Precursors of Sea Star Wasting: Immune and microbial disruption during initial disease outbreak in Southeast Alaska

McCracken, Andrew 1 ; Rodger, Aly1; Saratkar, Chanchal1; Sularz, Sage1; Brusch, Sophia1; Nunez, Joaquin1; Pespeni, Melissa1

Published Feb 27, 2026 on Dryad. https://doi.org/10.5061/dryad.t1g1jwtfh

Data files

Feb 27, 2026 version files 11.36 MB

DifferentiaMicrobialAbundance.tsv

40.68 KB
gene_micro_eigns_nf.csv

589.78 KB
geneid_annotations.tsv

1.21 MB
geneID_L2FC.tsv

1.93 MB
README.md

3.34 KB
transcriptID_PANTHER_GO.tsv

7.59 MB

Abstract

Sea Star Wasting Disease (SSW) has devastated sea star populations along the North American Pacific coast since 2013, yet the mechanisms of disease progression, particularly in natural environments, remain unclear. Here we integrate transcriptomic and microbial data from wild Pycnopodia helianthoides sampled across sites affected and unaffected by SSW in Southeast Alaska during the initial outbreak recorded in the region in 2016. Individuals exposed to SSW but lacking visible symptoms showed elevated expression of complement system components, pathogen recognition genes, immune regulatory and cell death pathways. Alongside signs of immune activation, genes involved in maintaining extracellular matrix composition, tissue remodeling, and cell adhesion were differentially expressed, indicating early disruption of tissue homeostasis preceding visible wasting symptoms. Gene ontology analysis revealed enrichment of immune response, cell-cell adhesion, response to oxygen levels and nervous system regulatory pathways. Furthermore, network analyses revealed differentially abundant microbes in Exposed individuals—notably Vibrio spp.—were highly correlated with immune response, tissue integrity, stress and detoxification genes in network modules. Together, our findings offer insight into early host-pathogen dynamics in wild populations, underscoring putative links between immune activation and microbial community shifts with the onset of SSW disease.

Overview

This dataset supports a study investigating early host-pathogen dynamics during the initial 2016 outbreak of Sea Star Wasting Disease (SSW) in Pycnopodia helianthoides (sunflower sea stars) in southeast Alaska. By integrating transcriptomic and microbial community profiles, this data was used to identify patterns of differential gene expression and microbial abundances in association with varying SSW exposure status.

Description of Data Files

All files are tab-separated values (.tsv) unless otherwise noted:

geneid_annotations.tsv

Functional annotations (NCBI-based) for each gene ID, derived from Diamond BLASTx searches.

Data Columns:
- geneID - numerical identifier from reference genome
- NCBI Annotation
transcriptID_PANTHER_GO.tsv

Gene Ontology (GO) annotations mapped to transcript IDs using PANTHER HMMs, based on the Pycnopodia helianthoides reference genome.
- Data Columns:
  - geneID - numerical identifier from reference genome
  - panthrID - PANTHER (Protein ANalysis THrough Evolutionary Relationships)
  - GO - gene ontology term
geneID_L2FC.tsv

Log2 fold-change values for differentially expressed genes between SSW-Exposed and Naive individuals, generated output of DESeq2.

Data Columns:
- gene_id- numerical identifier from reference genome
- log2FoldChange- LogFold2-Change (Relative to Naive animals)
- lfcSE - LogFold2-Change Standard Error
- pvalue - p.value
- qadj - adjusted p.value
- NCBI Annotation
DifferentiaMicrobialAbundance.tsv

Differential abundance results for microbial taxa (from 16S rRNA amplicon sequencing) between SSW-Exposed and Naive individuals, generated output of ANCOM-BC2.

Data Columns:
- Taxon- Microbial Taxa Classification
- LFC - LogFold Change (Relative to Naive animals)
- SE - Standard Error
- W - W Statistic
- pval - p.value
- qval - adjusted p.value
- significance - binary output of significance in differential abundance between Naive and Exposed animals.
gene_micro_eigns_nf.csv

Module eigengene membership scores for both host gene and microbial WGCNA modules used in network analysis.

Methods Summary

RNA-seq: Whole-body tissue samples were sequenced using paired-end 150 bp reads.
Gene expression analysis: Conducted using DESeq2 v1.40 with standard normalization and filtering.
Microbial profiling: 16S rRNA gene sequencing data analyzed using ANCOM-BC2 v2.11.1.
Gene ontology enrichment: Performed using topGO v3.21.
Network analysis: Weighted Gene Co-expression Network Analysis (WGCNA) performed using WGCNA v1.73 to detect modules of coexpressed genes and microbes.

Sample Classification

Each sea star individual was classified into one of the following categories:

Naive: Collected from regions not affected by SSW at the time of sampling.
Exposed: Collected from affected regions, but individuals showed no visible signs of wasting.

Citation and Use

If you use these data in your research, please cite the associated manuscript or this dataset directly via its Dryad DOI.