DOI: 10.5061/dryad.w3r2280w6
title: Data from: An adaptive biomolecular condensation response is conserved across environmentally divergent species

description: Cells must sense and respond to sudden maladaptive environmental
 changes—stresses—to survive and thrive. Across eukaryotes, stresses such
 as heat shock trigger conserved responses: growth arrest, a specific
 transcriptional response, and biomolecular condensation of protein and
 mRNA into structures known as stress granules under severe stress. The
 composition, formation mechanism, adaptive significance, and even
 evolutionary conservation of these condensed structures remain enigmatic.
 Here we provide an unprecedented view into stress-triggered condensation,
 its evolutionary conservation and tuning, and its integration into other
 well-studied aspects of the stress response. Using three morphologically
 near-identical budding yeast species adapted to different thermal
 environments and diverged by up to 100 million years, we show that
 proteome-scale biomolecular condensation is tuned to species-specific
 thermal niches, closely tracking corresponding growth and transcriptional
 responses. In each species, poly(A)-binding protein—a core marker of
 stress granules—condenses in isolation at species-specific temperatures,
 with conserved molecular features and conformational changes modulating
 condensation. From the ecological to the molecular scale, our results
 reveal previously unappreciated levels of evolutionary selection in the
 eukaryotic stress response, while establishing a rich, tractable system
 for further inquiry.
description: Eight folders contain raw and processed data as well as R or
 Python scripts to produce the figures in the paper.
description: This README file is for the data published in "An adaptive
 biomolecular condensation response is conserved across environmentally
 divergent species" by Keyport Kik et al. (2023) (preprint:
 ).
 These folders also contain custom R (version 4.2.2) scripts for data
 processing, analysis, and figure generation, organized into separate
 experimental folders. Some data have a separate R code for data processing
 and exporting data. The ordering of the figures may be different from
 what's shown in the paper. The code used to process raw RNA-seq data
 can be found on Github
 (.
 The rest of the data and scripts can be found on Dryad
 (). Below is a description
 of the contents of each folder. Raw RNA-seq data can be found under GEO
 accession code GSE234499. Please note: The most recent version of this
 package has been updated to include changes recommended by the reviewers,
 including microscopy data and further analysis for the HDX-MS section. ---
 supp-files/ Within this folder are three supplemtanal files: *
 `suppfile1/` (`master-orthologs.txt`) - contains ortholog calls for all
 three species, including gene and ORF name from S. cerevisiae *
 `suppfile2/` (`230215_labeled_genes_scer_all.tsv`) - were derived from the
 following sources. The targets of HSF1 and Msn2/4 were curated from Pincus
 et al. 2018 and Solís et al. 2016. The genes for core ribosomal proteins,
 ribosome biogenesis factors, and glycolytic enzymes (superpathway of
 glucose fermentation) as well as transcription factor regulation
 assignments were derived from the Saccharomyces Genome Database (Cherry et
 al. 2012; Engel et al. 2014, ). Genes
 for translation factors were derived from the KEGG BRITE database
 (Kanehisa et al. 2016).\ *`suppfile3/`
 (`20-04-21_sgd-all-regulators2.tsv`) - Transcription factor regulators
 were assigned according to (Triandafillou et al. 2020). --- fig-svgs/
 Final Figures 1-5 as well as Supplemental Figures 1-5 (in `*.svg` or
 `*.png` format) can be found in this folder. Table 1 and Table 2 can also
 be found here as `*.txt` files. --- dryad-upload/ Eight folders contain
 raw and processed data as well as R or Python scripts (open source
 versions) to produce the figures in the paper. All required packages and
 dependencies are listed at the top of each script. Both Windows v11 and
 MacOS can run all scripts. After downloading the `.zip` file, data are
 organized to be able to run efficiently within the structured directories
 in which they are found. Expected (actual) outputs are included for each
 script. To run all of the scripts and produce the figures should require
 less than a day's time. All input files are contained in this folder
 to test the code. Instructions for use are below. If there is a specific
 order to run the code, it is outlined below (e.g., `condensation-ms/`); in
 all other cases, scripts can be run in any order. * condensation-ms/ -
 mass spectrometry was performed at different control and treatment
 temperatures for each species. Outputs from Scaffold DIA 3.3.1 analysis
 perforfmed by MS Bioworks are in the `data/` folder (`MSB-9658A U. Chicago
 Keyport 042622.txt` - text file of S. kudriavzevii processed data from MS
 Bioworks, `MSB-9658B U. Chicago Keyport 042722.txt` - text file of S.
 cerevisiae processed data from MS Bioworks, `MSB-10835 U. Chicago Keyport
 022223.txt` - text file of K. marxianus processed data from MS Bioworks).
 Other sample name and proteome information called by the scripts are
 contained in `data/`, including `conditions.txt` (contains sample
 conditions), `sample_names.txt` (experimental names and assoiciated data),
 `kmarx-tsp-by-condition.txt` (tidy version of raw intensity data for K.
 marxianus). All analyses are performed in RStudio (R version 4.2.2).
 Analysis script `process-raw-dia.Rmd` processes raw data for S. cerevisiae
 and S. kudriavzevii and uses raw MS data, `sample_names.txt`,
 `uniprot-gene-orf.txt` (accession and gene information from Uniprot), and
 `skud_proteome_ygob.fasta` (`*.fasta` file for S. kudriavzevii from YGOB).
 This script produces `results/processed_data.tsv`, which is an input for
 mixing ratio calculation in `calculate-mixing-dia.Rmd` along with
 `data/kmarx-tsp-by-condition.txt` and `data/scer-kmarx-skud-orthologs.txt`
 (curated ortholog list for each species); this script outputs two files
 (`results/230501_mixing_ratios.tsv` and
 `results/psups-three-species.txt`). `psup-three-species.txt` was
 transformed into `psup_wide_data.txt` which is then used in
 `conservation-of-condensation.Rmd`, in combination with
 `data/scer-anno-proteins.txt` (annotations of protein processes as
 described in the Methods section, "Gene Annotation") and
 `230501_mixing_ratios.tsv`. This script,
 `conservation-of-condensation.Rmd`, ultimately produces the figures (found
 in `figures/`). Final figures can be found in the `fig-svg/` directory. A
 subset of custom functions called in scripts are defined in
 `utilityFunctions.R`. * dls/ - raw measurements from DLS temperature ramp
 experiments are contained in directories with dates as names as `*.csv`
 files, and associated sample information is also contained as
 `*-samples.csv`. All analyses are performed in RStudio (R version 4.2.2).
 Analysis script `analyze-dls.Rmd` reads in sample information and raw data
 to produce Figures 4a-d (output in in `figs/` folder). Raw data from
 Riback & Katanski et al. 2017 can be found in `riback-2017/` directory
 (WT Pab1 in `Pab1_15uM_DLSbuffpH6p4.csv`, MV to A Pab1 in
 `Pab1_MVtoA_15uM_pH6p4_4_14_15_1.csv`, and MV to I Pab1 in
 `Pab1_MVtoI_15uM_pH6p4.csv`), and is also used in `analyze-dls.Rmd`.
 Growth data to produce Figure 4b are generated from scripts in
 `growth-curves/` and output into this directory, including
 `conf-int-topt.txt` (confidence intervals from esptimation of optimal
 temperature in Figure 1b), `hsr-growth-max.txt` (maximum estimated growth
 temperature and heat shock temperature for each species), and
 `stop-growing.txt` (estimations the temperatures at which each species
 stops growing). Table S1 (`table1.txt`) and Table S2 (`table2.txt`) are
 output from the `analyze-dls.Rmd` script and contain baseline size
 calculations for each protein (S1) and temperature and doubling size of
 condensation for each protein (S2). Final figures can be found in the
 `fig-svg/` directory. * flow-cytometry/ - raw `*.fcs` files of each
 species +/- heat shock at various temperatures with 180 min of recovery
 are found here with condition discriptors in file names and contained
 directories with dates as names. All analyses are performed in RStudio (R
 version 4.2.2), and requires custom but publically available R packages.
 These custom packages can be found on GitHub
 (;
 ). The script
 `20220414-skud-scer-manytemps-endpoint.Rmd` reads in `*.fcs` files,
 processes flow data, and produces Figure S2a (found in `output/results/`).
 Another output file, `max-fc-hsr.txt`, which contains the fold change and
 statistics of maximum heat shock response for each species at each
 temperature, is produced here and also exported to `growth-cruves/data/`
 which will in part produce Figure 1c-d (also found in `output/results/`).
 Final figures can be found in the `fig-svg/` directory. * growth-curves/ -
 raw OD600 data for each WT species is found in `20230311/20230311.txt` and
 was sampled from log-phase growing yeast grown at different temperatures.
 All analyses are performed in RStudio (R version 4.2.2). Two R scripts are
 contained in this directory. First, `growth-curve-estimation.R` calculates
 the maximum specific growth rates by estimating the slope of the linear
 range of growth for each species and temperature. Resulting growth curves
 are then fit using the cardinal temperature model with inflection (Rosso,
 Lobry, and Flandrois 1993), and temperature of maximum growth is estimated
 from these curves and output into `data/dat-max.txt`. Optimal temperature
 statistics are calculated as confidence interval
 (`results/conf-int-topt.txt`, also output into `../dls/`) and standard
 deviation (`results/topt-sd.txt`). This script is also used to estimate
 the temperature at which each species stops growing, and is output as
 `data/stop-growing.txt` (also output into `../dls/`).
 `growth-curve-estimation.R` generates Figure 1b (found in `results/`). The
 second script, `plot-growth-hsr-correlation.Rmd` takes the output files
 from the first script as well as `data/max-fc-hsr.txt` produced from
 `../flow-cytometry/20220414-skud-scer-manytemps-endpoint.Rmd` to produce
 Figures 1c and 1d as well as `results/hsr-growth-max.txt`, which contains
 temperatures optima for growth and heat shock responses for each species.
 Final figures can be found in the `fig-svg/` directory. * hdx/ - HDX-MS
 data were collected for each species' Pab1 before and after
 condensation. Raw data for each species as `*_peptideUptake.csv`, as well
 as manually curated domain boudaries (`20221214_pab1DomainBoundaries`) and
 secondary structure predictions (`domain-ss.txt`) from Schäfer et al. 2019
 are found in this folder. Python3 scripts (`extract-hdx.py`, `hdx.py`, and
 `hdx_test.py`) produce %D values for each residue in aligned Pab1 using
 `aligned-positions.txt` based on the alignment (`align-pab1.txt`) that are
 used to compute means across the sequences. The outputs for Python3
 scripts are in `output/*hdx.txt`. Figure 5a-f generation (each found in
 `output/`) and other data processing are done in RStudio (R version 4.2.2)
 in `20230328_HDX_Pab1_SKK.Rmd`. Final figures can be found in the
 `fig-svg/` directory. * microscopy/ - Images are provided in `.tif` format
 and in `.jpg` format used for the final figure in the paper (in separate
 directories, `selections_tif/` and `selections_jpg/`). Files are labeled
 by species abbreviation (Sk, Sc, Km) and by temperature (in degrees
 Celsius). The Km_55 (K. marxianus, 55 degrees) seelction has a 5 micron
 scale bar added in ImageJ. The colorbar settings used for the `.jpg`
 images are as described in the final figure. There are 184 nm per pixel
 (5.43 pixels per micron). Final figures can be found in the `fig-svg/`
 directory. * rna-seq/ - Each species was treated with a species-specific
 heat shock (or control temperature) and then submitted for RNA-Seq.
 Upstream pre-processing was performed with a custom Snakemake pipeline
 which can be found on Github
 ().
 Downstream processing and analyses as well as figure generation are
 performed in RStudio (R version 4.2.2). The script
 `230503_analyze_counts.Rmd` aggregates count data (`counts/*_counts.tsv`)
 and sample information (`20230309-sample-info.txt`, also in `data/`) to
 produce `output/20230309-counts.tsv`. Gene lengths were extracted for each
 gene by first adding exon annotations to the GTF files (`*_genomic.gtf` or
 `*_genomic.cleaned.gtf`) using a custom script based on gffutils v0.11.1
 (, also in this directory as
 `gffutils_fix_missing_exon.py`). Gene lengths were then calculated using
 the GenomicFeatures package in R. These lengths (found in
 `output/*_merged_exon_length.tsv`) are then used to calculate transcript
 per million values (TPMs, `output/20230309_TPM.tsv`, also in `data/`)
 using the counts output (`output/20230309-counts.tsv`). Fold changes in
 transcript abundance were calculated using DESeq2 v3.16
 (`output/20230309-deseq.tsv`). We used pre-published genome annotations
 `src/label_Scer_genes/Saccharomyces-kudriavzevii-ZP591_genome.tab` from
 YGOB v8 (beta) to match RNA-Seq data to S. kudriavzevii strain ZP591.
 Figures 2a-2d and Figures S3a-c are produced using
 `230503_analyze_counts.Rmd`. Gene annotations were assigned in
 `src/label_Scer_genes/230215-label-genes.Rmd`, where targets of HSF1 and
 Msn2/4 were curated from Solis et al., 2016 and Pincus et al., 2018
 (`Solis_2016/mmc3.xlsx` and `200325-scer-features.txt` (also in `data/`)).
 The genes for core ribosomal proteins (`scer-ribosomal-proteins.txt`),
 ribosome biogenesis factors (`ribosome_biogenesis_annotations_sgd.txt`),
 and glycolytic enzymes (superpathway of glucose fermentation,
 `SGD_superpathway_glycolysis_221025.txt`) as well as transcription factor
 regulation assignments (`20-04-21_sgd-all-regulators2.tsv` and
 `tfs_and_targets_heatshock.tsv`) were derived from the Saccharomyces
 Genome Database (). Genes for
 translation factors were derived from the KEGG BRITE database
 (`translation_factors_kegg.tsv`). The annotation gene file was output into
 `230215_labeled_genes_scer_all.tsv`, which is subsequently used in
 `230503_analyze_counts.Rmd` to assign gene annotations. The script to
 produce the upset plots from Figures S2b-c is `txn-comparison.Rmd`, and
 also takes data from Brion et al. 2016
 (`data/brion2016-lkluyveri-stress-seq.txt`), sample information
 (`/data/20230309-sample-info.txt`), orthologs
 (`data/master-orthologs.txt`), TPMs (`data/20230309_TPM.tsv`), and gene
 annotations (`/data/230508_labeled_genes_scer.tsv`). GEO upload TPMs and
 counts (`Keyport-Kik_2023_TPMs.tsv` and `Keyport-Kik_2023_counts.tsv` are
 found in `output/` as well, and are identical to `output/20230309_*.txt`).
 A subset of custom functions called in scripts are defined in
 `utilityFunctions.R`. A file containing orthologs for all species (also
 Supplemental File 1), `master-orthologs.txt` is found in `data/` and
 `src/label_Scer_genes/` and is used as an input in the script
 `230503_analyze_counts.Rmd`. Final figures can be found in the `fig-svg/`
 directory. * spot-assays/ - Spot assays and plate growth assays were
 performed for each species at corresponding temperatures. Raw `*.tif`
 images for Figure 4e and Figure S1a-c (organized as directories) are
 contained here. Naming conventions for images in `fig4e/` and `figs1e/`are
 [date]*[time]*[strain/species]*[duration]*[temperature]. Naming
 conventions for images in `figs1a/`are
 [plate]*[duration]*[temp]*[species]. Naming conventions for images in
 `figs1c/`are [date]*[time]*[temperature]*[duration]. Final figures can be
 found in the `fig-svg/` directory.