**6.1 Metagenomics**

Koch's postulate was fundamental to the identification of disease-causing microorganisms [86]. In short, the strategy of isolating and cultivating the potential pathogen, and inoculating it into a healthy organism to confirm the symptoms of the disease, brought many advances to the study of infectious diseases [87]. More recently, due mainly to the advent of next-generation sequencing (NGS) technologies, the frontiers of microbiology expanded to those microorganisms that we cannot cultivate by classical microbiology techniques. That has opened the possibility to test the hypothesis that a microorganism not grown in vitro easily is the cause of FY [88]. If this is the case, metagenomics would be the technique to study FY.

Metagenomics is a culture-independent approach to study microbial communities. A metagenomics strategy allows one to skip the step of isolation and cultivation of microbial species. Metagenomics studies can contribute to elucidate the identity

#### **Figure 4.**

*Schematic showing a healthy oil palm tree (green leaves) and another one (yellow leaves) showing fatal yellowing (FY) symptoms. Different molecular techniques such as metagenomics, metabolomics and proteomics can be used to compare these contrasting biological situations. Metagenomics is a culture-independent technique that can be used to identify the microorganisms present. Metabolomics can used to identify and quantify cellular metabolites. Proteomics allows the identification of differentially expressed proteins. These 'omics' techniques are important high throughput tools that have been used to understand the biology of oil palm when challenged by FY disease. (credit: Clarissa Kruger).*

#### *Oil Palm Fatal Yellowing (FY), a Disease with an Elusive Causal Agent DOI: http://dx.doi.org/10.5772/intechopen.98856*

and/or the genetic and metabolic capabilities of the microorganisms present in a sample, including any that are potentially pathogenic [89].

In this sense, metagenomics complements the classic techniques of isolation and cultivation of microorganisms, and one can apply it to study different classes of microorganisms (e.g., viruses, bacteria, fungi, archaea) [22, 90–92]. Metagenomics protocols begin with the extraction of total DNA from the sample of interest, which contains microorganisms. Samples can be many different ones, such as soil or plant parts with FY disease symptoms. There are distinct ways to study the microbial community from this DNA. Many studies in different plants use the ribosomal RNA (rRNA) gene or ITS amplification approach (i.e., PCR amplification with specific primers) to identify the microorganisms present, including a potential pathogen [93–95].

16S rRNA gene-specific primers amplify bacterial and archaeal sequences (16S rDNA). Similarly, the 18S rRNA gene and the ITS-specific primers amplify fungal sequences. The ITS refers to the internal transcribed spacer, the DNA situated between the small-subunit ribosomal RNA and large-subunit rRNA genes. The 16S rDNA, 18S rDNA, and the ITS regions are highly polymorphic, thus allowing taxonomical identification of the microorganisms present in a sample. The PCR-amplified DNA is then sequenced and submitted to bioinformatics analysis to compare the obtained sequences with sequence databanks, leading to a putative microorganism. In summary, this metagenomics approach that combines PCR amplification with NGS allows identifying microorganisms present in the community [96].

The first metagenomics work to use ITS amplification and high throughput NGS to study FY in Brazil was performed by Costa et al. [22], who evaluated fungal communities associated with leaves of oil palm plants, with and without symptoms of FY. Leaves from health plants and from plants showing FY symptoms in three different disease stages (stages 2, 5, and 8) were obtained. Because of the similarities between PC and FY, using primers specific to the genus *Phythophtora,* the authors attempted PCR-amplification of oil palm leaf samples showing symptoms of FY. Weak amplification was obtained in only one sample. Thus, this study provided preliminary evidence that DNA of the genus *Phytophtora* may not be commonly present in Brazilian FY, contrary to what has been reported in Colombia [7]. However, further experiments with more samples, and additional controls are needed to clarify the validity of this initial observation.

The Costa et al. [22] study reported the analyses of fungal diversity using the ITS region. Results showed that the fungal community in different healthy asymptomatic oil palm leaves are more similar to each other than those presenting FY disease symptoms. The fungal communities were not the same among all the symptomatic samples, and were not consistent even between samples at the same stage of FY disease. Importantly, no fungal taxon had its relative proportion increased in leaves across all the FY diseased oil palm plants. It was hypothesized that the changes observed in the fungal community composition could be a secondary effect of FY disease. Similar metagenomic studies to analyze the viral, bacterial and archaeal communities associated with FY are needed.

A less common metagenomic approach that can also be used to study plant disease is to assemble genomes from the metagenome obtained from plants showing symptoms of disease. In this case, instead of using PCR to amplify a specific gene, one can completely sequence the DNA extracted from the samples of interest, and use bioinformatics tools to assemble genomes (metagenome-assembled genomes) of the microorganisms present. This type of methodology allows, in addition to identifying microorganisms present, access to their genomes. This creates the possibility of studying the genetic relationship among the species present, and predicting metabolic capabilities as well as the interactions between the organisms of the community [97]. One limitation to this method, however, is that the plant host genome sequence needs to be available and subtracted *in silico* from microbial community sequences. If possible, it is useful to find a way to selectively extract microbial DNA from the samples before sequencing to avoid or reduce the presence of the plant host DNA [98]. It should be noted that if the complexity of the microbial community is high or if a lot of host DNA is present in the sequenced samples, inadequate sequencing depth might be an important limitation to this method. To our knowledge this approach has not been used yet to search for the causal agent of FY.

### **6.2 Proteomics and metabolomics**

Proteome designates the set of proteins expressed by a cell, tissue, or organism at any given time [99]. Proteomic tools make it possible to obtain a protein profile with precision and sensitivity with the aid of electrophoresis, chromatography, mass spectrometry, and bioinformatics [99]. Proteomics is more and more used nowadays to understand plant responses to different biotic and abiotic stress conditions [100, 101].

In this context, and based on the hypothesis that the primary stress behind FY was abiotic and present in the soil, proteomics was applied to study this disease [21]. This hypothesis is based on observations regarding symptoms seen in the root system before they appeared in the aerial part [83]. Soil compaction, which hinders drainage and subject the roots to long periods of flooding in a hypoxia condition, would be in the origin of the stress [83].

Nascimento et al. [21] carried out a proteomic analysis to compare the protein profiles from symptomatic and asymptomatic oil palm plants, employing the mass spectrometry technique. The study looked for proteins linked to tolerance induction to relate the different areas collected and the distinct stages of the disease, analyzing the roots of symptomatic plants in early, intermediate, and final stages.

Proteins involved in the metabolism of phenylpropanoids and lignins, with a recognized role in reducing the effects of biotic and abiotic stress, were negatively regulated in symptomatic individuals, aggravating FY symptoms. In asymptomatic plants, enzymes such as S-adenosylmethionine - with a crucial role in methionine's biosynthetic metabolism - showed a recognized action in response to the stress. Plants with FY symptoms showed some pathogen-related proteins positively regulated, implying a progression of infection by biotic agents [21].

The hypothesis of a possible physiological dysfunction caused by factors present in the soil was reinforced by the large accumulation of antioxidant proteins in asymptomatic individuals [21]. The participation of the antioxidant system may indicate some level of resistance, considering that this system is vital for plants in conditions of soil flooding [102]. In addition, the accumulation of aldehyde dehydrogenase may indicate that the root system is under an anaerobic condition as it converts the acetaldehyde, promoting plant survival in this condition [21, 103]. Thus, these results indicate that plants affected by FY are in abiotic stress conditions and, with the damages done to the roots, it becomes a gateway for several opportunistic organisms [21].

In contrast to proteomics, metabolomics refers to a comprehensive analysis to identify the set of metabolites present in a sample with the aid of analytical techniques, such as liquid chromatographies or liquid–gas, associated or not with mass spectrometry, among others [104].

Rodrigues-Neto et al. [20] performed the first metabolomics work to study FY in Brazil using an untargeted metabolomics strategy to prospect metabolites differentially expressed in the leaves of FY symptomatic and asymptomatic plants. A high

*Oil Palm Fatal Yellowing (FY), a Disease with an Elusive Causal Agent DOI: http://dx.doi.org/10.5772/intechopen.98856*

throughput method based on metabolic fingerprinting MS, using UHPLC coupled to high-resolution mass spectrometry (HRMS), was employed, and chemometric analysis, PCA and PLS-DA, were used to evaluate metabolic differences. This study aimed at prospecting a biomarker for FY early diagnosis, besides gaining insights on pathways responsive to this disease valuable for future improvement studies.

Nine secondary metabolites were detected in a higher concentration in the healthy plants in comparison to the FY affected ones: Glycerophosphorylcholine, arginine, asparagine, paniculatin or apigenin 6,8-di-C-hexose, tyramine, Chlorophyllide, 1,2-dihexanoyl-sn-glycero-3-phosphoethanolamine, proline, malvidin 3-glucoside-5-(6″-malonylglucoside) or kaempferol 7-methyl ether 3-[3-hydroxy-3-methylglutaryl-(1–> 6)]-[apiosyl-(1–> 2)-galactoside]. These metabolites made possible to identify different metabolic pathways that have been affected by the FY, such as the glycerophospholipid metabolism, the isoquinoline alkaloid biosynthesis, the flavonoid biosynthesis, the tetrapyrrole biosynthesis and citrate cycle derivatives pathways.

Unfortunately, due to the fact that these metabolites are already described in the literature as linked to other types of stress, they are not good candidate for biomarkers; except for two of them, glycerophosphorylcholine and 1,2-dihexanoylsn-glycero-3-phosphoethanolamine [20].
