Many fungi and oomycete species are devasting plant pathogens. These eukaryotic filamentous pathogens secrete effector proteins to facilitate plant infection. Fungi and oomycete pathogens have diverse infection strategies and their effectors generally do not share sequence homology. However, they occupy similar host environments, either the plant apoplast or plant cytoplasm, and, therefore, may share some unifying properties based on the requirements of these host compartments. Here, we exploit these biological signals and present the first classifier (EffectorP 3.0) that uses two machine-learning models: one trained on apoplastic effectors and one trained on cytoplasmic effectors. EffectorP 3.0 accurately predicts known apoplastic and cytoplasmic effectors in fungal and oomycete secretomes with low estimated false-positive rates of 3 and 8%, respectively. Cytoplasmic effectors have a higher proportion of positively charged amino acids, whereas apoplastic effectors are enriched for cysteine residues. The combination of fungal and oomycete effectors in training leads to a higher number of predicted cytoplasmic effectors in biotrophic fungi. EffectorP 3.0 expands predicted effector repertoires beyond small, cysteine-rich secreted proteins in fungi and RxLR-motif containing secreted proteins in oomycetes. We show that signal peptide prediction is essential for accurate effector prediction, because EffectorP 3.0 recognizes a cytoplasmic signal also in intracellular, nonsecreted proteins. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license . 

EffectorP 3.0: Prediction of Apoplastic and Cytoplasmic Effectors in Fungi and Oomycetes

Many fungi and oomycete species are devasting plant pathogens. These eukaryotic filamentous pathogens secrete effector proteins to facilitate plant infection. Fungi and oomycete pathogens have diverse infection strategies and their effectors generally do not share sequence homology. However, they occupy similar host environments, either the plant apoplast or plant cytoplasm, and may therefore share some unifying properties based on the requirements of these host compartments. Here we exploit these biological signals and present the first classifier (EffectorP 3.0) that uses two machine learning models: one trained on apoplastic effectors and one trained on cytoplasmic effectors. EffectorP 3.0 accurately predicts known apoplastic and cytoplasmic effectors in fungal and oomycete secretomes with low estimated false positive rates of 3% and 8%, respectively. Cytoplasmic effectors have a higher proportion of positively charged amino acids, whereas apoplastic effectors are enriched for cysteine residues. The combination of fungal and oomycete effectors in training leads to a higher number of predicted cytoplasmic effectors in biotrophic fungi. EffectorP 3.0 expands predicted effector repertoires beyond small, cysteine-rich secreted proteins in fungi and RxLR-motif containing secreted proteins in oomycetes. We show that signal peptide prediction is essential for accurate effector prediction, as EffectorP 3.0 recognizes a cytoplasmic signal also in intracellular, non-secreted proteins. EffectorP 3.0 is available at http://effectorp.csiro.au.

https://biorxiv.org/content/10.1101/2021.07.28.454080v1.full.pdf

EffectorP 3.0: prediction of apoplastic and cytoplasmic effectors in fungi and oomycetes

SBSM-Pro: support bio-sequence machine for proteins

Many fungi and oomycete species are devasting plant pathogens. These eukaryotic filamentous pathogens secrete effector proteins to facilitate plant infection. Fungi and oomycete pathogens have dive...

EffectorP 3.0: prediction of apoplastic and cytoplasmic effectors in fungi and oomycetes.

Effectors play a central role in determining the outcome of plant-pathogen interactions. As key virulence proteins, effectors are collectively indispensable for disease development. By understanding the virulence mechanisms of effectors, fundamental knowledge of microbial pathogenesis and disease resistance have been revealed. Effectors are also considered double-edged swords because some of them activate immunity in disease resistant plants after being recognized by specific immune receptors, which evolved to monitor pathogen presence or activity. Characterization of effector recognition by their cognate immune receptors and the downstream immune signaling pathways is instrumental in implementing resistance. Over the past decades, substantial research effort has focused on effector biology, especially concerning their interactions with virulence targets or immune receptors in plant cells. A foundation of this research is robust identification of the effector repertoire from a given pathogen, which depends heavily on bioinformatic prediction. In this review, we summarize methodologies that have been used for effector mining in various microbial pathogens which use different effector delivery mechanisms. We also discuss current limitations and provide perspectives on how recently developed analytic tools and technologies may facilitate effector identification and hence generation of a more complete vision of host-pathogen interactions. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Effector Identification in Plant Pathogens.

Human genes form a large variety of isoforms after transcription, encoding distinct transcripts to exert different functions. Single-molecule RNA sequencing facilitates accurate identification of the isoforms by extending nucleotide read length significantly. However, the gene or isoform diversity is lowly represented by the mRNA molecules captured by single-molecule RNA sequencing. Here, we show that a cDNA normalization procedure before the library preparation for PacBio RS II sequencing captures 3.2-6.0 fold more full-length high-quality isoform species for different human samples, as compared to the non-normalized capture procedure. Many lowly expressed, functionally important isoforms can be detected. In addition, normalized PacBio RNA sequencing also resolves more allele-specific haplotype transcripts. Finally, we apply the cDNA normalization based long-read RNA sequencing method to profile the transcriptome of human gastric signet-ring cell carcinomas, identify new cancer-specific transcriptome signatures, and thus, bring out the utility of the improved protocols in gene expression studies.

/pdf/improving-the-diversity-of-captured-full-length-isoforms-3u7d3utr21.pdf

Improving the diversity of captured full-length isoforms using a normalized single-molecule RNA-sequencing method

Many Gram-negative bacteria infect hosts and cause diseases by translocating a variety of type III secreted effectors (T3SEs) into the host cell cytoplasm. However, despite a dramatic increase in the number of available whole-genome sequences, it remains challenging for accurate prediction of T3SEs. Traditional prediction models have focused on atypical sequence features buried in the N-terminal peptides of T3SEs, but unfortunately, these models have had high false-positive rates. In this research, we integrated promoter information along with characteristic protein features for signal regions, chaperone-binding domains, and effector domains for T3SE prediction. Machine learning algorithms, including deep learning, were adopted to predict the atypical features mainly buried in signal sequences of T3SEs, followed by development of a voting-based ensemble model integrating the individual prediction results. We assembled this into a unified T3SE prediction pipeline, T3SEpp, which integrated the results of individual modules, resulting in high accuracy (i.e., ∼0.94) and >1-fold reduction in the false-positive rate compared to that of state-of-the-art software tools. The T3SEpp pipeline and sequence features observed here will facilitate the accurate identification of new T3SEs, with numerous benefits for future studies on host-pathogen interactions.IMPORTANCE Type III secreted effector (T3SE) prediction remains a big computational challenge. In practical applications, current software tools often suffer problems of high false-positive rates. One of the causal factors could be the relatively unitary type of biological features used for the design and training of the models. In this research, we made a comprehensive survey on the sequence-based features of T3SEs, including signal sequences, chaperone-binding domains, effector domains, and transcription factor binding promoter sites, and assembled a unified prediction pipeline integrating multi-aspect biological features within homology-based and multiple machine learning models. To our knowledge, we have compiled the most comprehensive biological sequence feature analysis for T3SEs in this research. The T3SEpp pipeline integrating the variety of features and assembling different models showed high accuracy, which should facilitate more accurate identification of T3SEs in new and existing bacterial whole-genome sequences.

T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors.

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Improving the diversity of captured full-length isoforms using a normalized single-molecule RNA-sequencing method

T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors.