bioRxiv Science⌕ Search

bioRxiv · 10.1101/643627

Genome-wide identification of long non-coding RNAs and their regulatory networks involved in Apis mellifera ligustica response to Nosema ceranae infection

Abstract

Long non-coding RNAs (lncRNAs) are a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins, and lncRNAs have been proved to play pivotal roles in a wide range of biological processes in animals and plants. However, knowledge of expression pattern and potential role of honeybee lncRNAs response to Nosema ceranae infection is completely unknown. Here, we performed whole transcriptome strand-specific RNA sequencing of normal midguts of Apis mellifera ligustica workers (Am7CK, Am10CK) and N. ceranae-inoculated midguts (Am7T, Am10T), followed by comprehensive analyses using bioinformatic and molecular approaches. A total of 6353 A. m. ligustica lncRNAs were identified, including 4749 conserved lncRNAs and 1604 novel lncRNAs. These lncRNAs had low sequence similarities with other known lncRNAs in other species; however, their structural features were similar with counterparts in mammals and plants, including shorter exon and intron length, lower exon number, and lower expression level, compared with protein-coding transcripts. Further, 111 and 146 N. ceranae-responsive lncRNAs were identified from midguts at 7 day post inoculation (dpi) and 10 dpi compared with control midguts. 12 differentially expressed lncRNAs (DElncRNAs) were shared by Am7CK vs Am7T and Am10CK vs Am10T comparison groups, while the numbers of unique ones were 99 and 134, respectively. Functional annotation and pathway analysis showed the DElncRNAs may regulate the expression of neighboring genes by acting in cis. Moreover, we discovered 27 lncRNAs harboring eight known miRNA precursors and 513 lncRNAs harboring 2257 novel miRNA precursors. Additionally, hundreds of DElncRNAs and their target miRNAs were found to form complex competitive endogenous RNA (ceRNA) networks, suggesting these DElncRNAs may act as miRNA sponges. Furthermore, DElncRNA-miRNA-mRNA networks were constructed and investigated, the result demonstrated that part of DElncRNAs were likely to participate in regulating the material and energy metabolism as well as cellular and humoral immune during host responses to N. ceranae invasion. Finally, the expression pattern of 10 DElncRNAs was validated using RT-qPCR, confirming the reliability of our sequencing data. Our findings revealed here offer not only a rich genetic resource for further investigation of the functional roles of lncRNAs involved in A. m. ligustica response to N. ceranae infection, but also a novel insight into understanding host-pathogen interaction during microsporidiosis of honeybee.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Guo, R., Chen, H., Du, Y., zhou, d., geng, s., wang, h., zhu, z., shi, c., wan, j., xiong, c., zheng, y., Chen, D.. 2019-05-21. Genome-wide identification of long non-coding RNAs and their regulatory networks involved in Apis mellifera ligustica response to Nosema ceranae infection. https://doi.org/10.1101/643627

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗

Contrastive Learning Improves Generalization on M2OR Olfactory Binding Dataset

Olfaction-based biosensors stand to have profound impacts in fields such as medical diagnostics, national security, and drug discovery. A promising mode of vapor phase biodetection mimics volatile organic compound-olfactory receptor (VOC-OR) interactions with target-specific protein or peptide probes; however, lack of experimental coverage remains a core bottleneck. Current computational approaches aim to improve prediction of VOC-OR binding to reduce experimental burden, and current machine learning approaches fail to demonstrate a level of generalization that addresses current engineering challenges. Here, using the Molecular to Olfactory Dataset (M2OR) and Insect Olfactory Binding Protein Database (iOBPdb) datasets, data partitioning methods and their performance are evaluated on a neural network and contrastive learning framework on such VOC-OR binding datasets, varying: model training, architecture, and structure of the input data distribution. It was found that holding out certain chemical or proteomic motifs from the training data diminished model performance with a two layer neural network model. The model was able to generalize to state of the art performance (0.88 AUCROC) on every data split by applying a contrastive learning objective defined by shared molecular constituents and a stochastic sampler. This suggests the contrastive objective provides a strong inductive bias for neural networks to predict novel VOC-OR pairs. These findings highlight that model performance on pairwise biological interaction datasets can be inflated through identity leakage from constituent overlap, and emphasize the need for more robust evaluation protocols. Furthermore, we show contrastive regularization mitigates dataset degeneracy through enforcement of compatibility in the models latent geometry that may extend to other binder target-datasets where identity leakage confounds evaluation.

bioinformatics↗

Plate design confounds donor variation and model evaluation in single-cell perturbation screens

Single-cell screens of immune cells from many donors are used to study why individuals respond differently and to benchmark response models. Each donor-perturbation combination typically occupies one well, controls are grouped, and donors share one layout, so technical effects can look biological. Using a 90-cytokine screen, two compound screens, four cohorts and simulations, we show that well effects explain most replicate-control variation in abundant cell types, controls carry a shared offset, and identical control wells differ by hundreds of differentially expressed genes at a standard threshold, about as many as two receptor-negative proteins do. Negative controls, design-aware estimators and perturbation-specific evaluation change the interpretation of four recent analyses. A deep-learning model that scores below a perturbation-agnostic predictor under standard evaluation outperforms a simple baseline on perturbation-specific differences in two of three cell types (significantly in one with stricter controls), but predicts less well the component shared with inert wells.

bioinformatics↗