt molecular interactions and to ensure that each spot represents an individual polypeptide. Based on the type of sample to be analyzed and for the proteins of interest the published standard protocols have to be adapted and further optimized. The uniqueness of the 2DE is the easy visualization of protein isoforms which makes this technology itself extremely informative and for direct targeting of protein differences 2DE is the most rapid method. In a proteome, quantitation of protein expression provides the information about the cellular response to changes in its surrounding environments. The resulting differentially expressed proteins are supposed to play important roles in the precise regulation of cellular activities that are directly related to a given exogenous stimulus. Separation of membrane proteins, detection of low-abundance proteins, resolving alkaline proteins and detection of high molecular weight proteins are the major drawbacks in the 2D gels. Despite all these drawbacks, 2DE can demonstrate changes in relative abundance of visualized proteins and can detect protein isoforms, variants, polymer complexes and posttranslational modifications.

Gel-free methodologies that can be used to comparatively study protein expression profiles in different samples are shotgun proteomics, multidimensional protein identification technology (MudPIT) and isobaric tagging for relative and absolute quantification. Compared to gel-based methodologies shotgun proteomics approach permits the direct separation, quantification and identification of proteins in a complex mixture. These techniques are all based on the assumption that peak intensities recorded in a mass spectrum are directly proportional to ion concentrations in the sample (analyzed peptides). Reproducibility, column lifetime and high operating costs are the major disadvantages of gel-free proteomics (Chevalier, 2010).

Recently, an increasing number reports on proteomic analysis of plant−virus interactions was observed due to rapid technical advances in both bioinformatics and proteomics tools. The detection of both known and novel plant viruses through CP identification using MS-based proteomics methods such as high resolution LC−MS/MS demonstrating to be a useful tool for initial virus classification and characterization in infected plants (Cooper et al., 2003; Blouin et al., 2010). Viruses complete the steps of the infection process (replication and movement) exploiting plant metabolism and establishing a large net of interactions with host proteins in all types of interactions, compatible (susceptible host), incompatible (resistant host) and partially incompatible (partially resistant). Although these studies are limited by the shortage of sequence information that is needed for accurate identification of various proteins and by the relative insensitivity of the staining methods, they still allowed identifying multiple proteins or protein classes that are specifically regulated upon infection (Mehta et al., 2008). The comparative proteome-based studies of various plant−virus interactions, highlighting the class of proteins involved and the advantages or disadvantages of current technologies has been reviewed recently and given in Table 1.1(Carli et al., 2012).

Comparative proteome-based studies on several viral genera revealed major redirection of cell metabolism by a widespread repression of proteins associated with the photosynthetic apparatus, while energy metabolism/ protein synthesis and turnover are typically up-regulated. The modulations of metabolism concerning sugars, cell wall, and reactive oxygen species (ROS) as well as pathogenesis-related (PR) proteins are other common features in vial pathogenesis (Carli et al., 2012).

Table 1.1 Comparative proteomic studies on plant-virus interactions.

(Adapted from Carli et al., (2012)):

Plant host

Methodology (tissue used for the study)

Virus

Viral protein detected

Types of plant–virus interaction analyzed

Reference

Oryza sativa

gel exclusion chromatography/SDS-PAGE-nanoLC–MS/MS (leaf)

RYMV

viral CP

compatible, partially resistant, incompatible

Brizard et al., 2006

Oryza sativa cv.indica

2-DE/MS

RYMV

n.d.

compatible

Ventelon-Debout et al., 2004

Oryza sativa cv. japonica

2-DE/MS

RYMV

n.d.

partially resistant

Ventelon-Debout et al., 2004

Solanum lycopersicum

DIGE/nLC–ESI–IT-MS/MS (leaf)

CMV

viral CP

compatible

Carli et al., 2010.

Cucumis melo

2-DE/MALDI-TOF(stem or petioles)

CMV

n.d.

Malter and Wolf, 2011

Nicotiana benthamiana

2-DE/MS (leaf)

PMMoV-S

viral CP

compatible

Pineda et al., 2010

Nicotiana benthamiana

2-DE/Immunoassay/ N-terminal sequencing (leaf)

PMMoV-S

n.d.

compatible

Pérez-Bueno et al., 2004

Capsicum chinense

2-DE/N-terminal amino acid sequencing/ MALDI-TOF MS and MS/MS or n-ESI–IT-MS/MS (leaf)

PMMoV-S and

PMMoV-I

viral CP

compatible, incompatible

Elvira et al., 2008

Capsicum annum

2DE/MALDI-TOF (leaf)

TMV

n.d.

incompatible

Lee et al., 2006

Solanum lycopersicum

2-DE/MALDI-TOF MS or LC–MS/MS (fruit)

TMV

viral CP

compatible

Casado-Vela et al., 2006

Pisum sativumL.

2-DE/MALDI TOF and n-ESI–IT-MS/MS (leaf)

PPV

viral CP

compatible

Diaz Vivancos et al., 2008

Prunus persicaL.

2-DE/MALDI-TOF (leaf)

PPV

n.d.

compatible

Diaz Vivancos et al., 2006

Glycine max

2-DE/MS (leaf)

SMV

n.d.

partially resistant

Yang et al., 2011

Vigna mungo

2-DE/MALDI-TOF/TOF (leaf)

MYMIV

n.d.

compatible

Kundu et al., 2011

Zea maysL.

PEG prefractionation-2-DE/MS (leaf)

RBSDV

P9-1

compatible

Li et al., 2011

Vitis vinifera

2-DE/MALDI-TOF/TOF (berry pulp/skin)

GLRaV-1, GVA and RSPaV

RSPaV CP

compatible

Giribaldi et al., 2011

Citrus

2-DE/MALDI-TOF/TOF (stem bark)

Putative agents: CTV, CSDaV

n. d.

compatible, incompatible

Cantu et al., 2008

Beta vulgarisL.

Multidimensional protein fractionation system (ProteomeLab PF2D), MALDI-TOF/TOF (root)

BNYVV

n.d.

incompatible, compatible

Larson et al., 2008

Carica papayaL.

2-DE and DIGE/MS (leaf)

PMeV

n.d.

compatible

Rodrigues et al., 2011

Carica papayaL.

1-DE-LC–ESI–MS/MS (fruit latex)

PMeV

n.d.

Compatible

Rodrigues et al., 2012

Note: n.d; not detected. RYMV, Rice yellow mottle virus; CMV, Cucumber mosaic virus; TMV, Tobacco mosaic virus; PMMoV-S, Pepper mild mottle virus Spanish strain; PMMoV-I, Pepper mild mottle virus Italian strain; PMeV, Papaya meleira virus; SMV, Soybean mosaic virus; RBSDV, Rice black-streaked dwarf virus; CTV, Citrus tristeza virus; CSDaV, Citrus sudden death associated virus; PPV, Plum pox virus; GLRaV-1, Grapevine leaf roll associated virus-1; GVA, Grapevine virus A; RSPaV, Rupestris stem pitting associated virus; BNYVV, Beet necrotic yellow vein virus.

Yellow mosaic disease in legumes

Yellow mosaic disease (YMD) is one of the major constraints for legume productivity in India and other South and South-East Asian countries. YMD was first reported in the late 1940s in western India in lima bean (Phaseolus lunatus; Capoor and Varma, 1948), and in the 1950s in mungbean (Vigna radiata) in northern India (Nariani, 1960). Since then YMD has emerged throughout India causing enormous losses in the production of French bean (Phaseolus vulgaris), blackgram (Vigna mungo), cluster bean (Cyamopsis tetragonoloba), groundnut (Arachis hypogea), horsegram (Macrotyloma uniflorum), hyacinth bean (Lablab purpureus), mothbean (Vigna aconitifolia), mungbean, lima bean, pigeonpea (Cajanus cajan) and soybean (Glycine max) (Nene, 1973; Varma et al., 1992). The yield losses in India due to YMD in three important leguminous crops (blackgram, mungbean and soybean) are estimated to be about $300 million (Varma et al., 1992). Apart from India, YMD is a major threat to the legume productivity in Bangladesh, Pakistan, Nepal, Thailand and Sri Lanka. (Sivanathan, 1977; Jalaluddin and Shaikh, 1981; Malik, 1992; Chiemsombat, 1992).

It has been recognized that four species of bipartite begomoviruses (collectively known as legume yellow mosaic viruses (LYMVs)) are the causative agents of YMD in legumes of southern Asia which are closely related and have distinct but overlapping host ranges. Generally the symptoms in YMD range from small yellow specks along the veins to chlorosis. Severity depends on host species and susceptibility. Based on the sequence identities, two distinct begomovirus species, MYMIV and MYMV, causing yellow mosaic disease in legume crops including soybean (Glycine max), mungbean (Vigna radiata), blackgram (Vigna mungo), pigeonpea (Cajanus cajan), mothbean (Vigna aconitifolia) and common bean (Phaseolus vulgaris) have been recognized (Fauquet and Stanley, 2003; Karthikeyan et al., 2004; Girish and Usha, 2005; Malathi et al., 2005, Usharani et al., 2005). Dolichos yellow mosaic virus (DoYMV) has recently been recognized as a distinct species of begomovirus causing YMD in dolichos (Maruthi et al., 2006). The virus is reported to have a very narrow host range consisting of only the host from which it was isolated, dolichos (Lablab purpureus). Horsegram yellow mosaic virus (HgYMV) is the least characterized of the four viruses. This virus causes yellow mosaic disease in horsegram (Macrotyloma uniflorum) (Barnabas et al., 2010) and is reported to affect a number of legume species in view of the overlapping host ranges of the legume viruses including Cajanus cajan, Glycine max, hairy indigo (Indigofera hirsute), Phaseolus lunatus, P. vulgaris, V. mungo and V. radiata. It has been shown that The LYMVs are indicated as genetically isolated due to the lack of interaction of these viruses with that do not infect legumes (Qazi et al., 2007). In addition, phylogenetic analyses indicated that the legume-infecting begomoviruses from South and South-East Asia are a distinct group of viruses well separated from the New World and other Old World begomoviruses (Girish and Usha, 2005). This genetic isolation of these viruses clearly has implications for the development of resistance. Diversity is a strong counter indicator for the durability of either natural host plant resistance or engineered resistance (García-Arenal and McDonald, 2003).

Two soybean-infecting begomoviruses have been characterized from central and southern parts of India. Based on sequence analyses the isolate from central India was identified as a strain of MYMIV (MYMIV-Sb[MP]) and the southern Indian isolate was identified as a strain of MYMV (MYMV-Sb[Mad]) (Girish and Usha, 2005).

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