Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning

Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data. However, previous works mainly focused on understanding single graph inputs while many real-world applications require pair-wise analysis for graph-structured data (e.g., scene graph matching, code searching, and drug-drug interaction prediction). To this end, recent works have shifted their focus to learning the interaction between pairs of graphs. Despite their improved performance, these works were still limited in that the interactions were considered at the node-level, resulting in high computational costs and suboptimal performance. To address this issue, we propose a novel and efficient graph-level approach for extracting interaction representations using co-attention in graph pooling. Our method, Co-Attention Graph Pooling (CAGPool), exhibits competitive performance relative to existing methods in both classification and regression tasks using real-world datasets, while maintaining lower computational complexity.Comment: Published at IEEE Acces

Drought-induced susceptibility for Cenangium ferruginosum leads to progression of Cenangium-dieback disease in Pinus koraiensis

Recently, the occurrence of "Cenangium-dieback" has been frequent and devastating. Cenangium-dieback is caused by an endophytic fungus Cenangium ferruginosum in stressed pine trees. Progression of the disease in terms of molecular interaction between host and pathogen is not well studied and there is a need to develop preventive strategies. Thus, we simulated disease conditions and studied the associated transcriptomics, metabolomics, and hormonal changes. Pinus koraiensis seedlings inoculated with C. ferruginosum were analyzed both under drought and well-watered conditions. Transcriptomic analysis suggested decreased expression of defense-related genes in C. ferruginosum-infected seedlings experiencing water-deficit. Further, metabolomic analysis indicated a decrease in the key antimicrobial terpenoids, flavonoids, and phenolic acids. Hormonal analysis revealed a drought-induced accumulation of abscisic acid and a corresponding decline in the defense-associated jasmonic acid levels. Pathogen-associated changes were also studied by treating C. ferruginosum with metabolic extracts from pine seedlings (with and without drought) and polyethylene glycol to simulate the effects of direct drought. From RNA sequencing and metabolomic analysis it was determined that drought did not directly induce pathogenicity of C. ferruginosum. Collectively, we propose that drought weakens pine immunity, which facilitates increased C. ferruginosum growth and results in conversion of the endophyte into the phytopathogen causing dieback

EzMAP: Easy Microbiome Analysis Platform

Background The rapid advances in next-generation sequencing technologies have revolutionized the microbiome research by greatly increasing our ability to understand diversity of microbes in a given sample. Over the past decade, several computational pipelines have been developed to efficiently process and annotate these microbiome data. However, most of these pipelines require an implementation of additional tools for downstream analyses as well as advanced programming skills. Results Here we introduce a user-friendly microbiome analysis platform, EzMAP (Easy Microbiome Analysis Platform), which was developed using Java Swings, Java Script and R programming language. EzMAP is a standalone package providing graphical user interface, enabling easy access to all the functionalities of QIIME2 (Quantitative Insights Into Microbial Ecology) as well as streamlined downstream analyses using QIIME2 output as input. This platform is designed to give users the detailed reports and the intermediate output files that are generated progressively. The users are allowed to download the features/OTU table (.biom;.tsv;.xls), representative sequences (.fasta) and phylogenetic tree (.nwk), taxonomy assignment file (optional). For downstream analyses, users are allowed to perform relative abundances (at all taxonomical levels), community comparison (alpha and beta diversity, core microbiome), differential abundances (DESeq2 and linear discriminant analysis) and functional prediction (PICRust, Tax4Fun and FunGuilds). Our case study using a published rice microbiome dataset demonstrates intuitive user interface and great accessibility of the EzMAP. Conclusions This EzMAP allows users to consolidate the microbiome analysis processes from raw sequence processing to downstream analyses specific for individual projects. We believe that this will be an invaluable tool for the beginners in their microbiome data analysis. This platform is freely available at https://github.com/gnanibioinfo/EzMAP and will be continually updated for adoption of changes in methods and approaches.This work was supported by grants from Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry through Agricultural Microbiome R&D Program, funded by Ministry of Agriculture, Food, and Rural Afairs (MAFRA) (918017–04), by a grant from Rural Development Administration (PJ013178), and by a grant from National Research Foundation of Korea (NRF-2018R1A5A1023599). The funding bodies were not involved in the design of EzMAP and, analysis of data, and in writing the manuscript

A silver bullet in a golden age of functional genomics:the impact of Agrobacterium-mediated transformation of fungi

Abstract The implementation of Agrobacterium tumefaciens as a transformation tool revolutionized approaches to discover and understand gene functions in a large number of fungal species. A. tumefaciens mediated transformation (AtMT) is one of the most transformative technologies for research on fungi developed in the last 20 years, a development arguably only surpassed by the impact of genomics. AtMT has been widely applied in forward genetics, whereby generation of strain libraries using random T-DNA insertional mutagenesis, combined with phenotypic screening, has enabled the genetic basis of many processes to be elucidated. Alternatively, AtMT has been fundamental for reverse genetics, where mutant isolates are generated with targeted gene deletions or disruptions, enabling gene functional roles to be determined. When combined with concomitant advances in genomics, both forward and reverse approaches using AtMT have enabled complex fungal phenotypes to be dissected at the molecular and genetic level. Additionally, in several cases AtMT has paved the way for the development of new species to act as models for specific areas of fungal biology, particularly in plant pathogenic ascomycetes and in a number of basidiomycete species. Despite its impact, the implementation of AtMT has been uneven in the fungi. This review provides insight into the dynamics of expansion of new research tools into a large research community and across multiple organisms. As such, AtMT in the fungi, beyond the demonstrated and continuing power for gene discovery and as a facile transformation tool, provides a model to understand how other technologies that are just being pioneered, e.g. CRISPR/Cas, may play roles in fungi and other eukaryotic species

The PEX7-Mediated Peroxisomal Import System Is Required for Fungal Development and Pathogenicity in Magnaporthe oryzae

In eukaryotes, microbodies called peroxisomes play important roles in cellular activities during the life cycle. Previous studies indicate that peroxisomal functions are important for plant infection in many phytopathogenic fungi, but detailed relationships between fungal pathogenicity and peroxisomal function still remain unclear. Here we report the importance of peroxisomal protein import through PTS2 (Peroxisomal Targeting Signal 2) in fungal development and pathogenicity of Magnaporthe oryzae. Using an Agrobacterium tumefaciens-mediated transformation library, a pathogenicity-defective mutant was isolated from M. oryzae and identified as a T-DNA insert in the PTS2 receptor gene, MoPEX7. Gene disruption of MoPEX7 abolished peroxisomal localization of a thiolase (MoTHL1) containing PTS2, supporting its role in the peroxisomal protein import machinery. ΔMopex7 showed significantly reduced mycelial growth on media containing short-chain fatty acids as a sole carbon source. ΔMopex7 produced fewer conidiophores and conidia, but conidial germination was normal. Conidia of ΔMopex7 were able to develop appressoria, but failed to cause disease in plant cells, except after wound inoculation. Appressoria formed by ΔMopex7 showed a defect in turgor generation due to a delay in lipid degradation and increased cell wall porosity during maturation. Taken together, our results suggest that the MoPEX7-mediated peroxisomal matrix protein import system is required for fungal development and pathogenicity M. oryzae

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Computer-Aided Drug Discovery in Plant Pathology

Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides

Nuclear Effectors in Plant Pathogenic Fungi

AbstractThe nuclear import of proteins is a fundamental process in the eukaryotes including plant. It has become evident that such basic process is exploited by nuclear effectors that contain nuclear localization signal (NLS) and are secreted into host cells by fungal pathogens of plants. However, only a handful of nuclear effectors have been known and characterized to date. Here, we first summarize the types of NLSs and prediction tools available, and then delineate examples of fungal nuclear effectors and their roles in pathogenesis. Based on the knowledge on NLSs and what has been gleaned from the known nuclear effectors, we point out the gaps in our understanding of fungal nuclear effectors that need to be filled in the future researches

In Silico Identification of Potential Inhibitor Against a Fungal Histone Deacetylase, RPD3 from Magnaporthe Oryzae

Histone acetylation and deacetylation play an essential role in the epigenetic regulation of gene expression. Histone deacetylases (HDAC) are a group of zinc-binding metalloenzymes that catalyze the removal of acetyl moieties from lysine residues from histone tails. These enzymes are well known for their wide spread biological effects in eukaryotes. In rice blast fungus, Magnaporthe oryzae, MoRPD3 (an ortholog of Saccharomyces cerevisiae Rpd3) was shown to be required for growth and development. Thus in this study, the class I HDAC, MoRpd3 is considered as a potential drug target, and its 3D structure was modelled and validated. Based on the model, a total of 1880 compounds were virtually screened (molecular docking) against MoRpd3 and the activities of the compounds were assessed by docking scores. The in silico screening suggested that [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid (−8.7 kcal/mol) and [4-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid (−8.5 kcal/mol) are effective in comparison to trichostatin A (−7.9 kcal/mol), a well-known general HDAC inhibitor. The in vitro studies for inhibition of appressorium formation by [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid has resulted in the maximum inhibition at lower concentrations (1 μM), while the trichostatin A exhibited similar levels of inhibition at 1.5 μM. These findings thus suggest that 3D quantitative structure activity relationship studies on [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid compound can further guide the design of more potential and specific HDAC inhibitors