GO Tables from/for: Oligogalactolipid production during cold challenge is conserved in early diverging lineages
Data files
Jul 04, 2023 version files 749.90 KB
-
At2vs7-deseq-result-2rep-fold1-p05-2more.txt
-
At2vs7-deseq-result-2rep-fold1-p05-7more.txt
-
AtAAvsAtW-deseq-results-fold1-p05-AAmore.txt
-
AtAAvsAtW-deseq-results-fold1-p05-WEmore.txt
-
README_JXB.txt
-
README.md
-
Sb0vs2dot5-deseq-2rep-fold1-p05-0more.txt
-
Sb0vs2dot5-deseq-2rep-fold1-p05-2dot5more.txt
-
SbMeOHvsDNP-deseq-result-fold1-p05-DNPmore.txt
-
SbMeOHvsDNP-deseq-result-fold1-p05-MeOHmore.txt
Abstract
Severe cold, defined as a damaging cold beyond acclimation temperatures, has unique responses, but the signaling and evolution of these responses are not well understood. Production of oligogalactolipids, which is triggered by cytosolic acidification in Arabidopsis (Arabidopsis thaliana), contributes to survival in severe cold. Here, we investigated oligogalactolipid production in species from bryophytes to angiosperms. Production of oligogalactolipids differed within each clade, suggesting multiple evolutionary origins of severe cold tolerance. We also observed greater oligogalactolipid production in control samples instead of temperature-challenged samples of some species. Further examination of representative species revealed a tight association between temperature, damage, and oligogalactolipid production that scaled with the cold tolerance of each species. Based on oligogalactolipid production and transcript changes, multiple species share a signal of oligogalactolipid production initially described in Arabidopsis, cytosolic acidification. Together, these data suggest that oligogalactolipid production is a severe cold response that originated from an ancestral damage response that remains in many land plant lineages and that cytosolic acidification is a common signaling mechanism for its activation.
Methods
Plant material and growth conditions
Arabidopsis (Arabidopsis thaliana, Columbia-0 [Col-0]) plants were grown on a mixture of Sungrow Propagation Mix soil and Turface at 22°C under a 16-h-light/8-h-dark photoperiod. Plants were grown for 3 to 4 weeks before cold acclimation at 6°C under a 12-h-light/12-h-dark photoperiod for 1 week. Sorghum (Sorghum bicolor ‘BTx623’) was grown in chambers under a 16-h-light/8-h-dark photoperiod with 29°C during the day and 22°C at night on standard greenhouse soil mix (8:8:3:1 [w/w/w/w] peat moss: vermiculite: sand: screened topsoil, with 7.5:1:1:1 [w/w/w/w/] Waukesha fine lime, Micromax, Aquagro, and Green Guard per cubic yard). Acclimation was carried out at 16°C for 1 week for sorghum. All plants for all treatments were moved into low-temperature stress at the end of their respective day for treatment in the dark.
Ion leakage
Plants used for ion leakage were grown as described above, and all plants were cold acclimated for 1 week at the appropriate temperature. Ion leakage was determined using a refrigerated circulator (AP15R-40, VWR, Radnor, PA, USA) with leaf pieces or punches floated onto 3 mL of ddH2O. For Arabidopsis, an entire leaf was used. For sorghum, three leaf punches of 8 mm in diameter were used. Samples were collected from the second true leaf, except for Arabidopsis, where rosette leaves were sampled, with care taken to use older, expanded leaves and avoid cotyledons. Different plants were then chilled to temperatures sufficient to induce stress in the respective species. Stress was imposed on Arabidopsis as previously described (Warren et al., 1996). Briefly, samples were exposed to an initial equilibration at 2°C for 30 min, nucleation was initiated with a ddH2O ice chip at –1°C for 1 h, and subsequent chilling occurred at a rate of –2°C/h (Figure 1B). Samples for sorghum were collected at temperatures from 0°C to –4°C. Following a 30-min equilibration at 0°C, samples were cooled from 0°C to –1°C at a rate of –2°C/h, and ice nucleation was initiated at –1°C and held for 1 h before subsequent chilling at a rate of –1°C/h from –1°C to –3°C and then –2°C/h from –3°C to –4°C (Figure 1B). The slower chilling between –1°C and –3°C allowed for sampling in 0.5°C steps. At each temperature point, an equivalent sample for lipid analysis and fractional TGDG accumulation was collected for all species. For Arabidopsis and sorghum, an additional and equivalent sample was also taken for transcriptome analysis for Arabidopsis at –2°C (control) and –7°C (challenge), for sorghum at –0.5°C (control) and –2.5°C (challenge).
Cytosolic acidification
All experiments were performed on excised leaves. For Arabidopsis, the leaf was placed into a cup of acid solution or water after having removed the leaf from the rosette of a full-sized, 3-week-old plant. For sorghum, the sorghum stalk above the soil surface was cut using a new razor blade for each plant and shoots were inserted into a tube containing 20 mM 2,4-dinitrophenol, pH 5, in 18.2% (v/v) methanol, adjusted with KOH, or into 18.2% (v/v) methanol/water as a control. These samples were immediately placed into a humidity chamber for 3 h with a minimum relative humidity of 84%. Leaf punches were taken from the second true leaf to mimic the samples used in ion leakage tests.
RNA-seq data generation and processing
Total RNA was isolated for each sample using a Zymo Quick-RNA Plant Mini-prep Kit (Zymo Research Corp, Irvine, CA, USA), and RNA-seq libraries were prepared according to Illumina TruSeq Sample Preparation V2 using 1 μg of starting total RNA. Libraries were sequenced using a 75-bp paired-end Illumina Miseq instrument at the University of Nebraska Medical Center. Raw reads were deposited in the NCBI SRA (Sequence Read Archive) database under the BioProject ID PRJNA894306. Trimmomatic 0.36 (Bolger et al., 2014) was used to remove low-quality reads and adapters using default parameters. The resulting clean reads from Arabidopsis and sorghum samples were aligned to the Arabidopsis thaliana TAIR10 and Sorghum bicolor v3.1 genomes, respectively (retrieved from Phytozome v12.0) using GSNAP (2018-03-25) (Wu & Nacu, 2010). Alignment files were converted to bam format using Samtools (v1.9) (Li et al., 2009) and used as input to HTSeq (0.6.1) (Anders et al., 2015) for generation of raw counts per gene.
Differentially expressed genes
The formula design ≅ Replicate + Condition in DESeq2 was used to identify differentially expressed genes (DEGs) for each species for both artificial acidification and severe cold treatment using DESeq2 (Love et al., 2014). Two and three biological replicates were employed for the identification of DEGs during temporal and chemical treatment. Any gene in the condition factor (Control vs. Treatment) with an adjusted p-value < 0.05 and absolute Log2 fold-change > 1 was classified as DEGs. Overall, four gene categories in each species were generated: upregulated by chemical treatment, upregulated by temperature treatment, downregulated by chemical treatment, and downregulated by temperature treatment. To identify differentially expressed orthologs between Arabidopsis and sorghum, a list of corresponding orthologs between each sorghum gene model and the best hit Arabidopsis gene models was retrieved from Phytozome v12.0. To compare with randomly overlapping orthologs in either treatment, only genes with 1:1 orthologs between sorghum and Arabidopsis were considered as background. Sorghum genes in each category were assigned to the best hit Arabidopsis gene models. To determine the significance of co-upregulated or co-downregulated orthologs between Arabidopsis and sorghum, we randomly picked the equal number of upregulated or downregulated genes in Arabidopsis respectively and tested the number of genes with orthologs in sorghum. The F test was used to determine significance between real overlapping ortholog numbers and permutated overlapping ortholog numbers. In total, 100 permutations were performed.
Gene Ontology Analysis
Lists of genes obtained from DEG analysis were analyzed via the Gene Ontology Resource (Ashburner et al., 2000; Mi et al., 2019). GO Enrichment Analysis was performed, and significantly enriched categories were reported (Gene Ontology Consortium, 2021).