Oomycete Metabolism Is Highly Dynamic and Reflects Lifestyle Adaptations.

The selective pressure of pathogen-host symbiosis drives adaptations. How these interactions shape the metabolism of pathogens is largely unknown. Here, we use comparative genomics to systematically analyze the metabolic networks of oomycetes, a diverse group of eukaryotes that includes saprotrophs as well as animal and plant pathogens, with the latter causing devastating diseases with significant economic and/or ecological impacts. In our analyses of 44 oomycete species, we uncover considerable variation in metabolism that can be linked to lifestyle differences. Comparisons of metabolic gene content reveal that plant pathogenic oomycetes have a bipartite metabolism consisting of a conserved core and an accessory set. The accessory set can be associated with the degradation of defense compounds produced by plants when challenged by pathogens. Obligate biotrophic oomycetes have smaller metabolic networks, and taxonomically distantly related biotrophic lineages display convergent evolution by repeated gene losses in both the conserved as well as the accessory set of metabolisms. When investigating to what extent the metabolic networks in obligate biotrophs differ from those in hemibiotrophic plant pathogens, we observe that the losses of metabolic enzymes in obligate biotrophs are not random and that gene losses predominantly influence the terminal branches of the metabolic networks. Our analyses represent the first metabolism-focused comparison of oomycetes at this scale and will contribute to a better understanding of the evolution of oomycete metabolism in relation to lifestyle adaptation. Numerous oomycete species are devastating plant pathogens that cause major damage in crops and natural ecosystems. Their interactions with hosts are shaped by strong selection, but how selection affects adaptation of the primary metabolism to a pathogenic lifestyle is not yet well established. By pan-genome and metabolic network analyses of distantly related oomycete pathogens and their nonpathogenic relatives, we reveal considerable lifestyle- and lineage-specific adaptations. This study contributes to a better understanding of metabolic adaptations in pathogenic oomycetes in relation to lifestyle, host, and environment, and the findings will help in pinpointing potential targets for disease control. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal.

International cancer registries make real-world genomic and clinical data available, but their joint analysis remains a challenge. AACR Project GENIE, an international cancer registry collecting data from 19 cancer centers, makes data from >130,000 patients publicly available through the cBioPortal for Cancer Genomics (https://genie.cbioportal.org). For 25,000 patients, additional real-world longitudinal clinical data, including treatment and outcome data, are being collected by the AACR Project GENIE Biopharma Collaborative using the PRISSMM data curation model. Several thousand of these cases are now also available in cBioPortal. We have significantly enhanced the functionalities of cBioPortal to support the visualization and analysis of this rich clinico-genomic linked dataset, as well as datasets generated by other centers and consortia. Examples of these enhancements include (i) visualization of the longitudinal clinical and genomic data at the patient level, including timelines for diagnoses, treatments, and outcomes; (ii) the ability to select samples based on treatment status, facilitating a comparison of molecular and clinical attributes between samples before and after a specific treatment; and (iii) survival analysis estimates based on individual treatment regimens received. Together, these features provide cBioPortal users with a toolkit to interactively investigate complex clinico-genomic data to generate hypotheses and make discoveries about the impact of specific genomic variants on prognosis and therapeutic sensitivities in cancer. SIGNIFICANCE: Enhanced cBioPortal features allow clinicians and researchers to effectively investigate longitudinal clinico-genomic data from patients with cancer, which will improve exploration of data from the AACR Project GENIE Biopharma Collaborative and similar datasets.

Uncovering the Role of Metabolism in Oomycete-Host Interactions Using Genome-Scale Metabolic Models.

Metabolism is the set of biochemical reactions of an organism that enables it to assimilate nutrients from its environment and to generate building blocks for growth and proliferation. It forms a complex network that is intertwined with the many molecular and cellular processes that take place within cells. Systems biology aims to capture the complexity of cells, organisms, or communities by reconstructing models based on information gathered by high-throughput analyses (omics data) and prior knowledge. One type of model is a genome-scale metabolic model (GEM) that allows studying the distributions of metabolic fluxes, i.e., the "mass-flow" through the network of biochemical reactions. GEMs are nowadays widely applied and have been reconstructed for various microbial pathogens, either in a free-living state or in interaction with their hosts, with the aim to gain insight into mechanisms of pathogenicity. In this review, we first introduce the principles of systems biology and GEMs. We then describe how metabolic modeling can contribute to unraveling microbial pathogenesis and host-pathogen interactions, with a specific focus on oomycete plant pathogens and in particular Phytophthora infestans. Subsequently, we review achievements obtained so far and identify and discuss potential pitfalls of current models. Finally, we propose a workflow for reconstructing high-quality GEMs and elaborate on the resources needed to advance a system biology approach aimed at untangling the intimate interactions between plants and pathogens.

Mining oomycete proteomes for metalloproteases leads to identification of candidate virulence factors in Phytophthora infestans.

Pathogens deploy a wide range of pathogenicity factors, including a plethora of proteases, to modify host tissue or manipulate host defences. Metalloproteases (MPs) have been implicated in virulence in several animal and plant pathogens. Here we investigated the repertoire of MPs in 46 stramenopile species including 37 oomycetes, 5 diatoms, and 4 brown algae. Screening their complete proteomes using hidden Markov models (HMMs) trained for MP detection resulted in over 4,000 MPs, with most species having between 65 and 100 putative MPs. Classification in clans and families according to the MEROPS database showed a highly diverse MP repertoire in each species. Analyses of domain composition, orthologous groups, distribution, and abundance within the stramenopile lineage revealed a few oomycete-specific MPs and MPs potentially related to lifestyle. In-depth analyses of MPs in the plant pathogen Phytophthora infestans revealed 91 MPs, divided over 21 protein families, including 25 MPs with a predicted signal peptide or signal anchor. Expression profiling showed different patterns of MP gene expression during pre-infection and infection stages. When expressed in leaves of Nicotiana benthamiana, 12 MPs changed the sizes of lesions caused by inoculation with P. infestans; with 9 MPs the lesions were larger, suggesting a positive effect on the virulence of P. infestans, while 3 MPs had a negative effect, resulting in smaller lesions. To the best of our knowledge, this is the first systematic inventory of MPs in oomycetes and the first study pinpointing MPs as potential pathogenicity factors in Phytophthora.

Misannotated Multi-Nucleotide Variants in Public Cancer Genomics Datasets Lead to Inaccurate Mutation Calls with Significant Implications.

Although next-generation sequencing is widely used in cancer to profile tumors and detect variants, most somatic variant callers used in these pipelines identify variants at the lowest possible granularity, single-nucleotide variants (SNV). As a result, multiple adjacent SNVs are called individually instead of as a multi-nucleotide variants (MNV). With this approach, the amino acid change from the individual SNV within a codon could be different from the amino acid change based on the MNV that results from combining SNV, leading to incorrect conclusions about the downstream effects of the variants. Here, we analyzed 10,383 variant call files (VCF) from the Cancer Genome Atlas (TCGA) and found 12,141 incorrectly annotated MNVs. Analysis of seven commonly mutated genes from 178 studies in cBioPortal revealed that MNVs were consistently missed in 20 of these studies, whereas they were correctly annotated in 15 more recent studies. At the BRAF V600 locus, the most common example of MNV, several public datasets reported separate BRAF V600E and BRAF V600M variants instead of a single merged V600K variant. VCFs from the TCGA Mutect2 caller were used to develop a solution to merge SNV to MNV. Our custom script used the phasing information from the SNV VCF and determined whether SNVs were at the same codon and needed to be merged into MNV before variant annotation. This study shows that institutions performing NGS sequencing for cancer genomics should incorporate the step of merging MNV as a best practice in their pipelines. SIGNIFICANCE: Identification of incorrect mutation calls in TCGA, including clinically relevant BRAF V600 and KRAS G12, will influence research and potentially clinical decisions.

The Genome of Peronospora belbahrii Reveals High Heterozygosity, a Low Number of Canonical Effectors, and TC-Rich Promoters.

Along with Plasmopara destructor, Peronosopora belbahrii has arguably been the economically most important newly emerging downy mildew pathogen of the past two decades. Originating from Africa, it has started devastating basil production throughout the world, most likely due to the distribution of infested seed material. Here, we present the genome of this pathogen and results from comparisons of its genomic features to other oomycetes. The assembly of the nuclear genome was around 35.4 Mbp in length, with an N50 scaffold length of around 248 kbp and an L50 scaffold count of 46. The circular mitochondrial genome consisted of around 40.1 kbp. From the repeat-masked genome, 9,049 protein-coding genes were predicted, out of which 335 were predicted to have extracellular functions, representing the smallest secretome so far found in peronosporalean oomycetes. About 16% of the genome consists of repetitive sequences, and, based on simple sequence repeat regions, we provide a set of microsatellites that could be used for population genetic studies of P. belbahrii. P. belbahrii has undergone a high degree of convergent evolution with other obligate parasitic pathogen groups, reflecting its obligate biotrophic lifestyle. Features of its secretome, signaling networks, and promoters are presented, and some patterns are hypothesized to reflect the high degree of host specificity in Peronospora species. In addition, we suggest the presence of additional virulence factors apart from classical effector classes that are promising candidates for future functional studies.

Metabolic Model of the Phytophthora infestans-Tomato Interaction Reveals Metabolic Switches during Host Colonization.

The oomycete pathogen Phytophthora infestans causes potato and tomato late blight, a disease that is a serious threat to agriculture. P. infestans is a hemibiotrophic pathogen, and during infection, it scavenges nutrients from living host cells for its own proliferation. To date, the nutrient flux from host to pathogen during infection has hardly been studied, and the interlinked metabolisms of the pathogen and host remain poorly understood. Here, we reconstructed an integrated metabolic model of P. infestans and tomato (Solanum lycopersicum) by integrating two previously published models for both species. We used this integrated model to simulate metabolic fluxes from host to pathogen and explored the topology of the model to study the dependencies of the metabolism of P. infestans on that of tomato. This showed, for example, that P. infestans, a thiamine auxotroph, depends on certain metabolic reactions of the tomato thiamine biosynthesis. We also exploited dual-transcriptome data of a time course of a full late blight infection cycle on tomato leaves and integrated the expression of metabolic enzymes in the model. This revealed profound changes in pathogen-host metabolism during infection. As infection progresses, P. infestans performs less de novo synthesis of metabolites and scavenges more metabolites from tomato. This integrated metabolic model for the P. infestans-tomato interaction provides a framework to integrate data and generate hypotheses about in planta nutrition of P. infestans throughout its infection cycle.IMPORTANCE Late blight disease caused by the oomycete pathogen Phytophthora infestans leads to extensive yield losses in tomato and potato cultivation worldwide. To effectively control this pathogen, a thorough understanding of the mechanisms shaping the interaction with its hosts is paramount. While considerable work has focused on exploring host defense mechanisms and identifying P. infestans proteins contributing to virulence and pathogenicity, the nutritional strategies of the pathogen are mostly unresolved. Genome-scale metabolic models (GEMs) can be used to simulate metabolic fluxes and help in unravelling the complex nature of metabolism. We integrated a GEM of tomato with a GEM of P. infestans to simulate the metabolic fluxes that occur during infection. This yields insights into the nutrients that P. infestans obtains during different phases of the infection cycle and helps in generating hypotheses about nutrition in planta.

Functional Analysis of Mating Type Genes and Transcriptome Analysis during Fruiting Body Development of Botrytis cinerea.

Botrytis cinerea is a plant-pathogenic fungus producing apothecia as sexual fruiting bodies. To study the function of mating type (MAT) genes, single-gene deletion mutants were generated in both genes of the MAT1-1 locus and both genes of the MAT1-2 locus. Deletion mutants in two MAT genes were entirely sterile, while mutants in the other two MAT genes were able to develop stipes but never formed an apothecial disk. Little was known about the reprogramming of gene expression during apothecium development. We analyzed transcriptomes of sclerotia, three stages of apothecium development (primordia, stipes, and apothecial disks), and ascospores by RNA sequencing. Ten secondary metabolite gene clusters were upregulated at the onset of sexual development and downregulated in ascospores released from apothecia. Notably, more than 3,900 genes were differentially expressed in ascospores compared to mature apothecial disks. Among the genes that were upregulated in ascospores were numerous genes encoding virulence factors, which reveals that ascospores are transcriptionally primed for infection prior to their arrival on a host plant. Strikingly, the massive transcriptional changes at the initiation and completion of the sexual cycle often affected clusters of genes, rather than randomly dispersed genes. Thirty-five clusters of genes were jointly upregulated during the onset of sexual reproduction, while 99 clusters of genes (comprising >900 genes) were jointly downregulated in ascospores. These transcriptional changes coincided with changes in expression of genes encoding enzymes participating in chromatin organization, hinting at the occurrence of massive epigenetic regulation of gene expression during sexual reproduction.IMPORTANCE Fungal fruiting bodies are formed by sexual reproduction. We studied the development of fruiting bodies ("apothecia") of the ubiquitous plant-pathogenic ascomycete Botrytis cinerea The role of mating type genes in apothecium development was investigated by targeted mutation. Two genes are essential for the initiation of sexual development; mutants in these genes are sterile. Two other genes were not essential for development of stipes; however, they were essential for stipes to develop a disk and produce sexual ascospores. We examined gene expression profiles during apothecium development, as well as in ascospores sampled from apothecia. We provide the first study ever of the transcriptome of pure ascospores in a filamentous fungus. The expression of numerous genes involved in plant infection was induced in the ascospores, implying that ascospores are developmentally primed for infection before their release from apothecia.

Genome-wide characterization of Phytophthora infestans metabolism: a systems biology approach.

Genome-scale metabolic models (GEMs) provide a functional view of the complex network of biochemical reactions in the living cell. Initially mainly applied to reconstruct the metabolism of model organisms, the availability of increasingly sophisticated reconstruction methods and more extensive biochemical databases now make it possible to reconstruct GEMs for less well-characterized organisms, and have the potential to unravel the metabolism in pathogen-host systems. Here, we present a GEM for the oomycete plant pathogen Phytophthora infestans as a first step towards an integrative model with its host. We predict the biochemical reactions in different cellular compartments and investigate the gene-protein-reaction associations in this model to obtain an impression of the biochemical capabilities of P. infestans. Furthermore, we generate life stage-specific models to place the transcriptomic changes of the genes encoding metabolic enzymes into a functional context. In sporangia and zoospores, there is an overall down-regulation, most strikingly reflected in the fatty acid biosynthesis pathway. To investigate the robustness of the GEM, we simulate gene deletions to predict which enzymes are essential for in vitro growth. This model is an essential first step towards an understanding of P. infestans and its interactions with plants as a system, which will help to formulate new hypotheses on infection mechanisms and disease prevention.