Clasnip: a web-based intraspecies classifier and multi-locus sequence typing for pathogenic microorganisms using fragmented sequences

Identification of Rust Fungi Using High-Throughput Sequencing Data from Environmental Samples

Working with any plant-associated microbe comes with the inherent challenge that no environment is sterile, and a plant's metabiome is teeming with life. When collecting field samples outside of the laboratory, this issue is compounded further. Rust fungi, being obligate plant pathogens, are challenging to maintain, and strict protocols must be adhered to in the laboratory to prevent cross-contamination. In this era of big data and easy access to next generation sequencing (NGS), it is increasingly common for scientists to work with large sequencing datasets, which must first be evaluated for quality and filtered for potentially nontarget reads. Sequencing data files from environmental samples often contain genetic material from organisms not targeted by the experimental design. This situation can lead to issues if researchers assume the presence of only their intended subjects. Additionally, the origin of some samples may be inherently unknown, making the main objective of certain sequencing experiments to identify all organisms present, not just the expected ones.This chapter details common in silico approaches for identifying and classifying samples from sequencing data, drawing on experiences with cereal rust samples collected in the field. While the concepts are broadly applicable, they may require some tailoring for your species of interest. The chapter does not cover population-genomics level approaches to sample classification; instead, it describes essential quality control steps that researchers should implement before conducting downstream analyses to ensure that their data is appropriate for the intended tests.

The functional decline of tomato plants infected by Candidatus Liberbacter solanacearum: an RNA-seq transcriptomic analysis

Introduction

Candidatus Liberibacter solanacearum (CLso) is a regulated plant pathogen in European and some Asian countries, associated with severe diseases in economically important Apiaceous and Solanaceous crops, including potato, tomato, and carrot. Eleven haplotypes of CLso have been identified based on the difference in rRNA and conserved genes and host and pathogenicity. Although it is pathogenic to a wide range of plants, the mechanisms of plant response and functional decline of host plants are not well defined. This study aims to describe the underlying mechanism of the functional decline of tomato plants infected by CLso by analyzing the transcriptomic response of tomato plants to CLso haplotypes A and B.

Methods

Next-generation sequencing (NGS) data were generated from total RNA of tomato plants infected by CLso haplotypes A and B, and uninfected tomato plants, while qPCR analysis was used to validate the in-silico expression analysis. Gene Ontology and KEGG pathways were enriched using differentially expressed genes.

Results

Plants infected with CLso haplotype B saw 229 genes upregulated when compared to uninfected plants, while 1,135 were downregulated. Healthy tomato plants and plants infected by haplotype A had similar expression levels, which is consistent with the fact that CLso haplotype A does not show apparent symptoms in tomato plants. Photosynthesis and starch biosynthesis were impaired while starch amylolysis was promoted in plants infected by CLso haplotype B compared with uninfected plants. The changes in pathway gene expression suggest that carbohydrate consumption in infected plants was more extensive than accumulation. In addition, cell-wall-related genes, including steroid biosynthesis pathways, were downregulated in plants infected with CLso haplotype B suggesting a reduction in membrane fluidity, cell signaling, and defense against bacteria. In addition, genes in phenylpropanoid metabolism and DNA replication were generally suppressed by CLso infection, affecting plant growth and defense.

Discussion

This study provides insights into plants' defense and functional decline due to pathogenic CLso using whole transcriptome sequencing and qPCR validation. Our results show how tomato plants react in metabolic pathways during the deterioration caused by pathogenic CLso. Understanding the underlying mechanisms can enhance disease control and create opportunities for breeding resistant or tolerant varieties.