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Results for “Cross-domain RNA-Seq”

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PubMed2026

Cross-domain cooperation drives nutrient acquisition and metabolism in the bark beetle holobiont.

Microbial symbiosis underpins host adaptation, yet mechanisms of metabolic integration in holobionts remain unclear. Using metatranscriptomics, genomics, and metabolic assays, we investigated gut microbiome interactions in the European spruce bark beetle (Ips typographus). We observed metabolic complementarity among symbionts and host, forming cross-domain networks that support nutrient acquisition. Nitrogen recycling revealed strong interdependence: no single partner possessed a complete uric acid degradation pathway, but combined evidence supports a distributed pathway spanning beetle, Bacteria, and fungi. Additionally, bacterial nitrate reduction to ammonia indicates a potential nitrogen influx, making otherwise inaccessible inorganic nitrogen available to the host. Shaped by microbial interactions, symbionts also likely supply specific amino acids, while vitamin metabolism showed cross-domain co-metabolism, with Bacteria as main producers of B vitamins, while host and fungi modulated interconversion. Carbohydrate degradation was highly partitioned; bacteria target xylan and pectin, while fungi contribute to glucan breakdown. Crucially, our data provide indirect evidence that the beetle may contribute to complete cellulose degradation, highlighting an underappreciated host role in lignocellulose processing. In terms of enzymatic functional diversity, the bacteriome emerged as the most important microbiome component-an observation that contrasts with the traditional focus on fungi and underscores the need to consider bacterial contributions in insect symbioses. Despite life-stage variation, core metabolic functions remained stable. Overall, metabolic interdependence, rather than microbial composition alone, structures holobiont function. These results highlight functional redundancy and ecological resilience, emphasizing the importance of microbial cooperation and host-microbe metabolic evolution.

Bark beetle↗
PubMed2025

Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods.

Gene expression studies often use bulk RNA sequencing of mixed cell populations because single cell or sorted cell sequencing may be prohibitively expensive. However, mixed cell studies may miss expression patterns that are restricted to specific cell populations. Computational deconvolution can be used to estimate cell fractions from bulk expression data and infer average cell-type expression in a set of samples (e.g., cases or controls), but imputing sample-level cell-type expression is required for more detailed analyses, such as relating expression to quantitative traits, and is less commonly addressed. Here, we assessed the accuracy of imputing sample-level cell-type expression using a real dataset where mixed peripheral blood mononuclear cells (PBMC) and sorted (CD4, CD8, CD14, CD19) RNA sequencing data were generated from the same subjects (N=158), and pseudobulk datasets synthesised from eQTLgen single cell RNA-seq data. We compared three domain-specific methods, CIBERSORTx, bMIND and debCAM/swCAM, and two cross-domain machine learning methods, multiple response LASSO and ridge, that had not been used for this task before. We also assessed the methods according to their ability to recover differential gene expression (DGE) results. LASSO/ridge showed higher sensitivity but lower specificity for recovering DGE signals seen in observed data compared to deconvolution methods, although LASSO/ridge had higher area under curves than deconvolution methods. Machine learning methods have the potential to outperform domain-specific methods when suitable training data are available.

Humans↗