Genome-wide profiling of long non-coding RNA of the rice blast fungus Magnaporthe oryzae during infection.

BACKGROUND: Long non-coding RNAs (lncRNAs) play essential roles in developmental processes and disease development at the transcriptional and post-transcriptional levels across diverse taxa. However, only few studies have profiled fungal lncRNAs in a genome-wide manner during host infection. RESULTS: Infection-associated lncRNAs were identified using lncRNA profiling over six stages of host infection (e.g., vegetative growth, pre-penetration, biotrophic, and necrotrophic stages) in the model pathogenic fungus, Magnaporthe oryzae. We identified 2,601 novel lncRNAs, including 1,286 antisense lncRNAs and 980 intergenic lncRNAs. Among the identified lncRNAs, 755 were expressed in a stage-specific manner and 560 were infection-specifically expressed lncRNAs (ISELs). To decipher the potential roles of lncRNAs during infection, we identified 365 protein-coding genes that were associated with 214 ISELs. Analysis of the predicted functions of these associated genes suggested that lncRNAs regulate pathogenesis-related genes, including xylanases and effectors. CONCLUSIONS: The ISELs and their associated genes provide a comprehensive view of lncRNAs during fungal pathogen-plant interactions. This study expands new insights into the role of lncRNAs in the rice blast fungus, as well as other plant pathogenic fungi.

Alternative splicing diversifies the transcriptome and proteome of the rice blast fungus during host infection.

Alternative splicing (AS) contributes to diversifying and regulating cellular responses to environmental conditions and developmental cues by differentially producing multiple mRNA and protein isoforms from a single gene. Previous studies on AS in pathogenic fungi focused on profiling AS isoforms under a limited number of conditions. We analysed AS profiles in the rice blast fungus Magnaporthe oryzae, a global threat to rice production, using high-quality transcriptome data representing its vegetative growth (mycelia) and multiple host infection stages. We identified 4,270 AS isoforms derived from 2,413 genes, including 499 genes presumably regulated by infection-specific AS. AS appears to increase during infection, with 32.7% of the AS isoforms being produced during infection but absent in mycelia. Analysis of the isoforms observed at each infection stage showed that 636 AS isoforms were more abundant than corresponding annotated mRNAs, especially after initial hyphal penetration into host cell. Many such dominant isoforms were predicted to encode regulatory proteins such as transcription factors and phospho-transferases. We also identified the genes encoding distinct proteins via AS and confirmed the translation of some isoforms via a proteomic analysis, suggesting potential AS-mediated neo-functionalization of some genes during infection. Comprehensive profiling of the pattern of genome-wide AS during multiple stages of rice-M. oryzae interaction established a foundational resource that will help investigate the role and regulation of AS during rice infection.

Comparative genome analyses of four rice-infecting Rhizoctonia solani isolates reveal extensive enrichment of homogalacturonan modification genes.

BACKGROUND: Plant pathogenic isolates of Rhizoctonia solani anastomosis group 1-intraspecific group IA (AG1-IA) infect a wide range of crops causing diseases such as rice sheath blight (ShB). ShB has become a serious disease in rice production worldwide. Additional genome sequences of the rice-infecting R. solani isolates from different geographical regions will facilitate the identification of important pathogenicity-related genes in the fungus. RESULTS: Rice-infecting R. solani isolates B2 (USA), ADB (India), WGL (India), and YN-7 (China) were selected for whole-genome sequencing. Single-Molecule Real-Time (SMRT) and Illumina sequencing were used for de novo sequencing of the B2 genome. The genomes of the other three isolates were then sequenced with Illumina technology and assembled using the B2 genome as a reference. The four genomes ranged from 38.9 to 45.0 Mbp in size, contained 9715 to 11,505 protein-coding genes, and shared 5812 conserved orthogroups. The proportion of transposable elements (TEs) and average length of TE sequences in the B2 genome was nearly 3 times and 2 times greater, respectively, than those of ADB, WGL and YN-7. Although 818 to 888 putative secreted proteins were identified in the four isolates, only 30% of them were predicted to be small secreted proteins, which is a smaller proportion than what is usually found in the genomes of cereal necrotrophic fungi. Despite a lack of putative secondary metabolite biosynthesis gene clusters, the rice-infecting R. solani genomes were predicted to contain the most carbohydrate-active enzyme (CAZyme) genes among all 27 fungal genomes used in the comparative analysis. Specifically, extensive enrichment of pectin/homogalacturonan modification genes were found in all four rice-infecting R. solani genomes. CONCLUSION: Four R. solani genomes were sequenced, annotated, and compared to other fungal genomes to identify distinctive genomic features that may contribute to the pathogenicity of rice-infecting R. solani. Our analyses provided evidence that genomic conservation of R. solani genomes among neighboring AGs was more diversified than among AG1-IA isolates and the presence of numerous predicted pectin modification genes in the rice-infecting R. solani genomes that may contribute to the wide host range and virulence of this necrotrophic fungal pathogen.

Transcriptome Profiling of the Rice Blast Fungus Magnaporthe oryzae and Its Host Oryza sativa During Infection.

The rice blast (fungal pathogen: Magnaporthe oryzae and host: Oryza sativa) is one of the most important model pathosystems for understanding plant-microbe interactions. Although both genome sequences were published as the first cases of pathogen and host, only a few in planta transcriptome data during infection are available. Due to technical difficulties, previously reported fungal transcriptome data are not highly qualified to comprehensively profile the expression of fungal genes during infection. Here, we report the high-quality transcriptomes of M. oryzae and rice during infection using a sheath infection-based RNA sequencing approach. This comprehensive expression profiling of the fungal pathogen and its host will provide a better platform for understanding the plant-microbe interactions at the genomic level and serve as a valuable resource for the research community.

TGFam-Finder: a novel solution for target-gene family annotation in plants.

Whole-genome annotation error that omits essential protein-coding genes hinders further research. We developed Target Gene Family Finder (TGFam-Finder), an alternative tool for the structural annotation of protein-coding genes containing target domain(s) of interest in plant genomes. TGFam-Finder took considerably reduced annotation run-time and improved accuracy compared to conventional annotation tools. Large-scale re-annotation of 50 plant genomes identified an average of 150, 166 and 86 additional far-red-impaired response 1, nucleotide-binding and leucine-rich-repeat, and cytochrome P450 genes, respectively, that were missed in previous annotations. We detected significantly higher number of translated genes in the new annotations using mass spectrometry data from seven plant species compared to previous annotations. TGFam-Finder along with the new gene models can provide an optimized platform for comprehensive functional, comparative, and evolutionary studies in plants.

A High-Quality Draft Genome Sequence of Colletotrichum gloeosporioides sensu stricto SMCG1#C, a Causal Agent of Anthracnose on Cunninghamia lanceolata in China.

Colletotrichum has a broad host range and causes major yield losses of crops. The fungus Colletotrichum gloeosporioides is associated with anthracnose on Chinese fir. In this study, we present a high-quality draft genome sequence of C. gloeosporioides sensu stricto SMCG1#C, providing a reference genomic data for further research on anthracnose of Chinese fir and other hosts.

Genomic comparisons of the laurel wilt pathogen, Raffaelea lauricola, and related tree pathogens highlight an arsenal of pathogenicity related genes.

Raffaelea lauricola is an invasive fungal pathogen and symbiont of the redbay ambrosia beetle (Xyleborus glabratus) that has caused widespread mortality to redbay (Persea borbonia) and other Lauraceae species in the southeastern USA. We compare two genomes of R. lauricola (C2646 and RL570) to seven other related Ophiostomatales species including R. aguacate (nonpathogenic close relative of R. lauricola), R. quercus-mongolicae (associated with mortality of oaks in Korea), R. quercivora (associated with mortality of oaks in Japan), Grosmannia clavigera (cause of blue stain in conifers), Ophiostoma novo-ulmi (extremely virulent causal agent of Dutch elm disease), O. ulmi (moderately virulent pathogen that cause of Dutch elm disease), and O. piceae (blue-stain saprophyte of conifer logs and lumber). Structural and functional annotations were performed to determine genes that are potentially associated with disease development. Raffaelea lauricola and R. aguacate had the largest genomes, along with the largest number of protein-coding genes, genes encoding secreted proteins, small-secreted proteins, ABC transporters, cytochrome P450 enzymes, CAZYmes, and proteases. Our results indicate that this large genome size was not related to pathogenicity but was likely lineage specific, as the other pathogens in Raffaelea (R. quercus-mongolicae and R. quercivora) had similar genome characteristics to the Ophiostoma species. A diverse repertoire of wood-decaying enzymes were identified in each of the genomes, likely used for toxin neutralization rather than wood degradation. Lastly, a larger number of species-specific, secondary metabolite, synthesis clusters were identified in R. lauricola suggesting that it is well equipped as a pathogen, which could explain its success as a pathogen of a wide range of lauraceous hosts.

Role of the Histone Acetyltransferase Rtt109 in Development and Pathogenicity of the Rice Blast Fungus.

Acetylation of histone H3 lysine 56 (H3K56) by the fungal-specific histone acetyltransferase Rtt109 plays important roles in maintaining genome integrity and surviving DNA damage. Here, we investigated the implications of Rtt109-mediated response to DNA damage on development and pathogenesis of the rice blast fungus Magnaporthe oryzae (anamorph: Pyricularia oryzae). The ortholog of Rtt109 in M. oryzae (MoRtt109) was found via sequence homology and its functionality was confirmed by phenotypic complementation of the Saccharomyces cerevisiae Rtt109 deletion strain. Targeted deletion of MoRtt109 resulted in a significant reduction in acetylation of H3K56 and rendered the fungus defective in hyphal growth and asexual reproduction. Furthermore, the deletion mutant displayed hypersensitivity to genotoxic agents, confirming the conserved importance of Rtt109 in genome integrity maintenance and genotoxic stress tolerance. Elevated expression of DNA repair genes and the results of the comet assay were consistent with constitutive endogenous DNA damage. Although the conidia produced from the mutant were not impaired in germination and appressorium morphogenesis, the mutant was significantly less pathogenic on rice leaves. Transcriptomic analysis provided insight into the factors underlying phenotypic defects that are associated with deficiency of H3K56 acetylation. Overall, our results indicate that MoRtt109 is a conserved histone acetyltransferase that affects proliferation and asexual fecundity of M. oryzae through maintenance of genome integrity and response to DNA damage.

CFGP 2.0: a versatile web-based platform for supporting comparative and evolutionary genomics of fungi and Oomycetes.

In 2007, Comparative Fungal Genomics Platform (CFGP; http://cfgp.snu.ac.kr/) was publicly open with 65 genomes corresponding to 58 fungal and Oomycete species. The CFGP provided six bioinformatics tools, including a novel tool entitled BLASTMatrix that enables search homologous genes to queries in multiple species simultaneously. CFGP also introduced Favorite, a personalized virtual space for data storage and analysis with these six tools. Since 2007, CFGP has grown to archive 283 genomes corresponding to 152 fungal and Oomycete species as well as 201 genomes that correspond to seven bacteria, 39 plants and 105 animals. In addition, the number of tools in Favorite increased to 27. The Taxonomy Browser of CFGP 2.0 allows users to interactively navigate through a large number of genomes according to their taxonomic positions. The user interface of BLASTMatrix was also improved to facilitate subsequent analyses of retrieved data. A newly developed genome browser, Seoul National University Genome Browser (SNUGB), was integrated into CFGP 2.0 to support graphical presentation of diverse genomic contexts. Based on the standardized genome warehouse of CFGP 2.0, several systematic platforms designed to support studies on selected gene families have been developed. Most of them are connected through Favorite to allow of sharing data across the platforms.

funRNA: a fungi-centered genomics platform for genes encoding key components of RNAi.

BACKGROUND: RNA interference (RNAi) is involved in genome defense as well as diverse cellular, developmental, and physiological processes. Key components of RNAi are Argonaute, Dicer, and RNA-dependent RNA polymerase (RdRP), which have been functionally characterized mainly in model organisms. The key components are believed to exist throughout eukaryotes; however, there is no systematic platform for archiving and dissecting these important gene families. In addition, few fungi have been studied to date, limiting our understanding of RNAi in fungi. Here we present funRNA http://funrna.riceblast.snu.ac.kr/, a fungal kingdom-wide comparative genomics platform for putative genes encoding Argonaute, Dicer, and RdRP. DESCRIPTION: To identify and archive genes encoding the abovementioned key components, protein domain profiles were determined from reference sequences obtained from UniProtKB/SwissProt. The domain profiles were searched using fungal, metazoan, and plant genomes, as well as bacterial and archaeal genomes. 1,163, 442, and 678 genes encoding Argonaute, Dicer, and RdRP, respectively, were predicted. Based on the identification results, active site variation of Argonaute, diversification of Dicer, and sequence analysis of RdRP were discussed in a fungus-oriented manner. funRNA provides results from diverse bioinformatics programs and job submission forms for BLAST, BLASTMatrix, and ClustalW. Furthermore, sequence collections created in funRNA are synced with several gene family analysis portals and databases, offering further analysis opportunities. CONCLUSIONS: funRNA provides identification results from a broad taxonomic range and diverse analysis functions, and could be used in diverse comparative and evolutionary studies. It could serve as a versatile genomics workbench for key components of RNAi.

Exploring the DNA methylome of Korean patients with colorectal cancer consolidates the clinical implications of cancer-associated methylation markers.

Aberrant DNA methylation plays a critical role in the development and progression of colorectal cancer (CRC), which has high incidence and mortality rates in Korea. Various CRC-associated methylation markers for cancer diagnosis and prognosis have been developed; however, they have not been validated for Korean patients owing to the lack of comprehensive clinical and methylome data. Here, we obtained reliable methylation profiles for 228 tumor, 103 adjacent normal, and two unmatched normal colon tissues from Korean patients with CRC using an Illumina Infinium EPIC array; the data were corrected for biological and experiment biases. A comparative methylome analysis confirmed the previous findings that hypermethylated positions in the tumor were highly enriched in CpG island and promoter, 5' untranslated, and first exon regions. However, hypomethylated positions were enriched in the open-sea regions considerably distant from CpG islands. After applying a CpG island methylator phenotype (CIMP) to the methylome data of tumor samples to stratify the CRC patients, we consolidated the previously established clinicopathological findings that the tumors with high CIMP signatures were significantly enriched in the right colon. The results showed a higher prevalence of microsatellite instability status and MLH1 methylation in tumors with high CMP signatures than in those with low or non-CIMP signatures. Therefore, our methylome analysis and dataset provide insights into applying CRC-associated methylation markers for Korean patients regarding cancer diagnosis and prognosis. [BMB Reports 2024; 57(3): 161-166].

Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups.

BACKGROUND: Phenome-wide association studies (PheWASs) have been conducted on Asian populations, including Koreans, but many were based on chip or exome genotyping data. Such studies have limitations regarding whole genome-wide association analysis, making it crucial to have genome-to-phenome association information with the largest possible whole genome and matched phenome data to conduct further population-genome studies and develop health care services based on population genomics. RESULTS: Here, we present 4,157 whole genome sequences (Korea4K) coupled with 107 health check-up parameters as the largest genomic resource of the Korean Genome Project. It encompasses most of the variants with allele frequency >0.001 in Koreans, indicating that it sufficiently covered most of the common and rare genetic variants with commonly measured phenotypes for Koreans. Korea4K provides 45,537,252 variants, and half of them were not present in Korea1K (1,094 samples). We also identified 1,356 new genotype-phenotype associations that were not found by the Korea1K dataset. Phenomics analyses further revealed 24 significant genetic correlations, 14 pleiotropic associations, and 127 causal relationships based on Mendelian randomization among 37 traits. In addition, the Korea4K imputation reference panel, the largest Korean variants reference to date, showed a superior imputation performance to Korea1K across all allele frequency categories. CONCLUSIONS: Collectively, Korea4K provides not only the largest Korean genome data but also corresponding health check-up parameters and novel genome-phenome associations. The large-scale pathological whole genome-wide omics data will become a powerful set for genome-phenome level association studies to discover causal markers for the prediction and diagnosis of health conditions in future studies.

KoNA: Korean Nucleotide Archive as A New Data Repository for Nucleotide Sequence Data.

During the last decade, the generation and accumulation of petabase-scale high-throughput sequencing data have resulted in great challenges, including access to human data, as well as transfer, storage, and sharing of enormous amounts of data. To promote data-driven biological research, the Korean government announced that all biological data generated from government-funded research projects should be deposited at the Korea BioData Station (K-BDS), which consists of multiple databases for individual data types. Here, we introduce the Korean Nucleotide Archive (KoNA), a repository of nucleotide sequence data. As of July 2022, the Korean Read Archive in KoNA has collected over 477 TB of raw next-generation sequencing data from national genome projects. To ensure data quality and prepare for international alignment, a standard operating procedure was adopted, which is similar to that of the International Nucleotide Sequence Database Collaboration. The standard operating procedure includes quality control processes for submitted data and metadata using an automated pipeline, followed by manual examination. To ensure fast and stable data transfer, a high-speed transmission system called GBox is used in KoNA. Furthermore, the data uploaded to or downloaded from KoNA through GBox can be readily processed using a cloud computing service called Bio-Express. This seamless coupling of KoNA, GBox, and Bio-Express enhances the data experience, including submission, access, and analysis of raw nucleotide sequences. KoNA not only satisfies the unmet needs for a national sequence repository in Korea but also provides datasets to researchers globally and contributes to advances in genomics. The KoNA is available at https://www.kobic.re.kr/kona/.

Comprehensive profiling of DNA methylation in Korean patients with colorectal cancer.

Alterations in DNA methylation play an important pathophysiological role in the development and progression of colorectal cancer. We comprehensively profiled DNA methylation alterations in 165 Korean patients with colorectal cancer (CRC), and conducted an in-depth investigation of cancer-specific methylation patterns. Our analysis of the tumor samples revealed a significant presence of hypomethylated probes, primarily within the gene body regions; few hypermethylated sites were observed, which were mostly enriched in promoter-like and CpG island regions. The CpG Island Methylator PhenotypeHigh (CIMP-H) exhibited notable enrichment of microsatellite instability-high (MSI-H). Additionally, our findings indicated a significant correlation between methylation of the MLH1 gene and MSI-H status. Furthermore, we found that the CIMP-H had a higher tendency to affect the right-side of the colon tissues and was slightly more prevalent among older patients. Through our methylome profile analysis, we successfully verified the thylation patterns and clinical characteristics of Korean patients with CRC. This valuable dataset lays a strong foundation for exploring novel molecular insights and potential therapeutic targets for the treatment of CRC. [BMB Reports 2024; 57(2): 110-115].

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

Fungal plant cell wall-degrading enzyme database: a platform for comparative and evolutionary genomics in fungi and Oomycetes.

BACKGROUND: Plant cell wall-degrading enzymes (PCWDEs) play significant roles throughout the fungal life including acquisition of nutrients and decomposition of plant cell walls. In addition, many of PCWDEs are also utilized by biofuel and pulp industries. In order to develop a comparative genomics platform focused in fungal PCWDEs and provide a resource for evolutionary studies, Fungal PCWDE Database (FPDB) is constructed (http://pcwde.riceblast.snu.ac.kr/). RESULTS: In order to archive fungal PCWDEs, 22 sequence profiles were constructed and searched on 328 genomes of fungi, Oomycetes, plants and animals. A total of 6,682 putative genes encoding PCWDEs were predicted, showing differential distribution by their life styles, host ranges and taxonomy. Genes known to be involved in fungal pathogenicity, including polygalacturonase (PG) and pectin lyase, were enriched in plant pathogens. Furthermore, crop pathogens had more PCWDEs than those of rot fungi, implying that the PCWDEs analysed in this study are more needed for invading plant hosts than wood-decaying processes. Evolutionary analysis of PGs in 34 selected genomes revealed that gene duplication and loss events were mainly driven by taxonomic divergence and partly contributed by those events in species-level, especially in plant pathogens. CONCLUSIONS: The FPDB would provide a fungi-specialized genomics platform, a resource for evolutionary studies of PCWDE gene families and extended analysis option by implementing Favorite, which is a data exchange and analysis hub built in Comparative Fungal Genomics Platform (CFGP 2.0; http://cfgp.snu.ac.kr/).

Introduction of the Korea BioData Station (K-BDS) for sharing biological data.

A wave of new technologies has created opportunities for the cost-effective generation of high-throughput profiles of biological systems, foreshadowing a "data-driven science" era. The large variety of data available from biological research is also a rich resource that can be used for innovative endeavors. However, we are facing considerable challenges in big data deposition, integration, and translation due to the complexity of biological data and its production at unprecedented exponential rates. To address these problems, in 2020, the Korean government officially announced a national strategy to collect and manage the biological data produced through national R&D fund allocations and provide the collected data to researchers. To this end, the Korea Bioinformation Center (KOBIC) developed a new biological data repository, the Korea BioData Station (K-BDS), for sharing data from individual researchers and research programs to create a data-driven biological study environment. The K-BDS is dedicated to providing free open access to a suite of featured data resources in support of worldwide activities in both academia and industry.

Epigenetic insights into colorectal cancer: comprehensive genome-wide DNA methylation profiling of 294 patients in Korea.

DNA methylation regulates gene expression and contributes to tumorigenesis in the early stages of cancer. In colorectal cancer (CRC), CpG island methylator phenotype (CIMP) is recognized as a distinct subset that is associated with specific molecular and clinical features. In this study, we investigated the genomewide DNA methylation patterns among patients with CRC. The methylation data of 1 unmatched normal, 142 adjacent normal, and 294 tumor samples were analyzed. We identified 40,003 differentially methylated positions with 6,933 (79.8%) hypermethylated and 16,145 (51.6%) hypomethylated probes in the genic region. Hypermethylated probes were predominantly found in promoter-like regions, CpG islands, and N shore sites; hypomethylated probes were enriched in open-sea regions. CRC tumors were categorized into three CIMP subgroups, with 90 (30.6%) in the CIMP-high (CIMP-H), 115 (39.1%) in the CIMP-low (CIMP-L), and 89 (30.3%) in the non-CIMP group. The CIMP-H group was associated with microsatellite instabilityhigh tumors, hypermethylation of MLH1, older age, and rightsided tumors. Our results showed that genome-wide methylation analyses classified patients with CRC into three subgroups according to CIMP levels, with clinical and molecular features consistent with previous data. [BMB Reports 2023; 56(10): 563-568].

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research.

Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.