Variability in Fusarium species Causing Wilt Disease in Crops: A Transcriptomic Approach to Characterize Dialogue Between Host and Pathogen - Crop Improvement: New Approaches and Modern Techniques - page 280

2011 ). Predictions were made on the basis of the above model that various metabo-
lites such as allantoin, glutamate, glycine, which are synthesized by L. bicolor may
be used by aspen and in return; the latter provides sugars such as glucose or fructose
to the fungus and thus implying that these analyses could be applied in case of tran-
scriptomics data from other complex systems (Larsen et al. 2011 ). In addition, the
functional studies and analysis would be useful for the identification of functions of
genes, RNA (Fig. 8.1 ) and more importantly the functions of proteins and metabo-
lites during plant-microbe interactions. These studies will pin point the important
processes that control these interactions. The above vision can also be achieved by
developing genomic models, which will suit the analysis of metabolic flux. In these
models the microbes may be viewed at one level as one closely interacting super or-
ganisms, whereas the interacting plant may be viewed by genomic models at several
levels based on compartmentalization and thus distinguishing metabolic processes
in vacuoles, mitochondria, chloroplasts, cytoplasm and peroxisomes (Schenk et al.
2012 ). The genome-scale models have been recently constructed for Arabidopsis,
C4 plants and more than 25 bacterial species based on primary metabolites (Ober-
hardt et al. 2009 ; de Oliveira Dal'Molin et al. 2010a , b ). In addition the quantitative
data obtained from gene expression studies and metabolomics can also be included
in both types of model. Although the transcriptomics approaches are useful for un-
derstanding the plant-microbe interactions, there is a scope for improvements as in
case of transcriptional profiling studies the data generated has not been replicated
and thus defy statistical analyses. It is due to the reason that it has high cost and
is very complex. The less complex environmental samples from sea water (micro-
bial complexity compared to soil) revealed among the unique reference genes only
17 % overlap by repeated pyrosequencing (Stewart et al. 2010 ) which suggests that
current sequencing platforms need to evolve further for gasping the complexities
within communities. The sequencing platforms of with better coverage are cur-
rently being developed and then are coupled with replicate profiling and statistical
analyses and thus these hold a great promise for representation of the expression
profiles of interactions accurately. Currently, HiSeq 2000 (maximum 600 Gb, cor-
responding to 3 billion reads using TreSeq v3 reagent kits; Illumina, Inc.) platform
delivers the largest amount of sequence data. New insights will be provided by
understanding the detrimental or beneficial plant— Fusarium interactions for bio-
technological purposes. For the association studies between grasses and endophytes
the use of transcriptomics is a tractable system. Similarly, transcriptomics of detri-
mental association identified candidate genes which play pivotal role in infection
processes and in which the interaction switches from being mutalistic to pathogenic
between interacting partners (Eaton et al. 2011 ; Beatty and Good 2011 ). Thus the
knowledge obtained from interactions (beneficial/detrimental) between plants and
microbes will provide tremendous opportunities to increase crop productivity. Fur-
thermore, transcriptomics of the environment (soil/water) should be included in
systems biology as an essential component and thus integrated along with other
omics technologies. The future prospect would be if we as scientists could harness
the potential of microbes by engineering such crop plants, which are particularly
suited for beneficial interactions with microbes. An example in this direction could