Advances in Sequencing Technologies in Plant Pathology

Author: Prashanth Kumar A.

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Abstract

Molecular techniques offer enhanced precision in species identification, such as Sanger sequencing for fungi and environmental DNA samples. High-throughput DNA sequencing (HTS) methodologies have significantly transformed research in plant and soil biology, enabling a more accurate evaluation of biodiversity within terrestrial ecosystems. However, certain biases persist and require resolution. Metabarcoding, which involves examining the identification of fungi and oomycetes, is another method used to evaluate fungal biodiversity across various settings. The effectiveness of DNA metabarcoding depends on the careful selection of a suitable DNA marker gene. Researchers commonly use DNA barcoding and metabarcoding techniques to analyze fungal communities, but certain groups may not exhibit complete resolution at the species level using the ITS region. Amplicon sequence variant (ASV) methodologies enhance taxonomic identification, while databases like FUN Guild and Fungal Traits can identify ITS sequences from fungal and other eukaryotic organisms. Metabarcoding is a molecular technique used to identify and quantify species within environmental samples, providing cost-efficient approaches for characterizing microbial communities. It has been used to examine plant illnesses such as Fusarium Head blight; grapevine trunk diseases and apple replant disease. High-throughput sequencing has enhanced our capacity to evaluate biodiversity in fungal communities across ecosystems. High-throughput sequencing (HTS) enables the sequencing of the entire transcriptome, facilitating the identification of isoforms, unique transcripts, alternative splice variants and genomic variants. However, the accurate taxonomic classification of fungal transcripts at the species level heavily relies on the presence of full genomes. Metabarcoding sequencing is the most widely employed method for plant pest detection and identification due to its favorable cost-efficiency ratio and low risk of false-positive results. However, there is a lack of research focused on the validation of high-throughput sequencing (HTS) approaches for diagnosing phytopathogenic fungi. The analytical sensitivity of high-throughput sequencing can be influenced by factors such as the number of reads produced per sample, the DNA extraction technique employed, and the competition for primers in the PCR reaction. The importance of HTS technologies for the diagnosis of filamentous plant diseases is now recognized, but the cost of sequencing per sample remains unaffordable for several facilities. Long-read sequencing techniques are proposed to address the presence of soil sample sequences lacking homologies in many databases. Next-generation sequencing (NGS) technology can be used for routine detection of fungal pathogens, but the volume of NGS data requires enhancements in management. A specialized pipeline has been created using machine learning classifiers to benefit metabarcoding studies.

Keywords

Epidemic, Metabarcoding, High-throughput sequencing, PCR, transcriptome, DNA extraction and next generation sequencing

Conclusion

The importance of HTS technologies for the diagnosis of filamentous plant diseases caused by fungi and oomycetes, as well as their role in enhancing plant disease management, is now generally recognized. Nevertheless, it is imperative to acknowledge that there remain economic and technical factors that necessitate careful consideration prior to the realization and widespread implementation of this aspiration. Despite the potential solution of adding additional samples in each run, the cost of sequencing per sample remains unaffordable for several facilities. This suggests that more measures of DNA purification are necessary in order to prevent the occurrence of unanticipated and undesirable sequencing artifacts. These measures may include the utilization of a dummy sample and the inclusion of DNA derived from healthy plant tissues as controls. The presence of soil sample sequences lacking homologies in many databases, sometimes referred to as "dark taxa" or "dark matter fungi," has been frequently seen (Page et al., 2016). The aforementioned phenomenon can be attributed to the substantial presence of fungal species that are non-cultivable and have not yet been adequately documented. Additionally, the limited taxonomic resolution achieved by the utilization of "short-reads" sequences of the rRNA barcodes has also played a role in this matter (Tedersoo et al., 2014). The proposed solution to address this issue is the utilization of long-read sequencing techniques to sequence the complete rRNA operon, encompassing the large subunit (LSU), internal transcribed spacer (ITS), and small subunit (SSU) (Jamy et al., 2020; Tedersoo et al., 2017; Latz et al., 2022). The scientific community has not fully embraced the suggestion to include intracellular DNA, sometimes known as metagenomic DNA or mgDNA, as a type (Burgaz et al., 2018; Lucking et al., 2021; Hongsanan et al., 2018). One problem with using Next-Generation Sequencing (NGS) technology to find fungal pathogens on a regular basis is that it needs to be made easier to manage the large amount of NGS data. This includes things like server capacity and memory power, as well as the availability of bioinformatic skills, such as algorithms and expert personnel. In order to achieve this objective, a specialized pipeline has been created, utilizing machine learning classifiers, as a viable approach for assigning error-prone sequence-long readings to certain taxa (Krause et al., 2021; Enjes et al., 2021; Yang et al., 2020). Metabarcoding studies, which encompass investigations into human diseases, can benefit from the application of machine learning (ML) modeling. ML modeling can aid in the prediction of disease outcomes and the analysis of environmental factors that influence microbial composition. This approach is relevant not just in the context of agricultural and natural ecosystems but also in the broader field of metabarcoding research (Chang et al., 2017; Zhou et al., 2019; Sharma et al., 2022; Namkung et al., 2020).

References

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How to cite this article

Prashanth Kumar A. (2023). Advances in Sequencing Technologies in Plant Pathology. Biological Forum – An International Journal, 15(5a): 538-548.