bioRxiv Science⌕ Search

bioRxiv · 10.1101/528166

Comparative identification of microRNAs in Apis cerana cerana workers'midguts responding to Nosema ceranae invasion

Abstract

MicroRNAs (miRNAs) are endogenous small noncoding RNAs that post transcriptionally regulate gene expression and are involved in many biological processes including host-pathogen interactions. However, the potential role of miRNAs in the responses of eastern honeybees to Nosema ceranae invasion is completely unknown. Here, the expression profiles and differentially expressed miRNAs (DEmiRNAs) in the midguts of Apis cerana cerana workers 7 and 10 days post infection (dpi) with N. ceranae were investigated via small RNA sequencing and bioinformatics. In total, 529 miRNAs highly conserved between various species and 25 novel miRNAs with varied expressions were identified for the first time. In addition, stem-loop RT-PCR confirmed the expression of 16 predicted miRNAs, validating their existence. Eight up-regulated miRNAs and six down-regulated miRNAs were detected in midguts at 7 dpi, while nine and three miRNAs were significantly up-regulated and down-regulated, respectively, in midguts at 10 dpi. In addition, Venn analysis showed that five DEmiRNAs were shared, while nine and seven DEmiRNAs were specifically expressed in midguts at 7 and 10 dpi, respectively. Gene ontology analysis suggested that a portion of the DEmiRNAs and corresponding target genes were involved in various biological processes, cellular components, and molecular functions including immune system processes and response to stimulus and signaling. Moreover, KEGG pathway analysis shed light on the potential functions of some DEmiRNAs in the regulation of target genes engaged in material and energy metabolism, cellular immunity such as endocytosis and phagosome, and the humoral immune system, including the Jak-STAT and MAPK signaling pathways. Further investigation demonstrated a complex regulation network between DEmiRNAs and their target mRNAs, with miR-598-y, miR-252-y, miR-92-x and miR-3654-y at the center of the network, implying their key parts in host responses. This comprehensive miRNA transcriptome analysis demonstrated that N. ceranae invasion influenced the expression of miRNAs in the midguts of A. c. ceranae workers; the results can not only facilitate future exploration of the regulatory roles and mechanisms of miRNAs in hosts responses, especially their immune responses to N. ceranae, but also provide potential candidates for further investigation of the molecular mechanisms underlying eastern honeybee-microsporidian interactions.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Guo, R., Chen, D., Du, Y., Chen, H., Wang, H., Xiong, C., Zheng, Y., Hou, C., Diao, Q.. 2019-01-23. Comparative identification of microRNAs in Apis cerana cerana workers'midguts responding to Nosema ceranae invasion. https://doi.org/10.1101/528166

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

KEEP EXPLORING

Related preprints

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗

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↗