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

Rieseberg, Tim P.; Dadras, Armin; Darienko, Tatyana; Post, Sina; Herrfurth, Cornelia; Fürst-Jansen, Janine M. R.; Hohnhorst, Nils; Petroll, Romy; Rensing, Stefan A.; Pröschold, Thomas; de Vries, Sophie; Irisarri, Iker; Feussner, Ivo; de Vries, Jan

doi:10.1038/s41467-025-56939-y

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Published: 19 February 2025

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

Nature Communications volume 16, Article number: 1780 (2025) Cite this article

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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 combine time-course stress profiling using photophysiology, transcriptomics on 2.7 Tbp of data, and metabolite profiling analyses on 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 convergences 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.

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Introduction

Earth’s surface teems with photosynthesizing life. Biodiverse cyanobacteria and algae form green biofilms on rocks and tree barks, 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 streptophytes². Phylogenomic analyses established that the Zygnematophyceae are the closest streptophyte algal relatives of land plants^2,3,4 and comparative genomics have ushered in major progress in establishing a shared catalog of genes for key traits between streptophyte algae and land plants^5,6,7,8,9,10. 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 land¹¹. Several synergistic properties that hinge on a complex genetic chassis have shaped the plants that conquered land¹², including multicellular development^13,14, propagation¹⁵, symbiosis^16,17, and stress response¹⁸. 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 physiology¹⁹. 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 availability¹⁸. 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^20,21,22. 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^{27,28,29,30,31}. The diversity of apocarotenoids is vast, including land plant hormones like abscisic acid (ABA)^32,33 and strigolactones³⁴ but also small volatiles like β-ionone (β-IO) and β-cyclocitral (β-CC) with a growing number of recognized functions^{27,28,29,30,31,35}. 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 radiation³⁸ might have been higher rates of apocarotenogenesis—even 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 circumcarinatum¹⁰ and Mesotaenium endlicherianum^7,11, and the land plant Physcomitrium patens³⁹. Of data on pigment profiles and photophysiology correlations were calculated with time-resolved global gene expression profiles from 270 biological samples in total. Using gene 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 and discussion

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 growth conditions (see Methods; briefly, 20/25 °C, 80–100 μmol photons m⁻² s⁻¹): two representatives of the diverse and species-rich⁴⁰ closest algal relatives of land plants, the filamentous alga Zygnema circumcarinatum¹⁰ SAG 698-1b (henceforth Zc) and the unicellular alga Mesotaenium endlicherianum^7,11 SAG 12.97 (Me) and the bryophyte model Physcomitrium patens³⁹ 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 6 h followed by (ii) 4 h of recovery at standard growth conditions, and 24 h of (iii) low temperature at 12 °C colder and (iv) high temperature at 12 °C warmer than standard growth conditions; 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).

**Fig. 1: A time-series setup for stress kinetics in three 600-million-year-divergent streptophytes.**

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 t₀) across all three species, yielding a total of 270 biological 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 showed the swiftest separation upon temporal progression in all three streptophytes followed by HL for the two algae (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.

**Fig. 2: Divergent temporal stress progression retraced by global differential gene expression analysis.**

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 (Supplementary Fig. 1a), yielding up to more than 3000 significantly differentially expressed genes (Supplementary Fig. 1b), and performed GO term enrichment across time and treatments (Fig. 2c); irrespective of the treatment, more than 50% of the differentially expressed genes fall into the hierarchical orthogroups (HOG) shared by all species (Supplementary Fig. 2a). 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 (Supplementary Fig. 1b). Further, on balance, Zc showed the most delayed response (Fig. 2c)—even in the recovery (Supplementary Fig. 2b); here, Pp appeared to recover the fastest (Supplementary Fig. 2), consistent with its physiological profile (Fig. 1c). This subdued response of Zc (Fig. 2b, c; and in the following) is likely due to its natural ecophysiology and growth, which allows the formation of highly resilient algal mats that even withstand the harsh environments of the High Arctic⁴¹.

Recurrent themes corroborated previous findings^11,42: among the responses, light quality signaling, response to ROS, and photosynthesis-associated processes stuck out (Supplementary Fig. 3; Supplementary Data 1). In Me calcium signaling 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.

Xanthophyll cycle and apocarotenogenesis were the most dynamic in Mesotaenium

Under stress, carotenoid levels adjust and yield apocarotenoid signals through oxidative cleavage²³ (Fig. 3a). To assess their profiles, we combined RP-C₃₀-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 species Zc and Pp than in the unicellular Me (Fig. 3b, c, e); for example, the de-epoxidation state changed after 15 min 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 t₀ (Fig. 3c), suggesting that the overall response is mainly determined by the altered physicochemical stress conditions. Transcript levels of the enzyme-coding genes (e.g., PDS, CRTISO, CYP97A) likely underpinning (apo)carotenogenesis were downregulated, suggesting a feedback loop: 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 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. 3d). The pigment pools of Me and Pp are of similar sizes (compared to Pp, t₀ 95% and 100%) while Zc has a much smaller pool (13%; Fig. 3c)—but it could be that the weight of mucilage skews the values for Zc. The pigment pool of Me was the most dynamic, especially with regard to the ratios of the xanthophylls antheraxanthin (A) + zeaxanthin (Z) to violaxanthin (V) + A + Z (AZ/VAZ) (Fig. 3c, e). Further, the ratio of β-carotene to the apocarotenoids β-CC, β-IO, and dihydroactinidiolide (DHA) had changed after 2 h 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 stress (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 as well as HL, and we observe decreasing transcript levels: in Me and Pp, CCD1 showed the most dynamic response in the first 2 h of treatment, decreasing 3.3- and 5.5-fold in Me upon heat and HL stress (BH-corrected P = 7.63 × 10⁻⁷ and 8.05 × 10⁻¹³) and 3.9-fold in Pp upon heat stress (BH-corrected P = 1.56 × 10⁻¹⁵). MeCCD1 constantly rose during the first 6 h cold stress up to 6.4-fold (BH-corrected P = 1.12 × 10⁻¹¹), while β-CC, β-IO levels reached their peak at 4 h 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^27,43. Overall, we recovered the presence of well-known apocarotenoid signals with the most pronounced stress kinetics in Mesotaenium.

**Fig. 3: Carotenoid and apocarotenoid dynamics upon stress.**

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 13,125, 9445, and 15,778 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 expression patterns of the genes assigned to 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 Jaccard distances (Fig. 4c, d).

**Fig. 4: Unsupervised gene co-expression networks recover shared programs.**

The gene clusters consist of meaningful biological cohorts, such as Medarkred and Ppdarkgrey that are Jaccard-similar (Fig. 4d) and enriched in ribosomal GO-terms, and ribosome component-coding genes that are hubs in these networks (Supplementary Figs. 4 and 5). Physiologically coherent behaviors were recovered. For example, cluster Meyellow shows negative correlation with temperature (r = −0.73, P = 10⁻¹³) and the ratios of AZ/VAZ (r = −0.52, P = 2 × 10⁻⁶), with the Jaccard similar cluster Ppyellow showing a negative correlation with temperature (r = −0.67, P = 10⁻¹⁰) and similar terms and hub genes associated with chloroplast physiology (Fig. 4c; Supplementary Fig. 5) which was as in previous work¹¹ a recurrent theme (Fig. 4k); in contrast, for example, Medarkorange has weak correlation with temperature (r = 0.35, P = 0.003) and light intensity (r = 0.33, P = 0.006; Supplementary Fig. 5). Clusters of Zc seldom show correlations with the tested environmental factors but this is likely due to Zc’s subdued responses. Overall, the clearest correlations are with temperature, where highly Jaccard-similar clusters (Fig. 4c, d) such as Mebrown (Fig. 4e), Ppbrown (Fig. 4f), Zcbrown (Supplementary Fig. 5), and MeTurquoise (Fig. 4g) show strong positive correlations (r = 0.85, P = 5 × 10⁻²³; r = 0.64, P = 10⁻⁹; r = 0.74, P = 2 × 10⁻¹³; and r = 0.39, P = 7 × 10⁻⁴; Fig. 4b) and similar terms across species associated with protein homeostasis.

Several clusters reflect signaling processes, including genes for kinases and HISTIDINE KINASE (AHK) hubs (Fig. 4f, h, i; Supplementary Fig. 5), calcium and Ca²⁺-DEPENDENT KINASE (CDPK) hubs (Fig. 4i, j), photomorphogenesis (Fig. 4h), and links of kinases and carotenogenesis (Fig. 4h). Overall, the 1860 top₂₀ hub genes found among each of the total of 93 clusters computed by WGCNA for all three species belong to 1473 HOGs (Supplementary Data 3). Of these, 1047 (71.1%) are HOGs containing hubs across all three species (Fig. 4l), indicating their shared high connectivity across 600 million years of divergent evolution.

The clearest correlation with the apocarotenoid signals show Pppink and Meblack (r = 0.61, P = 10⁻⁸ and r = 0.41, P = 3 × 10⁻⁴). Pppink is enriched in light intensity including genes for the phytochrome signaling hub LONG AFTER FAR-RED 3 (LAF3)⁴⁴ and oxidoreductase activity including a superoxide dismutase hub (Supplementary Fig. 5). Meblack shows 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 model⁴⁵ (DPGP). Using DPGP, we clustered all 11,670, 9781, and 3887 genes that passed the expression cutoff in Me, Zc, and Pp into 12–16, 13–20, and 11–13 clusters (four types of clusters per condition, i.e. cold, heat, HL, HL + recovery; each set of four for the three species; Supplementary Data 2). We filtered these clusters, retaining only those that had a Gaussian probability of at least 0.7 and calculated their Jaccard distances (Fig. 5a; Supplementary Data 2). As expected, the most similar clusters occurred within a species. However, there are several similar clusters across species, for example, a cohort of genes downregulated upon heat in PpC6 and MeC3 related to photosynthesis (Fig. 5b). Pp showed pronounced dynamics upon heat treatment (cf. Fig. 1d); we explored this a bit further based on temporally clustered Jaccard similar genes that show distinct temporal behaviors and found that Pp apparently responds with an early upregulation of genes enriched for functions in multicellular developmental processes (PpH7; links of morphogenesis, cell–cell interaction, and stress response; Supplementary Fig. 6c–e). 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. Certain DPGP clusters are richer in WGCNA-defined hubs; for example, hubs of Meyellow are mainly in a cohort of early down-regulated genes upon HL MeC1 and Ppyellow shows a similar behavior (chloroplast and photosynthesis; Supplementary Figs. 5d and 6). Early upregulated hubs include those of protein homeostasis in Mebrown.

**Fig. 5: Time-course gene expression clusters pinpoint shared responders.**

Research on model land plants has established a rich framework of genetic cascades that act in information processing and are structured into a hierarchy. To understand how the temporal expression patterns 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)⁴⁶. Note that the predicted GRN has been reconstructed based on Granger causality which is not the same as biological regulatory interactions; it rather reflects whether gene expression changes in one gene are a significant explanatory factor in the expression change of another gene. For SWING-RF, we worked with a subset of 1897, 3694, and 1629 genes and 24 metabolite levels and ratios in Me, Zc, and Pp, predicting 375,273–3,358,660 non-zero interactions between genes (and metabolites), yielding putative gene regulatory networks (GRNs; Supplementary 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; see Methods); in addition, we also investigated the topology of the network if we (i) group genes simply using best BLAST hits (BBH; Supplementary 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 (Fig. 6c; Supplementary Fig. 9), and (iii) applied the same DPGP-based filtering (Fig. 6d; Supplementary Fig. 10). The topology of the predicted GRN highlighted several major points of convergence—hubs (Fig. 7a–c) signified by a high number of in degrees and characterized by high authority scores (Fig. 6a, inset); while the exact upstream and downstream relationships sometimes changed, highly connected hubs were consistently recovered throughout all analyses (cf. Fig. 6a and Supplementary Figs. 7–10; Fig. 7c) and networks followed the behavior of a scale-free network (Supplementary Fig. 11).

**Fig. 6: A predicted gene regulatory network shared across 600 million years of streptophyte evolution.**

**Fig. 7: Robust hubs in the predicted gene regulatory network.**

Physical feedback

Mechanosensitive channels regulate ion flow in response to mechanical cues. We recover homologs coding for proteins of the OSCA family, calcium-permeable channels sensitive to hyperosmolarity⁴⁷, as the 18^th most connected point of convergence (Fig. 6a; Fig. 7a, b; Supplementary Figs. 7–10). Feeding into them is a PDS1-PDS3 network (Fig. 6a), 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 include those with general functions such as homologs of CYTOSOLIC IRON-SULFUR PROTEIN ASSEMBLY 1 (CIA1) but also SOUL homologs (Fig. 6a). SOUL are heme-binding proteins⁴⁸ that can translocate to the chloroplast and are key for oxidative homeostasis⁴⁹.

Directly influenced by the OSCA hub is a large HOG of subtilases—the second most connected hub in the predicted GRN (Fig. 7a). These are known growth regulators^50,51, pointing to a potential key role of peptide signaling shared by non-vascular streptophytes (see also refs. ^52,53,54). Indeed, highly predictive for the subtilase hub genes are a CXE HOG that also contained the growth regulating gibberellin receptor gene GIBBERELLIN INSENSITIVE DWARF1 (GID1)⁵⁵—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 is a HOG of GROWTH-REGULATING FACTORs (GFR). This speaks for feedback of (i) osmo-sensing—via OSCA and AHKs^56,57 — and (ii) growth programs in which subtilases are a point of convergence. In the analysis with the extended dataset, OSCA and the subtilases remain in close proximity but another gene coding for growth-regulating proteins moved closer, a HOG of TRICHOME BIREFRINGENCE-LIKE (TBL) that act in cell wall biosynthesis^58,59 (Supplementary Figs. 9 and 10).

Convergence at ethylene programs

Ethylene signaling stands as the sole example of a phytohormone with a clear signaling cascade reported to be functionally conserved since the common ancestor of land plants and Zygnematophyceae, although its biosynthetic pathway may not be necessarily conserved^60,61. Exogenous ethylene treatment of the zygnematophyceaen alga Spirogyra induces growth phenotypes and the differential expression of stress- and photosynthesis-associated genes identified in land plants⁶². Crucially, Spirogyra homologs complement knockout lines in Arabidopsis ethylene signaling⁶³. 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 ETHYLENE RESPONSE 1 (ETR1) as a major hub, being the 6^th most connected HOG and the topmost connected single gene in all three species (Fig. 6c, d). Both in Arabidopsis and Pp, ethylene is detected by a two-component histidine kinase receptor subfamily of ETR1 homologs^64,65. Upstream of ETR1 in our predicted GRN are genes for 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)^66,67 and ATTENUATED FAR-RED RESPONSE (AFR)⁶⁸. Indeed, further upstream are genes for C2H2 zinc-finger transcription factors (TFs)—TFs generally prominent in our SWING network—including SUF4 and YY1 and further well known to be among the apocarotenoid-activated TF families^28,37,69, CONSTANS-like B-box TF (that appear to encode the CCT domain) involved in light-dependent regulation^70,71,72, 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) TF-coding genes forms the 13^th most connected hub; this HOG includes homologs of ETHYLENE RESPONSE FACTORs (ERFs) but have diverse functions in environmental and hormonal responses⁷³. 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 signaling⁷⁴; our work adds in the conservation in the two Zygnematophyceae (Fig. 6a, d). A HOG that includes the heat stress-relevant ARABIDOPSIS HOMOLOG OF TRITHORAX 1 and 2 (ATX)⁷⁵ 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^76,77,78. 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. Such kinases form converging points in all our predicted GRNs (Figs. 6a, c, d, 8; Supplementary Figs. 7–10). They are the receivers of diverse inputs, integrating environmental input in biological programs.

**Fig. 8: Evolutionary assembly of environmental response cascades gleaned from transcriptome data.**

As the single most connected receiver in our DPGP-filtered SWING-inferred GRN was a HOG of Ser/Thr kinases (Figs. 6a–d and 7a, b), which include several receptor-like kinases⁷⁹. In our analyses, these receive input from the apocarotenoid ß-IO and SUPPRESSOR OF FRIGIDA (SUF4), a putative zinc-finger-containing TF gene, regulating flowering time; recently it was shown that SUF4 acts as a thermosensor, showing thermo-sensitive assembly and thus activity⁸⁰. This warrants attention, as we recovered in the BBH-based analysis a whole putative regulatory chain of ß-IO–SUF4–a gene for a transmembrane protein–MYB4R1 (Supplementary Fig. 8). Furthermore, a HOG including the circadian regulators⁸¹ LUX ARRHYTHMO (LUX) and BROTHER OF LUX ARRHYTHMO, were the fifth most predictive (Fig. 7b) on the Ser/Thr kinase hub expression pattern(s), aligning with their previously noted importance in photomorphogenesis co-expression hubs in Mesotaenium¹¹. The expression of these Ser/Thr kinase-coding genes were highly dynamic across species, in particular fast spikes early upon HL in the algae and temperature in Pp (Supplementary Fig. 12) reflecting the physiological responsiveness (Fig. 1c–e).

Around this hub coding for Ser/Thr kinases we recovered several genes that are known to be major hubs in regulation themselves. This included genes coding for TOR, eukaryote-wide regulators of diverse developmental and metabolic processes^82,83, which are part of the kinase network. As potential regulator of the major kinase hub, genes for PP2A B subunits appeared. This aligns with the regulatory subunit determining activation of the phosphatase complex and various subfamilies interact with different kinases⁸⁴. Furthermore, several genes coding for proteins in calcium signaling appeared, including CALCIUM DEPENDENT PROTEIN KINASEs (CPDKs), CDPK-RELATED KINASEs (CRKs), CALCINEURIN B-LIKE PROTEIN (CBL), and even genes for putative Na⁺/H⁺ antiporters; this bolsters previous findings¹¹ and the idea that these represent ancient regulators^85,86.

Next to the major hub of genes for Ser/Thr kinases, other kinase HOGs included AHKs. AHKs are best known as cytokinin signaling components⁸⁷ but also include respond to diverse abiotic stressors^56,57. These AHKs form another hub (being the 2^nd to 10^th most connected single genes) upstream of the major Ser/Thr kinase-coding gene hub. As highlighted by the previous environmental gradient analysis on Mesotaenium¹¹ alone, we find chains of kinase-coding genes (BSK, SERK, MKK) linked with a large group of EXORDIUM-like genes, which are modulators of cell expansion⁸⁸ 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^89,90 and mTERF4 interplays with GUN1 (see also Zcdarkgreen, Supplementary Fig. 5) that is implicated in retrograde signaling⁹¹.

Overall, the kinases form a major track throughout the all predicted GRNs. Many of the kinases recovered are homologous to well-known integrators of environmental cues, via intracellular signaling, to growth and acclimation programs (Fig. 8).

On the evolutionary assembly of environmental response cascades

Interconnections of environmental input to internal programs underpin the defining capacities of land plants to adequately respond to adverse environmental conditions¹⁹. In this circuitry, kinase-based cascades are some of the key wires and these cascades must have been present during the infancy of land plants. It is thus an intriguing avenue to explore the deep evolution of the respective cascades, which can be revealed through comparative analyses, such as the one performed here on three divergent streptophytes, allowing to illuminate deep origins. Research in the plant evolutionary community has yielded great insights by analyzing the evolutionary origins of important cascades through homology-guided approaches. Yet, these cascades do not always correspond to their land plant-biased “archetypal” route. Important cascade members might hence be missed, sometimes even those constituting the ancient conserved gene cores⁹². Further, since a shared signaling circuit operated in a common ancestor, divergent evolution likely brought variation to the signaling paths, despite that they might still work following a homologous biological program⁹². To find these cases, kinases can guide our search^54,92. And that such kinase-guided searches can yield thus far unrecognized response programs that differ from the textbook is exemplified by the recent finding that auxin can act through an ancient RAF-like protein kinases mediated response system that operates in land plants and streptophyte algae⁹³.

Here, we use an approach that does not rely on prioritization based on homology to known key genes and pinpoint several points of predicted regulatory bundling of information (Fig. 6a inset; Fig. 8). These include several hubs of genes coding for proteins that are homologous to kinases that are known responders to recurrent themes in stress perception: sensing of the cell wall status, osmotic imbalance, recognitions of molecular patterns that can also include peptide signals and more. Co-occurring with these are genes that can underpin the generation of peptide signals (subtilase-coding genes) as well as growth regulatory that are key to cell wall homeostasis such as EXL. To these cascades that build on kinases we can also add the apocarotenoids that derive from pigments that any phtosynthestic organisms must have and whose production is due to physical forces and an inevitable consequence of the oxidative challenges posed by the terrestrial habitat. All mentioned aspects are recurrent themes in—and core to—the biology of the stretptophyte cell⁵⁴. We here started piecing together parts of their molecular perception through ancient signaling cascades. These cascades constitute the ancient and shared core of how the earliest land plants responded to stress; from this core, the diversification of environmental signaling started.

Conclusions on cue processing and the evolution of layered genetic networks in stress response

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^24,94. 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 show shared stress-mitigation programs and apocarotenogenesis, driven by physicochemical conditions. What diversified were the genetic hubs and enzymes they interact with, which fine-tuned signal formation and responses likely favorable during terrestrialization. Indeed, plant terrestrialiazation was conceivably accompanied by increased oxidative force that consequently led to more apocarotenogenesis, all fostered by elevated O₂ levels, reduced CO₂ levels, temperature dynamics, and generally more abiotic stress.

Since their emergence, traits of land plants have been dependent on complex, but flexible pathways that concertedly bring about their adaptive potential. And in many cases the signature genes that underpin these traits emerged prior to the origin of land plants^6,10,54. Genetic networks of plants can be conceptualized in a multi-layered structure¹⁹. Our findings pinpoint to genetic hubs in which gene expressional changes are bundled (Fig. 6a inset), establishing links between environmental input that influences a whole layer of genes and the points in which this input consolidates. This consolidation appears to a major extent mediated by kinases (Figs. 6–8) that are known to integrate in signaling cascades that facilitate a cross-talk between different inputs, for example bundling signaling molecules, Ca²⁺, and osmotic cues^57,95, and their link to developmental programs; surprisingly, this is in the analyses here gleaned from transcriptional changes. These kinases appear to 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 environmental programs that regulate the transcriptional level across 600-million-year divergent streptophytes.

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 described⁹⁶ prior to stress treatment with the exception that in both cases agarized (1%) Woods Hole Medium (WHM)⁹⁷ 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; Supplementary 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)⁹⁸ 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 Ø15 cm, 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 10× 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 N₂ 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 10×, 20×, 40× and 100× 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-D₃ (≥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.⁹⁹. 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 N₂ and homogenized quickly in reaction vials placed in a metal block (blockage of light and homogenous temperature) cooled by liquid N₂ (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 N₂ 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 N₂ 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 × 4.6 mm I.D.) from YMC Europe was integrated. The solvent system used was initially inspired by Gupta et al.¹⁰⁰ 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 antheraxanthin¹⁰⁰ and respective cis/trans-isomers of carotenoids¹⁰⁰ 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 (Supplementary Methods Figs. 2–4, and Supplementary Methods Table 1). 9-cis-β-carotene was quantified based on β-carotene calibration due to similar spectral properties¹⁰¹. 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 instead¹⁰² (Supplementary Methods Table 2). Antheraxanthin (Supplementary Methods Table 2) was quantified based on zeaxanthin calibration respecting differences in extinction coefficients¹⁰². Chlorophyll a and b were quantified at 660 nm based on calibration curves of the respective standards (Supplementary Methods Figs. 2–4, and Supplementary Methods Table 1).

HS-SPME-GC-MS analysis of volatile apocarotenoids

The method established here was initially based on Rivers et al.¹⁰³. 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 × 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 ionization 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-D₃ (Supplementary Methods Fig. 5); for respective chromatograms and fragmentation patterns, see Supplementary Methods Figs. 6–12. 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-D₃ (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-D₃ 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 cooling 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 the 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 N₂, 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 min of heat shock. Protocol B was continued as described by the vendor.

Functional annotation

In order to assign functional information to the sequences we employed a comprehensive set of tools that we bundled, together with all the other tools and code used, in a workflow deposited on Zenodo¹⁰⁴. These tools included InterProScan¹⁰⁵ (v5.64-96.0 and -pa -goterms flags), eggNOG-mapper^106,107,108 (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), BLAST¹⁰⁹ (v2.15.0) against protein files of A. thaliana¹¹⁰ and genome scale gene family analysis using Orthofinder^{111,112,113,114} (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 oxford¹¹⁵, Azolla filiculoides¹¹⁶, A. thaliana¹¹⁰, Brachypodium distachyon¹¹⁷, Chara braunii⁶, Chlorokybus melkonianii^9,118, Chlamydomonas reinhardtii¹¹⁹, Closterium sp. NIES-67¹²⁰, Klebsormidium nitens⁵, Mesotaenium endlicherianum^7,11, Marchantia polymorpha¹²¹, Mesostigma viride⁹, Ostreococcus lucimarinus¹²², Oryza sativa¹²³, Prasinoderma coloniale¹²⁴, Penium margaritaceum⁸, Physcomitrium patens³⁹, Solanum lycopersicum¹²⁵, Selaginella moellendorffii¹²⁶, Spirogloea muscicola⁷, Zygnema circumcarinatum¹⁰ SAG 698-1a, Z. circumcarinatum¹⁰ SAG 698-1b, Z. circumcarinatum¹⁰ UTEX 1559, Z. circumcarinatum¹⁰ UTEX 1560, and Zea mays¹²⁷. 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)¹²⁸. We also used Tapscan (v3)¹²⁹ 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)¹³⁰ and available on GitHub (https://github.com/dadrasarmin/rnaseq_quantification_kallisto_pipeline). Briefly, we used FastQC (v0.12.1)¹³¹ and MultiQC (v1.16)¹³² to perform quality control, Trimmomatic (v0.39)¹³³ to perform trimming and filtering, and Kallisto (v0.48.0)¹³⁴ to quantify gene expressions (see also Supplementary Methods).

We used R (v4.3.2)¹³⁵ and tidyverse (v2.0.0)¹³⁶ for data analysis and visualization. We used tximport (v1.30.0)¹³⁷ 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)¹³⁸ 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 dataset¹³⁹. We used qsmooth (v1.18.0)¹⁴⁰ with treatments as group_factor to perform the normalization.

Differential gene expression analysis

We used limma (v3.58.1)¹⁴¹ to model gene expression changes under each treatment samples compared to the same time point in the control condition using lmFit, contrasts.fit, eBayes, and 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)¹⁴². 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)¹⁴³ 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 networks¹⁴⁴. 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)¹⁴⁵ 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 R² of Scale free topology model fit (above 0.8). We picked 20, 20, 14 as soft thresholds 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 the 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)¹⁴⁶ 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. All results obtained via WGCNA have been uploaded on Zenodo.

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 model⁴⁵. 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)⁴⁶ to account for our temporal information which is one of the best method for this purpose according to independent benchmarkings¹⁴⁷. 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 benchmarking¹⁴⁷. 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.

Based on the input, theoretically possible network could consist of 3,596,712, 13,641,942, and 2,652,012 edges for the networks of Me, Pp, and Zc, respectively. However, we recovered networks with 3,557,677 (98.9147%), 3,910,293 (28.66376%), and 1,415,350 (53.36891%) non-zero predictive pairs of genes (Me, Pp, and Zc, respectively). To calculate the conserved network, we mapped gene IDs to HOGs to find a common concept across species. The number of edges (the predictive pairs) are 1,465,716, 1,246,870, 856,719 for Me, Pp, and Zc, respectively. We had 5552 HOGs that are shared between Zc, Me, and Pp. Therefore, in theory the maximum number of predictions (pairs of genes) is 30,819,152. The network that was conserved among Zc, Pp, and Me had 383,984 (1.245927%) unique pairs. Finally, we added an extra layer of filtering by just looking at the genes that were among conserved DPGP clusters across species with probability bigger than 0.7. Here, we are looking at genes that are both tightly clustered across species (DPGP) and have conservation of clustering across species (Orthofinder and Jaccard distance). Using these as filters on top of the SWING method, significantly reduce the number of genes, but it is the set of genes that are show tight regulation at expression level as well as homology besides the Granger causality. Here, we had 923 HOGs and metabolites that were present in conserved DPGP clusters. Therefore, in theory, we could have maximum 851,006 edges in the conserved network. We had 923 edges (0.1084599%). In summary, the conserved gene pairs (923) filtered based on similar clusters of co-expression networks (DPGP) and homology (Orthofinder) are a very small portion of the potential/theoretical network—obtained after applying several filters for conservation and robustness.

Statistics & reproducibility

Sample sizes (for RNA-Seq as well as the other experiments) were chosen based on the best practices of the field (at least 3 biological replicates per condition). Each analysis involved millions of pooled cells, all can be assumed to behave similarly (as they were vegetative cells from the same starting culture). For each species, at minimum 90 samples for RNAseq and physiological data points were analyzed. Sequencing was then performed to a depth that was chosen based on approaching saturation level (based on obtaining differential expression patterns among the given number of genes). All data were used except for an additional set of samples that turned out to be irrelevant for the study (constant light, only acquired for Me). The experiments were not randomized, yet all experiments are based on a random selection of millions of cells from a minimum of three pooled cultures on agar plate per replicate. The start culture was one homogenous culture that was equally distributed to inoculate the plates. A random selection of millions of cells thus ended up in one plate that was exposed to a certain condition. Prior to start, all plates were thus equal. The Investigators were not blinded to allocation during experiments and outcome assessment. Blinding was not relavant for this study. It is irrelevant for the bioinformatics because we worked with all versus all comparisons, unsupervised methods and all pipelines are fully transparent. All cell-based evaluation is quantifyable and unambigous. Further, the information is fully provided and re-evaluable.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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). Raw metabolite profiling data are available on Zenodo: https://doi.org/10.5281/zenodo.10805605. Details on WGCNA are available on Zenodo: https://doi.org/10.5281/zenodo.14234484. Data can be interactively explored at https://rshiny.gwdg.de/apps/streptotime/ Source data are provided with this paper.

Code availability

Code is available under: https://gitlab.gwdg.de/armin.dadras/time-resolved-oxidative-signal-convergence-across-the-algae-embryophyte-divide. Code is also available on Zenodo: https://doi.org/10.5281/zenodo.14710622.

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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. I.I. was supported by MICIU/AEI/10.13039/501100011033, ESF+ and ERDF/EU (Grants RyC2022-038245-I and PID2023-152168NB-I00). Illustrations of algae and Physcomitrium in Fig. 1b by Debbie Maizels, Zoobotanica Scientific Illustration. We are grateful to T. Friedl for supporting us with access to the facilities of the Department of Experimental Phycology and SAG Culture Collection of Algae. We thank the Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG) for providing excellent computational infrastructure.

Funding

Open Access funding enabled and organized by Projekt DEAL.

Author information

These authors contributed equally: Tim P. Rieseberg, Armin Dadras.

Authors and Affiliations

University of Göttingen, Institute of Microbiology and Genetics, Department of Applied Bioinformatics, Goldschmidtstr. 1, 37077, Göttingen, Germany
Tim P. Rieseberg, Armin Dadras, Tatyana Darienko, Janine M. R. Fürst-Jansen, Nils Hohnhorst, Thomas Pröschold, Sophie de Vries, Iker Irisarri & Jan de Vries
University of Göttingen, Albrecht Haller Institute of Plant Science, Experimental Phycology and Culture Collection of Algae at Göttingen University (EPSAG), Nikolausberger Weg 18, 37073, Göttingen, Germany
Tatyana Darienko
University of Göttingen, Albrecht Haller Institute of Plant Science, Department of Plant Biochemistry, Justus-von-Liebig-Weg, 37077, Göttingen, Germany
Sina Post, Cornelia Herrfurth & Ivo Feussner
University of Göttingen, Goettingen Center for Molecular Biosciences (GZMB), Service Unit for Goettingen Metabolomics and Lipidomics, Justus-von-Liebig Weg 11, 37077, Göttingen, Germany
Cornelia Herrfurth & Ivo Feussner
Department of Algal Development and Evolution, Max Planck Institute for Biology Tübingen, Tübingen, Germany
Romy Petroll
University of Freiburg, Centre for Biological Signalling Studies (BIOSS), Freiburg, Germany
Stefan A. Rensing
University of Innsbruck, Research Department for Limnology, 5310, Mondsee, Austria
Thomas Pröschold
University of Göttingen, Campus Institute Data Science (CIDAS), Goldschmidtstr. 1, 37077, Göttingen, Germany
Iker Irisarri & Jan de Vries
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
Iker Irisarri
Museo Nacional de Ciencias Naturales (MNCN-CSIC), Department of Biodiversity and Evolutionary Biology, José Gutiérrez Abascal 2, 28006, Madrid, Spain
Iker Irisarri
University of Göttingen, Göttingen Center for Molecular Biosciences (GZMB), Department of Plant Biochemistry, Justus- von-Liebig Weg 11, 37077, Göttingen, Germany
Ivo Feussner
University of Göttingen, Göttingen Center for Molecular Biosciences (GZMB), Department of Applied Bioinformatics, Goldschmidtstr. 1, 37077, Göttingen, Germany
Sophie de Vries & Jan de Vries

Authors

Tim P. Rieseberg
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Armin Dadras
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Ivo Feussner
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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., J.M.R.F.-J. 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., J.M.R.F.-J., T.P.R., and S.d.V. designed figures. 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.

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Correspondence to Tim P. Rieseberg or Jan de Vries.

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Rieseberg, T.P., Dadras, A., Darienko, T. et al. Time-resolved oxidative signal convergence across the algae–embryophyte divide. Nat Commun 16, 1780 (2025). https://doi.org/10.1038/s41467-025-56939-y

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Received: 27 May 2024
Accepted: 06 February 2025
Published: 19 February 2025
DOI: https://doi.org/10.1038/s41467-025-56939-y