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Unveiling metabolic integration in psyllids and their nutritional endosymbionts through comparative transcriptomics analysis.

ABSTRACT: Psyllids, a group of insects that feed on plant sap, have a symbiotic relationship with an endosymbiont called Carsonella. Carsonella synthesizes essential amino acids and vitamins for its psyllid host, but lacks certain genes required for this process, suggesting a compensatory role of psyllid host genes. To investigate this, gene expression was compared between two psyllid species, Bactericera cockerelli and Diaphorina citri, in specialized cells where Carsonella resides (bacteriomes). Collaborative psyllid genes, including horizontally transferred genes, showed patterns of conserved gene expression; however, species-specific patterns were also observed, suggesting differences in the nutritional metabolism between psyllid species. Also, the recycling of nitrogen in bacteriomes may primarily rely on glutamate dehydrogenase (GDH). Additionally, lineage-specific gene clusters were differentially expressed in B. cockerelli and D. citri bacteriomes and are highlighted here. These findings shed light on potential host adaptations for the regulation of this symbiosis due to host, microbiome, and environmental differences.

SUBMITTER: Kwak Y

PROVIDER: S-EPMC10558732 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Unveiling metabolic integration in psyllids and their nutritional endosymbionts through comparative transcriptomics analysis.

Kwak Younghwan Y Hansen Allison K AK

iScience 20230915 10

Psyllids, a group of insects that feed on plant sap, have a symbiotic relationship with an endosymbiont called Carsonella. Carsonella synthesizes essential amino acids and vitamins for its psyllid host, but lacks certain genes required for this process, suggesting a compensatory role of psyllid host genes. To investigate this, gene expression was compared between two psyllid species, Bactericera cockerelli and Diaphorina citri, in specialized cells where Carsonella ...[more]

PMID: 37810228

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Project description:Current integration methods for single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data are typically designed for specific tasks, such as deconvolution of cell types or spatial distribution prediction of RNA transcripts. These methods usually only offer a partial analysis of ST data, neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk. Here, we present eMCI, an explainable multimodal correlation integration model based on deep neural network framework. eMCI leverages the fusion of scRNA-seq and ST data using different spot-cell correlations to integrate multiple synthetic analysis tasks of ST data at cellular level. First, eMCI can achieve better or comparable accuracy in cell-type classification and deconvolution according to wide evaluations and comparisons with state-of-the-art methods on both simulated and real ST datasets. Second, eMCI can identify key components across spatial domains responsible for different cell types and elucidate the spatial expression patterns underlying cell-type specificity and intercellular communication, by employing an attribution algorithm to dissect the visual input. Especially, eMCI has been applied to 3 cross-species datasets, including zebrafish melanomas, soybean nodule maturation, and human embryonic lung, which accurately and efficiently estimate per-spot cell composition and infer proximal and distal cellular interactions within the spatial and temporal context. In summary, eMCI serves as an integrative analytical framework to better resolve the spatial transcriptome based on existing single-cell datasets and elucidate proximal and distal intercellular signal transduction mechanisms over spatial domains without requirement of biological prior reference. This approach is expected to facilitate the discovery of spatial expression patterns of potential biomolecules with cell type and cell-cell communication specificity.

Project description:MotivationTranscriptome-based gene co-expression analysis has become a standard procedure for structured and contextualized understanding and comparison of different conditions and phenotypes. Since large study designs with a broad variety of conditions are costly and laborious, extensive comparisons are hindered when utilizing only a single dataset. Thus, there is an increased need for tools that allow the integration of multiple transcriptomic datasets with subsequent joint analysis, which can provide a more systematic understanding of gene co-expression and co-functionality within and across conditions. To make such an integrative analysis accessible to a wide spectrum of users with differing levels of programming expertise it is essential to provide user-friendliness and customizability as well as thorough documentation.ResultsThis article introduces horizontal CoCena (hCoCena: horizontal construction of co-expression networks and analysis), an R-package for network-based co-expression analysis that allows the analysis of a single transcriptomic dataset as well as the joint analysis of multiple datasets. With hCoCena, we provide a freely available, user-friendly and adaptable tool for integrative multi-study or single-study transcriptomics analyses alongside extensive comparisons to other existing tools.Availability and implementationThe hCoCena R-package is provided together with R Markdowns that implement an exemplary analysis workflow including extensive documentation and detailed descriptions of data structures and objects. Such efforts not only make the tool easy to use but also enable the seamless integration of user-written scripts and functions into the workflow, creating a tool that provides a clear design while remaining flexible and highly customizable. The package and additional information including an extensive Wiki are freely available on GitHub: https://github.com/MarieOestreich/hCoCena. The version at the time of writing has been added to Zenodo under the following link: https://doi.org/10.5281/zenodo.6911782.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Unveiling metabolic integration in psyllids and their nutritional endosymbionts through comparative transcriptomics analysis.

Publications

Unveiling metabolic integration in psyllids and their nutritional endosymbionts through comparative transcriptomics analysis.

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