Time-resolved oxidative signal convergence across the algae–embryophyte divide

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Rieseberg , View ORCID Profile Armin Dadras , View ORCID Profile Tatyana Darienko , Sina Post , View ORCID Profile Cornelia Herrfurth , View ORCID Profile Janine M. R. Fürst-Jansen , Nils Hohnhorst , View ORCID Profile Romy Petroll , View ORCID Profile Stefan A. Rensing , View ORCID Profile Thomas Pröschold , Sophie de Vries , View ORCID Profile Iker Irisarri , View ORCID Profile Ivo Feussner , View ORCID Profile Jan de Vries doi: https://doi.org/10.1101/2024.03.11.584470 Tim P. Rieseberg 1 University of Goettingen, Institute of Microbiology and Genetics, Department of Applied Bioinformatics , Goldschmidtstr. 1, 37077 Goettingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tim P. Rieseberg For correspondence: timphilipp.rieseberg{at}uni-goettingen.de devries.jan{at}uni-goettingen.de Armin Dadras 1 University of Goettingen, Institute of Microbiology and Genetics, Department of Applied Bioinformatics , Goldschmidtstr. 1, 37077 Goettingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Armin Dadras Tatyana Darienko 1 University of Goettingen, Institute of Microbiology and Genetics, Department of Applied Bioinformatics , Goldschmidtstr. 1, 37077 Goettingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tatyana Darienko Sina Post 2 University of Goettingen, Albrecht Haller Institute of Plant Science, Department of Plant Biochemistry , Justus-von-Liebig-Weg, 37077 Goettingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cornelia Herrfurth 2 University of Goettingen, Albrecht Haller Institute of Plant Science, Department of Plant Biochemistry , Justus-von-Liebig-Weg, 37077 Goettingen, Germany 3 University of Goettingen, Goettingen Center for Molecular Biosciences (GZMB), Service Unit for Goettingen Metabolomics and Lipidomics , Justus-von-Liebig Weg 11, 37077 Goettingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cornelia Herrfurth Janine M. R. Fürst-Jansen 1 University of Goettingen, Institute of Microbiology and Genetics, Department of Applied Bioinformatics , Goldschmidtstr. 1, 37077 Goettingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Janine M. R. Fürst-Jansen Nils Hohnhorst 1 University of Goettingen, Institute of Microbiology and Genetics, Department of Applied Bioinformatics , Goldschmidtstr. 1, 37077 Goettingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Romy Petroll 4 Department of Algal Development and Evolution, Max Planck Institute for Biology Tübingen , Tübingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Romy Petroll Stefan A. Rensing 5 University of Freiburg, Centre for Biological Signalling Studies (BIOSS) , Freiburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stefan A. Rensing Thomas Pröschold 1 University of Goettingen, Institute of Microbiology and Genetics, Department of Applied Bioinformatics , Goldschmidtstr. 1, 37077 Goettingen, Germany 6 University of Innsbruck, Research Department for Limnology , 5310 Mondsee, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thomas Pröschold Sophie de Vries 1 University of Goettingen, Institute of Microbiology and Genetics, Department of Applied Bioinformatics , Goldschmidtstr. 1, 37077 Goettingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Iker Irisarri 1 University of Goettingen, Institute of Microbiology and Genetics, Department of Applied Bioinformatics , Goldschmidtstr. 1, 37077 Goettingen, Germany 7 University of Goettingen, Campus Institute Data Science (CIDAS) , Goldschmidtstr. 1, 37077 Goettingen, Germany 8 Section Phylogenomics, Centre for Molecular Biodiversity Research, Leibniz Institute for the Analysis of Biodiversity Change (LIB) , Museum of Nature, Hamburg, Martin-Luther-King Platz 3, 20146 Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Iker Irisarri Ivo Feussner 2 University of Goettingen, Albrecht Haller Institute of Plant Science, Department of Plant Biochemistry , Justus-von-Liebig-Weg, 37077 Goettingen, Germany 3 University of Goettingen, Goettingen Center for Molecular Biosciences (GZMB), Service Unit for Goettingen Metabolomics and Lipidomics , Justus-von-Liebig Weg 11, 37077 Goettingen, Germany 9 University of Goettingen, Goettingen Center for Molecular Biosciences (GZMB), Department of Plant Biochemistry , Justus-von-Liebig Weg 11, 37077 Goettingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ivo Feussner Jan de Vries 1 University of Goettingen, Institute of Microbiology and Genetics, Department of Applied Bioinformatics , Goldschmidtstr. 1, 37077 Goettingen, Germany 2 University of Goettingen, Albrecht Haller Institute of Plant Science, Department of Plant Biochemistry , Justus-von-Liebig-Weg, 37077 Goettingen, Germany 7 University of Goettingen, Campus Institute Data Science (CIDAS) , Goldschmidtstr. 1, 37077 Goettingen, Germany 10 University of Goettingen, Goettingen Center for Molecular Biosciences (GZMB), Department of Applied Bioinformatics , Goldschmidtstr. 1, 37077 Goettingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jan de Vries For correspondence: timphilipp.rieseberg{at}uni-goettingen.de devries.jan{at}uni-goettingen.de Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT The earliest land plants faced a significant challenge in adapting to environmental stressors. Stress on land is unique in its dynamics, entailing swift and drastic changes in light and temperature. While we know that land plants share with their closest streptophyte algal relatives key components of the genetic makeup for dynamic stress responses, their concerted action is little understood. Here, we combined time-course stress profiling using photophysiology, transcriptomics on 2.7 Tbp of data, and metabolite profiling analyses on more than 270 distinct samples, to study stress kinetics across three 600-million-year-divergent streptophytes. Through co-expression analysis and Granger causal inference we predict a gene regulatory network that retraces a web of ancient signal convergence at ethylene signaling components, osmosensors, and chains of major kinases. These kinase hubs already integrated diverse environmental inputs since before the dawn of plants on land. INTRODUCTION Earth’s surface teems with photosynthesizing life. Biodiverse cyanobacteria and algae form green biofilms on rocks and tree bark, and lichens thrive on the bleakest mountaintops. All of this is however dwarfed by the lineage that conquered land globally: the land plants (embryophytes) 1 . Together with streptophyte algae, land plants belong to the streptophytes 2 . Phylogenomic analyses established that the Zygnematophyceae are the closest streptophyte algal relatives of land plants 2 – 4 and comparative genomics have ushered in major progress in establishing a shared catalogue of traits between streptophyte algae and land plants 5 – 10 . Yet, we are only beginning to understand how these genes might have been used in a functional advantage at the time of the conquest of land 11 . Several synergistic properties have shaped land conquering plants 12 , including multicellular development 13 , 14 , propagation 15 , symbiosis 16 , 17 , and stress response 18 . In case of the latter, the earliest land plants had to overcome a diverse range of stressors to which modern land plants dynamically respond by adjusting their growth and physiology 19 . One of the hallmarks of abiotic stress on land in contrast to water is its more dynamic nature: life on land involves rapid and drastic shifts in temperature, light or water availability 18 . We focus on two terrestrial stressors — strongly fluctuating temperatures (cold and heat stress) and light conditions (high light stress and recovery). Terrestrial stressors impact plant and algal physiology especially through the generation of reactive oxygen species (ROS) in the plastid; the plastid acts as a signaling hub upon environmental challenge 20 – 22 . Carotenoids are integral in the oxidative stress mitigation networks of Chloroplastida and found in nearly any photosynthetic organism 23 , 24 . By quenching oxidative stress of different nature, oxidative breakdown products of the polyene backbone called apocarotenoids are a consequence 25 , 26 . Apocarotenoids act as signals in attuning plant and plastid physiology to stress 27 – 31 . The diversity of apocarotenoids is vast, including land plant hormones like abscisic acid (ABA) 32 , 33 and strigolactones 34 but also small volatiles like β-ionone (β-IO) and β-cyclocitral (β-CC) with a growing number of recognized functions 27 – 31 , 35 . The involvement of β-CC in high light stress response was confirmed by several studies in Arabidopsis thaliana 27 , 29 and some data also suggests a role in retrograde signaling 36 , 37 . A physicochemical consequence of the elevation of atmospheric oxygen levels due to plant terrestrialization and radiation 38 might have been higher rates in apocarotenogenesis— independent of the evolution of carotenoid-cleaving enzymes. The utilization of signals derived from carotenoids in the first land plants is hence plausible and adaptive. Here, we studied the integration of apocarotenoid signals and oxidative stress networks in three genome-sequenced non-vascular streptophytes: the algae Zygnema circumcarinatum 10 and Mesotaenium endlicherianum 7 , 11 , and the land plant Physcomitrium patens 39 . Data on pigment profiles and photophysiology were correlated with time-resolved global gene expression profiles from more than 270 samples in total. Using co-expression and gene regulatory network inference, we retrace a web of ancient kinase hubs where environmental and apocarotenoid signals converged already in the last common ancestor of embryophytes and algae. RESULTS A comparative framework for stress dynamics across 600 million years of streptophyte evolution We worked with three genome-sequenced streptophytes that are 600-million-years-divergent ( Fig. 1a ) under comparable conditions (see Methods; briefly, 20/25 °C, 80–100 μmol photons m − 2 s − 1 ): two representatives of the diverse and species-rich 40 closest algal relatives of land plants, the filamentous alga Zygnema circumcarinatum 10 SAG 698-1b (henceforth Zc ) and the unicellular alga Mesotaenium endlicherianum 7 , 11 SAG 12.97 ( Me ) and the bryophyte model Physcomitrium patens 39 strain Gransden 2004 ( Pp ). After growth to apt density, we challenged the organisms by shifting them to (i) high light stress (HL) at 10x growth light intensity (i.e., 800-1050 μmol photons m − 2 s − 1 ) for 6h followed by (ii) 4h of recovery at standard growth conditions, and 24h of (iii) low temperature at 12 °C colder and (iv) high temperature at 12 °C warmer than standard growth condition; all experimentation was repeated in three successive biological replicates ( Fig. 1b ). To track the physiological response of the three streptophytes, we measured their relative photosynthetic yield (Fq’/Fm’) using pulse-amplitude-modulation (PAM) fluorometry ( Fig. 1c-e ) at 13 time points ( Fig. 1b ). Download figure Open in new tab Figure 1: A time-series setup for stress kinetics in three 600-million-year-divergent streptophytes. (a) Cladogram of Streptophyta; Phragmoplastophyta studied herein are highlighted in bold. (b) Summary of the stress experiment time grids and the respective investigations including RNAseq, metabolite profiling, photophysiology, and morphology. Me specific metabolite profiling is indicated by black dots. For high light stress experiments morphology was investigated at maximum stress (yellow dot) and maximum recovery. (c) Boxplots of relative quantum yield of PSII (Fq’/Fm’) during stress exposure and control, category time scale. (d) Boxplots of aggregated relative quantum yield of PSII (Fq’/Fm’) due to stress exposure and in control. (e) Scatterplots with Loess fit of relative quantum yield of PSII (Fq’/Fm’) during stress exposure and control of first six hours of the experiment, absolute time scale. (f) Morphological observations before and after stress exposure with time points corresponding to b. (g) Quantification of observed morphological effects; statistics was performed using a Kruskal-Wallis plus Dunn post-hoc test. LDs = accumulated lipid droplets/small droplets, red/brown = overall color change of cell to a more red-brown shade, DPN = droplets accumulated around the nucleus. On balance, responses upon stress were well conserved in the three studied organisms but acted in slightly differing kinetics ( Fig. 1c ). HL brought the swiftest changes in Fq’/Fm’ ( Fig. 1e ), with an average drop by 0.49±0.08 at 30 min ( Fig. 1c ). While Me and Pp recovered progressively after HL, Zc gusted to the initial values but recovered later ( Fig. 1c ). In all three studied species HL caused a stronger and faster response in Fq’/Fm’ than temperature stress ( Fig. 1c ). In Zc and Me the stressors additionally induced morphological alterations ( Fig. 1f )—in both causing significant changes in chloroplast shape upon HL and heat as well as LD accumulation upon cold ( Fig. 1g ); in Zc, heat and HL further induced pigment alterations and plasmolysis ( Fig. 1g ). Overall, our setup captures quantifiable changes in physiology that follow a stress kinetic. We next turned to the molecular consequences. Global transcriptomics bear out divergent time-course dynamics To understand how the temporal and amplitudinal differences in physiology relate to genetic changes, we used global gene expression analysis. We generated RNA-seq data on samples from 29 different conditions in biological triplicates (sextuplicates for t 0 ) across all three species, yielding a total of 270 samples. All sequencing was performed on the NovaSeq6000 platform (Illumina) using paired-end 150 bp reads, yielding 2.8 Tbp (2,806,144,787,400 bp), of which 2.7 Tbp were retained after quality filtering (2,710,780,974,000 bp). We mapped the data onto the respective genomes based on current gene models 10 , 11 , 39 with an average alignment rate of 83.39%, 67.39%, and 92.58 % for Me , Zc , and Pp , respectively. A principal component analysis (PCA) of gene expression data recovered a clear separation between stress and control conditions ( Fig. 2a ). Along PC1 (describing between 26.9% to 44% of the variance; Fig. 2a ) heat followed by HL showed the swiftest separation upon temporal progression in all three streptophytes ( Fig. 2b ); recovery caused time-successive rapprochement to control conditions ( Fig. 2a ). Treatment with cold generally induced less differences from control conditions and resembled characteristics of a resting state despite slight fluctuations likely due to circadian rhythms. Download figure Open in new tab Figure 2: Divergent temporal stress progression retraced by global differential gene expression analysis. (a) PCA analyses for all three species with color and symbol code for the respective RNAseq samples on the right; below the PCAs, time-dependent changes in the data are highlighted in plots of time versus PC1 and PC2. (b) Time-resolved alluvial diagrams of most prominent GO terms found across at least three time points based on significantly differentially regulated genes (term size indicated by line width) during cold, heat and high light stress exposure, category time scale. To pinpoint the functions that underpin the temporal dynamics of stress response, we performed differential gene expression analysis of each treatment to its respective control. We carried out 22 comparisons for each species (Extended Data Fig. 1a), yielding up to more than 3000 significantly differentially expressed genes (Extended Data Fig. 1b), and performed GO term enrichment across time and treatments ( Fig. 2c ). HL induces rapid responses whereas responses to cold were more delayed except in Me . In general, Me showed the highest responsiveness towards the studied environmental cues (Extended Data Fig. 1b). Further, on balance, Zc showed the most delayed response ( Fig. 2c )—even in the recovery (Extended Data Fig. 2). Recurrent themes corroborated previous findings 11 , 41 : among the responses, light quality signaling, response to ROS, and chloroplast as well as photosynthesis-associated processes stuck out (Extended Data Fig. 3). In Me calcium and kinases featured prominently in the temperature stresses ( Fig. 2c ). Overall, the gene expression data align with the physiological data. We next turned to a physiological stress mitigation mechanism that generates signals: carotenoid metabolism and apocarotenoid signals. Dynamic xanthophyll cycle and apocarotenogenesis in Mesotaenium Under stress, carotenoid levels adjust and yield apocarotenoid signals through oxidative cleavage 23 ( Fig. 3a ). To assess their profiles, we combined RP-C 30 -HPLC-UV-Vis (carotenoids and chlorophylls) with HS-SPME-GC-MS (volatile apocarotenoids; Fig. 3b,c,e ) and investigated transcript levels of relevant genes ( Fig. 3d ). In general, pigment fluctuations due to stress exposure seem more similar in the two studied multicellular organisms than in the unicellular Me ( Fig. 3b,c,e ); for example, the de-epoxidation state changed after 15 minutes of HL by 1.98-fold in Me , 1.09-fold in Zc , and 1.35-fold in Pp . Changes in the relative xanthophyll pool size were ±20% upon 2 h of treatment by any stressor relative to t 0 ( Fig. 3c ), suggesting that the overall response is mainly determined by the altered physicochemical stress conditions. This is also reflected by transcript level changes in homologs of enzymes responsible for gross flux regulation ( Fig. 3d ). Since enzymatic reactions of the carotenoid pathway are favored by heat and other biosynthetic steps are favored by light in both conditions, transcript levels of these enzymes, including homologs of PDS, CRTISO, CYP97A, are downregulated: for example, after 6 h PDS was downregulated—relative to control—in Me 44-fold (heat) and 13.5-fold (HL) (BH-corrected P = 1.28×10 -17 and 1.52 ×10 -11 ), and in Zc downregulated by 2.4-fold (heat) and 1.6-fold (HL) (BH-corrected P = 7.64×10 -6 and 0.016); in Pp PDS expression did not change or was upregulated by 3.2-fold under cold (BH-corrected P = 8.68×10 -12 ; Fig. 3e ). The pigment pools of Me and Pp are of similar sizes (compared to Pp , t 0 95% and 100%) while Zc has a much smaller pool (13%; Fig. 3c ). An explanation is that the weight of mucilage skews the values for Zc . The pigment pool of Me was the most dynamic ( Fig. 3c,e ). The ratio of β-carotene to the apocarotenoids β-CC, β-IO, and dihydroactinidiolide (DHA) had changed after 2h of treatment most pronouncedly upon HL, down to 0.36, 0.59, and 0.31 for Me, Zc , and Pp compared to 1.05, 0.59, and 0.41 in cold and 0.35, 0.9, and 0.65 upon heat ( Fig. 3b,e ). As expected, there was no direct correlation between changes in apocarotenoid levels and transcript levels, because apocarotenoids are also formed by non-enzymatic cleavage. The non-enzymatic cleavage reaction is favored under elevated temperatures and HL and we observe decreasing transcript levels: in Me and Pp , CCD1 showed the most dynamic in the first 2h of treatment, decreasing 3.3- and 5.5-fold in Me upon heat and HL (BH-corrected P = 7.63×10 -7 and 8.05×10 -13 ) and 3.9-fold in Pp upon heat (BH-corrected P = 1.56×10 -15 ). Me CCD1 constantly rose during the first 6h cold stress up to 6.4-fold (BH-corrected P = 1.12×10 -11 ), while β-CC, β-IO levels reached their peak at 4h cold. β-IO appears to be swiftly converted to DHA in a non-enzymatic reaction 26 , 27 . DHA has also shown a stronger effect on oxidative stress-responsive genes in earlier studies 27 , 42 . Overall, we recovered the presence of well-known apocarotenoid signals with the most pronounced stress kinetics in Mesotaenium . Download figure Open in new tab Figure 3: Carotenoid and apocarotenoid dynamics upon stress. (a) Simplified schematic representation of the conserved and intertwined carotenoid and apocarotenoid pathways in the three organisms of this work. (b) Heatmap of metabolite flux and its correlation with the different conditions/samples; the color and symbol codes are as in Figure 2 and can be found in the bottom right corner of the figure. (c) Pigment pool fluctuations due to stress exposure. Areas relative to size of pigment pool/contribution of individual pigment to the pool normalized on Pp t 0 . (d) Expression changes of important genes in the first 6 h of stress exposure (only one respective homolog shown also in some cases more were present). (e) Relative change of selected metabolite levels/ratios in the first 6 h of stress exposure relative to the average value of t 0 normalized by DW of sample, recovery rate of measurement, and molecular mass of the respective metabolites. 6MHO, 6-methyl-5-hepten-2-one, 9-cis, 9- cis - β-Carotene, 9- cis -neo, 9- cis -Neoxanthin, (A+Z)/(V+A+Z), (Antheraxanthin+Zeaxanthin) / (Violaxanthin+Antheraxanthin+Zeaxanthin), α-car, α-Carotene, antherax, Antheraxanthin, β- car, β-Carotene, β-car/9- cis -neox, β-Carotene / 9- cis - β-Carotene, β-Car/ β-CC, β-Carotene / β-Cyclocitral, β-Car/ β-IO, β-Carotene / β-Ionone, β-Car/DHA, beta β-Carotene / Dihydroactinidiolide, β-Car/ β-CC+β-IO+DHA, β-Carotene / (β-Cyclocitral + β-Ionone + Dihydroactinidiolide), β-CC, β-Cyclocitral, β-IO, β-Ionone, Chl a , Chlorophyll a , Chl a / b , Chlorophyll a / Chlorophyll b , Chl b , Chlorophyll b , DHA, Dihydroactinidiolide, DHA/β-IO, Dihydroactinidiolide / β-Ionone, violax, Violaxanthin, V+A+Z/Chl a +Chl b , (Violaxanthin+Antheraxanthin+Zeaxanthin) / (Chlorophyll b ), Zeax, Zeaxanthin. Biological programs correlate with the environmental triggers and pigment profiles Each organism reacted to the environmental cues by the altered expression of up to thousands of genes. To understand their cooperative action, we clustered all 13125, 9445, and 15778 genes that passed the expression cutoff in Me , Zc , and Pp into 27, 34, and 29 clusters respectively, to which we refer by color ( Fig. 4a ). We then asked the questions of how these clusters (i) correlate with the environmental cues and apocarotenoid levels ( Fig. 4b ) and (ii) are similar across species ( Fig. 4c,d ). For the latter, we worked with Orthofinder’s hierarchical orthogroups (HOGs) and calculated the Jaccard distance ( Fig. 4c,d ). Download figure Open in new tab Figure 4: Unsupervised gene co-expression networks recover shared programs. WGCNA was used to compute co-expression networks. (a) Distance dendograms of module correlation with metabolites, condition, and photophysiology. (b) Heatmaps of module trait correlation with metabolites, condition, and photophysiology corresponding to distance trees in a. (c) Scaled Jaccard distances that illustrate similarity (pink) and dissimilarity (blue) between modules based on HOGs. (d) The 50 most similar modules. (e) cnetplot of the GO terms enriched in the cluster Me brown (f-j) Selected WGCNA clusters with the top 20 most connected genes annotated based on homology. (k) WGCNA cluster correlation of assigned most enriched GO terms in Zygnema circumcarinatum , Physcomitrium patens and Mesotaenium endlicherianum . The gene clusters consist of meaningful biological cohorts, such as Me darkred and Pp darkgrey that are similar ( Fig. 4d ) and enriched in ribosomal GO-terms, and ribosome component-coding genes that are hubs in these networks (Extended Data Figs. 4 and 5). Physiologically coherent behaviors were recovered. For example, cluster Me yellow shows negative correlation with temperature ( r = −0.73, P = 10 −1 3 ) and the ratios of antheraxanthin (A) + zeaxanthin (Z) to violaxanthin (V) + A + Z (AZ/VAZ) ( r = −0.52, P = 2×10 −6 ), with the similar cluster Pp yellow showing a negative correlation with temperature ( r = −0.67, P = 10 −10 ) and similar terms associated with ( Fig 4c ; Extended Data Fig. 5); in contrast, for example, Me darkorange showed weak correlation with temperature ( r = 0.35, P = 0.003) and light intensity ( r = 0.33, P = 0.006; Extended Data Fig. 5). Clusters of Zc seldom showed correlations with the tested environmental factors but this is likely due to Zc ’s subdued responses. Overall, the clearest correlations were with temperature, where highly similar clusters ( Fig. 4c,d ) such as Me brown ( Fig. 4e ), Pp brown ( Fig. 4f ), Zc brown (Extended Data Fig. 5), and Me Turquoise ( Fig. 4g ) show strong positive correlations ( r = 0.85, P = 5×10 −2 3 ; r = 0.64, P = 10 −9 ; r = 0.74, P = 2×10 −1 3 ; and r = 0.39, P = 7×10 −4 ) and similar terms associated with protein homeostasis. Several clusters reflect signaling processes, including kinases and HISTIDINE KINASE (AHK) hubs ( Fig. 4f,h,i ,j; Extended Data Fig. 5), calcium and Ca 2+ -DEPENDENT KINASE (CDPK) hubs ( Fig. 4i ), photomorphogenesis ( Fig. 4g ), and links of kinases and carotenogenesis ( Fig. 4f ). The clearest correlation with the apocarotenoid signals showed Pp pink and Me black ( r = 0.61, P = 10 −8 and r = 0.41, P = 3×10 −4 ). Pp pink was enriched in light intensity including the phytochrome signaling hub LONG AFTER FAR-RED 3 (LAF3) 43 and oxidoreductase activity including a superoxide dismutase hub (Extended Data Fig. 5). Me black showed a very complex and broad enrichment of genes associated with general transcription processes. To disentangle these genetic networks, we next used temporal clustering. Temporal stress co-expression and Granger causal inference of gene regulatory networks To understand the time-course dynamics of the responses, we modeled clusters of gene expression along their time course using a Dirichlet process Gaussian process mixture model 44 (DPGP). Using DPGP, we clustered all 11670, 9781, and 3887 genes that passed the expression cutoff in Me , Zc , and Pp into 12 to 16, 13 to 20, and 11 to 13 clusters. We filtered these clusters, retaining only those that had a Gaussian probability of at least 0.7 and calculated their Jaccard distances ( Fig. 5a ). As expected, the most similar clusters occurred within a species. However, there were several similar clusters across species, including a cohort of genes downregulated upon heat in Pp C6 and Me C3 related to photosynthesis ( Fig. 5b ). Download figure Open in new tab Figure 5: Time-course gene expression clusters pinpoint shared responders. Dirichlet Process Gaussian Process (DPGP) time-resolved clusters of stress samples. (a) Top: Heatmap of Jaccard distances computed for all DPGP clusters based on HOGs; bottom: zoom-in on the top 50 most similar cluster (b) Line plots of the expression behavior of cohorts of genes recovered by DPGP clustering for 20 pairs of DPGP clusters high Jaccard similarity; the Z-score standardized log 2 fold change (CPM) over the first 6 hours of stress exposure is shown. (c) Left: Black ticks indicate into which DPGP clusters the hub genes recovered by WGCNA (indicated by colors) fall; right: line plots of the expression behavior of cohorts of genes in DPGP clusters that are particularly rich in WGCNA-defined hub genes. We next turned to understand how the hubs of the co-expression clusters identified via WGCNA behave along the temporal gradient, with a focus on signaling hubs. Hub genes show focal occurrence among DPGP clusters. For example, hubs of Me yellow (chloroplast and photosynthesis; Extended Data Fig. 5d) were mainly in cohort of early down-regulated genes upon HL Me C1 ( Fig. 5c ); Pp yellow showed a similar behavior ( Fig. 5c ). Early upregulated hubs included those of protein homeostasis in Me brown. Research on model land plants has established a rich framework of genetic cascades that act in information processing and are structured a hierarchy. To understand how the temporal expression behaviors of the genes are linked in the 600-million-year divergent streptophytes, we predicted gene regulatory relationships based on Granger causality using Sliding Window Inference for Network Generation (SWING) and a Random Forrest (RF) approach (i.e., SWING-RF) 45 . For this, we worked with a subset of 1897, 3694, and 1629 genes and 24 metabolite levels and ratios in Me , Zc , and Pp , predicting 375,273 to 3,358,660 non-zero interactions between genes (and metabolites), yielding putative gene regulatory networks (GRNs; Extended Data Fig. 7). We then asked the question of how the genes that group into HOGs and show conserved behavior in DPGP clusters predict each other ( Fig. 6a,b ) and recovered a network of 923 conserved gene pairs (1239, 1188, and 1369 in Me, Zc, Pp ); in addition, we also investigated the topology of the network if we (i) group genes simply using best BLAST hits (BBH; Extended Data Fig. 8), recovering a network of 757 conserved genes, and (ii) re-compute the network using all time points of stress treatment—also those for which no metabolite data were generated, recovering a network of 900 (Extended Data Fig. 9). The topology of the predicted GRN highlighted several major points of convergence—hubs ( Fig. 6c,d ). Download figure Open in new tab Figure 6: A predicted gene regulatory network shared across 600 million years of streptophyte evolution. SWING-RF network filtered based on conserved DPGP clusters of most predictive (enlarged circles) HOGs and metabolites conserved in all three species. (a) Predicted conserved stress responsive network with some important hubs highlighted. (b) Zoom into the central ‘kinome’ (LRR) SPS kinases hub. (c) Ranked position of top 50 most connected nodes in the conserved network among all networks predicted by SWING (not filtered). (d) Ranked position of top 50 most connected nodes in the conserved network among all networks predicted by SWING and filtered for genes in conserved DPGP clusters. Physical feedback Mechanosensitive channels, regulate ion flow in response to mechanical cues. We recover protein homologs from the OSCA family, calcium-permeable channels sensitive to hyperosmolality 46 , as highly connected points of convergence ( Fig. 6a,c,d ). Feeding into them was a PDS1-PDS3 network ( Fig. 6 ), genes with highly responsive expression change under stress ( Fig. 3d ), underpinning a central link of carotenogenesis to all kinds of oxidative stress responses. Connected genes included those with general functions such as homologs of CYTOSOLIC IRON-SULFUR PROTEIN ASSEMBLY 1 (CIA) but also SOUL homologs ( Fig. 6a ). SOUL are heme-binding proteins 47 that can translocate to the chloroplast and are key for oxidative homeostasis 48 . Directly influenced by the OSCA hub was a large HOG of subtilases, which are known growth regulators 49 , 50 . Indeed, highly predictive for this subtilase hubs were a CXE HOG that also contained the growth regulating gibberellin receptor GIBBERELLIN INSENSITIVE DWARF1 (GID1) 51 —some Me homologs even bearing a homologous region to the unique n-terminal extension of GID1—and AHK1, which if interpreted as cytokinin-relevant, also speaks of ancient developmental programs. Indeed, upstream of CXE/GID was a HOG of GROWTH-REGULATING FACTORs (GFR). This speaks for a feedback of osmo-sensing—as done by OSCA and AHKs 52 , 53 —and growth programs in which subtilases are a point of convergence. Convergence at ethylene programs Ethylene signaling stands as the sole example of a phytohormone with a clear signaling cascade reported to be functionally conserved between land plants and Zygnematophyceae, although its biosynthetic pathway may not need to be necessarily conserved 54 , 55 . Exogenous ethylene treatment of the zygnematophyceaen alga Spirogyra induces growth phenotypes and the differential expression of stress- and photosynthesis-associated genes identified in land plants 56 . Crucially, Spirogyra homologs complement knockout lines in Arabidopsis ethylene signaling 57 . Ethylene thus both completes the stress and photosynthesis response cycle and represents the phytohormone with the most evident data supporting its conserved functional role in stress response in both land plants and Zygnematophyceae. Our predicted GRN recovers several signature genes from ethylene signaling. We recover an ETR among the most highly connected homologs ( Fig. 6c,d ) and HOGs. Both in Arabidopsis and Pp , ethylene is detected by a two-component histidine kinase receptor subfamily of ETR1 homologs 58 , 59 . Upstream were meaningful regulatory cassettes including a HOG of Kelch repeat F-Box that includes homologs of the stress response and specialized metabolism regulators SMALL AND GLOSSY LEAVES (SAGL1) 60 , 61 and ATTENUATED FAR-RED RESPONSE (AFR) 62 . Indeed, further upstream were C2H2 zinc-finger TFs—TFs generally prominent in our SWING network—including SUF4, YY1 and are well known to be among the apocarotenoid-activated TF families 28 , 37 , 63 , CONSTANS-like B-box TFs that include the CCT domain involved in light-dependent regulation 64 – 66 , and phytochromes in line with their essential role in stress, light signaling, and several phytochrome signatures in our transcriptomes. And upstream of this is a HOG of various APETALA2 (AP2) TFs that include homologs of Ethylene Response Factors (ERFs) but have diverse functions in environmental and hormonal responses 67 . That said, in Pp , an ETR kinases play a crucial role in facilitating effective environmental stress responses through ethylene-mediated submergence signaling and ABA-mediated osmo-stress signaling 68 ; our work adds in the conservation in the two Zygnematophyceae ( Fig. 6 ). A HOG that includes the heat stress-relevant ARABIDOPSIS HOMOLOG OF TRITHORAX 1 and 2 (ATX) 69 was downstream, providing the single broker that linked it to the most connected hub in the predicted GRN: a kinase. Environmental and apocarotenoid input converges at hubs of the kinome In land plants, a network of kinases regulates responses to environmental input by phosphorylating proteins like TFs and enzymes, impacting synthesis of specialized metabolites, gene activation, and more 70 – 72 . Several factors modulate the activity of these kinases, including protein phosphatases acting as mediators but also direct signals if linked to a sensor, affecting response dynamics and outcomes. These kinases form converging points in our predicted GRN. They are the receivers of diverse inputs, integrating environmental input in biological programs. As the single most connected receiver in our DPGP-filtered SWING-inferred GRN was an HOG of serine / threonine kinases (SPS; Fig. 6a-d ), which include several receptor-like kinases 73 . In our analyses, these receive input from the apocarotenoid ß-IO and SUPPRESSOR OF FRIGIDA (SUF4), a putative zinc-finger-containing TF, regulating for delayed flowering in winter-annual Arabidopsis by suppressing the late-flowering FRI mutants through reduced H3K4 trimethylation at FLC; recently it was shown that SUF4 acts as a thermosensor, showing thermo-sensitive assembly and thus activity 74 . This warrants attention, as we recovered in the BBH-based analysis a whole putative regulatory chain of ß-IO–SUF4–a transmembrane protein–MYB4R1 (Extended Data Fig. 8). Furthermore, a HOG including the circadian regulators 75 LUX ARRHYTHMO (LUX) and BROTHER OF LUX ARRHYTHMO, were the fifth most predictive ( Fig. 6d ) on the SPS kinase hub expression behavior, aligning with their previously noted importance in photomorphogenesis co-expression hubs in Mesotaenium 11 . Around this SPS hub we recovered several genes that are known to be major hubs in regulation themselves. This included TOR, eukaryote-wide regulators of diverse developmental and metabolic processes 76 , 77 , which are part of the kinase network. As potential regulator of the major kinase hub, a hub of PP2A B subunits appeared. This aligns with the regulatory subunit determining activation of the phosphatase complex and various subfamilies interact with different kinases 78 . Next to the major hub of SPS kinases, other kinase HOGs included AHKs. AHKs are best known as cytokinin signaling components 79 but also include respond to diverse abiotic stressors 52 , 53 . These AHKs form another hub upstream of the major SPS hub. As highlighted by the previous environmental gradient analysis on Mesotaenium 11 alone, we find chains of kinases (BSK, SERK, MKK) linked with a large group of EXORDIUM-like proteins, which are modulators of cell expansion 80 and thus likely the downstream target that modifies growth upon environmental input. These EXORDIUM-likes are connected via a mTERF4-like HOG to the central kinome ( Fig. 6 ). mTERF transcription termination factors harbor many important functions in regulating organellar gene expression 81 , 82 and mTERF4 interplaying with GUN1 (see also Zc darkgreen, Extended Data Fig. 5) that is implicated in retrograde signaling 83 . Overall, the kinases form a major track throughout the entire GRN. Many of the kinases recovered are homologous to well-known integrators of environmental cues, via intracellular signaling, to growth and acclimation programs. DISCUSSION The success of the earliest land plants likely hinged on their capacity to perceive and react to environmental conditions. These environmental conditions are integrated into a network that bundles information and triggers developmental plasticity. Integrated in this web are key links between plastid and cell physiology: carotenoids and the signals they give rise to due to oxidative cleavage. Carotenoids are among the best conserved and most ancient oxidative stress-mitigating molecules in photosynthetic organisms 24 , 84 . With the synthesis of the first carotenoid non-enzymatically formed apocarotenoids were born – enzymes for controlled oxidative cleavage of the polyene backbone likely evolved later. Our data shows that apocarotenogenesis driven by physicochemical conditions is shared. What diversified were the genetic hubs and enzymes acting on them and controlling the formation and response likely favorable for terrestrialization—a process that came with more oxidative force and as a physicochemical consequence more apocarotenogenesis due to elevated O 2 levels, reduced CO 2 levels, a drastic drop in temperature and generally more abiotic stress due to terrestrial environmental conditions. We found several of these stress-mitigation hubs conserved in the two investigated Zygnematophyceae and the moss P. patens . Traits of land plants are under an adaptability-driven selection since their emergence—and in many cases these signature genes emerged prior to the origin of land plants. Our findings pinpoint the hubs in which this information is bundled. Largely mediated by kinases that are known to integrate in signaling cascades that facilitate cross-talk between different inputs, for example bundling signaling molecules, Ca 2+ , and osmotic 53 , 85 , and their link to developmental programs. These kinases span a web, serving as points of convergence between inputs and redistribute these on the level of protein phosphorylation to the respective output. We here found that environmental programs regulate the transcriptional level across 600-million-year divergent streptophytes. MATERIALS AND METHODS Algae and moss cultivation Mesotaenium endlicherianum SAG 12.97 and Zygnema circumcarinatum SAG 698-1b were obtained from the Culture Collection of Algae at Göttingen University (SAG) and grown on cellophane disks (folia®, Max Bringmann KG) as described 86 prior to stress treatment with the exception that in both cases agarized (1%) Woods Hole Medium (WHM) 87 was used. In short, fully-grown plates of stock cultures were suspended and inoculated on fresh WHM plates. Mesotaenium endlicherianum SAG 12.97 was grown for 8 d at 20 ± 1 °C with 80–90 μmol photons m − 2 s − 1 (Niello® LED 300 W, 380– 740 nm spectrum; Suppl. Fig. 1) under 16/8-h light/dark cycle. Zygnema circumcarinatum SAG 698-1b was grown for 48 h at 20 ± 1 °C with 20–25 μmol photons m − 2 s − 1 (Niello® LED 300 W, 380–740 nm spectrum) and afterward for 11 d at 20 ± 1 °C with 80–90 μmol photons m − 2 s − 1 with the same 16/8-h light/dark cycle (13 d of total growth until the start of the experiment). Physcomitrium patens Gransden 2004 strain 40001 protonema was used for the experiments. Agarized (0.55 %) basal minimum medium with ammonium tartrate (BCD-AT) 88 was used and inoculated with 2 mL of a suspension of the stock culture (one fully-grown plate per 20 mL sterile tab water) homogenized with an IKA® ULTRA-TURRAX®. Cultures were kept at 25 ± 1 °C during the light phase with 100 μmol photons m − 2 s − 1 (growth chambers of Percival Scientific, Inc., Perry, IA, USA) and at 18 ± 1 °C during dark phase under 16/8-h light/dark cycle for 7 d prior to stress treatment or sub-cultivation. Stress treatments Time-series stress experiments were performed as visualized in Fig. 1b in biological triplicates. Transcriptome and metabolite samples were harvested at the time points indicated by rectangular lines in Fig. 1b . Also, relative quantum yield was measured at these time points (for details see the photophysiological measurements section). For the control timeline, cultures stayed for another 24 h at the cultivation conditions described above. For temperature stress treatments control light conditions were used but the temperature was 12 °C lower (cold stress) or higher (heat stress). Due to the reduced efficiency of the LEDs at elevated temperature, the light intensity dropped by 10 μmol photons m − 2 s − 1 during algae heat stress experiments (not the case for Physcomitrium patens ). Algae temperature stress treatments were performed in HS80 growth tents (Secret Jardin, Manage, Belgium) in temperature-controlled rooms. To ensure homogeneous temperature in the tents, cultures were put on an elevated grid and two small Clip-Fans (Garden Highpro Clip-Ventilator Ø15cm, 5 Watt) were added. Physcomitrium patens temperature stress series were performed in the Percival’s with altered temperature programs. For HL treatments cultures were transferred to a 10x light intensity regime compared to control conditions 800–900 μmol photons m − 2 s − 1 (algae) and 950–1050 μmol photons m − 2 s − 1 (moss) respectively, and afterwards 4 h to control conditions for recovery. In the case of algae high light treatments, the increased light intensity also led to a rise in temperature by 12 °C. The temperature stayed constant during Physcomitrium patens HL treatments. To achieve this light intensity two lamps with higher output (Niello® LED 900 W, 380–740 nm spectrum) were used in the case of the algae treatments and for Physcomitrium patens HL treatments, 4 additional lamps (Niello® LED 300 W, 380–740 nm spectrum) were installed in the Percival. Harvesting and storage of transcriptome and metabolite samples Samples for transcriptomic analysis and metabolite profiling were harvested with a spatula at the respective timepoints and immediately frozen in liquid N 2 in reaction vials. Afterwards, samples were stored at –70 or –80 °C and metabolite samples additionally overlaid with argon before storage. To ensure a higher reproducibility, each transcriptome and metabolite sample was pooled from three different algae or moss plates (technical triplicate) at each timepoint. This was done in biological triplicates as described above. Photophysiological measurements Fq’/Fm was determined using a MINI-PAM II (Heinz Walz GmbH) to access the photophysiological perturbations due to stress exposure at the time-points represented by rectangular lines in Fig. 1b . Only one culture of the three technical replicates (pooled for transcriptome and metabolite analysis) per timepoint and per biological replicate was measured to reduce the perturbations by the light pulse (intensity = 8 (pulse of 4000 μmol photons m − 2 s − 1 ), frequency = 3, gain = 3) on metabolite and transcriptomic level. Microscopy Microscope pictures were taken in biological triplicates. For control, cold and heat stress conditions pictures were taken after 0 h and 24 h. For the HL/recovery time series after 6 h of stress and 4 h of recovery. The microscopical setup was comprised of a Carl Zeiss Axioscope 7 RL BF/DF/ C-DIC, TL LED with 10x, 20x, 40x and 100x objectives (Carl Zeiss Microscopy) connected to an Axiocam 208 color. The data was processed with the ZEN (blue edition; version 3.0) imaging system (Carl Zeiss Microscopy). For statistical analysis 6-10 (once 6 and once 8 for Zygnema circumcarinatum ) pictures per replicate with 4-8 cells were evaluated (total of 40 cells). Chemicals Carotenoid standards of 9- cis -neoxanthin (≥ 97%), violaxanthin (≥ 90%), lutein (≥ 99%), α-carotene (≥ 95%), β-carotene (≥ 93%), and lycopene (≥ 90%) were obtained from Sigma Aldrich Chemie GmbH and Zeaxanthin (≥ 90%) from ChemPur GmbH. Chlorophyll a (≥ 95%) and b (≥ 95%) were obtained from Sigma Aldrich Chemie GmbH. Apocarotenoid standards of 6-methyl-5-hepten-2-one (≥ 99%), β-CC (≥ 97%) and β-IO (≥ 96%) were obtained from Sigma Aldrich Chemie GmbH. Dihydroactinidiolide (≥ 98%) was synthesized by abcr GmbH. β-IO-D 3 (≥ 95%) used as the internal standard for apocarotenoid quantification was obtained from Eptes. MTBE (≥99.5%) and methanol (≥99.8%) for HPLC analysis were obtained from Fisher Scientific GmbH and acetone for extraction and standard dilutions from Carl Roth GmbH (≥ 99.9%). Ethylacetate (≥ 99.9%) for apocarotenoid standard dilutions was obtained from Sigma Aldrich Chemie GmbH and methanol (≥ 99.9%) for standard dilutions from Fisher Scientific GmbH. Further solvents for carotenoid standard dilutions were dichloromethane (≥ 99.9%) from Carl Roth GmbH and acetonitrile (≥ 99.9%) from Fisher Scientific GmbH. BHT (≥ 99.7%) was obtained from Carl Roth GmbH. The water used for HPLC analysis and extraction was purified by ultrapure water system arium pro (Sartorius). Carotenoid and chlorophyll extraction Initially, the extraction protocol was inspired by Aronsson et al. 89 Several alterations were made to optimize the protocol for organisms investigated in this study, as follows. Prior to extraction, algae samples were lyophilized (ZIRBUS technology GmbH, Bad Grund, Germany) for 18–20 h and moss samples for circa 24 h. Samples were shielded from light during this process. Next, samples were immediately frozen again in liquid N 2 and homogenized quickly in reaction vials placed in a metal block (blockage of light and homogenous temperature) cooled by liquid N 2 (cooling and replacement of oxygen) with a sharp conical spatula to prevent oxidation or degradation. Samples were overlaid with argon again and stored at –80 °C until extraction. Directly before extraction samples were frozen in liquid N 2 again. All extractions were performed in a temperature-controlled room at 4 °C in the dark (only indirect light with an intensity below detection limit) to prevent alterations during extraction. Extraction solvents contained 0.1w% BHT to additionally prevent oxidation. Biomass was weighted quickly in the same room and the remaining sample was directly frozen again in liquid N 2 until HS-SPME-GC-MS analysis. 250–700 μL solvent A (acetone:water (80:20) + 0.1w% BHT) were added immediately to the weighted sample. Extraction volume was adjusted to the available/weighted biomass to ensure similar biomass:solvent ratios. This ratio was slightly optimized for each organism (average biomass used per mL of solvent-mix: Mesotaenium endlicherianum 5 mgDW/mL, Zygnema circumcarinatum 12 mgDW/mL, Physcomitrium patens 5 mgDW/mL) due to different pigment concentrations in different species. After adding solvent A the mixture was directly vortexed for circa 1-2 min (2-4 min for Zygnema circumcarinatum due to its worse extractability). Then, the sample was centrifuged for 1 min at 4 °C at max. speed 20238 rcf (Eppendorf 5424), and the supernatant was collected in another reaction tube. The procedure was repeated with solvent B (acetone + 0.1w% BHT) but the pellet was broken by stirring it up with a pipette tip before vortexing. The supernatants were combined, vortexed for 10 s and centrifuged again at max. speed to clear the extract from insolubles. 200 μL of the extract were transferred to a brown glass analysis vial with glass insert and PTFE septum. Samples were injected (40 μL) into the HPLC within the next 5–25 min after extraction and stored on a cooled (10 °C) light shielded sample rack until analysis. HPLC-UV-Vis analysis of carotenoids and chlorophylls The HPLC system Agilent 1100 series equipped with a UV-Vis-DAD detector was used for simultaneous carotenoid and chlorophyl measurements. For separation a YMC Carotenoid C30 S-3 µm column (250 x 4.6 mm I.D.) from YMC Europe was integrated. The solvent system used was initially inspired by Gupta et al. 90 but modified for this study, as follows. Eluents were degassed in an ultrasonic sonic bath (≥15 min) before connection with the HPLC system. A ternary gradient of eluent A (methanol:water, 98:2, v/v), eluent B (methanol:water, 95:2, v/v), and eluent C (MTBE) was applied as follows: 0 min A:C (80:20, flow rate 1.4 mL/min), 0–2.00 min gradient to A:C (70:30, flow rate 1.4 mL/min), 2.01 min B:C (70:30, flow rate 1.0 mL/min), 2.01–18.00 min gradient to B:C (0:100, flow rate 1.0 mL/min), 18:00–19.00 min gradient to A:C (80:20, flow rate 1.0 mL/min), 19.00–25.00 flow rate gradient from 1.0 mL/min to 1.4 mL/min A:C (80:20), hold for 1 min on this condition (total run time 26 min). The oven temperature was 20 °C. Identities of the detected molecules were determined by absorption spectra, retention time, and comparison of both with authentical commercial standards (if available). In case of antheraxanthin 90 and respective cis / trans -isomers of carotenoids 90 identities of the detected molecules were determined by absorption spectra by comparison with literature data. Violaxanthin, lutein, α-carotene, β-carotene, and zeaxanthin were quantified at 451 nm based on calibration curves of the respective standards (Suppl. Figs. 2, 3 , 4 , and Suppl. Table 1). 9- cis -β-carotene was quantified based on β-carotene calibration due to similar spectral properties 91 . The analytical standard of 9- cis -neoxanthin was an almost equal mixture of isomers, so it was quantified based on violaxanthin calibration respecting differences in extinction coefficients instead 92 (Supplemental Table 2). Antheraxanthin (Supplemental Table 2) was quantified based on zeaxanthin calibration respecting differences in extinction coefficients 92 . Chlorophyll a and b were quantified at 660 nm based on calibration curves of the respective standards (Suppl. Figs. 2, 3, 4, and Suppl. Table 1). HS-SPME-GC-MS analysis of volatile apocarotenoids The method established here was initially based on Rivers et al. 93 The system used for volatile apocarotenoid measurements was comprised of the following technical compartments: GC/MSD instrument (Agilent Technologies 7890B) coupled to a 5977B MSD quadrupole, PAL3 Auto sampler system with Robotic Tool Change (RTC 120), polydimethylsiloxane/divinylbenzene/carboxen (50/30 µM DVB/CAR on PDMS) adsorbent SPME fiber from Supelco, HP-5MS UI column (30 m x 0,25 mm; 0,25 µm coating thickness; Agilent). The temperature program for elution was the following: Inlet 250 °C, 60 °C 2 min hold, ramp 5 °C/min till 185 °C, ramp 25 °C/min till 320 °C followed by a 2 min hold, in total 34.4 min. Post run was set to 60 °C and Aux heater (MSD Transfer Line) to 280 °C. Helium gas flow rate was set to 1 mL/min. Metabolite adsorption and desorption program: 30 min pre-conditioning at 270°C, 5 min sample equilibration at 70 °C with agitation, 40 min sample adsorption on fiber at 70 °C with agitation at a penetration depth of 40 mm followed by 35 min of sample desorption in the inlet at 250°C. The electron impact ionisation energy (EI) was set to 70 eV and the ion source temperature was 230°C. Identities of the apocarotenoids were determined by fragmentation patterns and retention times by comparison with authentical analytical apocarotenoid standards in each species. For that purpose, total ion chromatograms (TIC) were recorded (m/z 40 – 300). All apocarotenoids were confirmed in all organisms by this measure except 6-methyl-5-hepten-2-one which was only detectable in selective ion monitoring (SIM) in Zc and Pp . For quantification SIM was used and recovery rates were accessed by co-injection and co-calibration (of each standard) with β-IO-D 3 (Suppl. Fig. 5); for respective chromatograms and fragmentation patterns, see Suppl. Figs. 6 to 12. Ions for quantification of the respective molecules: 6-methyl-5-hepten-2-one (m/Z: 108), β-cyclocitral (m/Z: 137) and β-IO (m/Z: 177), β-IO-D 3 (m/Z: 179 + 180), and dihydroactinidiolide (m/Z: 111). The remaining refrozen samples used beforehand for carotenoid quantification (see above carotenoid and chlorophyll extraction) were weighted swiftly into brown glass HS-SPME-vials (20 mL, magnetic caps with silicone/PTFE septa) in a temperature-controlled room at 4 °C in the dark (only indirect light with an intensity below detection limit) to prevent alterations by carotenoid degradation and co-injected with 4 μL β-IO-D 3 standard solution (same amount and concentration as for co-calibration). To reduce contamination by production residues and other volatiles in the HS-SPME-vials, they were precleaned as follows: Vials and caps were washed two times with methanol (≥ 99.9%) and dried overnight at 80 °C to emit remaining volatiles. After cooled down the vials were closed with the caps and stored until analysis. RNA extraction The RNA extraction was based on Dadras et al. 11 and slightly optimized for each organism. In general, the protocol described by the vendor of Spectrum Plant Total RNA Kit (Sigma) was used. Changes are described in the following. Frozen Me SAG 12.97 samples were put on ice, mixed immediately with 500 μL lysis buffer containing 2-mercaptoethanol (10 μL/mL lysis buffer), vortexed briefly and transferred for one minute to an ultrasonic bath for optimal cell penetration followed by 5 min of heat shock (56 °C). Protocol B (increased binding solution (750 μL)) was continued as described by vendor. For Pp extraction, the same protocol was used as for Me but with 1 mL lysis buffer and four minutes of ultrasonic bath. Frozen Zc SAG 698-1b samples were lyophilized freshly before extraction for 18-20 h (ZIRBUS technology GmbH, Bad Grund, Germany), frozen again in liquid N 2 , put on ice, mixed immediately with 1 mL lysis buffer, vortexed briefly and transferred for four minutes to an ultrasonic bath for optimal cell penetration followed by 5 minutes of heat shock. Protocol B was continued as described by vendor. Functional annotation In order to assign functional information to the sequences we employed an comprehensive set of tools including InterProScan 94 (v5.64-96.0 and -pa -goterms flags), eggNOG-mapper 95 – 97 (v2.1.12 and -m diamond--dmnd_iterate yes--dbmem --cpu 0 --evalue 1e-10 --sensmode ultra-sensitive --tax_scope 33090 --dmnd_db eggnog_proteins_default_viridiplantae.dmnd flags), BLAST 98 (v2.15.0) against protein files of A. thaliana 99 and genome scale gene family analysis using Orthofinder 100 – 103 (v2.5.5). Me , Zc SAG698-1b and Pp had 74.5, 93.9, and 78.1% of their genes in HOGs and they have 8.2, 2.2, 12% or their genes in species-specific orthogroups, respectively. We first ran Orthofinder with these settings: -S diamond -M msa -A mafft -T fasttree -t 200 -a 6 -y. Building on this, we redid the analysis by providing a user-defined rooted species tree to increase the accuracy of the inference and this tree includes the following species: Anthoceros agrestis oxford 104 , Azolla filiculoides 105 , A. thaliana 99 , Brachypodium distachyon 106 , Chara braunii 6 , Chlorokybus melkonianii 9 , 107 , Chlamydomonas reinhardtii 108 , Closterium sp. NIES-67 109 , Klebsormidium nitens 5 , Mesotaenium endlicherianum 7 , 11 , Marchantia polymorpha 110 , Mesostigma viride 111 , Ostreococcus lucimarinus 112 , Oryza sativa 113 , Prasinoderma coloniale 114 , Penium margaritaceum 115 , Physcomitrium patens 39 , Solanum lycopersicum 116 , Selaginella moellendorffii 117 , Spirogloea muscicola 7 , Zygnema circumcarinatum 10 SAG 698-1a, Z. circumcarinatum 10 SAG 698-1b, Z. circumcarinatum 10 UTEX 1559, Z. circumcarinatum 10 UTEX 1560, and Zea mays 118 . To assign GO terms to each gene, we combined the functional annotation of InterProScan and eggNOG-mapper into a table for each species using the ontologyIndex package (v2.11) 119 . We also used Tapscan (v2) 120 to identify transcription factors for each species. Quality control and gene expression quantification and exploratory data analysis We used the pipeline of Dadras et al. 11 built using Snakemake (v7.7.0) 121 and available on GitHub ( https://github.com/dadrasarmin/rnaseq_quantification_kallisto_pipeline ). Briefly, we used FastQC (v0.12.1) 122 and MultiQC (v1.16) 123 to perform quality control, Trimmomatic (v0.39) 124 to perform trimming and filtering, and Kallisto (v0.48.0) 125 to quantify gene expressions. We used R (v4.3.2) 126 and tidyverse (v2.0.0) 127 for data analysis and visualization. We used tximport (v1.30.0) 128 to import and summarize count tables at gene-level into R and normalized count tables for both sequencing depth and gene length using the following settings: “countsFromAbundance = “lengthScaledTPM”, txOut = F”. We used edgeR (v4.0.6) 129 to keep only genes with expression levels higher than 10 counts-per-milion (CPM) in at least 3 samples. Based on the experimental design of this study, we chose to perform global normalization (quantile normalization) to remove technical unwanted variations in our dataset 130 . We used qsmooth (v1.18.0) 131 with treatments as group_factor to perform the normalization. Differential gene expression analysis We used limma (v3.58.1) 132 to model gene expression changes under each treatment samples compared to the same time point in the control condition using lmFit, contrasts.fit, eBayes, decideTests functions. We picked |log2(fold change)| >= 1 as well as Benjamini-Hochberg method for p-value adjustment and a threshold of 0.05 to determine differentially expressed genes (DEGs). We used GO-gene tables that we prepared in the functional annotation step to perform Over-Representation Analysis (ORA) using clusterProfiler (v4.10.0) 133 . In this section, we only focused on “Biological Process” domain of GO terms, using only expressed genes in our dataset as background, adjusted p. value cut off <= 0.05 and q. value cut off <= 0.05 for enrichment analysis. To visualize the general pattern of GO term enrichment over-time under each treatment, we used alluvial (v0.2.0) 134 and picked top 10 GO terms that are enriched in as many as possible time points and sorted them on Y-axis based on the enriched gene count of the GO term. The thickness of each stratum is visualized based on the number of enriched genes in each GO term. Co-expression network analysis It is well known that gene co-expression methods, each with its own strengths and weaknesses, can lead the different final networks 135 . In this study, we used two methods from different classes of co-expression network analysis. First, we use Weighted Gene Co-expression Network Analysis (WGCNA v1.72.5) 136 to infer one network from all treatments and time points per species. In this method, correlation measures are used to calculate an adjacency matrix using a beta and a network type. Next, the topological overlap matrix is calculated based on the adjacency matrix, then a distance matrix will be calculated and using hierarchical clustering genes will be divided into various modules. Finally, modules that are very similar based on their Eigenvalues will be merged. We followed the authors’ recommendations for the parameters for this last step. In summary, we screened soft-thresholding powers from 1 to 50 for each species and picked a soft threshold based on mean connectivity (around 50), median connectivity (around 20), and signed R 2 of Scale free topology model fit (above 0.8). We picked 20, 20, 14 as soft threshold for Me , Pp , and Zc . We built our networks using the following settings: Merging threshold=0.20, correlation method= biweight midcorrelation, network type=signed, TOMType=signed, minimum module size=30, and maximum percentile of outliers=0.05. We calculated Pearson’s correlation coefficient and gene significance based on module’s Eigengene values and various physiological measurements and metabolite concentration changes. We also calculated inter- and intra-modular connectivity for each module and picked top 20 highly connected genes as the hubs of that module. For each module, we performed GO enrichment analysis similar to the differential gene expression analysis mentioned above. Biological theme comparison plots were made using clusterProfiler to discover patterns of GO enrichment among different modules. We used igraph (v1.6.0) 137 to visualize co-expression network for each module and annotate the hubs. We annotated hubs based on the blast results described in the functional annotation above in this order: (a) gene symbol > (b) A. thaliana best hit gene ID > (c) species gene ID. The second method is the Dirichlet Process Gaussian Process mixture model (DPGP), a non-parametric model-based method that is designed to perform gene co-expression analysis for time series datasets. It solves the problem of the number of clusters using a Dirichlet process and then model the dependencies in gene expression profiles between time points using a Gaussian model 44 . We used fold change values that has a significant adjusted p-value ≤ 0.05 compared to the same time point in control as the input of the software. Due to assumptions of this method, we had to make one network per species ( Me, Zc, Pp ) and per treatment (cold, heat, HL); nine networks in total. We visualized expression profiles and performed GO enrichment analysis as mentioned above. The authors of DPGP suggested that this tool can be used to look for tightly regulated genes by filtering for gene assignments to clusters with a specific threshold in the final probability. We picked probability ≥ 0.7 as our threshold as suggest by the DPGP authors and compared inter- and intra-species similarities between clusters using Jaccard distance. We picked a collection of the most similar filtered gene clusters based on Jaccard distances to investigate further. We normalized the data between 0 to 1 to visualize it as a heatmap. Also, we put a minimum cap of 0.9 Jaccard distance for both clustering methods since the heatmap was not informative due to presence of few outliers in pair-wise combinations (very close clusters). Gene regulatory network (GRN) inference There are various methods to calculate GRN based on time series transcriptomics but the balance between run time and accuracy makes it hard to pick a gold standard among all methods. Here, we used Sliding Window Inference for Network Generation (SWING) 45 to account for our temporal information which is one of the best method for this purpose according to independent benchmarkings 138 . SWING uses a multivariate Granger causality to infer network topology from time series data. We combined the transcriptomics data with metabolite concentrations as inputs and used the Random Forest (RF, i.e. SWING-RF) method to infer the network which has the best performance compared to LASSO and PLSR in the benchmarking 138 . The parameters that should be defined to infer the network were decided based on the best practice that was suggested by the authors of SWING as follows; For Me, we had more metabolite data and we picked: minimum lag= 0, maximum lag = 1, fixed-length of sliding window = 4 and number of trees = 500. For Pp and Zc, we picked these parameters: minimum lag = 0, maximum lag = 1, fixed-length of sliding window = 2 and number of tress = 500. We performed Z-score transformation on the input datasets. To integrate scores from many windows and delays into a single score (regulator-regulated pairs), we utilized this package’s mean-mean aggregation approach. Confidence values from windowed subsets are combined into a single network by calculating the mean rank of the edge at each delay k, followed by the average rank of the edge over all delays. The outcome of this method is a ranked list of all possible pairs ordered from the most to the less confident one. We first filtered out pairs with 0 support, extracted the top 0.1%, and visualized the result via igraph. Since it was still a very big network, then investigated the network with more filtering. Basically we created file list based on keywords downloaded from TAIR. (a) Cold consists of “cold acclimation”, “response to cold”, and “cellular response to cold” (b) Heat consists of “response to heat”, “heat acclimation”, “cellular response to heat”, and “cellular heat acclimation” (c) HL consists of “response to high light intensity”, and “cellular response to high light intensity” (d) Oxidative consists of “response to oxidative stress”, “cellular response to oxidative stress”, “cellular response to reactive oxygen species”, “response to photooxidative stress”, and “regulation of response to oxidative stress” (e) “Carotenoid metabolic process” (f) “Apocarotenoid metabolic process”. We then used the BLAST results mentioned above to find possible homologs of these genes in our species of interest. We used these gene sets as well as metabolite list and TF list extracted using TapScan to look for top 0.1% edges of GRN for each of these subsets. We used igraph to visualize the data and annotate the top 100 nodes in the edge list as explained in the co-expression network section. Data availability All RNAseq reads have been uploaded to NCBI SRA and can be accessed under Bioproject PRJNA895341 ( Mesotaenium ) and PRJNA939006 ( Zygnema and Physcomitrium ) and SRA accessions from SRR22077315 to SRR22077409 ( Mesotaenium ) and from SRR23625966 to SRR23626145 ( Zygnema and Physcomitrium ). Code is available here: https://gitlab.gwdg.de/armin.dadras/time-resolved-oxidative-signal-convergence-across-the-algae-embryophyte-divide Raw metabolite profiling data are available on Zenodo: 10.5281/zenodo.10805605 Data can be interactively explored at https://rshiny.gwdg.de CONTRIBUTIONS J.d.V. conceived the project. J.d.V. coordinated the project with I.F., J.d.V. and T.P.R. designed the experiments. T.P.R., T.D., S.P., N.H., C.H., S.d.V., I.I., and T.P. performed experimental work. A.D. and J.d.V. designed the computational analysis. A.D. carried out computational analysis. T.P.R. and C.H. performed analytics. C.H. and I.F. supervised the analytics. R.P. and S.A.R. predicted streptophyte transcription factors. J.d.V., A.D., and T.P.R. contributed to writing the manuscript. J.d.V. organized and wrote the final manuscript. All authors commented, discussed, and provided input on the final manuscript. COMPETING INTERESTS The authors declare no competing interests. ACKNOWLEDGEMENTS We thank René Heise, Sabine Freitag, Malte Bürsing, and Tarek Morsi for excellent technical support and Greta Lisbach for help during experimentation. J.d.V. thanks the European Research Council for funding under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 852725; ERC-StG “TerreStriAL”). J.d.V. and I.F. are grateful for support through the German Research Foundation (DFG): on the grant SHOAL (514060973; VR132/11-1) and within the framework of the Priority Programme “MAdLand – Molecular Adaptation to Land: Plant Evolution to Change” (SPP 2237; 440231723 VR 132/4-1; VR 132/4-2; FE 446/14-1), in which T.R. is a PhD student and A.D., J.M.R.F.-J, and I.I. partake as associate members. A.D. is grateful for being supported through the International Max Planck Research School (IMPRS) for Genome Science. J.M.R.F.-J. and T.R. gratefully acknowledge support by the Ph.D. program “Microbiology and Biochemistry” within the framework of the “Göttingen Graduate Center for Neurosciences, Biophysics, and Molecular Biosciences” (GGNB) at the University of Goettingen. REFERENCES ↵ Bar-On , Y. M. , Phillips , R. & Milo , R . The biomass distribution on Earth . Proc Natl Acad Sci U S A 115 , 6506 – 6511 ( 2018 ). doi: 10.1073/pnas.1711842115 OpenUrl Abstract / FREE Full Text ↵ Initiative , O. T. P. T . One thousand plant transcriptomes and the phylogenomics of green plants . Nature 574 , 679 – 685 ( 2019 ). OpenUrl ↵ Wickett , N. J. et al. Phylotranscriptomic analysis of the origin and early diversification of land plants . Proceedings of the National Academy of Sciences 111 , E4859 – E4868 ( 2014 ). OpenUrl Abstract / FREE Full Text ↵ Puttick , M. N. et al. The interrelationships of land plants and the nature of the ancestral embryophyte . Current Biology 28 , 733 – 745 . e732 ( 2018 ). OpenUrl CrossRef PubMed ↵ Hori , K. et al. Klebsormidium flaccidum genome reveals primary factors for plant terrestrial adaptation . Nature communications 5 , 3978 ( 2014 ). OpenUrl ↵ Nishiyama , T. et al. The Chara genome: secondary complexity and implications for plant terrestrialization . Cell 174 , 448 – 464 . e424 ( 2018 ). OpenUrl CrossRef PubMed ↵ Cheng , S. et al. Genomes of subaerial Zygnematophyceae provide insights into land plant evolution . Cell 179 , 1057 – 1067 . e1014 ( 2019 ). OpenUrl CrossRef PubMed Jiao , C. et al. The Penium margaritaceum genome: hallmarks of the origins of land plants . Cell 181 , 1097 – 1111 . e1012 ( 2020 ). OpenUrl CrossRef PubMed ↵ Wang , S. et al. Genomes of early-diverging streptophyte algae shed light on plant terrestrialization . Nature Plants 6 , 95 – 106 ( 2020 ). OpenUrl ↵ Feng , X. et al. Chromosome-level genomes of multicellular algal sisters to land plants illuminate signaling network evolution . bioRxiv ( 2023 ). doi: 10.1101/2023.01.31.526407 OpenUrl Abstract / FREE Full Text ↵ Dadras , A. et al. Environmental gradients reveal stress hubs pre-dating plant terrestrialization . Nature Plants ( 2023 ). doi: 10.1038/s41477-023-01491-0 OpenUrl CrossRef ↵ Donoghue , P. C. J. , Harrison , C. J. , Paps , J. & Schneider , H . The evolutionary emergence of land plants . Curr Biol 31 , R1281 – R1298 ( 2021 ). doi: 10.1016/j.cub.2021.07.038 OpenUrl CrossRef ↵ Mulvey , H. & Dolan , L . RHO of plant signaling was established early in streptophyte evolution . Curr Biol ( 2023 ). doi: 10.1016/j.cub.2023.11.007 OpenUrl CrossRef ↵ Bierenbroodspot , M. J. et al. Phylogenomic insights into the first multicellular streptophyte . Current Biology 34 ( 2024 ). doi: 10.1016/j.cub.2023.12.070 OpenUrl CrossRef ↵ Bowman , J. L. , Sakakibara , K. , Furumizu , C. & Dierschke , T . Evolution in the Cycles of Life . Annu Rev Genet 50 , 133 – 154 ( 2016 ). doi: 10.1146/annurev-genet-120215-035227 OpenUrl CrossRef PubMed ↵ Delaux , P. M. et al. Algal ancestor of land plants was preadapted for symbiosis . Proc Natl Acad Sci U S A 112 , 13390 – 13395 ( 2015 ). doi: 10.1073/pnas.1515426112 OpenUrl Abstract / FREE Full Text ↵ Delaux , P. M. & Schornack , S . Plant evolution driven by interactions with symbiotic and pathogenic microbes . Science 371 ( 2021 ). doi: 10.1126/science.aba6605 OpenUrl Abstract / FREE Full Text ↵ Fürst-Jansen , J. M. , de Vries , S. & de Vries , J . Evo-physio: on stress responses and the earliest land plants . Journal of Experimental Botany 71 , 3254 – 3269 ( 2020 ). OpenUrl CrossRef ↵ Scheres , B. & van der Putten , W. H . The plant perceptron connects environment to development . Nature 543 , 337 – 345 ( 2017 ). doi: 10.1038/nature22010 OpenUrl CrossRef PubMed ↵ Glasser , C. et al. Meta-analysis of retrograde signaling in Arabidopsis thaliana reveals a core module of genes embedded in complex cellular signaling networks . Mol Plant 7 , 1167 – 1190 ( 2014 ). doi: 10.1093/mp/ssu042 OpenUrl CrossRef PubMed Web of Science Pfannschmidt , T . Chloroplast redox signals: how photosynthesis controls its own genes . Trends Plant Sci 8 , 33 – 41 ( 2003 ). doi: 10.1016/s1360-1385(02)00005-5 OpenUrl CrossRef PubMed Web of Science ↵ Kleine , T. et al. Acclimation in plants - the Green Hub consortium . Plant J 106 , 23 – 40 ( 2021 ). doi: 10.1111/tpj.15144 OpenUrl CrossRef ↵ Nisar , N. , Li , L. , Lu , S. , Khin , N. C. & Pogson , B. J . Carotenoid metabolism in plants . Mol Plant 8 , 68 – 82 ( 2015 ). doi: 10.1016/j.molp.2014.12.007 OpenUrl CrossRef PubMed ↵ Sandmann , G . Diversity and origin of carotenoid biosynthesis: its history of coevolution towards plant photosynthesis . New Phytol 232 , 479 – 493 ( 2021 ). doi: 10.1111/nph.17655 OpenUrl CrossRef ↵ Hou , X. , Rivers , J. , Leon , P. , McQuinn , R. P. & Pogson , B. J . Synthesis and Function of Apocarotenoid Signals in Plants . Trends Plant Sci 21 , 792 – 803 ( 2016 ). doi: 10.1016/j.tplants.2016.06.001 OpenUrl CrossRef ↵ Moreno , J. C. , Mi , J. , Alagoz , Y. & Al-Babili , S . Plant apocarotenoids: from retrograde signaling to interspecific communication . Plant J 105 , 351 – 375 ( 2021 ). doi: 10.1111/tpj.15102 OpenUrl CrossRef ↵ Ramel , F. et al. Carotenoid oxidation products are stress signals that mediate gene responses to singlet oxygen in plants . Proc Natl Acad Sci U S A 109 , 5535 – 5540 ( 2012 ). doi: 10.1073/pnas.1115982109 OpenUrl Abstract / FREE Full Text ↵ D’Alessandro , S. , Mizokami , Y. , Legeret , B. & Havaux , M . The Apocarotenoid beta-Cyclocitric Acid Elicits Drought Tolerance in Plants . iScience 19 , 461 – 473 ( 2019 ). doi: 10.1016/j.isci.2019.08.003 OpenUrl CrossRef ↵ Lundquist , P. K. et al. Loss of plastoglobule kinases ABC1K1 and ABC1K3 causes conditional degreening, modified prenyl-lipids, and recruitment of the jasmonic acid pathway . Plant Cell 25 , 1818 – 1839 ( 2013 ). doi: 10.1105/tpc.113.111120 OpenUrl Abstract / FREE Full Text Mitra , S. et al. Negative regulation of plastidial isoprenoid pathway by herbivore-induced beta-cyclocitral in Arabidopsis thaliana . Proc Natl Acad Sci U S A 118 ( 2021 ). doi: 10.1073/pnas.2008747118 OpenUrl Abstract / FREE Full Text ↵ Felemban , A. et al. The apocarotenoid beta-ionone regulates the transcriptome of Arabidopsis thaliana and increases its resistance against Botrytis cinerea . Plant J ( 2023 ). doi: 10.1111/tpj.16510 OpenUrl CrossRef ↵ Nambara , E. & Marion-Poll , A. Abscisic acid biosynthesis and catabolism . Annu Rev Plant Biol 56 , 165 – 185 ( 2005 ). doi: 10.1146/annurev.arplant.56.032604.144046 OpenUrl CrossRef PubMed ↵ Cutler , S. R. , Rodriguez , P. L. , Finkelstein , R. R. & Abrams , S. R . Abscisic acid: emergence of a core signaling network . Annu Rev Plant Biol 61 , 651 – 679 ( 2010 ). doi: 10.1146/annurev-arplant-042809-112122 OpenUrl CrossRef PubMed Web of Science ↵ Barbier , F. , Fichtner , F. & Beveridge , C . The strigolactone pathway plays a crucial role in integrating metabolic and nutritional signals in plants . Nat Plants 9 , 1191 – 1200 ( 2023 ). doi: 10.1038/s41477-023-01453-6 OpenUrl CrossRef ↵ Kleine , T. & Leister , D . Retrograde signaling: Organelles go networking . Biochim Biophys Acta 1857 , 1313 – 1325 ( 2016 ). doi: 10.1016/j.bbabio.2016.03.017 OpenUrl CrossRef PubMed ↵ Havaux , M. beta-Cyclocitral and derivatives: Emerging molecular signals serving multiple biological functions . Plant Physiol Biochem 155 , 35 – 41 ( 2020 ). doi: 10.1016/j.plaphy.2020.07.032 OpenUrl CrossRef ↵ D’Alessandro , S. & Havaux , M . Sensing beta-carotene oxidation in photosystem II to master plant stress tolerance . New Phytol 223 , 1776 – 1783 ( 2019 ). doi: 10.1111/nph.15924 OpenUrl CrossRef ↵ Lenton , T. M. et al. Earliest land plants created modern levels of atmospheric oxygen . Proc Natl Acad Sci U S A 113 , 9704 – 9709 ( 2016 ). doi: 10.1073/pnas.1604787113 OpenUrl Abstract / FREE Full Text ↵ Lang , D. et al. The Physcomitrella patens chromosome-scale assembly reveals moss genome structure and evolution . Plant J 93 , 515 – 533 ( 2018 ). doi: 10.1111/tpj.13801 OpenUrl CrossRef PubMed ↵ Hess , S. et al. A phylogenomically informed five-order system for the closest relatives of land plants . Current Biology 32 , 4473 – 4482 . e4477 ( 2022 ). OpenUrl ↵ de Vries , J. et al. Heat stress response in the closest algal relatives of land plants reveals conserved stress signaling circuits . The Plant Journal 103 , 1025 – 1048 ( 2020 ). OpenUrl ↵ Shumbe , L. , Bott , R. & Havaux , M . Dihydroactinidiolide, a high light-induced beta-carotene derivative that can regulate gene expression and photoacclimation in Arabidopsis . Mol Plant 7 , 1248 – 1251 ( 2014 ). doi: 10.1093/mp/ssu028 OpenUrl CrossRef PubMed ↵ Hare , P. D. , Moller , S. G. , Huang , L. F. & Chua , N. H . LAF3, a novel factor required for normal phytochrome A signaling . Plant Physiol 133 , 1592 – 1604 ( 2003 ). doi: 10.1104/pp.103.028480 OpenUrl Abstract / FREE Full Text ↵ McDowell , I. C. et al. Clustering gene expression time series data using an infinite Gaussian process mixture model . PLoS computational biology 14 , e1005896 ( 2018 ). OpenUrl ↵ Finkle , J. D. , Wu , J. J. & Bagheri , N . Windowed Granger causal inference strategy improves discovery of gene regulatory networks . Proc Natl Acad Sci U S A 115 , 2252 – 2257 ( 2018 ). doi: 10.1073/pnas.1710936115 OpenUrl Abstract / FREE Full Text ↵ Yuan , F. et al. OSCA1 mediates osmotic-stress-evoked Ca2+ increases vital for osmosensing in Arabidopsis . Nature 514 , 367 – 371 ( 2014 ). doi: 10.1038/nature13593 OpenUrl CrossRef PubMed Web of Science ↵ Takahashi , S. , Ogawa , T. , Inoue , K. & Masuda , T . Characterization of cytosolic tetrapyrrole-binding proteins in Arabidopsis thaliana . Photochem Photobiol Sci 7 , 1216 – 1224 ( 2008 ). doi: 10.1039/b802588f OpenUrl CrossRef PubMed Web of Science ↵ Lee , H. J. , Mochizuki , N. , Masuda , T. & Buckhout , T. J . Disrupting the bimolecular binding of the haem-binding protein 5 (AtHBP5) to haem oxygenase 1 (HY1) leads to oxidative stress in Arabidopsis . J Exp Bot 63 , 5967 – 5978 ( 2012 ). doi: 10.1093/jxb/ers242 OpenUrl CrossRef PubMed ↵ Xing , Q. et al. ZHOUPI controls embryonic cuticle formation via a signalling pathway involving the subtilisin protease ABNORMAL LEAF-SHAPE1 and the receptor kinases GASSHO1 and GASSHO2 . Development 140 , 770 – 779 ( 2013 ). doi: 10.1242/dev.088898 OpenUrl Abstract / FREE Full Text ↵ Tanaka , H. et al. A subtilisin-like serine protease is required for epidermal surface formation in Arabidopsis embryos and juvenile plants . Development 128 , 4681 – 4689 ( 2001 ). doi: 10.1242/dev.128.23.4681 OpenUrl Abstract / FREE Full Text ↵ Murase , K. , Hirano , Y. , Sun , T. P. & Hakoshima , T . Gibberellin-induced DELLA recognition by the gibberellin receptor GID1 . Nature 456 , 459 – 463 ( 2008 ). doi: 10.1038/nature07519 OpenUrl CrossRef PubMed Web of Science ↵ Jeon , J. et al. A subset of cytokinin two-component signaling system plays a role in cold temperature stress response in Arabidopsis . J Biol Chem 285 , 23371 – 23386 ( 2010 ). doi: 10.1074/jbc.M109.096644 OpenUrl Abstract / FREE Full Text ↵ Tran , L. S. et al. Functional analysis of AHK1/ATHK1 and cytokinin receptor histidine kinases in response to abscisic acid, drought, and salt stress in Arabidopsis . Proc Natl Acad Sci U S A 104 , 20623 – 20628 ( 2007 ). doi: 10.1073/pnas.0706547105 OpenUrl Abstract / FREE Full Text ↵ Bowman , J. L. , Briginshaw , L. N. , Fisher , T. J. & Flores-Sandoval , E . Something ancient and something neofunctionalized—evolution of land plant hormone signaling pathways . Current Opinion in Plant Biology 47 , 64 – 72 ( 2019 ). OpenUrl ↵ Van de Poel , B. & de Vries , J . Evolution of ethylene as an abiotic stress hormone in streptophytes . Environ Exp Bot 214 , 105456 ( 2023 ). doi: 10.1016/j.envexpbot.2023.105456 OpenUrl CrossRef ↵ Van de Poel , B. , Cooper , E. D. , Van Der Straeten , D. , Chang , C. & Delwiche , C. F . Transcriptome profiling of the green alga Spirogyra pratensis (Charophyta) suggests an ancestral role for ethylene in cell wall metabolism, photosynthesis, and abiotic stress responses . Plant Physiology 172 , 533 – 545 ( 2016 ). OpenUrl Abstract / FREE Full Text ↵ Ju , C. et al. Conservation of ethylene as a plant hormone over 450 million years of evolution . Nature plants 1 , 14004 ( 2015 ). OpenUrl ↵ Yasumura , Y. , Pierik , R. , Fricker , M. D. , Voesenek , L. A. & Harberd , N. P . Studies of Physcomitrella patens reveal that ethylene-mediated submergence responses arose relatively early in land-plant evolution . Plant J 72 , 947 – 959 ( 2012 ). doi: 10.1111/tpj.12005 OpenUrl CrossRef PubMed Web of Science ↵ O’Malley , R. C. et al. Ethylene-binding activity, gene expression levels, and receptor system output for ethylene receptor family members from Arabidopsis and tomato . Plant J 41 , 651 – 659 ( 2005 ). doi: 10.1111/j.1365-313X.2004.02331.x OpenUrl CrossRef PubMed Web of Science ↵ Yu , K. et al. The Kelch-F-box protein SMALL AND GLOSSY LEAVES 1 (SAGL1) negatively influences salicylic acid biosynthesis in Arabidopsis thaliana by promoting the turn-over of transcription factor SYSTEMIC ACQUIRED RESISTANCE DEFICIENT 1 (SARD1) . New Phytol 235 , 885 – 897 ( 2022 ). doi: 10.1111/nph.18197 OpenUrl CrossRef ↵ Yu , S. I. , Kim , H. , Yun , D. J. , Suh , M. C. & Lee , B. H . Post-translational and transcriptional regulation of phenylpropanoid biosynthesis pathway by Kelch repeat F-box protein SAGL1 . Plant Mol Biol 99 , 135 – 148 ( 2019 ). doi: 10.1007/s11103-018-0808-8 OpenUrl CrossRef ↵ Harmon , F. G. & Kay , S. A . The F box protein AFR is a positive regulator of phytochrome A-mediated light signaling . Curr Biol 13 , 2091 – 2096 ( 2003 ). doi: 10.1016/j.cub.2003.11.019 OpenUrl CrossRef PubMed Web of Science ↵ Shumbe , L. et al. METHYLENE BLUE SENSITIVITY 1 (MBS1) is required for acclimation of Arabidopsis to singlet oxygen and acts downstream of beta-cyclocitral . Plant Cell Environ 40 , 216 – 226 ( 2017 ). doi: 10.1111/pce.12856 OpenUrl CrossRef ↵ Atanasov , V. et al. Arabidopsis BBX14 is involved in high light acclimation and seedling development . Plant J ( 2023 ). doi: 10.1111/tpj.16597 OpenUrl CrossRef Zhang , Z. et al. CONSTANS-LIKE 7 (COL7) is involved in phytochrome B (phyB)-mediated light-quality regulation of auxin homeostasis . Mol Plant 7 , 1429 – 1440 ( 2014 ). doi: 10.1093/mp/ssu058 OpenUrl CrossRef PubMed ↵ Liu , L. J. et al. COP1-mediated ubiquitination of CONSTANS is implicated in cryptochrome regulation of flowering in Arabidopsis . Plant Cell 20 , 292 – 306 ( 2008 ). doi: 10.1105/tpc.107.057281 OpenUrl Abstract / FREE Full Text ↵ Muller , M. & Munne-Bosch , S . Ethylene Response Factors: A Key Regulatory Hub in Hormone and Stress Signaling . Plant Physiol 169 , 32 – 41 ( 2015 ). doi: 10.1104/pp.15.00677 OpenUrl Abstract / FREE Full Text ↵ Toriyama , T. et al. Sensor histidine kinases mediate ABA and osmostress signaling in the moss Physcomitrium patens . Curr Biol 32 , 164 – 175 e168 ( 2022 ). doi: 10.1016/j.cub.2021.10.068 OpenUrl CrossRef ↵ Song , Z. T. , Zhang , L. L. , Han , J. J. , Zhou , M. & Liu , J. X . Histone H3K4 methyltransferases SDG25 and ATX1 maintain heat-stress gene expression during recovery in Arabidopsis . Plant J 105 , 1326 – 1338 ( 2021 ). doi: 10.1111/tpj.15114 OpenUrl CrossRef ↵ Meng , X. & Zhang , S . MAPK cascades in plant disease resistance signaling . Annu Rev Phytopathol 51 , 245 – 266 ( 2013 ). doi: 10.1146/annurev-phyto-082712-102314 OpenUrl CrossRef PubMed Web of Science Hohmann , U. , Lau , K. & Hothorn , M . The Structural Basis of Ligand Perception and Signal Activation by Receptor Kinases . Annu Rev Plant Biol 68 , 109 – 137 ( 2017 ). doi: 10.1146/annurev-arplant-042916-040957 OpenUrl CrossRef ↵ Hoang , X. L. T. et al. Histidine Kinases: Diverse Functions in Plant Development and Responses to Environmental Conditions . Annu Rev Plant Biol 72 , 297 – 323 ( 2021 ). doi: 10.1146/annurev-arplant-080720-093057 OpenUrl CrossRef ↵ Dievart , A. , Gottin , C. , Perin , C. , Ranwez , V. & Chantret , N . Origin and Diversity of Plant Receptor-Like Kinases . Annu Rev Plant Biol 71 , 131 – 156 ( 2020 ). doi: 10.1146/annurev-arplant-073019-025927 OpenUrl CrossRef ↵ Meyer , H. M. , Hotta , T. , Malkovskiy , A. , Zheng , Y. & Ehrhardt , D. W . ( 2023 ). doi: 10.1101/2023.11.01.565081 OpenUrl Abstract / FREE Full Text ↵ Dai , S. et al. BROTHER OF LUX ARRHYTHMO is a component of the Arabidopsis circadian clock . Plant Cell 23 , 961 – 972 ( 2011 ). doi: 10.1105/tpc.111.084293 OpenUrl Abstract / FREE Full Text ↵ Shi , L. , Wu , Y. & Sheen , J . TOR signaling in plants: conservation and innovation . Development 145 ( 2018 ). doi: 10.1242/dev.160887 OpenUrl Abstract / FREE Full Text ↵ Burkart , G. M. & Brandizzi , F . A Tour of TOR Complex Signaling in Plants . Trends Biochem Sci 46 , 417 – 428 ( 2021 ). doi: 10.1016/j.tibs.2020.11.004 OpenUrl CrossRef ↵ Uhrig , R. G. , Labandera , A. M. & Moorhead , G. B . Arabidopsis PPP family of serine/threonine protein phosphatases: many targets but few engines . Trends Plant Sci 18 , 505 – 513 ( 2013 ). doi: 10.1016/j.tplants.2013.05.004 OpenUrl CrossRef PubMed Web of Science ↵ Kieber , J. J. & Schaller , G. E . Cytokinin signaling in plant development . Development 145 ( 2018 ). doi: 10.1242/dev.149344 OpenUrl Abstract / FREE Full Text ↵ Schroder , F. , Lisso , J. , Lange , P. & Mussig , C . The extracellular EXO protein mediates cell expansion in Arabidopsis leaves . BMC Plant Biol 9 , 20 ( 2009 ). doi: 10.1186/1471-2229-9-20 OpenUrl CrossRef PubMed ↵ Meteignier , L. V. et al. The Arabidopsis mTERF-repeat MDA1 protein plays a dual function in transcription and stabilization of specific chloroplast transcripts within the psbE and ndhH operons . New Phytol 227 , 1376 – 1391 ( 2020 ). doi: 10.1111/nph.16625 OpenUrl CrossRef ↵ Hammani , K. & Barkan , A . An mTERF domain protein functions in group II intron splicing in maize chloroplasts . Nucleic Acids Res 42 , 5033 – 5042 ( 2014 ). doi: 10.1093/nar/gku112 OpenUrl CrossRef PubMed ↵ Sun , X. , Xu , D. , Liu , Z. , Kleine , T. & Leister , D . Functional relationship between mTERF4 and GUN1 in retrograde signaling . J Exp Bot 67 , 3909 – 3924 ( 2016 ). doi: 10.1093/jxb/erv525 OpenUrl CrossRef PubMed ↵ Rieseberg , T. P. et al. Crossroads in the evolution of plant specialized metabolism . Semin Cell Dev Biol 134 , 37 – 58 ( 2023 ). doi: 10.1016/j.semcdb.2022.03.004 OpenUrl CrossRef ↵ Chen , X. et al. Protein kinases in plant responses to drought, salt, and cold stress . J Integr Plant Biol 63 , 53 – 78 ( 2021 ). doi: 10.1111/jipb.13061 OpenUrl CrossRef ↵ Rieseberg , T. P. et al. Divergent responses in desiccation experiments in two ecophysiologically different Zygnematophyceae . Physiol Plant 175 , e14056 ( 2023 ). doi: 10.1111/ppl.14056 OpenUrl CrossRef ↵ Nichols , H. Handbook of Phycological Methods . 16 – 17 ( Cambridge University Press , 1973 ). ↵ Ashton , N. W. & Cove , D. J . The isolation and preliminary characterisation of auxotrophic and analogue resistant mutants of the moss , Physcomitrella patens. Molecular and General Genetics MGG 154 , 87 – 95 ( 1977 ). doi: 10.1007/bf00265581 OpenUrl CrossRef ↵ Aronsson , H. et al. Monogalactosyldiacylglycerol deficiency in Arabidopsis affects pigment composition in the prolamellar body and impairs thylakoid membrane energization and photoprotection in leaves . Plant Physiol 148 , 580 – 592 ( 2008 ). doi: 10.1104/pp.108.123372 OpenUrl Abstract / FREE Full Text ↵ Gupta , P. , Sreelakshmi , Y. & Sharma , R . A rapid and sensitive method for determination of carotenoids in plant tissues by high performance liquid chromatography . Plant Methods 11 , 5 ( 2015 ). doi: 10.1186/s13007-015-0051-0 OpenUrl CrossRef PubMed ↵ Lin , C. H. & Chen , B. H . Determination of carotenoids in tomato juice by liquid chromatography . J Chromatogr A 1012 , 103 – 109 ( 2003 ). doi: 10.1016/s0021-9673(03)01138-5 OpenUrl CrossRef PubMed ↵ Thrane , J. E. et al. Spectrophotometric Analysis of Pigments: A Critical Assessment of a High-Throughput Method for Analysis of Algal Pigment Mixtures by Spectral Deconvolution . PLoS One 10 , e0137645 ( 2015 ). doi: 10.1371/journal.pone.0137645 OpenUrl CrossRef ↵ Rivers , J. Y. , Truong , T. T. , Pogson , B. J. & McQuinn , R. P . Volatile apocarotenoid discovery and quantification in Arabidopsis thaliana: optimized sensitive analysis via HS-SPME-GC/MS . Metabolomics 15 , 79 ( 2019 ). doi: 10.1007/s11306-019-1529-y OpenUrl CrossRef ↵ Jones , P. et al. InterProScan 5: genome-scale protein function classification . Bioinformatics 30 , 1236 – 1240 ( 2014 ). doi: 10.1093/bioinformatics/btu031 OpenUrl CrossRef PubMed Web of Science ↵ Buchfink , B. , Reuter , K. & Drost , H.-G . Sensitive protein alignments at tree-of-life scale using DIAMOND . Nat Methods 18 , 366 – 368 ( 2021 ). doi: 10.1038/s41592-021-01101-x OpenUrl CrossRef PubMed Cantalapiedra , C. P. , Hernández-Plaza , A. , Letunic , I. , Bork , P. & Huerta-Cepas , J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale . ( Bioinformatics , 2021 ). ↵ Huerta-Cepas , J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses . Nucleic Acids Research 47 , D309 – D314 ( 2019 ). doi: 10.1093/nar/gky1085 OpenUrl CrossRef PubMed ↵ Camacho , C. , et al. BLAST+: architecture and applications . BMC Bioinformatics 10 , 421 ( 2009 ). doi: 10.1186/1471-2105-10-421 OpenUrl CrossRef PubMed ↵ Cheng , C. Y. et al. Araport11: a complete reannotation of the Arabidopsis thaliana reference genome . The Plant Journal 89 , 789 – 804 ( 2017 ). doi: 10.1111/tpj.13415 OpenUrl CrossRef PubMed ↵ Emms , D. M. & Kelly , S . OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy . Genome Biol 16 , 157 ( 2015 ). doi: 10.1186/s13059-015-0721-2 OpenUrl CrossRef PubMed Emms , D. M. & Kelly , S . OrthoFinder: phylogenetic orthology inference for comparative genomics . Genome Biol 20 , 238 ( 2019 ). doi: 10.1186/s13059-019-1832-y OpenUrl CrossRef PubMed Katoh , K. & Standley , D. M . MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability . Molecular Biology and Evolution 30 , 772 – 780 ( 2013 ). doi: 10.1093/molbev/mst010 OpenUrl CrossRef PubMed Web of Science ↵ Price , M. N. , Dehal , P. S. & Arkin , A. P . FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments . PLoS ONE 5 , e9490 ( 2010 ). doi: 10.1371/journal.pone.0009490 OpenUrl CrossRef PubMed ↵ Li , F.-W. et al. Anthoceros genomes illuminate the origin of land plants and the unique biology of hornworts . Nature Plants 6 , 259 – 272 ( 2020 ). doi: 10.1038/s41477-020-0618-2 OpenUrl CrossRef PubMed ↵ Li , F.-W. et al. Fern genomes elucidate land plant evolution and cyanobacterial symbioses . Nature Plants 4 , 460 – 472 ( 2018 ). doi: 10.1038/s41477-018-0188-8 OpenUrl CrossRef ↵ The International Brachypodium , I. Genome sequencing and analysis of the model grass Brachypodium distachyon . Nature 463 , 763 – 768 ( 2010 ). doi: 10.1038/nature08747 OpenUrl CrossRef PubMed Web of Science ↵ Irisarri , I. et al. Unexpected cryptic species among streptophyte algae most distant to land plants . Proc Biol Sci 288 , 20212168 ( 2021 ). doi: 10.1098/rspb.2021.2168 OpenUrl CrossRef ↵ Merchant , S. S. et al. The Chlamydomonas Genome Reveals the Evolution of Key Animal and Plant Functions . Science 318 , 245 – 250 ( 2007 ). doi: 10.1126/science.1143609 OpenUrl Abstract / FREE Full Text ↵ Sekimoto , H. et al. A divergent RWP-RK transcription factor determines mating type in heterothallic Closterium. New Phytologist 237 , 1636 – 1651 ( 2023 ). doi: 10.1111/nph.18662 OpenUrl CrossRef ↵ Montgomery , S. A. et al. Chromatin Organization in Early Land Plants Reveals an Ancestral Association between H3K27me3, Transposons, and Constitutive Heterochromatin . Current Biology 30 , 573 – 588 .e577 ( 2020 ). doi: 10.1016/j.cub.2019.12.015 OpenUrl CrossRef PubMed ↵ Cheng , S. et al. Genomes of Subaerial Zygnematophyceae Provide Insights into Land Plant Evolution . Cell 179 , 1057 – 1067 .e1014 ( 2019 ). doi: 10.1016/j.cell.2019.10.019 OpenUrl CrossRef PubMed ↵ Palenik , B. et al. The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation . Proc. Natl. Acad. Sci. U.S.A . 104 , 7705 – 7710 ( 2007 ). doi: 10.1073/pnas.0611046104 OpenUrl Abstract / FREE Full Text ↵ Ouyang , S. et al. The TIGR Rice Genome Annotation Resource: improvements and new features . Nucleic Acids Research 35 , D883 – D887 ( 2007 ). doi: 10.1093/nar/gkl976 OpenUrl CrossRef PubMed Web of Science ↵ Li , L. et al. The genome of Prasinoderma coloniale unveils the existence of a third phylum within green plants . Nat Ecol Evol 4 , 1220 – 1231 ( 2020 ). doi: 10.1038/s41559-020-1221-7 OpenUrl CrossRef ↵ Jiao , C. et al. The Penium margaritaceum Genome: Hallmarks of the Origins of Land Plants . Cell 181 , 1097 – 1111 .e1012 ( 2020 ). doi: 10.1016/j.cell.2020.04.019 OpenUrl CrossRef PubMed ↵ The Tomato Genome , C. The tomato genome sequence provides insights into fleshy fruit evolution . Nature 485 , 635 – 641 ( 2012 ). doi: 10.1038/nature11119 OpenUrl CrossRef PubMed Web of Science ↵ Banks , J. A. et al. The Selaginella Genome Identifies Genetic Changes Associated with the Evolution of Vascular Plants . Science 332 , 960 – 963 ( 2011 ). doi: 10.1126/science.1203810 OpenUrl Abstract / FREE Full Text ↵ Jiao , Y. et al. Improved maize reference genome with single-molecule technologies . Nature 546 , 524 – 527 ( 2017 ). doi: 10.1038/nature22971 OpenUrl CrossRef PubMed ↵ Greene , D. , Richardson , S. & Turro , E . ontologyX: a suite of R packages for working with ontological data . Bioinformatics 33 , 1104 – 1106 ( 2017 ). doi: 10.1093/bioinformatics/btw763 OpenUrl CrossRef PubMed ↵ Wilhelmsson , P. K. I. , Mühlich , C. , Ullrich , K. K. & Rensing , S. A . Comprehensive Genome-Wide Classification Reveals That Many Plant-Specific Transcription Factors Evolved in Streptophyte Algae . Genome Biology and Evolution 9 , 3384 – 3397 ( 2017 ). doi: 10.1093/gbe/evx258 OpenUrl CrossRef ↵ Mölder , F. et al. Sustainable data analysis with Snakemake . F1000Res 10 , 33 ( 2021 ). doi: 10.12688/f1000research.29032.1 OpenUrl CrossRef PubMed ↵ Andrews , S. ( Babraham Bioinformatics, Babraham Institute, Cambridge, United Kingdom , 2010 ). ↵ Ewels , P. , Magnusson , M. , Lundin , S. & Käller , M . MultiQC: summarize analysis results for multiple tools and samples in a single report . Bioinformatics 32 , 3047 – 3048 ( 2016 ). doi: 10.1093/bioinformatics/btw354 OpenUrl CrossRef PubMed ↵ Bolger , A. M. , Lohse , M. & Usadel , B . Trimmomatic: a flexible trimmer for Illumina sequence data . Bioinformatics 30 , 2114 – 2120 ( 2014 ). doi: 10.1093/bioinformatics/btu170 OpenUrl CrossRef PubMed Web of Science ↵ Bray , N. L. , Pimentel , H. , Melsted , P. & Pachter , L . Near-optimal probabilistic RNA-seq quantification . Nat Biotechnol 34 , 525 – 527 ( 2016 ). doi: 10.1038/nbt.3519 OpenUrl CrossRef PubMed ↵ R Core Team, A. & Team, R. C. ( 2022 ). ↵ Wickham , H. et al. Welcome to the Tidyverse . Journal of open source software 4 , 1686 ( 2019 ). OpenUrl ↵ Soneson , C. , Love , M. I. & Robinson , M. D . Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences . F1000Res 4 ( 2015 ). ↵ Robinson , M. D. , McCarthy , D. J. & Smyth , G. K . edgeR: a Bioconductor package for differential expression analysis of digital gene expression data . bioinformatics 26 , 139 – 140 ( 2010 ). OpenUrl CrossRef PubMed Web of Science ↵ Hicks , S. C. & Irizarry , R. A . Quantro: a data-driven approach to guide the choice of an appropriate normalization method . Genome Biol 16 , 1 – 8 ( 2015 ). OpenUrl CrossRef PubMed ↵ Hicks , S. C. , et al. Smooth quantile normalization . Biostatistics 19 , 185 – 198 ( 2018 ). OpenUrl CrossRef ↵ Ritchie , M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies . Nucleic Acids Research 43 , e47 – e47 ( 2015 ). OpenUrl CrossRef PubMed ↵ Wu , T. , et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data . The innovation 2 ( 2021 ). ↵ Brunson , J. C . Ggalluvial: layered grammar for alluvial plots . Journal of Open Source Software 5 ( 2020 ). ↵ Chowdhury , H. A. , Bhattacharyya , D. K. & Kalita , J. K . (Differential) co-expression analysis of gene expression: a survey of best practices . IEEE/ACM transactions on computational biology and bioinformatics 17 , 1154 – 1173 ( 2019 ). OpenUrl ↵ Langfelder , P. & Horvath , S . WGCNA: an R package for weighted correlation network analysis . BMC Bioinformatics 9 , 1 – 13 ( 2008 ). OpenUrl CrossRef PubMed ↵ Csardi , G. & Nepusz , T . The igraph software package for complex network research . InterJournal, complex systems 1695 , 1 – 9 ( 2006 ). OpenUrl CrossRef ↵ Lu , J. et al. Causal network inference from gene transcriptional time-series response to glucocorticoids . PLoS computational biology 17 , e1008223 ( 2021 ). OpenUrl View the discussion thread. Back to top Previous Next Posted March 13, 2024. 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