PhyEffector, the First Algorithm That Identifies Classical and Non-Classical Effectors in Phytoplasmas

Phytoplasmas are the causal agents of more than 100 plant diseases in economically important crops. Eleven genomes have been fully sequenced and have allowed us to gain a better understanding of the biology and evolution of phytoplasmas. Effectors are key players in pathogenicity and virulence, and their identification and description are becoming an essential practice in the description of phytoplasma genomes. This is of particular importance because effectors are possible candidates for the development of new strategies for the control of plant diseases. To date, the prediction of effectors in phytoplasmas has been a great challenge; the reliable comparison of effectoromes has been hindered because research teams have used the combination of different programs in their predictions. This is not trivial since significant differences in the results can arise, depending on the predictive pipeline used. Here, we tested different predictive pipelines to create the PhyEffector algorithm; the average value of the F1 score for PhyEffector was 0.9761 when applied to different databases or genomes, demonstrating its robustness as a predictive tool. PhyEffector can recover both classical and non-classical phytoplasma effectors, making it an invaluable tool to accelerate effectoromics in phytoplasmas

Fungal Effectoromics: A World in Constant Evolution

Effectors are small, secreted molecules that mediate the establishment of interactions in nature. While some concepts of effector biology have stood the test of time, this area of study is ever-evolving as new effectors and associated characteristics are being revealed. In the present review, the different characteristics that underly effector classifications are discussed, contrasting past and present knowledge regarding these molecules to foster a more comprehensive understanding of effectors for the reader. Research gaps in effector identification and perspectives for effector application in plant disease management are also presented, with a focus on fungal effectors in the plant-microbe interaction and interactions beyond the plant host. In summary, the review provides an amenable yet thorough introduction to fungal effector biology, presenting noteworthy examples of effectors and effector studies that have shaped our present understanding of the field

Microbial Effectors: Key Determinants in Plant Health and Disease

Effectors are small, secreted molecules that alter host cell structure and function, thereby facilitating infection or triggering a defense response. Effectoromics studies have focused on effectors in plant–pathogen interactions, where their contributions to virulence are determined in the plant host, i.e., whether the effector induces resistance or susceptibility to plant disease. Effector molecules from plant pathogenic microorganisms such as fungi, oomycetes and bacteria are major disease determinants. Interestingly, the effectors of non-pathogenic plant organisms such as endophytes display similar functions but have different outcomes for plant health. Endophyte effectors commonly aid in the establishment of mutualistic interactions with the plant and contribute to plant health through the induction of systemic resistance against pathogens, while pathogenic effectors mainly debilitate the plant’s immune response, resulting in the establishment of disease. Effectors of plant pathogens as well as plant endophytes are tools to be considered in effectoromics for the development of novel strategies for disease management. This review aims to present effectors in their roles as promotors of health or disease for the plant host

EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases

Pathogens are able to deliver small-secreted, cysteine-rich proteins into plant cells to enable infection. The computational prediction of effector proteins remains one of the most challenging areas in the study of plant fungi interactions. At present, there are several bioinformatic programs that can help in the identification of these proteins; however, in most cases, these programs are managed independently. Here, we present EffHunter, an easy and fast bioinformatics tool for the identification of effectors. This predictor was used to identify putative effectors in 88 proteomes using characteristics such as size, cysteine residue content, secretion signal and transmembrane domains

WideEffHunter: An Algorithm to Predict Canonical and Non-Canonical Effectors in Fungi and Oomycetes

Newer effectorome prediction algorithms are considering effectors that may not comply with the canonical characteristics of small, secreted, cysteine-rich proteins. The use of effector-related motifs and domains is an emerging strategy for effector identification, but its use has been limited to individual species, whether oomycete or fungal, and certain domains and motifs have only been associated with one or the other. The use of these strategies is important for the identification of novel, non-canonical effectors (NCEs) which we have found to constitute approximately 90% of the effectoromes. We produced an algorithm in Bash called WideEffHunter that is founded on integrating three key characteristics: the presence of effector motifs, effector domains and homology to validated existing effectors. Interestingly, we found similar numbers of effectors with motifs and domains within two different taxonomic kingdoms: fungi and oomycetes, indicating that with respect to their effector content, the two organisms may be more similar than previously believed. WideEffHunter can identify the entire effectorome (non-canonical and canonical effectors) of oomycetes and fungi whether pathogenic or non-pathogenic, unifying effector prediction in these two kingdoms as well as the two different lifestyles. The elucidation of complete effectoromes is a crucial step towards advancing effectoromics and disease management in agriculture