Unveiling putative modulators of mutable collagenous tissue in the brittle star Ophiomastix wendtii: an RNA-Seq analysis
Creators: name: Nouri, Reyhaneh
Publisher: name: Zenodo
Year: 2024

description: Abstract

Collagenous connective tissue, found throughout the bodies of metazoans, plays a crucial role in maintaining structural integrity. This versatile tissue has the potential for numerous biomedical applications, including the development of innovative collagen-based biomaterials. Inspiration for such advancements can be drawn from echinoderms, a group of marine invertebrates that includes sea stars, sea cucumbers, brittle stars, sea urchins, and sea lilies. Through their nervous system, these organisms can reversibly control the pliability of their connective tissue components (i.e., tendons and ligaments) that are composed of mutable collagenous tissue (MCT). The variable tensile properties of the MCT allow echinoderms to perform unique functions, including postural maintenance, reduction of muscular energy use, autotomy to avoid predators, and asexual reproduction through fission. The changes in the tensile strength of MCT structures are specifically controlled by specialized neurosecretory cells called juxtaligamental cells. These cells release substances that either soften or stiffen the MCT. So far, only a few of these substances have been purified and characterized, and the genetic underpinning of MCT biology remains unknown. Therefore, we have conducted this research to identify MCT-related genes in echinoderms as a first step towards a better understanding of the MCT molecular control mechanisms. Our ultimate goal is to unlock new biomaterial applications based on this knowledge. In this project, we used RNA-Seq to identify and annotate differentially expressed genes in the MCT structures of the brittle star Ophiomastix wendtii. As a result, we present a list of 16 putative MCT modulator genes, which will be validated and characterized in forthcoming functional analyses.

Contents

Additional file 1

File Additional_file_1.zip: A spreadsheet with selected annotations for the 94 shared upregulated genes in thewhole arm and inner arm core. For each gene, we listed a representative isoformcontig, best BLAST hit, putative function, the status of the recovered coding region(ORF), predicted protein product size, the outcome of the signal peptide predictionby SignalP, and the predicted localization of the protein product by DeepLoc.

Additional file 2

File Additional_file_2.zip: Combined pathway analysis for gene ontology (GO) enrichment. This file includesthree graphs that represent the pathway enrichment analysis for biological processes, molecular functions, and cellular components in O. wendtii transcripts dataset. The analysis aims to identify significantly enriched pathways in the dataset, revealing key biological processes, molecular functions, and cellular components that are overrepresented. The analysis was performed by OmicsBox with a significance threshold of p-value < 0.05. The dataset comprised 586,658 sequences, with 62,325 sequences linked to Gene Ontology terms, providing insights into the functional roles of these genes in the studied condition. The gradient of colors from light to dark indicates the number of sequences associated with each GO term, with darker shades representing higher sequence counts.

Additional file 3

File Additional_file_3.zip: A spreadsheet containing detailed annotations for all transcripts in the dataset.

Additional file 4

File Additional_file_4.zip: A file containing amino acid sequences of proteins encoded by the 16 candidate genes.

Additional file 5

File Additional_file_5.zip: A file with detailed annotations for the amino acid sequences of proteins encoded by the 16 candidate genes.

 

Funding and acknowledgement

Funding for this project was generously provided by Startup funds and the Faculty Research Grant (FRG) from UNC Charlotte.

We acknowledge several entities at the University of North Carolina at Charlotte for their support. This project was made possible through the collaboration and resources provided by the College of Computing and Informatics, the Department of Bioinformatics and Genomics sequencing facility, the University Research Computing, and the Center for Computational Intelligence to Predict Health and Environmental Risks (CIPHER).We also acknowledge the Wake Forest Institute of Regenerative Medicine for their contributions.We would like to thank Sarah Calaway, Ph.D., Senior Field Applications Scientist from Illumina, for her exceptional technical and bioinformatic expertise and assistance during sequencing experiments. We are equally grateful to Lauren Roppolo Brazell for her dedicated help and support during sequencing.