Kunk, D. | Search bioRxiv

bioRxiv2024

Title: Diurnal rhythms in durum wheat triggered by Rhopalosiphum padi (bird cherry-oat 2 aphid)

Wheat is a staple crop and one of the most widely consumed grains globally. Wheat yields can experience significant losses due to the damaging effects of herbivore infestation. However, little is known about the effect aphids have on the natural diurnal rhythms in plants. Our time-series transcriptomics and metabolomics study reveal intriguing molecular changes occurring in plant diurnal rhythmicity upon aphid infestation. Under control conditions, 15,366 out of the 66,559 genes in the tetraploid wheat cultivar Svevo, representing approximately 25% of the transcriptome, exhibited diurnal rhythmicity. Upon aphid infestation, 5,682 genes lost their rhythmicity, while additional 5,203 genes began to exhibit diurnal rhythmicity. The aphid-induced rhythmic genes were enriched in GO terms associated with plant defense, such as protein phosphorylation and cellular response to ABA and were enriched with motifs of the WRKY transcription factor families. Conversely, the genes that lost rhythmicity due to aphid infestation were enriched with motifs of the TCP and ERF transcription factor families. While the core circadian clock genes maintain their rhythmicity during infestation, we observed that approximately 60% of rhythmic genes experience disruptions in their rhythms during aphid infestation. These changes can influence both the plants growth and development processes as well as defense responses. Furthermore, analysis of rhythmic metabolite composition revealed that several monoterpenoids gained rhythmic activity under infestation, while saccharides retained their rhythmic patterns. Our findings highlight the ability of insect infestation to disrupt the natural diurnal cycles in plants, expanding our knowledge of the complex interactions between plants and insects.

plant biology↗

bioRxiv2024

Machine learning for characterizing plant-insect interactions through electrical penetration graphic signal

The electrical penetration graph (EPG) is a well-known technique that provides insights into the feeding behavior of insects with piercing-sucking mouthparts, mostly hemipterans. Since its inception in the 1960s, EPG has become indispensable in studying plant-insect interactions, revealing critical information about host plant selection, plant resistance, virus transmission, and responses to environmental factors. By integrating the plant and insect into an electrical circuit, EPG allows researchers to identify specific feeding behaviors based on distinct waveform patterns associated with activities within plant tissues. However, the traditional manual analysis of EPG waveform data is time-consuming and labor-intensive, limiting research throughput. This study presents a novel machine-learning approach to automate the segmentation and classification of EPG signals. We rigorously evaluated six diverse machine learning models, including neural networks, tree-based models, and logistic regressions, using an extensive dataset from aphid feeding experiments. Our results demonstrate that a Residual Network (ResNet) architecture achieved the highest overall waveform classification accuracy of 96.8% and highest segmentation overlap rate of 84.4%, highlighting the potential of machine learning for accurate and efficient EPG analysis. This automated approach promises to accelerate research in this field significantly and has the potential to be generalized to other insect species and experimental settings. Our findings underscore the value of applying advanced computational techniques to complex biological datasets, paving the way for a more comprehensive understanding of insect-plant interactions and their broader ecological implications. The source code for all experiments conducted within this study is publicly available at https://github.com/HySonLab/ML4Insects. Author summaryInsect pests of the order Hemiptera pose a significant threat to global agriculture, causing substantial crop losses due to direct feeding and serving as vectors for many economically important plant viruses. Understanding plant-insect interactions is crucial for mitigating these impacts. The electrical penetration graph (EPG) is a valuable tool that provides detailed insights into these interactions. However, the analysis of EPG data is a time-consuming, labor-intensive process that can also be prone to operator errors. State-of-the-art machine learning (ML) algorithms can be trained to perform this task accurately and consistently. These advanced algorithms can automate identifying and classifying specific EPG waveform patterns associated with distinct insect feeding behaviors. Our machine learning models, trained on extensive aphid feeding data demonstrated high accuracy in classifying these waveforms, with Residual Network (ResNet) architecture achieving the best performance. The automated approach saves time and resources, eliminates operator error, and also enables the identification of novel feeding patterns, providing a deeper understanding of the mechanisms underlying plant-aphid interactions. Moreover, our evaluation of a large, diverse dataset of four aphid species on four host plants indicates the potential for generalizing these models to different experimental settings. By applying advanced computational techniques to EPG data, we are pioneering the intelligent surveillance of aphid feeding habits. This approach promises to significantly enhance our efforts in developing a better understanding of factors that affect aphid feeding.

ecology↗

bioRxiv2024

Diurnal rhythmicity in salivary effector expression and metabolism shapes aphid performance on wheat plants

Diurnal rhythms influence insect behavior, physiology, and metabolism, optimizing their performance by adapting to daily changes in the environment. While their impact on agricultural pests has been briefly explored, our understanding of how these rhythms drive adaptative responses in pest biology and influence host colonization remains elusive. Here, we show that a notorious global aphid pest, Rhopalosiphum padi, exhibits distinct diurnal patterns in feeding behavior, with elevated honeydew excretion at night and extended phloem salivation during early nighttime. Temporal aphid transcriptome profiling reveals four diurnally rhythmic clusters, two of which peak at night, exhibiting enrichment in carbohydrate and amino acid metabolism. Beyond the established role in manipulating host responses and allowing sustained feeding, our study reveals novel evidence of cyclical fluctuations in salivary effector expression in an insect species. Silencing key effector genes, peaking in expression during the increased nighttime salivation, results in a more pronounced reduction in aphid excretion activity on host plants during the night compared to the day, a phenomenon not observed on artificial diets. A better understanding of aphid diurnal rhythms and their roles in shaping aphid performance provides a promising avenue to refine and optimize pest management, granting a strategic advantage for minimizing crop damage.

systems biology↗