Metabolomics-based biomarker discovery for bee health monitoring : a proof of concept study concerning nutritional stress in Bombus terrestris

Bee pollinators are exposed to multiple natural and anthropogenic stressors. Understanding the effects of a single stressor in the complex environmental context of antagonistic/synergistic interactions is critical to pollinator monitoring and may serve as early warning system before a pollination crisis. This study aimed to methodically improve the diagnosis of bee stressors using a simultaneous untargeted and targeted metabolomics-based approach. Analysis of 84 Bombus terrestris hemolymph samples found 8 metabolites retained as potential biomarkers that showed excellent discrimination for nutritional stress. In parallel, 8 significantly altered metabolites, as revealed by targeted profiling, were also assigned as candidate biomarkers. Furthermore, machine learning algorithms were applied to the above-described two biomarker sets, whereby the untargeted eight components showed the best classification performance with sensitivity and specificity up to 99% and 100%, respectively. Based on pathway and biochemistry analysis, we propose that gluconeogenesis contributed significantly to blood sugar stability in bumblebees maintained on a low carbohydrate diet. Taken together, this study demonstrates that metabolomics-based biomarker discovery holds promising potential for improving bee health monitoring and to identify stressor related to energy intake and other environmental stressors

Metabolomic analysis of cricket paralysis virus infection in Drosophila S2 cells reveals divergent effects on central carbon metabolism as compared with silkworm Bm5 cells

High-throughput approaches have opened new opportunities for understanding biological processes such as persistent virus infections, which are widespread. However, the potential of persistent infections to develop towards pathogenesis remains to be investigated, particularly with respect to the role of host metabolism. To explore the interactions between cellular metabolism and persistent/pathogenic virus infection, we performed untargeted and targeted metabolomic analysis to examine the effects of Cricket paralysis virus (CrPV, Dicistroviridae) in persistently infected silkworm Bm5 cells and acutely infected Drosophila S2 cells. Our previous study (Viruses 2019, 11, 861) established that both glucose and glutamine levels significantly increased during the persistent period of CrPV infection of Bm5 cells, while they decreased steeply during the pathogenic stages. Strikingly, in this study, an almost opposite pattern in change of metabolites was observed during different stages of acute infection of S2 cells. More specifically, a significant decrease in amino acids and carbohydrates was observed prior to pathogenesis, while their abundance significantly increased again during pathogenesis. Our study illustrates the occurrence of diametrically opposite changes in central carbon mechanisms during CrPV infection of S2 and Bm5 cells that is possibly related to the type of infection (acute or persistent) that is triggered by the virus

A metabolomics approach to unravel cricket paralysis virus infection in silkworm Bm5 cells

How a host metabolism responds to infection with insect viruses and how it relates to pathogenesis, is little investigated. Our previous study observed that Cricket paralysis virus (CrPV, Dicistroviridae) causes short term persistence in silkworm Bm5 cells before proceeding to acute infection. In this study, a metabolomics approach based on high resolution mass spectrometry was applied to investigate how a host metabolism is altered during the course of CrPV infection in Bm5 cells and which changes are characteristic for the transition from persistence to pathogenicity. We observed that CrPV infection led to significant and stage-specific metabolic changes in Bm5 cells. Differential metabolites abundance and pathway analysis further identified specific metabolic features at different stages in the viral life cycle. Notably, both glucose and glutamine levels significantly increased during CrPV persistent infection followed by a steep decrease during the pathogenic stages, suggesting that the central carbon metabolism was significantly modified during CrPV infection in Bm5 cells. In addition, dynamic changes in levels of polyamines were detected. Taken together, this study characterized for the first time the metabolic dynamics of CrPV infection in insect cells, proposing a central role for the regulation of both amino acid and carbohydrate metabolism during the period of persistent infection of CrPV in Bm5 cells

Generation of virus- and dsRNA-derived siRNAs with species-dependent length in insects

Double-stranded RNA (dsRNA) molecules of viral origin trigger a post-transcriptional gene-silencing mechanism called RNA interference (RNAi). Specifically, virally derived dsRNA is recognized and cleaved by the enzyme Dicer2 into short interfering RNAs (siRNAs), which further direct sequence-specific RNA silencing, ultimately silencing replication of the virus. Notably, RNAi can also be artificially triggered by the delivery of gene-specific dsRNA, thereby leading to endogenous gene silencing. This is a widely used technology that holds great potential to contribute to novel pest control strategies. In this regard, research efforts have been set to find methods to efficiently trigger RNAi in the field. In this article, we demonstrate the generation of dsRNA- and/or virus-derived siRNAs-the main RNAi effectors-in six insect species belonging to five economically important orders (Lepidoptera, Orthoptera, Hymenoptera, Coleoptera, and Diptera). In addition, we describe that the siRNA length distribution is species-dependent. Taken together, our results reveal interspecies variability in the (antiviral) RNAi mechanism in insects and show promise to contribute to future research on (viral-based) RNAi-triggering mechanisms in this class of animals

Construction of A Nomogram Prediction Model for PD-L1 Expression in Non-small Cell Lung Cancer Based on 18F-FDG PET/CT Metabolic Parameters

Background and objective In recent years, immunotherapy represented by programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunosuppressants has greatly changed the status of non-small cell lung cancer (NSCLC) treatment. PD-L1 has become an important biomarker for screening NSCLC immunotherapy beneficiaries, but how to easily and accurately detect whether PD-L1 is expressed in NSCLC patients is a difficult problem for clinicians. The aim of this study was to construct a Nomogram prediction model of PD-L1 expression in NSCLC patients based on 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography/conputed tomography (PET/CT) metabolic parameters and to evaluate its predictive value. Methods Retrospective collection of 18F-FDG PET/CT metabolic parameters, clinicopathological information and PD-L1 test results of 155 NSCLC patients from Inner Mongolia People's Hospital between September 2016 and July 2021. The patients were divided into the training group (n=117) and the internal validation group (n=38), and another 51 cases of NSCLC patients in our hospital between August 2021 and July 2022 were collected as the external validation group according to the same criteria. Then all of them were categorized according to the results of PD-L1 assay into PD-L1+ group and PD-L1- group. The metabolic parameters and clinicopathological information of patients in the training group were analyzed by univariate and binary Logistic regression, and a Nomogram prediction model was constructed based on the screened independent influencing factors. The effect of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) in both the training group and the internal and external validation groups. Results Binary Logistic regression analysis showed that metabolic tumor volume (MTV), gender and tumor diameter were independent influences on PD-L1 expression. Then a Nomogram prediction model was constructed based on the above independent influences. The ROC curve for the model in the training group shows an area under the curve (AUC) of 0.769 (95%CI: 0.683-0.856) with an optimal cutoff value of 0.538. The AUC was 0.775 (95%CI: 0.614-0.936) in the internal validation group and 0.752 (95%CI: 0.612-0.893) in the external validation group. The calibration curves were tested by the Hosmer-Lemeshow test and showed that the training group (χ2=0.040, P=0.979), the internal validation group (χ2=2.605, P=0.271), and the external validation group (χ2=0.396, P=0.820) were well calibrated. The DCA curves show that the model provides clinical benefit to patients over a wide range of thresholds (training group: 0.00-0.72, internal validation group: 0.00-0.87, external validation group: 0.00-0.66). Conclusion The Nomogram prediction model constructed on the basis of 18F-FDG PET/CT metabolic parameters has greater application value in predicting PD-L1 expression in NSCLC patients

Functional characterization of BdB1, a well-conserved carboxylesterase among tephritid fruit flies associated with malathion resistance in Bactrocera dorsalis (Hendel)

There are many evidences that insect carboxylesterase possess important physiological roles in xenobiotic metabolism and are implicated in the detoxification of organophosphate (OP) insecticides. Despite the ongoing resistance development in the oriental fruit fly, Bactrocera dorsalis (Hendel), the molecular basis of carboxylesterase and its ability to confer OP resistance remain largely obscure. This study was initiated to provide a better understanding of carboxylesterase-mediated resistance mechanism in a tephritid pest fly. Here, we narrow this research gap by demonstrating a well-conserved esterase B1 gene, BdB1, mediates malathion resistance development via gene upregulation with the use of a laboratory selected malathion-resistant strain (MR) of B. dorsalis. No sequence mutation of BdB1 was detected between MR and the susceptible strain (MS) of B. dorsalis. BdB1 is predominantly expressed in the midgut, a key insect tissue for detoxification. As compared with transcripts in MS, BdB1 was significantly more abundant in multiple tissues in the MR. RNA interference (RNAi)-mediated knockdown of BdB1 significantly increased malathion susceptibility. Furthermore, heterologous expression along with cytotoxicity assay revealed BdB1 could probably have the function of malathion detoxification

The Bee Hemolymph Metabolome: A Window into the Impact of Viruses on Bumble Bees

State-of-the-art virus detection technology has advanced a lot, yet technology to evaluate the impacts of viruses on bee physiology and health is basically lacking. However, such technology is sorely needed to understand how multi-host viruses can impact the composition of the bee community. Here, we evaluated the potential of hemolymph metabolites as biomarkers to identify the viral infection status in bees. A metabolomics strategy based on ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry was implemented. First, we constructed a predictive model for standardized bumble bees, in which non-infected bees were metabolically differentiated from an overt Israeli acute paralysis virus (IAPV) infection ((RY)-Y-2 = 0.993; Q(2) = 0.906), as well as a covert slow bee paralysis virus (SBPV) infection ((RY)-Y-2 = 0.999; Q(2) = 0.875). Second, two sets of potential biomarkers were identified, being descriptors for the metabolomic changes in the bee's hemolymph following viral infection. Third, the biomarker sets were evaluated in a new dataset only containing wild bees and successfully discriminated virus infection versus non-virus infection with an AUC of 0.985. We concluded that screening hemolymph metabolite markers can underpin physiological changes linked to virus infection dynamics, opening promising avenues to identify, monitor, and predict the effects of virus infection in a bee community within a specific environment

The Impact of Microbiome and Microbiota-Derived Sodium Butyrate on Drosophila Transcriptome and Metabolome Revealed by Multi-Omics Analysis

The host microbiome plays an important role in regulating physiology through microbiota-derived metabolites during host-microbiome interactions. However, molecular mechanism underly host-microbiome interactions remains to be explored. In this study, we used Drosophila as the model to investigate the influence of microbiome and microbiota-derived metabolite sodium butyrate on host transcriptome and metabolome. We established both a sterile Drosophila model and a conventional Drosophila model to demonstrate the role of sodium butyrate. Using multi-omics analysis, we found that microbiome and sodium butyrate could impact host gene expression patterns in both the sterile Drosophila model and the conventional Drosophila model. The analysis of gut microbial using 16S rRNA sequencing showed sodium butyrate treatment also influenced Drosophila bacterial structures. In addition, Drosophila metabolites identified by ultra-high performance liquid chromatography-MS/MS were shown to be affected by sodium butyrate treatment with lipids as the dominant changed components. Our integrative analysis of the transcriptome, the microbiome, and the metabolome data identified candidate transcripts that are coregulated by sodium butyrate. Taken together, our results reveal the impact of the microbiome and microbiota-derived sodium butyrate on host transcriptome and metabolome, and our work provides a better understanding of host-microbiome interactions at the molecular level with multi-omics data

Effect of Juglone against Pseudomonas syringae pv Actinidiae Planktonic Growth and Biofilm Formation

Pseudomonas syringaepv Actinidiae (P. syringae) is a common pathogen causing plant diseases. Limoli proved that its strong pathogenicity is closely related to biofilm state. As a natural bacteriostatic agent with broad-spectrum bactericidal properties, juglone can be used as a substitute for synthetic bacteriostatic agents. To explore the antibacterial mechanism, this study was carried out to examine the inhibitory effect of juglone on cell membrane destruction, abnormal oxidative stress, DNA insertion and biofilm prevention of P. syringae. Results showed that juglone at 20 μg/mL can act against planktogenic P. syringae (107 CFU/mL). Specially, the application of juglone significantly damaged the permeability and integrity of the cell membrane of P. syringae. Additionally, juglone caused abnormal intracellular oxidative stress, and also embedded in genomic DNA, which affected the normal function of the DNA of P. syringae. In addition, environmental scanning electron microscope (ESEM) and other methods showed that juglone effectively restricted the production of extracellular polymers, and then affected the formation of the cell membrane. This study provided a possibility for the development and utilization of natural juglone in plants, especially P. syringae