Novel insights into host-pathogen interactions of large yellow croakers ( Larimichthys crocea) and pathogenic bacterium Pseudomonas plecoglossicida using time-resolved dual RNA-seq of infected spleens.
ABSTRACT: Host-pathogen interactions are highly complex, involving large dynamic changes in gene expression during infection. These interactions are fundamental to understanding anti-infection immunity of hosts, as well as the pathogenesis of pathogens. For bacterial pathogens interacting with animal hosts, time-resolved dual RNA-seq of infected tissue is difficult to perform due to low pathogen load in infected tissue. In this study, an acute infection model of Larimichthys crocea infected by Pseudomonas plecoglossicida was established. The spleens of infected fish exhibited typical symptoms, with a maximum bacterial load at two days post-injection (dpi). Time-resolved dual RNA-seq of infected spleens was successfully applied to study host-pathogen interactions between L. crocea and P. plecoglossicida. The spleens of infected L. crocea were subjected to dual RNA-seq, and transcriptome data were compared with those of noninfected spleens or in vitro cultured bacteria. Results showed that pathogen-host interactions were highly dynamically regulated, with corresponding fluctuations in host and pathogen transcriptomes during infection. The expression levels of many immunogenes involved in cytokine-cytokine receptor, Toll-like receptor signaling, and other immune-related pathways were significantly up-regulated during the infection period. Furthermore, metabolic processes and the use of oxygen in L. crocea were strongly affected by P. plecoglossicida infection. The WGCNA results showed that the metabolic process was strongly related to the entire immune process. For P. plecoglossicida, the expression levels of motility-related genes and flagellum assembly-related genes were significantly up-regulated. The results of this study may help to elucidate the interactions between L. crocea and P. plecoglossicida.
Project description:In the present study, Larimichthys crocea and Pseudomonas plecoglossicida were selected as a host-pathogen interaction model for teleosts and prokaryotic pathogens. Five shRNAs were designed and synthesized to silence the fliA gene, all of which resulted in pronounced reductions in fliA mRNA; the mutant strain with the best silencing efficiency of 92.16% was chosen for subsequent analysis. A significant decrease in motility, intracellular survival and escape was observed for the fliA-RNAi strain of P. plecoglossicida, whereby silencing of the fliA gene led to a 30% decrease in mortality and a four-day delay in the onset of infection in L. crocea. Moreover, silencing of P. plecoglossicida fliA resulted in a significant change in both the pathogen and host transcriptome in the spleens of infected L. crocea. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of pathogen transcriptome data showed that silencing fliA resulted in downregulation of 18 flagellum-related genes; KEGG analysis of host transcriptome data revealed that infection with the fliA-RNAi strain caused upregulation of 47 and downregulation of 106 immune-related genes. These pathogen-host interactions might facilitate clearance of P. plecoglossicida by L. crocea, with a significant decrease in fliA-RNAi P. plecoglossicida strain virulence in L. crocea.
Project description:Pseudomonas plecoglossicida is a rod-shaped, gram-negative bacterium with flagella. It causes visceral white spot disease and high mortality in Larimichthys crocea during culture, resulting in serious economic loss. Analysis of transcriptome and quantitative real-time polymerase chain reaction (PCR) data showed that dksA gene expression was significantly up-regulated after 48 h of infection with Epinephelus coioides (log 2FC=3.12, P<0.001). RNAi of five shRNAs significantly reduced the expression of dksA in P. plecoglossicida, and the optimal silencing efficiency was 96.23%. Compared with wild-type strains, the symptoms of visceral white spot disease in L. crocea infected with RNAi strains were reduced, with time of death delayed by 48 h and mortality reduced by 25%. The dksA silencing led to a substantial down-regulation in cellular component-, flagellum-, and ribosome assembly-related genes in P. plecoglossicida, and the significant up-regulation of fliC may be a way in which virulence is maintained in P. plecoglossicida. The GO and KEGG results showed that RNAi strain infection in L. crocea led to the down-regulation of inflammatory factor genes in immune-related pathways, which were associated with multiple immune response processes. Results also showed that dksA was a virulence gene in P. plecoglossicida. Compared with the wild-type strains, RNAi strain infection induced a weaker immune response in L. crocea.
Project description:Pseudomonas plecoglossicida is a temperature-dependent opportunistic pathogen which is associated with a variety of diseases in fish. During the development of "white nodules" disease, the expression of htpG in P. plecoglossicida was found to be significantly up-regulated at its virulent temperature of 18°C. The infection of htpG-RNAi strain resulted in the onset time delay, reduction in mortality and infection symptoms in spleen of Epinephelus coioides, and affected the bacterial tissue colonization. In order to reveal the effect of htpG silencing of P. plecoglossicida on the virulence regulation in P. plecoglossicida and immune response in E. coioides, dual RNA-seq was performed and a pathogen-host integration network was constructed. Our results showed that infection induced the expression of host genes related to immune response, but attenuated the expression of bacterial virulence genes. Novel integration was found between host immune genes and bacterial virulence genes, while IL6, IL1R2, IL1B, and TLR5 played key roles in the network. Further analysis with GeneMANIA indicated that flgD and rplF might play key roles during the htpG-dependent virulence regulation, which was in accordance with the reduced biofilm production, motility and virulence in htpG-RNAi strain. Meanwhile, IL6 and IL1B were found to play key roles during the defense against P. plecoglossicida, while CELA2, TRY, CPA1, CPA2, and CPB1 were important targets for P. plecoglossicida attacking to the host.
Project description:Pseudomonas plecoglossicida is a Gram-negative fish pathogen responsible for visceral granular disease in large yellow croaker, Larimichthys crocea. Here, we report the complete genome sequence of a verified virulent strain, XSDHY-P, that was isolated from spleen tissue of a diseased large yellow croaker.
Project description:The transcriptome is a powerful proxy for the physiological state of a cell, healthy or diseased. As a result, transcriptome analysis has become a key tool in understanding the molecular changes that accompany bacterial infections of eukaryotic cells. Until recently, such transcriptomic studies have been technically limited to analyzing mRNA expression changes in either the bacterial pathogen or the infected eukaryotic host cell. However, the increasing sensitivity of high-throughput RNA sequencing now enables "dual RNA-seq" studies, simultaneously capturing all classes of coding and noncoding transcripts in both the pathogen and the host. In the five years since the concept of dual RNA-seq was introduced, the technique has been applied to a range of infection models. This has not only led to a better understanding of the physiological changes in pathogen and host during the course of an infection but has also revealed hidden molecular phenotypes of virulence-associated small noncoding RNAs that were not visible in standard infection assays. Here, we use the knowledge gained from these recent studies to suggest experimental and computational guidelines for the design of future dual RNA-seq studies. We conclude this review by discussing prospective applications of the technique.
Project description:Nutrient acquisition from the host environment is crucial for the survival of intracellular pathogens, but conceptual and technical challenges limit our knowledge of pathogen diets. To overcome some of these technical roadblocks, we exploited an experimentally accessible model for early infection of human macrophages by Mycobacterium tuberculosis, the etiological agent of tuberculosis, to study host-pathogen interactions with a multi-omics approach. We collected metabolomics and complete transcriptome RNA sequencing (dual RNA-seq) data of the infected macrophages, integrated them in a genome-wide reaction pair network, and identified metabolic subnetworks in host cells and M. tuberculosis that are modularly regulated during infection. Up- and downregulation of these metabolic subnetworks suggested that the pathogen utilizes a wide range of host-derived compounds, concomitant with the measured metabolic and transcriptional changes in both bacteria and host. To quantify metabolic interactions between the host and intracellular pathogen, we used a combined genome-scale model of macrophage and M. tuberculosis metabolism constrained by the dual RNA-seq data. Metabolic flux balance analysis predicted coutilization of a total of 33 different carbon sources and enabled us to distinguish between the pathogen's substrates directly used as biomass precursors and the ones further metabolized to gain energy or to synthesize building blocks. This multiple-substrate fueling confers high robustness to interventions with the pathogen's metabolism. The presented approach combining multi-omics data as a starting point to simulate system-wide host-pathogen metabolic interactions is a useful tool to better understand the intracellular lifestyle of pathogens and their metabolic robustness and resistance to metabolic interventions. IMPORTANCE The nutrients consumed by intracellular pathogens are mostly unknown. This is mainly due to the challenge of disentangling host and pathogen metabolism sharing the majority of metabolic pathways and hence metabolites. Here, we investigated the metabolic changes of Mycobacterium tuberculosis, the causative agent of tuberculosis, and its human host cell during early infection. To this aim, we combined gene expression data of both organisms and metabolite changes during the course of infection through integration into a genome-wide metabolic network. This led to the identification of infection-specific metabolic alterations, which we further exploited to model host-pathogen interactions quantitatively by flux balance analysis. These in silico data suggested that tubercle bacilli consume up to 33 different nutrients during early macrophage infection, which the bacteria utilize to generate energy and biomass to establish intracellular growth. Such multisubstrate fueling strategy renders the pathogen's metabolism robust toward perturbations, such as innate immune responses or antibiotic treatments.
Project description:A fundamental goal of contemporary biomedical research is to understand the molecular basis of disease pathogenesis and exploit this information to develop targeted and more-effective therapies. Necrotizing myositis caused by the bacterial pathogen Streptococcus pyogenes is a devastating human infection with a high mortality rate and few successful therapeutic options. We used dual transcriptome sequencing (RNA-seq) to analyze the transcriptomes of S. pyogenes and host skeletal muscle recovered contemporaneously from infected nonhuman primates. The in vivo bacterial transcriptome was strikingly remodeled compared to organisms grown in vitro, with significant upregulation of genes contributing to virulence and altered regulation of metabolic genes. The transcriptome of muscle tissue from infected nonhuman primates (NHPs) differed significantly from that of mock-infected animals, due in part to substantial changes in genes contributing to inflammation and host defense processes. We discovered significant positive correlations between group A streptococcus (GAS) virulence factor transcripts and genes involved in the host immune response and inflammation. We also discovered significant correlations between the magnitude of bacterial virulence gene expression in vivo and pathogen fitness, as assessed by previously conducted genome-wide transposon-directed insertion site sequencing (TraDIS). By integrating the bacterial RNA-seq data with the fitness data generated by TraDIS, we discovered five new pathogen genes, namely, S. pyogenes 0281 (Spy0281 [dahA]), ihk-irr, slr, isp, and ciaH, that contribute to necrotizing myositis and confirmed these findings using isogenic deletion-mutant strains. Taken together, our study results provide rich new information about the molecular events occurring in severe invasive infection of primate skeletal muscle that has extensive translational research implications.IMPORTANCE Necrotizing myositis caused by Streptococcus pyogenes has high morbidity and mortality rates and relatively few successful therapeutic options. In addition, there is no licensed human S. pyogenes vaccine. To gain enhanced understanding of the molecular basis of this infection, we employed a multidimensional analysis strategy that included dual RNA-seq and other data derived from experimental infection of nonhuman primates. The data were used to target five streptococcal genes for pathogenesis research, resulting in the unambiguous demonstration that these genes contribute to pathogen-host molecular interactions in necrotizing infections. We exploited fitness data derived from a recently conducted genome-wide transposon mutagenesis study to discover significant correlation between the magnitude of bacterial virulence gene expression in vivo and pathogen fitness. Collectively, our findings have significant implications for translational research, potentially including vaccine efforts.
2020-01-01 | S-EPMC7029145 | BioStudies
Project description:Dual RNA-Seq data from spleens of Larimichthys crocea infected with clpV-RNAi strain of Pseudomonas plecoglossicida
Project description:The complexity of the pathogenic mechanism underlying the host immune response to Actinobacillus pleuropneumonia (App) makes the use of preventive measures difficult, and a more global view of the host-pathogen interactions and new insights into this process are urgently needed to reveal the pathogenic and immune mechanisms underlying App infection. Here, we infected specific pathogen-free Mus musculus with App serotype 7 by intranasal inoculation to construct an acute hemorrhagic pneumonia infection model and isolated the infected lungs for analysis of the interactions by dual RNA-seq.Four cDNA libraries were constructed, and 2428 differentially expressed genes (DEGs) of the host and 333 DEGs of App were detected. The host DEGs were mainly enriched in inflammatory signaling pathways, such as the TLR, NLR, RLR, BCR and TCR signaling pathways, resulting in large-scale cytokine up-regulation and thereby yielding a cytokine cascade for anti-infection and lung damage. The majority of the up-regulated cytokines are involved in the IL-23/IL-17 cytokine-regulated network, which is crucial for host defense against bacterial infection. The DEGs of App were mainly related to the transport and metabolism of energy and materials. Most of these genes are metabolic genes involved in anaerobic metabolism and important for challenging the host and adapting to the anaerobic stress conditions observed in acute hemorrhagic pneumonia. Some of these genes, such as adhE, dmsA, and aspA, might be potential virulence genes. In addition, the up-regulation of genes associated with peptidoglycan and urease synthesis and the restriction of major virulence genes might be immune evasion strategies of App. The regulation of metabolic genes and major virulence genes indicate that the dominant antigens might differ during the infection process and that vaccines based on these antigens might allow establishment of a precise and targeted immune response during the early phase of infection.Through an analysis of transcriptional data by dual RNA-seq, our study presents a novel global view of the interactions of App with its host and provides a basis for further study.
Project description:Organisms constantly interact with other species through physical contact which leads to changes on the molecular level, for example the transcriptome. These changes can be monitored for all genes, with the help of high-throughput experiments such as RNA-seq or microarrays. The adaptation of the gene expression to environmental changes within cells is mediated through complex gene regulatory networks. Often, our knowledge of these networks is incomplete. Network inference predicts gene regulatory interactions based on transcriptome data. An emerging application of high-throughput transcriptome studies are dual transcriptomics experiments. Here, the transcriptome of two or more interacting species is measured simultaneously. Based on a dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candida albicans, the software tool NetGenerator was applied to predict an inter-species gene regulatory network. To promote further investigations of molecular inter-species interactions, we recently discussed dual RNA-seq experiments for host-pathogen interactions and extended the applied tool NetGenerator (Schulze et al., 2015). The updated version of NetGenerator makes use of measurement variances in the algorithmic procedure and accepts gene expression time series data with missing values. Additionally, we tested multiple modeling scenarios regarding the stimuli functions of the gene regulatory network. Here, we summarize the work by Schulze et al. (2015) and put it into a broader context. We review various studies making use of the dual transcriptomics approach to investigate the molecular basis of interacting species. Besides the application to host-pathogen interactions, dual transcriptomics data are also utilized to study mutualistic and commensalistic interactions. Furthermore, we give a short introduction into additional approaches for the prediction of gene regulatory networks and discuss their application to dual transcriptomics data. We conclude that the application of network inference on dual-transcriptomics data is a promising approach to predict molecular inter-species interactions.