DOI: 10.5061/dryad.t1g1jwtfh
title: Precursors of Sea Star Wasting: Immune and microbial disruption during initial disease outbreak in Southeast Alaska

description: 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.
description: # Precursors of Sea Star Wasting: Immune and microbial disruption during
 initial disease uutbreak in Southeast Alaska ## 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.