Detection and editing of the updated Arabidopsis plastid- and mitochondrial-encoded proteomes through PeptideAtlas.

Arabidopsis (Arabidopsis thaliana) ecotype Col-0 has plastid and mitochondrial genomes encoding over 100 proteins. Public databases (e.g. Araport11) have redundancy and discrepancies in gene identifiers for these organelle-encoded proteins. RNA editing results in changes to specific amino acid residues or creation of start and stop codons for many of these proteins, but the impact of RNA editing at the protein level is largely unexplored due to the complexities of detection. Here, we assembled the nonredundant set of identifiers, their correct protein sequences, and 452 predicted nonsynonymous editing sites of which 56 are edited at lower frequency. We then determined accumulation of edited and/or unedited proteoforms by searching ∼259 million raw tandem MS spectra from ProteomeXchange, which is part of PeptideAtlas (www.peptideatlas.org/builds/arabidopsis/). We identified all mitochondrial proteins and all except 3 plastid-encoded proteins (NdhG/Ndh6, PsbM, and Rps16), but no proteins predicted from the 4 ORFs were identified. We suggest that Rps16 and 3 of the ORFs are pseudogenes. Detection frequencies for each edit site and type of edit (e.g. S to L/F) were determined at the protein level, cross-referenced against the metadata (e.g. tissue), and evaluated for technical detection challenges. We detected 167 predicted edit sites at the proteome level. Minor frequency sites were edited at low frequency at the protein level except for cytochrome C biogenesis 382 at residue 124 (Ccb382-124). Major frequency sites (>50% editing of RNA) only accumulated in edited form (>98% to 100% edited) at the protein level, with the exception of Rpl5-22. We conclude that RNA editing for major editing sites is required for stable protein accumulation.

The ProteomeXchange consortium at 10 years: 2023 update.

Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas.

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

Universal Spectrum Identifier for mass spectra.

Mass spectra provide the ultimate evidence to support the findings of mass spectrometry proteomics studies in publications, and it is therefore crucial to be able to trace the conclusions back to the spectra. The Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to any mass spectrum contained in datasets deposited to public proteomics repositories. USI enables greater transparency of spectral evidence, with more than 1 billion USI identifications from over 3 billion spectra already available through ProteomeXchange repositories.

ARAX: a graph-based modular reasoning tool for translational biomedicine.

MOTIVATION: With the rapidly growing volume of knowledge and data in biomedical databases, improved methods for knowledge-graph-based computational reasoning are needed in order to answer translational questions. Previous efforts to solve such challenging computational reasoning problems have contributed tools and approaches, but progress has been hindered by the lack of an expressive analysis workflow language for translational reasoning and by the lack of a reasoning engine-supporting that language-that federates semantically integrated knowledge-bases. RESULTS: We introduce ARAX, a new reasoning system for translational biomedicine that provides a web browser user interface and an application programming interface (API). ARAX enables users to encode translational biomedical questions and to integrate knowledge across sources to answer the user's query and facilitate exploration of results. For ARAX, we developed new approaches to query planning, knowledge-gathering, reasoning and result ranking and dynamically integrate knowledge providers for answering biomedical questions. To illustrate ARAX's application and utility in specific disease contexts, we present several use-case examples. AVAILABILITY AND IMPLEMENTATION: The source code and technical documentation for building the ARAX server-side software and its built-in knowledge database are freely available online (https://github.com/RTXteam/RTX). We provide a hosted ARAX service with a web browser interface at arax.rtx.ai and a web API endpoint at arax.rtx.ai/api/arax/v1.3/ui/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Arabidopsis PeptideAtlas: Harnessing worldwide proteomics data to create a comprehensive community proteomics resource.

We developed a resource, the Arabidopsis PeptideAtlas (www.peptideatlas.org/builds/arabidopsis/), to solve central questions about the Arabidopsis thaliana proteome, such as the significance of protein splice forms and post-translational modifications (PTMs), or simply to obtain reliable information about specific proteins. PeptideAtlas is based on published mass spectrometry (MS) data collected through ProteomeXchange and reanalyzed through a uniform processing and metadata annotation pipeline. All matched MS-derived peptide data are linked to spectral, technical, and biological metadata. Nearly 40 million out of ∼143 million MS/MS (tandem MS) spectra were matched to the reference genome Araport11, identifying ∼0.5 million unique peptides and 17,858 uniquely identified proteins (only isoform per gene) at the highest confidence level (false discovery rate 0.0004; 2 non-nested peptides ≥9 amino acid each), assigned canonical proteins, and 3,543 lower-confidence proteins. Physicochemical protein properties were evaluated for targeted identification of unobserved proteins. Additional proteins and isoforms currently not in Araport11 were identified that were generated from pseudogenes, alternative start, stops, and/or splice variants, and small Open Reading Frames; these features should be considered when updating the Arabidopsis genome. Phosphorylation can be inspected through a sophisticated PTM viewer. PeptideAtlas is integrated with community resources including TAIR, tracks in JBrowse, PPDB, and UniProtKB. Subsequent PeptideAtlas builds will incorporate millions more MS/MS data.

Effective Control of Sclerotinia Stem Rot in Canola Plants Through Application of Exogenous Hairpin RNA of Multiple Sclerotinia sclerotiorum Genes.

Sclerotinia stem rot is a globally destructive plant disease caused by Sclerotinia sclerotiorum. Current management of Sclerotinia stem rot primarily relies on chemical fungicides and crop rotation, raising environmental concerns. In this study, we developed an eco-friendly RNA bio-fungicide targeting S. sclerotiorum. Six S. sclerotiorum genes were selected for double-stranded RNA (dsRNA) synthesis. Four genes, a chitin-binding domain, mitogen-activated protein kinase, oxaloacetate acetylhydrolase, and abhydrolase-3, were combined to express hairpin RNA in Escherichia coli HT115. The effect of application of total RNA extracted from E. coli HT115 expressing hairpin RNA on disease progressive and necrosis lesions was evaluated. Gene expression analysis using real-time PCR showed silencing of the target genes using 5 ng/µl of dsRNA in a fungal liquid culture. A detached leaf assay and greenhouse application of dsRNA on canola stem and leaves showed variation in the reduction of necrosis symptoms by dsRNA of different genes, with abhydrolase-3 being the most effective. The dsRNA from a combination of four genes reduced disease severity significantly (P = 0.01). Plants sprayed with hairpin RNA from four genes had lesions that were almost 30% smaller than those of plants treated with abhydrolase-3 alone, in lab and greenhouse assays. The results of this study highlight the potential of RNA interference to manage diseases caused by S. sclerotiorum; however, additional research is necessary to optimize its efficacy.

Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite.

The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work.

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

Predicting Optimal Antimalarial Drug Combinations from a Standardized Plasmodium falciparum Humanized Mouse Model.

The development of new combinations of antimalarial drugs is urgently needed to prevent the spread of parasites resistant to drugs in clinical use and contribute to the control and eradication of malaria. In this work, we evaluated a standardized humanized mouse model of erythrocyte asexual stages of Plasmodium falciparum (PfalcHuMouse) for the selection of optimal drug combinations. First, we showed that the replication of P. falciparum was robust and highly reproducible in the PfalcHuMouse model by retrospective analysis of historical data. Second, we compared the relative value of parasite clearance from blood, parasite regrowth after suboptimal treatment (recrudescence), and cure as variables of therapeutic response to measure the contributions of partner drugs to combinations in vivo. To address the comparison, we first formalized and validated the day of recrudescence (DoR) as a new variable and found that there was a log-linear relationship with the number of viable parasites per mouse. Then, using historical data on monotherapy and two small cohorts of PfalcHuMice evaluated with ferroquine plus artefenomel or piperaquine plus artefenomel, we found that only measurements of parasite killing (i.e., cure of mice) as a function of drug exposure in blood allowed direct estimation of the individual drug contribution to efficacy by using multivariate statistical modeling and intuitive graphic displays. Overall, the analysis of parasite killing in the PfalcHuMouse model is a unique and robust experimental in vivo tool to inform the selection of optimal combinations by pharmacometric pharmacokinetic and pharmacodynamic (PK/PD) modeling.

A Boolean network model of the double-strand break repair pathway choice.

Double strand break (DSB) repair is critical to maintaining the integrity of the genome. DSB repair deficiency underlies multiple pathologies, including cancer, chromosome instability syndromes, and, potentially, neurodevelopmental defects. DSB repair is mainly handled by two pathways: highly accurate homologous recombination (HR), which requires a sister chromatid for template-based repair, limited to S/G2 phases of the cell cycle, and canonical non-homologous end joining (c-NHEJ), available throughout the cell cycle in which minimum homology is sufficient for highly efficient yet error-prone repair. Some circumstances, such as cancer, require alternative highly mutagenic DSB repair pathways like microhomology-mediated end-joining (MMEJ) and single-strand annealing (SSA), which are triggered to attend to DNA damage. These non-canonical repair alternatives are emerging as prominent drivers of resistance in drug-based tumor therapies. Multiple DSB repair options require tight inter-pathway regulation to prevent unscheduled activities. In addition to this complexity, epigenetic modifications of the histones surrounding the DSB region are emerging as critical regulators of the DSB repair pathway choice. Modeling approaches to understanding DSBs repair pathway choice are advantageous to perform simulations and generate predictions on previously uncharacterized aspects of DSBs response. In this work, we present a Boolean network model of the DSB repair pathway choice that incorporates the knowledge, into a dynamic system, of the inter-pathways regulation involved in DSB repair, i.e., HR, c-NHEJ, SSA, and MMEJ. Our model recapitulates the well-characterized HR activity observed in wild-type cells in response to DSBs. It also recovers clinically relevant behaviors of BRCA1/FANCS mutants, and their corresponding drug resistance mechanisms ascribed to DNA repair gain-of-function pathogenic variants. Since epigenetic modifiers are dynamic and possible druggable targets, we incorporated them into our model to better characterize their involvement in DSB repair. Our model predicted that loss of the TIP60 complex and its corresponding histone acetylation activity leads to activation of SSA in response to DSBs. Our experimental validation showed that TIP60 effectively prevents activation of RAD52, a key SSA executor, and confirms the suitable use of Boolean network modeling for understanding DNA DSB repair.

Publications on COVID-19 in radiology journals in 2020 and 2021: bibliometric citation and co-citation network analysis.

OBJECTIVES: The pandemic caused by SARS-CoV-2 has led to the rapid publication of numerous radiology articles, primarily focused on disease diagnosis. The objective of this study is to analyze the intellectual structure of radiology research on COVID-19 using a citation and co-citation analysis. METHODS: We identified all documents about COVID-19 published in radiology journals included in the Web of Science in the period 2020-2021, conducting a citation analysis. Then we identified all bibliographic references that were cited by these documents, generating a co-citation matrix that was used to perform a co-citation network. RESULTS: Of the 3418 documents indexed in WoS, 857 were initially "Early Access," 2223 had citations, 393 had more than 20 citations, and 83 had more than 100 citations. The USA had the highest number of publications (32.62%) and China had the highest rate of funded studies (45.38%). The three authors with the most publications were affiliated with Italian institutions, while the five most cited authors were Chinese. A total of 647 publications were co-cited at least 12 times and were published in 206 different journals, with 49% of the documents found in radiology journals. The institutions with the greatest presence among these co-cited articles were Chinese and American. CONCLUSION: This co-citation analysis is the first to focus exclusively on radiology articles on COVID-19. Our study confirms the existence of interrelated thematic clusters with different specific weights. KEY POINTS: • As the pandemic caused by SARS-Cov-2 has led to the rapid publication of numerous radiology studies in a short time period, a bibliometric review based on citation and co-citation analysis has been conducted. • The co-citation analysis supported the identification of key themes in the study of COVID-19 in radiology publications. • Many of the most co-cited articles belong to a heterogeneous group of publications, with authors from countries that are far apart and even from different disciplines.

First Report of Verticillium stripe of Canola Caused by Verticillium longisporum in North Dakota.

North Dakota leads the U.S.A. in canola (Brassica napus L.) production (5) and approximately one-third of the acreage is located in the northeastern portion of the state. A field survey conducted at the end of the 2021 growing season in northeastern North Dakota revealed the presence of a single field with approximately 15% plants exhibiting whitish discoloration symptoms in the lower third of the stems. The epidermis on the discolored areas was peeling, and the exposed tissues were dark grey to dark-brown. Stem samples taken to the laboratory were surface disinfested with a 5% aqueous solution of NaOCl for 60 sec and rinsed thrice with sterile water. Under aseptic conditions, small stem pieces were plated on V-8 medium amended with 150 mg each of penicillin and streptomycin per liter of medium and incubated at 22 oC under 16 h light daily. Under the microscope, conidiophores were verticillate, hyaline, and had three branches. Conidia were single-celled, hyaline, and measured on average 9.2 + 1.8 µm. Microsclerotia were irregularly shaped. These features match the description of Verticillium longisporum (Stark) Karapapa Bainbr. & Heale, (4). Genomic DNA was extracted from a single-spore culture of an isolate as described by Azizi et al. (1). PCR assays were conducted twice on two independent DNA samples extracted from the same isolate using V. longisporum species-specific primer set VlspF1 and VlspR4 (2) with denaturation at 95 oC for 3 min, followed by 35 cycles of amplification at 94 oC for 1 min then 56 oC for 30 sec and 72 oC for 1 min, followed by a final period at 72 oC for 5 min. The sequenced PCR product, which had 100% homology with GenBank V. longisporum reference samples KY704097 and HE972063, was assigned GenBank accession number OR088215. Pathogenicity tests were conducted in greenhouse. Briefly, twenty seeds of the canola cv. Westar were incubated on a sterilized wet paper towel for five days at 22 oC. The seedlings were carefully lifted, and their root tips cut with scissors. Ten wounded seedlings were immersed in a V. longisporum spore suspension with 2.07 x 106 spores per ml for 30 minutes and the other ten in distilled water (controls). The plants were transplanted into pots (10x10x13 cm) containing Sunshine Mix # 1 potting mix (Fison Horticulture, Vancouver, B. C.). The study was conducted twice, with individual plants as replications. Three weeks later, 20% of inoculated plants had died and at physiological maturity, the rest of them had stunted growth and blackened internal stem tissues while external stem symptoms resembled those found in the field. All control plants reached maturity without symptoms. The pathogen re-isolated from inoculated plants were morphologically identical to the one retrieved from the field. These results confirmed the isolate as Verticillium longisporum. This is the first report of Verticillium stripe on canola in the US. In North America, the disease was first reported in Manitoba, Canada, in 2014 but subsequent surveys showed it is widespread in Canada (3). Identification of genetic resistance against this disease is required to ameliorate the threat this disease represents to US canola production.

Histopathological Investigation of Varietal Responses to Cercospora beticola Infection Process on Sugar Beet Leaves.

Cercospora leaf spot (CLS) is the most destructive foliar disease in sugar beet (Beta vulgaris). It is caused by Cercospora beticola Sacc., a fungal pathogen that produces toxins and enzymes which affect membrane permeability and cause cell death during infection. In spite of its importance, little is known about the initial stages of leaf infection by C. beticola. Therefore, we investigated the progression of C. beticola on leaf tissues of susceptible and resistant sugar beet varieties at 12-h intervals during the first 5 days after inoculation using confocal microscopy. Inoculated leaf samples were collected and stored in DAB (3,3'-diaminobenzidine) solution until processed. Samples were stained with Alexa Fluor-488-WGA dye to visualize fungal structures. Fungal biomass accumulation, reactive oxygen species (ROS) production, and the area under the disease progress curve were evaluated and compared. ROS production was not detected on any variety before 36 h postinoculation (hpi). C. beticola biomass accumulation, percentage leaf cell death, and disease severity were all significantly greater in the susceptible variety compared with the resistant variety (P < 0.05). Conidia penetrated directly through stomata between 48 to 60 hpi and produced appressoria on stomatal guard cells at 60 to 72 hpi in susceptible and resistant varieties, respectively. Penetration of hyphae inside the parenchymatous tissues varied in accordance with time postinoculation and varietal genotypes. Overall, this study provides a detailed account to date of events leading to CLS disease development in two contrasting varieties.

First Report of Leaf Spot of Sugar Beet Caused by Stemphylium vesicarium in Minnesota, USA.

In July 2021, sugar beet (Beta vulgaris L.) leaves with numerous tan to brown spots with white-bleached center and oval to irregularly shaped were collected from a field in Minnesota (MN) (46.2774° N, 96.3100° W), with 15% disease incidence and 30% disease severity. Leaves were washed with tap water then surface disinfected in 1% NaOCl aqueous solution for 1 min. Samples were rinsed thrice with sterile distilled water and dried in a laminar flow hood. A 2-cm leaf disc was plated on potato dextrose agar amended with streptomycin sulfate (200 mg/L) and incubated for four days at 25°C under 12-h light/dark cycle. Single spore cultures were obtained by suspending in sterile water spores harvested from a single colony. The suspension was streaked on a dish with V8 agar media and incubated as described. Five pure cultures were transferred to clarified V8 agar media for morphological feature observations. Colonies were uniform in appearance and developed light to olivaceous green mycelium. Conidia were dark brown to olivaceous green in color and measured 30 × 18 µm (n=20). They were oblong to broadly oval shaped muriform, and multiseptated (1 to 5 septa). Hyphae were septate and pale brown. Conidiophores were short, septate, and light to dark brown in color. Based on the morphological characteristics, isolates were identified as Stemphylium vesicarium (Simmons 1969). Genomic DNA of all five isolates were extracted using the DNeasy Plant Mini Kit (Qiagen, Hilden, Germany). PCR amplification and sequencing of the internal transcribed spacer (ITS) region (ITS1/ITS4 primers), the largest subunit of RNA polymerase II (5F2/7cR primers) (O'Donnell et al. 2009), the plasma membrane ATPase (ATPD-F1/ATPD-R1) gene (Lawrence et al. 2013), glyceraldehyde-3-phosphate-dehydrogenase gene (GAPDH) (gpd1/gpd2) (Berbee et al. 1999), and ß-tubulin gene (Bt2a/Bt2b primers) (Glass and Donaldson 1995) were done using standard procedures. Sequences were submitted to GenBank under accession numbers OP584331 (ITS), OP589289 (RPB2), OP589290 (ATPase), OP994239 (GAPDH) and OP382477 (ß-tubulin). The BLASTN search of the sequences showed 100% similarity with MT629829 (ITS) (525/525 bp), KC584471 (RPB2) (859/859 bp), JQ671770 (ATPase) (794/794 bp), MK105974 (GAPDH) (519/519 bp) and MN410922 (ß-tubulin) (320/320 bp) reference sequences of S. vesicarium. Pathogenicity tests were done using four cv. Maribo MA 504 plants. S. vesicarium spore suspensions (1 × 106/ml) were sprayed on three leaves from each plant. This trial was repeated with three replicates. A similar group of plants were sprayed with autoclaved distilled water to serve as non-inoculated control. All plants were incubated in the mist chamber for 5 days at 25°C, under daily 14/10 light-dark cycles, and >80% relative humidity, then transferred to the greenhouse kept at 23 ± 2°C and a 12-h photoperiod. Fifteen days post-inoculation, all inoculated plants had multiple lesions with dark brown margins with a grayish center, and non-inoculated control plants were asymptomatic. The re-isolated fungus was morphologically similar to isolates retrieved from the field. S. vesicarium was reported on sugar beet in Michigan (Metheny et al. 2022). This is the first report of S. vesicarium causing disease on sugar beet in MN. Stemphylium sp. is a major problem of sugar beet in the Netherlands (Hanse et al. 2015). Efforts should be made to prevent introduction of susceptible beet cultivars so that the disease does not become widespread in the USA.

Resistance to QoI and DMI Fungicides Does Not Reduce Virulence of C. beticola Isolates in North Central United States.

Cercospora leaf spot (CLS) is a destructive disease limiting sugar beet production and is managed using resistant cultivars, crop rotation, and timely applications of effective fungicides. Since 2016, its causal agent, Cercospora beticola, has been reported to be resistant to quinone outside inhibitors (QoIs) and to have reduced sensitive to demethylation inhibitors (DMIs) in sugar beet growing areas in North Dakota and Minnesota. Isolates of C. beticola resistant to QoIs, DMIs, and both QoIs and DMIs were collected from fields in Foxhome, Minnesota, in 2017. Fitness of these resistant isolates was compared with that of QoI- and DMI-sensitive isolates in laboratory and greenhouse studies. In the lab, mycelial growth, spore production, and spore germination were measured. The results showed that resistant isolates had significantly less mycelial growth and spore production than sensitive isolates, while no significant difference in spore germination was detected. In the greenhouse, six leaf-stage sugar beets were inoculated with a spore suspension made from each resistant group and incubated in separate humidity chambers. CLS disease severity was evaluated visually at 7, 14, and 21 days after inoculation (DAI), and the areas under disease progress curve (AUDPC) were calculated. Resistant isolates had significantly smaller AUDPC but still caused as high disease severity as the sensitive ones at 21 DAI. Although QoI- and/or DMI-resistant isolates had a relatively slower disease development, they still caused high disease severity and need to be factored in disease management practices.

Gluten-induced RNA methylation changes regulate intestinal inflammation via allele-specific XPO1 translation in epithelial cells.

OBJECTIVES: Coeliac disease (CD) is a complex autoimmune disorder that develops in genetically susceptible individuals. Dietary gluten triggers an immune response for which the only available treatment so far is a strict, lifelong gluten free diet. Human leucocyte antigen (HLA) genes and several non-HLA regions have been associated with the genetic susceptibility to CD, but their role in the pathogenesis of the disease is still essentially unknown, making it complicated to develop much needed non-dietary treatments. Here, we describe the functional involvement of a CD-associated single-nucleotide polymorphism (SNP) located in the 5'UTR of XPO1 in the inflammatory environment characteristic of the coeliac intestinal epithelium. DESIGN: The function of the CD-associated SNP was investigated using an intestinal cell line heterozygous for the SNP, N6-methyladenosine (m6A)-related knock-out and HLA-DQ2 mice, and human samples from patients with CD. RESULTS: Individuals harbouring the risk allele had higher m6A methylation in the 5'UTR of XPO1 RNA, rendering greater XPO1 protein amounts that led to downstream nuclear factor kappa B (NFkB) activity and subsequent inflammation. Furthermore, gluten exposure increased overall m6A methylation in humans as well as in in vitro and in vivo models. CONCLUSION: We identify a novel m6A-XPO1-NFkB pathway that is activated in CD patients. The findings will prompt the development of new therapeutic approaches directed at m6A proteins and XPO1, a target under evaluation for the treatment of intestinal disorders.

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine.

BACKGROUND: Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API). RESULTS: To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building-and hosting a web API for querying-a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink. CONCLUSION: RTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json . The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2 .

Genetic mapping and genomic prediction of sclerotinia stem rot resistance to rapeseed/canola (Brassica napus L.) at seedling stage.

KEY MESSAGE: GWAS detected ninety-eight significant SNPs associated with Sclerotinia sclerotiorum resistance. Six statistical models resulted in medium to high predictive ability, depending on trait, indicating potential of genomic prediction for disease resistance breeding. The lack of complete host resistance and a complex resistance inheritance nature between rapeseed/canola and Sclerotinia sclerotiorum often limits the development of functional molecular markers that enable breeding for sclerotinia stem rot (SSR) resistance. However, genomics-assisted selection has the potential to accelerate the breeding for SSR resistance. Therefore, genome-wide association (GWA) mapping and genomic prediction (GP) were performed using a diverse panel of 337 rapeseed/canola genotypes. Three-week-old seedlings were screened using the petiole inoculation technique (PIT). Days to wilt (DW) up to 2 weeks and lesion phenotypes (LP) at 3, 4, and 7 days post-inoculation (dpi) were recorded. A strong correlation (r = - 0.90) between DW and LP_4dpi implied that a single time point scoring at four days could be used as a proxy trait. GWA analyses using single-locus (SL) and multi-locus (ML) models identified a total of 41, and 208 significantly associated SNPs, respectively. Out of these, ninety-eight SNPs were identified by a combination of the SL model and any of the ML models, at least two ML models, or two traits. These SNPs explained 1.25-12.22% of the phenotypic variance and considered as significant, could be associated with SSR resistance. Eighty-three candidate genes with a function in disease resistance were associated with the significant SNPs. Six GP models resulted in moderate to high (0.42-0.67) predictive ability depending on SSR resistance traits. The resistant genotypes and significant SNPs will serve as valuable resources for future SSR resistance breeding. Our results also highlight the potential of genomic selection to improve rapeseed/canola breeding for SSR resistance.

Pea seed-borne mosaic virus (PSbMV) Risk Analysis of Field Pea Based on Susceptibility, Yield Loss, and Seed Transmission.

Pea seed-borne mosaic virus (PSbMV), a nonpersistently aphid-transmitted potyvirus, has been reported in field pea (Pisum sativum L.)-growing regions worldwide. In 2014, PSbMV was first identified in field peas in North Dakota, U.S.A. Susceptibility and yield losses attributed to PSbMV infection are influenced by viral pathotype and host genotype. Isolate ND14-1, recovered from North Dakota infected seed and identified as pathotype 4 (P4), was mechanically inoculated onto 20 field pea cultivars under greenhouse conditions. PSbMV susceptibility, number of seeds and pods per plant, yield, symptom expression, and PSbMV seed transmission rates were assessed by cultivar. A risk assessment was developed based on cultivar susceptibility, yield reduction, and PSbMV seed transmission. Risk factors were weighted based on perceived importance to commercial field pea producers. Three cultivars were classified as low risk, seven cultivars were classified as intermediate risk, and 10 cultivars were classified as high risk. Two of the low-risk cultivars, Aragorn and Cruiser, were confirmed to be resistant to this isolate of PSbMV. Cultivar Arcadia was susceptible to PSbMV infection with mild expression of symptoms, but was classified as low risk based on a low seed transmission rate and diminished yield losses. This risk assessment could prove a useful tool for growers in field pea cultivar selection where PSbMV is prevalent.