Proto-oncogenes in a eukaryotic unicellular organism play essential roles in plasmodial growth in host cells

Metabonomic profiling of clubroot-susceptible and clubroot-resistant radish and the assessment of disease-resistant metabolites

Plasmodiophora brassicae causes a serious threat to cruciferous plants including radish (Raphanus sativus L.). Knowledge on the pathogenic regularity and molecular mechanism of P. brassicae and radish is limited, especially on the metabolism level. In the present study, clubroot-susceptible and clubroot-resistant cultivars were inoculated with P. brassicae Race 4, root hairs initial infection of resting spores (10⁷ CFU/mL) at 24 h post-inoculation and root galls symptom arising at cortex splitting stage were identified on both cultivars. Root samples of cortex splitting stage of two cultivars were collected and used for untargeted metabonomic analysis. We demonstrated changes in metabolite regulation and pathways during the cortex splitting stage of diseased roots between clubroot-susceptible and clubroot-resistant cultivars using untargeted metabonomic analysis. We identified a larger number of differentially regulated metabolites and heavier metabolite profile changes in the susceptible cultivar than in the resistant counterpart. The metabolites that were differentially regulated in both cultivars were mostly lipids and lipid-like molecules. Significantly regulated metabolites and pathways according to the P value and variable important in projection score were identified. Moreover, four compounds, including ethyl α-D-thioglucopyranoside, imipenem, ginsenoside Rg1, and 6-gingerol, were selected, and their anti-P. brassicae ability and effects on seedling growth were verified on the susceptible cultivar. Except for ethyl α-D-thioglucopyranoside, the remaining could inhibit clubroot development of varing degree. The use of 5 mg/L ginsenoside Rg1 + 5 mg/L 6-gingerol resulted in the lowest disease incidence and disease index among all treatments and enhanced seedling growth. The regulation of pathways or metabolites of carbapenem and ginsenoside was further explored. The results provide a preliminary understanding of the interaction between radish and P. brassicae at the metabolism level, as well as the development of measures for preventing clubroot.

What Can We Learn from -Omics Approaches to Understand Clubroot Disease?

Clubroot is one of the most economically significant diseases worldwide. As a result, many investigations focus on both curing the disease and in-depth molecular studies. Although the first transcriptome dataset for the clubroot disease describing the clubroot disease was published in 2006, many different pathogen-host plant combinations have only recently been investigated and published. Articles presenting -omics data and the clubroot pathogen Plasmodiophora brassicae as well as different host plants were analyzed to summarize the findings in the richness of these datasets. Although genome data for the protist have only recently become available, many effector candidates have been identified, but their functional characterization is incomplete. A better understanding of the life cycle is clearly required to comprehend its function. While only a few proteome studies and metabolome analyses were performed, the majority of studies used microarrays and RNAseq approaches to study transcriptomes. Metabolites, comprising chemical groups like hormones were generally studied in a more targeted manner. Furthermore, functional approaches based on such datasets have been carried out employing mutants, transgenic lines, or ecotypes/cultivars of either Arabidopsis thaliana or other economically important host plants of the Brassica family. This has led to new discoveries of potential genes involved in disease development or in (partial) resistance or tolerance to P. brassicae. The overall contribution of individual experimental setups to a larger picture will be discussed in this review.

Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein–protein interaction (PPI) network. We found that the breast cancer–associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer–associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer–associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer–associated genes, and the top predicted genes are better enriched on known breast cancer–associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer–associated genes, which could be used for further in vitro and in vivo experimental validation.

Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer-associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer-associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein-protein interaction (PPI) network. We found that the breast cancer-associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer-associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer-associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer-associated genes, and the top predicted genes are better enriched on known breast cancer-associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer-associated genes, which could be used for further in vitro and in vivo experimental validation.

Nitrogen Supply and Host-Plant Genotype Modulate the Transcriptomic Profile of Plasmodiophora brassicae

Nitrogen fertilization can affect the susceptibility of Brassica napus to the telluric pathogen Plasmodiophora brassicae. Our previous works highlighted that the influence of nitrogen can strongly vary regarding plant cultivar/pathogen strain combinations, but the underlying mechanisms are unknown. The present work aims to explore how nitrogen supply can affect the molecular physiology of P. brassicae through its life epidemiological cycle. A time-course transcriptome experiment was conducted to study the interaction, under two conditions of nitrogen supply, between isolate eH and two B. napus genotypes (Yudal and HD-018), harboring (or not harboring) low nitrogen-conditional resistance toward this isolate (respectively). P. brassicae transcriptional patterns were modulated by nitrogen supply, these modulations being dependent on both host-plant genotype and kinetic time. Functional analysis allowed the identification of P. brassicae genes expressed during the secondary phase of infection, which may play a role in the reduction of Yudal disease symptoms in low-nitrogen conditions. Candidate genes included pathogenicity-related genes ("NUDIX," "carboxypeptidase," and "NEP-proteins") and genes associated to obligate biotrophic functions of P. brassicae. This work illustrates the importance of considering pathogen's physiological responses to get a better understanding of the influence of abiotic factors on clubroot resistance/susceptibility.

Correction to: Proto-oncogenes in a eukaryotic unicellular organism play essential roles in plasmodial growth in host cells

Following the publication of this article [1], the authors noted the following errors.