Biology subjects

Nalam, V. J.

Publications and source records attributed to Nalam, V. J..

9 recordsLinked to original sources

bioRxiv2026

Host Plant and Light Regime Shape Limited, Whole-Body Transcriptional Rhythmicity in the Bird Cherry-Oat Aphid (Rhopalosiphum padi)

Background Daily cycles in environmental conditions shape the biology of plants and their insect herbivores. Because aphids feed nearly continuously from the phloem to sustain rapid population growth, they maintain prolonged, direct contact with their hosts, which exposes them to plant-derived daily rhythms in addition to those arising from the environment. We investigated whole-body transcriptional rhythmicity in the bird cherry-oat aphid (Rhopalosiphum padi) under light-dark (LD) and constant darkness (DD) conditions while aphids fed on either wheat or an artificial diet sachet, sampling transcript expression every 4 h over a 48 h window. Results Only a small fraction of the transcriptome in R. padi displayed 24 h rhythms, and this fraction declined further as light and host cues were removed. Unexpectedly, more transcripts were rhythmic in DD than in LD on wheat. This trend was not present in diet-fed aphids and was not consistently detected across rhythmic detection tools or significance thresholds. Transcripts that were rhythmic in aphids feeding on wheat hosts had higher amplitudes compared to those feeding on diet. Peak expression (i.e., phase) for rhythmic transcripts clustered near dawn and pre-dusk (zeitgeber time, ZT0 and ZT12), except for transcripts detected as rhythmic in darkness-only on wheat, which peaked approximately 4 h later. Rhythmic transcripts detected in this study were functionally enriched for processes relevant to plant-aphid interactions, including carbohydrate metabolism and detoxification. Subsequent promoter analysis found E-box motifs in both rhythmic and non-rhythmic transcripts, and de novo promoter motif discovery on rhythmic transcripts identified recurring Mad/SMAD- and PAR-bZIP-like motifs. Conclusions Whole-body transcriptional rhythmicity in R. padi is limited in scope and appears strongly dependent on environmental and host-derived cues. The candidate transcripts and regulatory pathways identified in this study provide clear targets for tissue-specific, functionally minded follow-up studies to investigate the extent and regulatory mechanisms of endogenous physiological and behavioral rhythms in R. padi.

molecular biology↗

bioRxiv2025

Ancient pangenomic origins of noncanonical NLR genes underlying the recent evolutionary rescue of a staple crop

Evolutionary rescue occurs when populations in deteriorating environments avoid extinction by rapid adaptation. The recent evolutionary rescue of the cereal crop sorghum via RMES1 aphid resistance is among a few known in situ cases, but its pangenomic origins and molecular basis is not yet known. Here, we describe the behavioral effects, molecular endophenotypes, and pangenomic evolution underlying this evolutionary rescue. Analysis of near-isogenic lines show that RMES1 disrupts phloem feeding via global immunity activation of conserved defense networks. A growth-to-defense transition is evidenced by extensive transcriptome remodeling (>15% of expressed genes) and mediated by salicylic acid signaling. Nucleotide-binding leucine-rich repeat (NLR) immune receptor genes at the RMES1 locus harbored on a large tandem duplication have extensive copy number variation across the sorghum pangenome. The likely causative NLRs (RMES1A and RMES1B) were inferred from expression and structural variation. The NLRs encoded at RMES1 lack an N-terminal signaling domain and have nucleotide-binding domain sequence variation expected to result in a loss of ATP binding, suggesting RMES1 NLRs function via a noncanonical mechanism. The RMES1 NLR family is shared across the grass super-pangenome and includes the brown planthopper resistance gene BPH40 in Oryza sativa, which is syntenic to RMES1. Finally, k-mer analysis of RMES1 haplotypes in the sorghum pangenome established the East African origin of rare standing variation for resistance. Thus, the birth-and-death process at an ancient gene cluster generated pangenomic variation that was recruited to activate coordinated defense pathways and provide evolutionary rescue.

genetics↗

bioRxiv2024

DiscoEPG: A Python package for characterization of insect electrical penetration graph (EPG) signals

The Electrical Penetration Graph (EPG) technique is a well-known specialized tool that entomologists use to monitor and analyze the feeding behavior of piercing-sucking insects, such as aphids, whiteflies, and leafhoppers, on plants. Traditionally, the annotation is conducted by a well-trained technician who uses expert knowledge to compare the targeted waveforms with standard waveforms of aphid feeding behavior, which takes approximately 30 minutes to annotate an 8-hour recording depending on the complexity of the insect behaviors. Machine learning (ML) models, which shown great potential in monitoring insects behaviors, have recently been used to speed up this process. However, most publicly available tools that provide automatic annotation suffer from low prediction accuracy due to only using simple distinction rules to classify waveforms. For this reason, we develop DiscoEPG, an open-source Python package which performs accurate automatic EPG signal annotation. Various ML algorithms were experimented rigorously, which reports greater prediction power and improved accuracy in comparison to previous studies. In addition, we equipped our package with novel tools for generating journal-level plots to facilitate visual inspection, while including the computation of various EPG parameters and necessary statistical analysis which are popular in the research of aphids. With DiscoEPG, we aim to facilitate the rapid characterization of analysis of piercing-sucking insects feeding behavior through EPG signal, making this technique more viable to researchers who share the same interest. Our package is publicly available at: https://github.com/HySonLab/ML4Insects.

bioinformatics↗

bioRxiv2024

Plant responses to wheat curl mites and wheat streak mosaic virus (WSMV): first evidence of virus tolerance in wheat

Wheat curl mites (WCM) are arthropod pests that pose significant threats to wheat crops by causing direct damage by feeding, and transmitting viruses such as Wheat Streak Mosaic Virus (WSMV), Triticum Mosaic Virus (TriMV), and High Plains Wheat Mosaic Virus (HPWMoV), leading to substantial losses in wheat, barley, oats, and rye. Over three years of field screening, we found that the cultivar Hatcher consistently produced higher yields under high WSMV disease pressure, outperforming Mace and TAM112, which carry the Wsm1 gene and a QTL for curl mite resistance, respectively, indicating tolerance. To investigate the mechanisms underlying the tolerance phenotype in Hatcher, we compared its response to WCM and WSMV infection with a susceptible genotype, CO15D173R. Transcriptomic analysis revealed a nuanced interplay between plant defense and growth in Hatcher, with upregulation of genes related to jasmonic acid (JA), salicylic acid (SA), and abscisic acid (ABA) pathways, indicating a coordinated defense response. The activation of lignin biosynthesis genes points to a potential role of cell wall strengthening in deterring WCM and WSMV. Additionally, the regulation of genes involved in growth-related hormonal pathways such as gibberellic acid (GA), and brassinosteroids (BR) highlights Hatchers ability to maintain growth disease pressure. Our findings provide insight into the intricate network of phytohormones, growth-defense trade-offs, and cell wall modifications contributing to Hatchers tolerance to WCM and WSMV. This knowledge can inform the development of tolerant wheat varieties and enhance integrated pest management strategies, ultimately safeguarding wheat production.

plant biology↗

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↗

bioRxiv2022

Interplay between MYZUS PERSICAE-INDUCED LIPASE 1 and OPDA signaling in controlling green peach aphid infestation on Arabidopsis thaliana

MYZUS PERSICAE-INDUCED LIPASE1 (MPL1) encodes a lipase in Arabidopsis thaliana that is required for controlling infestation by the green peach aphid (GPA; Myzus persicae), an important phloem sap-consuming insect pest. Previously, we demonstrated that MPL1 expression was upregulated in response to GPA infestation, and GPA fecundity was higher on the mpl1 mutant, compared to the wild-type (WT), and lower on 35S:MPL1 plants that constitutively expressed MPL1 from the 35S promoter. Here, we show that the MPL1 promoter is active in the phloem and expression of the MPL1 coding sequence from the phloem-specific SUC2 promoter is sufficient to restore resistance to the GPA in the mpl1 mutant. The GPA infestation-associated upregulation of MPL1 requires CYCLOPHILIN 20-3 (CYP20-3), which encodes a 12-oxo- phytodienoic acid (OPDA)-binding protein that is involved in OPDA signaling and is required for controlling GPA infestation. OPDA promotes MPL1 expression to limit GPA fecundity, a process that requires CYP20-3 function. These results along with our observation that constitutive expression of MPL1 from the 35S promoter restores resistance to the GPA in the cyp20-3 mutant, and MPL1 feedbacks to limit OPDA levels in GPA-infested plants, suggest that an interplay between MPL1, OPDA, and CYP20-3 contributes to resistance to the GPA. HighlightInteraction between MYZUS PERSICAE-INDUCED LIPASE 1 function in the phloem, and 12-oxo-phytodienoic acid (OPDA) and CYCLOPHILIN 20-3, which encodes an OPDA-binding protein that is involved in OPDA signaling, is involved in controlling green peach aphid infestation on Arabidopsis thaliana.

plant biology↗

bioRxiv2020

Location, location, location: Feeding site affects aphid performance by altering access and quality of nutrients

Feeding location on a plant can affect aphid reproduction and survival, however little is known about factors that influence aphid performance on specific plant parts. We determined performance and feeding behavior of soybean aphid (Aphis glycines) on stem, adaxial (upper) and abaxial (lower) leaf surfaces during early vegetative growth of soybean plants and analyzed the associated phloem sap composition. Stems harbored greater aphid populations and aphids had shorter development time on stems compared to adaxial and abaxial leaf surfaces. While aphids feeding on the stem took the longest time to begin probing, potentially due to higher density and length of trichomes, this did not impact aphid population growth. Once aphids began probing, the sieve elements were more conducive to feeding as evidenced by less salivation from the stem as compared to either leaf surface. Moreover, vascular sap-enriched exudates from stems had higher sugars and amino acids, which supported higher aphid populations in artificial diet feeding assays. The high quality of stems as a food source may in part explain the shorter development time and overall greater population of aphids observed on stems. In summary, our findings suggest that the choice of feeding location and performance of aphids on a specific plant is driven largely by accessibility to and the quality of nutrients rather than morphological factors.

ecology↗