A Phakopsora pachyrhizi Effector Suppresses PAMP-Triggered Immunity and Interacts with a Soybean Glucan Endo-1,3-ß-Glucosidase to Promote Virulence.

Asian soybean rust, caused by the fungus Phakopsora pachyrhizi, is one of the most important diseases affecting soybean production in tropical areas. During infection, P. pachyrhizi secretes proteins from haustoria that are transferred into plant cells to promote virulence. To date, only one candidate P. pachyrhizi effector protein has been characterized in detail to understand the mechanism by which it suppresses plant defenses to enhance infection. Here, we aimed to extend understanding of the pathogenic mechanisms of P. pachyrhizi based on the discovery of host proteins that interact with the effector candidate Phapa-7431740. We demonstrated that Phapa-7431740 suppresses pathogen-associated molecular pattern-triggered immunity (PTI) and that it interacts with a soybean glucan endo-1,3-ß-glucosidase (GmßGLU), a pathogenesis-related (PR) protein belonging to the PR-2 family. Structural and phylogenetic characterization of the PR-2 protein family predicted in the soybean genome and comparison to PR-2 family members in Arabidopsis thaliana and cotton, demonstrated that GmßGLU is a type IV ß-1,3-glucanase. Transcriptional profiling during an infection time course showed that the GmßGLU mRNA is highly induced during the initial hours after infection, coinciding with peak of expression of Phapa-7431740. The effector was able to interfere with the activity of GmßGLU in vitro, with a dose-dependent inhibition. Our results suggest that Phapa-7431740 may suppress PTI by interfering with glucan endo-1,3-ß-glucosidase activity. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2022.

Rational Design of Profile HMMs for Sensitive and Specific Sequence Detection with Case Studies Applied to Viruses, Bacteriophages, and Casposons.

Profile hidden Markov models (HMMs) are a powerful way of modeling biological sequence diversity and constitute a very sensitive approach to detecting divergent sequences. Here, we report the development of protocols for the rational design of profile HMMs. These methods were implemented on TABAJARA, a program that can be used to either detect all biological sequences of a group or discriminate specific groups of sequences. By calculating position-specific information scores along a multiple sequence alignment, TABAJARA automatically identifies the most informative sequence motifs and uses them to construct profile HMMs. As a proof-of-principle, we applied TABAJARA to generate profile HMMs for the detection and classification of two viral groups presenting different evolutionary rates: bacteriophages of the Microviridae family and viruses of the Flavivirus genus. We obtained conserved models for the generic detection of any Microviridae or Flavivirus sequence, and profile HMMs that can specifically discriminate Microviridae subfamilies or Flavivirus species. In another application, we constructed Cas1 endonuclease-derived profile HMMs that can discriminate CRISPRs and casposons, two evolutionarily related transposable elements. We believe that the protocols described here, and implemented on TABAJARA, constitute a generic toolbox for generating profile HMMs for the highly sensitive and specific detection of sequence classes.

A Bioinformatics Tool for Efficient Retrieval of High-Confidence Terpene Synthases (TPS) and Application to the Identification of TPS in Coffea and Quillaja.

Terpenoids are a class of compounds that are found in all living organisms. In plants, some terpenoids are part of primary metabolism, but most terpenes found in plants are classified as specialized metabolites, encoded by terpene synthases (TPS). It is not obvious how to assign the putative product of a given TPS using bioinformatics tools. Phylogenetic analyses easily assign TPS into families; however members of the same TPS family can synthetize more than one terpenoid-and, in many biotechnological applications, researchers are more interested in the product of a given TPS rather than its phylogenetic profile. Automated protein annotation can be used to classify TPS based on their products, despite the family they belong to. Here, we implement an automated bioinformatics method, search_TPS, to identify TPS proteins that synthesize mono, sesqui and diterpenes in Angiosperms. We verified the applicability of the method by classifying wet lab validated TPS and applying it to find TPS proteins in Coffea arabica, C. canephora, C. eugenioides, and Quillaja saponaria. Search_TPS is a computational tool based on PERL scripts that carries out a series of HMMER searches against a curated database of TPS profile hidden Markov models. The tool is freely available at https://github.com/liliane-sntn/TPS .

GenSeed-HMM: A Tool for Progressive Assembly Using Profile HMMs as Seeds and its Application in Alpavirinae Viral Discovery from Metagenomic Data.

This work reports the development of GenSeed-HMM, a program that implements seed-driven progressive assembly, an approach to reconstruct specific sequences from unassembled data, starting from short nucleotide or protein seed sequences or profile Hidden Markov Models (HMM). The program can use any one of a number of sequence assemblers. Assembly is performed in multiple steps and relatively few reads are used in each cycle, consequently the program demands low computational resources. As a proof-of-concept and to demonstrate the power of HMM-driven progressive assemblies, GenSeed-HMM was applied to metagenomic datasets in the search for diverse ssDNA bacteriophages from the recently described Alpavirinae subfamily. Profile HMMs were built using Alpavirinae-specific regions from multiple sequence alignments (MSA) using either the viral protein 1 (VP1; major capsid protein) or VP4 (genome replication initiation protein). These profile HMMs were used by GenSeed-HMM (running Newbler assembler) as seeds to reconstruct viral genomes from sequencing datasets of human fecal samples. All contigs obtained were annotated and taxonomically classified using similarity searches and phylogenetic analyses. The most specific profile HMM seed enabled the reconstruction of 45 partial or complete Alpavirinae genomic sequences. A comparison with conventional (global) assembly of the same original dataset, using Newbler in a standalone execution, revealed that GenSeed-HMM outperformed global genomic assembly in several metrics employed. This approach is capable of detecting organisms that have not been used in the construction of the profile HMM, which opens up the possibility of diagnosing novel viruses, without previous specific information, constituting a de novo diagnosis. Additional applications include, but are not limited to, the specific assembly of extrachromosomal elements such as plastid and mitochondrial genomes from metagenomic data. Profile HMM seeds can also be used to reconstruct specific protein coding genes for gene diversity studies, and to determine all possible gene variants present in a metagenomic sample. Such surveys could be useful to detect the emergence of drug-resistance variants in sensitive environments such as hospitals and animal production facilities, where antibiotics are regularly used. Finally, GenSeed-HMM can be used as an adjunct for gap closure on assembly finishing projects, by using multiple contig ends as anchored seeds.