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RNA samples were collected from Synechococcus sp. WH8102 cultures grown under continuous laboratory phosphorus treatments (High-P, Mid-P, and Low-P), with three biological replicates per treatment. On the final day of the experiment, ~700 mL of each culture was filtered through 47 mm, 0.4 μm GFF filters (previously combusted at 450°C) using a peristaltic pump and stored at -80°C until analysis.

Total RNA was extracted from cells collected on filters using a modified TRIzol-based extraction protocol. Filters were transferred aseptically into screw-cap bead tubes containing 0.1 mm disruptor beads and kept on ice after removal from −80 °C storage. Cells were enzymatically lysed in lysis buffer (30 mM Tris, 10 mM EDTA, and 10 mg mL⁻¹ lysozyme) and incubated at 37 °C for 30 min with slow rotation. Samples were subsequently bead-beaten by vortexing at maximum speed for 5 min to ensure mechanical disruption. Following lysis, RNA was extracted using TRIzol Reagent (Thermo Fisher Scientific, Waltham, MA, USA). TRIzol and chloroform were added to each sample, followed by vigorous mixing and incubation at room temperature prior to centrifugation to separate phases. The aqueous phase containing RNA was transferred to a new tube and RNA was precipitated with saline solution and ice-cold isopropanol. Samples were incubated at −20 °C and centrifuged to pellet RNA. The RNA pellet was washed with 70% ethanol, briefly air-dried, and resuspended in nuclease-free water. To remove contaminating DNA, RNA samples were treated with TURBO DNase (Thermo Fisher Scientific) according to the manufacturer’s instructions. A subsequent phenol–chloroform–isoamyl alcohol purification step was performed, followed by chloroform extraction and ethanol precipitation to further purify RNA. The final RNA pellet was washed with 70% ethanol, air-dried, and resuspended in nuclease-free water. RNA concentration and purity were assessed using NanoDrop Spectrophotometer (Thermo Fisher Scientific) and Qubit Fluorometer (Thermo Fisher Scientific).

Total RNA samples were submitted to SeqCoast for library preparation and sequencing. Ribosomal RNA (rRNA) was depleted and sequencing libraries were prepared using the Illumina Stranded Total RNA Prep Ligation Kit with Ribo-Zero Plus Microbiome (Illumina, #20072063) and Illumina Unique Dual Indexes, following the manufacturer’s protocol. Sequencing was performed on an Illumina NextSeq 2000 platform using a 300-cycle XLEAP-SBS flow cell to generate 2 × 150 bp paired-end reads. A 1–2% PhiX spike-in control was included to support optimal base calling. Base calling, demultiplexing, adapter trimming, and initial run quality control were performed using DRAGEN (v4.2.7; Illumina) onboard the NextSeq 2000 system. Sequencing quality was assessed at both the run level and per-sample level, including inspection of FastQC reports. FastQ files corresponding to paired-end reads were generated for downstream analysis.

The reads were mapped to the Synechococcus sp. WH8102 genome obtained from the JGI Genome Portal using Bowtie2 v.2.4.1 (Langmead et al., 2012) with default parameters in paired-end mode. Counts were generated with the featureCounts function in the Rsubread v.2.12.3 R Bioconductor package (Liao et al., 2019).

Differential gene expression analysis was performed using DESeq2 (v1.42.1). Raw read counts were imported into a DESeqDataSet object with experimental conditions (Low-P, Mid-P, High-P) specified as the design factor. Size-factor normalization was applied using the median-of-ratios method to account for differences in sequencing depth, and gene-wise dispersion estimates were fitted under a negative binomial generalized linear model framework. Two complementary DESeq2 approaches were used to assess differential expression. First, a likelihood ratio test (LRT) was applied to compare a full model including the condition effect against a reduced model excluding it, in order to identify genes exhibiting any significant expression variation across treatments. Second, pairwise contrasts between conditions (Low-P vs High-P, Mid-P vs High-P, and Low-P vs Mid-P) were performed using Wald tests to estimate log₂ fold changes (log2FC). These effect sizes represent shrinkage-adjusted estimates from the DESeq2 model, where positive values indicate higher expression in the first condition of each comparison. P-values from Wald tests were adjusted for multiple testing using the Benjamini–Hochberg false discovery rate (FDR) procedure, and genes with adjusted p-values (padj) < 0.05 were considered statistically significant. For visualization and exploratory analyses, variance-stabilizing transformation (VST) was applied to normalized counts using DESeq2. Mean expression values per condition were computed from VST-transformed data across biological replicates. These values were used exclusively for visualization and interpretation of expression patterns and were not used for statistical inference.

Notes on experimental design:

Treatments: High-P, Mid-P, Low-P
Replicates: 3 biological replicates per treatment
RNA extractions using TRIzol reagent

16:T851,

The submitted dataset consists of normalized RNA-seq gene expression counts derived from raw sequencing reads. Raw sequencing data have been deposited in the Gene Expression Omnibus (GEO) at National Center for Biotechnology Information (NCBI) under accession number GSE327000. Data processing included the following steps:

Quality control and trimming:
- Demultiplexing, adapter trimming, and quality control were performed using BCL Convert v3.9.3.
- Raw read quality was assessed with FastQC v0.73 via Galaxy (galaxy0).
Alignment:
- Reads were mapped to the Synechococcus sp. WH8102 reference genome using Bowtie2 v2.4.1 in paired-end mode with default parameters.
BAM file processing:
- SAM files were converted to BAM sorted and indexed.
Count generation:
- Gene-, CDS-, and exon-level counts were generated using the featureCounts function from Rsubread v2.12.3.
Counts normalization:
- Raw counts were normalized using DESeq2 v1.42.1 (median-of-ratios size-factor method).
- Normalized counts were exported as a wide table (norm_counts_annotated) containing locus_tag, gene_name (when available), product annotation, and counts for each biological replicate and treatment.
Software and computing environment:
- Analyses were performed in R 4.3.3 (GUI 1.80, Big Sur ARM build 8340) using RStudio Version 2023.12.1+402.
- R packages: DESeq2 v1.42.1, Rsubread v2.12.3, and standard R packages for data manipulation and plotting.

This workflow yields a comprehensive dataset combining normalized expression values, statistical significance, pairwise comparisons, and gene functional annotations.

17:T5b3,This section documents curation actions performed prior to publication review with the submitter, and additional information relevant to understanding and reusing this dataset. It distinguishes changes made to the submitted (meta)data from unresolved issues and/or enhancements that improve future reuse and interoperability. CURATION ACTIONS PERFORMED ON DATA - Loaded data from log2_FC_final_table.xlsx (Sheet1), using row 1 as header, with empty string and "nd" values interpreted as missing - Renamed three columns to remove hyphens for BCO-DMO parameter-naming compliance: mean_Low-P to mean_Low_P, mean_Mid-P to mean_Mid_P, mean_High-P to mean_High_P - Output table as 998248_v1_log2fc_gene_expression_wh8102.csv - Syn_WH8102_normalized_counts_annotated.xlsx was converted to CSV format and parameter descriptions were added to the description. The resulting file, 998248_v1_Syn_WH8102_normalized_counts_annotated.csv, is listed as a supplemental file. - Some descriptive context was added to the description of the README – Synechococcus WH8102 RNA-seq Data (2025 P Experiment).txt file and it was listed as a supplemental file. CURATION ACTIONS PERFORMED ON METADATA - BCO-DMO's standard metadata entry and text formatting steps were performed. See: https://www.bco-dmo.org/how-to/standard-curation-edits - Used information from README and methods to expand on parameter definitions ISSUES POTENTIALLY IMPACTING REUSE - N/A18:T54b,This dataset contains normalized RNA-seq gene expression counts from Synechococcus sp. WH8102 grown under controlled laboratory phosphorus treatments (High-P, Mid-P, and Low-P) in 2025. Gene expression was measured across three biological replicates per treatment and includes 2,718 genes with locus_tag identifiers, along with gene names and functional product annotations where available. Normalized counts were generated using DESeq2 (median-of-ratios size factor method), enabling comparison across samples and are provided in the supplemental table 998248_v1_Syn_WH8102_normalized_counts_annotated.csv. Many genes lack assigned names, reflecting the prevalence of hypothetical or predicted proteins typical of cyanobacterial genomes. In addition to normalized counts, the dataset includes a differential expression table reporting pairwise comparisons between treatments as log2 fold changes (primary dataset: 998248_v1_log2fc_gene_expression_wh8102.csv), along with adjusted p-values and directionality of expression changes. Mean expression values derived from variance-stabilized (VST) data are also provided to facilitate interpretation of expression trends. Together, these data tables provide a comprehensive resource for examining phosphorus-responsive gene expression and patterns of differential regulation in Synechococcus sp. WH8102.19:T66d,This table contains normalized RNA-seq gene expression counts for Synechococcus sp. WH8102 across biological replicates grown under three phosphorus treatments (High-P, Mid-P, and Low-P). Counts were normalized using DESeq2 to account for differences in library size. Each row corresponds to a gene, and columns include functional annotations as well as normalized expression values for individual biological replicates. column name, description, units locus_tag, Unique gene identifier, unitless gene_name, Standard gene name (if assigned), unitless product, Gene product annotation, unitless go_process, Gene Ontology (GO) biological process, unitless go_function, GO molecular function, unitless go_component, GO cellular component annotation, unitless High-P-A, Normalized counts for High-P replicate A, counts (normalized; unitless but count-derived) High-P-B, Normalized counts for High-P replicate B, counts (normalized; unitless but count-derived) High-P-C, Normalized counts for High-P replicate C, counts (normalized; unitless but count-derived) Mid-P-A, Normalized counts for Mid-P replicate A, counts (normalized; unitless but count-derived) Mid-P-B, Normalized counts for Mid-P replicate B, counts (normalized; unitless but count-derived) Mid-P-C, Normalized counts for Mid-P replicate C, counts (normalized; unitless but count-derived) Low-P-A, Normalized counts for Low-P replicate A, counts (normalized; unitless but count-derived) Low-P-B, Normalized counts for Low-P replicate B, counts (normalized; unitless but count-derived) Low-P-C, Normalized counts for Low-P replicate C, counts (normalized; unitless but count-derived)1a:T10b6,NSF Award Abstract: Phosphorus (P) is a vital nutrient required by all forms of life. In the ocean, which sustains half of global photosynthesis and oxygen supply, P can be scarce enough to constrain biological productivity, carbon dioxide uptake, and therefore climate. Human activity is accelerating the delivery of nutrients like P to the ocean, but rates of nitrogen inputs are even greater. This imbalance may increase levels of P stress in marine ecosystems, placing ocean productivity more and more under the control of P supply. Given the critical role of P in sustaining ocean health and ecosystem services now and into the future, a comprehensive understanding of its utilization and fate in the marine environment is necessary. In this project, the research team investigates marine polyphosphate (polyP), a ubiquitous yet poorly understood form of P made by all living organisms. To close major knowledge gaps on marine polyP, the investigators are overcoming major technical barriers to produce the first quantitative measurements of polyP in marine microorganisms. These measurements are being conducted on laboratory microbial cultures, as well as field samples from environments with high P supply, such as the California Current Ecosystem, or very low P supply, such as the Mediterranean Sea. In addition, the research team is resolving the cellular function of marine polyP across these different organisms and environments in order to clarify its role in biological P nutrition. This work helps advance polyP research across disciplines, including terrestrial science and even cancer research, and has broad application to P bioremediation. This project supports a postdoctoral researcher, a graduate student, and several undergraduate students in the labs of two female scientists. New educational tools to teach the public about marine polyP are being produced and disseminated through this project. A K12 teacher is participating in the work and communicating the findings to their classrooms and to broad audiences online. PolyP is ubiquitous in marine systems, where it plays critical roles in microbial P nutrition and P mineral formation and sequestration. In these ways, polyP has the potential to shape long- and short-term marine P cycling, primary productivity, microbial ecology, and global climate. However, major knowledge gaps still exist. Due to technical limitations, the scientific community currently lacks a quantitative understanding of marine polyP pools, their chain lengths, and biological origins. Furthermore, given the view of polyP as a P storage molecule, recent observations pointing to the preferential retention of particulate polyP in low phosphate (Pi) environments raise new questions about its ecophysiological functions. To close these knowledge gaps, two research questions are addressed: Q1: What is the total content and chain length distribution of polyP across different microbial groups and environmental conditions? Q2: How do the production of particulate polyP contribute to microbial P demand and stoichiometry across a broad range in Pi availability? The following hypotheses are tested: H1 (Q1): Functionally and environmentally diverse plankton produce a broad range of polyP chain lengths and concentrations. H2 (Q2): The preferential retention of polyP in low Pi environments can be reconciled with its role as a P storage molecule by a combination of taxonomic and physiological factors. These hypotheses are tested in the laboratory using representative cultures of marine plankton and in the field using observational approaches along natural Pi gradients in the Pacific Ocean and Mediterranean Sea. Applying a new P-targeted method using mass spectrometry, the team is resolving an unprecedented level of detail in marine polyP content and speciation. By combining cell sorting with elemental, biochemical, and radiotracer analyses, the team is gaining a mechanistic understanding of polyP physiology and its cycling in the ocean. This project is supported by the Biological Oceanography and Chemical Oceanography Programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.6:["$","$L14",null,{"dataset":{"_id":"998248","description":"","problem":"","acquisition":"$15","processing":"$16","bcodmoProcessing":"$17","title":"RNA-seq gene expression data for Synechococcus sp. 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Unique gene identifier in Synechococcus sp. WH8102

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NCBI locus tag is a unique, systematic identifier systematically applied to every gene in a genome

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Functional description of gene product

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Gene Ontology (GO) molecular function annotation

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Gene Ontology (GO) cellular component annotation

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