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. 2022 Jul 1;190(3):1927–1940. doi: 10.1093/plphys/kiac321

How abiotic stress-induced socialization leads to the formation of massive aggregates in Chlamydomonas

Félix de Carpentier 1,2,3, Alexandre Maes 4, Christophe H Marchand 5,6, Céline Chung 7, Cyrielle Durand 8, Pierre Crozet 9,10,11, Stéphane D Lemaire 12,13, Antoine Danon 14,15,✉
PMCID: PMC9614484  PMID: 35775951

Abstract

Multicellular organisms implement a set of reactions involving signaling and cooperation between different types of cells. Unicellular organisms, on the other hand, activate defense systems that involve collective behaviors between individual organisms. In the unicellular model alga Chlamydomonas (Chlamydomonas reinhardtii), the existence and the function of collective behaviors mechanisms in response to stress remain mostly at the level of the formation of small structures called palmelloids. Here, we report the characterization of a mechanism of abiotic stress response that Chlamydomonas can trigger to form massive multicellular structures. We showed that these aggregates constitute an effective bulwark within which the cells are efficiently protected from the toxic environment. We generated a family of mutants that aggregate spontaneously, the socializer (saz) mutants, of which saz1 is described here in detail. We took advantage of the saz mutants to implement a large-scale multiomics approach that allowed us to show that aggregation is not the result of passive agglutination, but rather genetic reprogramming and substantial modification of the secretome. The reverse genetic analysis we conducted allowed us to identify positive and negative regulators of aggregation and to make hypotheses on how this process is controlled in Chlamydomonas.

Identification of the first regulators of the formation of massive aggregates in response to abiotic stress, a new strategy revealed to allow the survival of the unicellular alga Chlamydomonas reinhardtii under hostile conditions.

Introduction

When confronted with environmental stress, multicellular organisms implement a set of reactions involving signaling and cooperation between cells from the same tissue. A typical example is the hypersensitive response triggered by plants against pathogens, during which certain cells will be led to self-destruction to enable the entire organism to survive (Saur, 2021). In harsh environments, some unicellular organisms such as yeast or bacteria activate defense systems, which also favor the emergence of collective behaviors between cells. These mechanisms involve the formation of multicellular structures such as biofilms (Flemming et al., 2016; Váchová and Palková, 2018). Biofilms, as opposed to aggregates, are surface-anchored structures composed of an extracellular matrix (ECM) made of proteins and sugars, in which cells are embedded to better tolerate environmental stresses (Vlamakis et al., 2013; Váchová and Palková, 2018).

In the unicellular green alga Chlamydomonas (Chlamydomonas reinhardtii), the formation of multicellular structures has mostly been described as palmelloids, which are small clonal structures, resulting from the non-separation of cells after division. The formation of palmelloids has been reported mostly in response to abiotic stresses (Thiriet-Rupert et al., 2021), and more recently in another form, the gloeocapsids, in the context of microbial interactions (Krespach et al., 2021). Bigger multicellular structures have been reported in response to predators (Sathe and Durand, 2016), or by selection of sedimenting cells over a long period of time (Ratcliff et al., 2013; Herron et al., 2018), and only mentioned in the context of the response to abiotic stress (de Carpentier et al., 2019). However, the function of these multicellular structures, their composition, and how their formation is controlled remain to be explored.

Multicellular structures are remarkably interesting from an evolutionary point of view, since they are classically referred to as an intermediate stage in the transition to multicellularity. This is particularly beautifully illustrated in the order Volvocales, which includes unicellular organisms such as Chlamydomonas, life in undifferentiated colonial form, such as Gonium pectorale, as well as differentiated multicellular organisms such as Volvox carteri (Nishii and Miller, 2010; Hanschen et al., 2016).

In this study, we unravel that the formation of large aggregates containing several thousand cells can be induced by a wide range of abiotic stresses, and that this process is a crucial pro-survival mechanism for Chlamydomonas cells. We demonstrate that these structures are clearly different from palmelloids, not only because of the number of cells involved, but also because of their aggregative mode of formation, their specific secretome and their structure. To understand the genetic basis of this collective behavior, we have generated and screened 13,000 insertional mutants, and identified 16 socializer (saz) mutants, which spontaneously form multicellular structures. We characterize here in detail the saz1 mutant which is defective for the vegetative lytic enzyme (VLE) gene (Kubo et al., 2009). The aggregates formed by saz1 result from an aggregative process, and also contain a sugar-rich ECM. Moreover, saz1 was found to be more tolerant to several abiotic stresses. For both saz1 and stress-induced aggregates, the extracellular compartment had a central role in the formation of multicellular structures. Finally, a large-scale multiomics approach taking advantage of the saz mutant collection revealed a network of regulators of aggregation in Chlamydomonas. The nature of these regulators, the number of long periods of stress during evolution, and the fact that aggregates allow higher tolerance, prompts us to propose that the phenomenon of socialization that we have identified could be an intermediate stage that led to the appearance of multicellularity in Volvocales.

Results

Characterization of aggregation in response to stress

Wild-type cells from the D66 strain (CC4425) (Schnell and Lefebvre, 1993) were subjected to various treatments representing different types of stresses already described in Chlamydomonas: S-Nitrosoglutathione (GSNO, nitrosative stress; Morisse et al., 2014), rose bengal (singlet oxygen; Fischer et al., 2005), paraquat (superoxide/H2O2; Esperanza et al., 2015), and heat shock (Schroda et al., 2015). For all the stresses tested, at low stress intensity no impact could be visualized in the cultures, while at higher stress intensities cells died. Interestingly, at intermediate stress levels, large aggregates visible to the naked eye were detected and their surface area quantified (Figure 1A). Aggregates could be detected after 1 day and their size continued to increase steadily with time, until the stress subsides, and they eventually disappear. An example of the evolution of aggregate size in response to GSNO treatment is shown in Supplemental Figure S1. Since our model strain is cell wall-deficient, we verified that two wild-type strains that have a cell wall, CC-5100 (Wang et al., 2017) and CC-5101 (Ozawa et al., 2020) are also capable of forming aggregates. Supplemental Figure S2 indicates that the presence of the cell wall does not prevent the formation of aggregates in response to GSNO and that this cellular compartment does not play a preponderant role in this process. To determine whether Chlamydomonas cells form multicellular structures through aggregation or clonal assembly under our conditions, we constructed a strain expressing mVenus, a cytosolic yellow fluorescent protein (YFP). The Venus strain was grown together with the wild type and subjected to heat shock. The multicellular structures obtained were then analyzed and found to be aggregative, since they were composed of mVenus and wild-type cells (Figure 1B). During our observations, we found that within the aggregates an ECM was very easily visible (Figure 1C, upper part). Knowing that in bacteria and yeasts the ECM present in biofilms are rich in sugar, we tried to detect the presence of these compounds in our aggregates. We induced aggregation using rose bengal and stained the cells in the presence of fluorescently labeled Concanavalin-A (ConA-FITC), which binds glucosyl and mannosyl residues (Cavada et al., 2018). In Figure 1C (lower part), the ECM clearly exhibited a strong fluorescence showing the presence of sugar. To test whether the formation of aggregates could enable increased stress tolerance, we treated wild-type cultures with rose bengal, and stained dead cells using Evans blue (Preethi et al., 2017). We observed that most of the time, within multicellular structures, cells were protected from the toxic environment, while the others were dead (Figure 1D). We found similar results after treating the cells with heat shock or GSNO (Supplemental Figure S3). To quantify this phenomenon, we developed an ImageJ (Schindelin et al., 2012) macro allowing detection of all the structures, their size, and whether the cells are alive (green) or dead (blue). These analyses clearly showed that the larger the structures to which the cells belong, the more the phenomenon of protection is exacerbated (Figure 1E).

Figure 1.

Figure 1

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Aggregation in response to stress. A, Wild-type (wt) (CC4425) cultures were exposed to heat shock (50°C, 3 min), GSNO (0.5 mM), rose bengal (2 µM), and paraquat (0.1 µM) and placed in a 24-well plate for 48 h. For each well aggregates were detected, and their individual surface areas measured. The histogram represents the average total surface area of the four replicates. B, Microscopic observations of a representative heterogenous aggregate induced by heat shock (50°C, 3 min). Red fluorescence represents chlorophyll and yellow fluorescence represents mVenus. C, Sugar detection in a representative ECM (arrow), in an aggregate induced by rose bengal, upper panel imaged in brightfield, lower panel chlorophyll (red), and ConA-FITC fluorescence (green). D, Labeling of dead cells with Evans blue (arrow) 48 h after treatment with rose bengal (4 µM). E, Relationship between the viability of the structures detected and their surface area after a 48-h treatment with rose bengal (4 µM), from four biological replicates. Error bars (A) and shaded area (D) indicates ±sem and for t test: *P ≤ 0.05 or **P ≤ 0.01. Scale bars represents 50 µm (B and D) and 100 µm (C).

Socializer 1 mutant

To understand the genetic mechanisms that control the process of aggregation, we created and screened a library of 13,000 insertional transformants. This allowed identification of 16 mutants dubbed saz for their ability to aggregate spontaneously. In the present study, we have extensively characterized the mutant saz1 which forms large aggregates visible to the naked eye and containing several thousand cells (Figure 2A). The size of saz1 aggregates varied between 100 µm2 and several mm2, in the same range as stress-induced aggregates of wild-type cells (Figure 2C). When observed under the microscope, we saw that the cells form a homogeneous structure in which they appear to be attached to each other (Figure 2D). The SAZ1 locus was characterized using the protocol described previously (González-Ballester et al., 2005). The saz1 mutant harbors an insertion in the Cre01.g049950 gene (Figure 2B), previously named VLE (Matsuda et al., 1995) or sporangin (SPO) (Kubo et al., 2009). We precisely localized the insertion in the exon 21 of Cre01.g049950 and verified that a wild-type copy of the corresponding mRNA was no longer expressed in saz1 (Supplemental Figure S4). To confirm that a mutation in the VLE gene leads to a spontaneous aggregation phenotype, we analyzed three additional insertional vle mutants obtained from the CLiP library (Li et al., 2019). In these mutants with an insertion respectively in the 1st exon, the 6th exon, and the 17th intron, the VLE mRNA could not be detected (Supplemental Figure S4). Similarly to saz1, these three mutants aggregated spontaneously as opposed to the corresponding wild-type strain (CC5325) (Figure 2C). To determine the way aggregates are formed in saz1, we grew cultures composed of a combination of saz1 and the mVenus strain described in Figure 1B. As in the case of the aggregates formed in response to stress by wild-type cells, the multicellular structures spontaneously formed by saz1 were heterogenous and therefore the result of an aggregative process (Figure 2D). A sugar-rich ECM could be detected in saz1 aggregates using ConA-FITC (Figure 2E). The formation of wild-type aggregates allowed increased tolerance of the cells to stress conditions (Figure 1, C and D). Consistently, saz1, which forms aggregates spontaneously, was also more tolerant to stress. Indeed, saz1 showed a higher tolerance to all the stresses tested (GSNO, rose Bengal, and heat shock) (Figure 2, F–H), confirming the protective role of aggregates.

Figure 2.

Figure 2

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Socializer1 characterization. A, Representative microscopic observation of cultures of the wt (CC4425) and saz1 mutant under standard conditions. B, Insertion sites of saz1 and three CLiP mutants (LMJ.RY0402.131694, LMJ.RY0402.140798, and LMJ.RY0402.057262) in the VLE gene (Cre01.g049950). C, Analysis of saz1, the CLiP mutants, and the corresponding wts (CC4425 and CC5325) in 24-well plate. For each well aggregates were detected and their individual surface areas measured. The histogram represents the average total surface area of the six replicates. D, Microscopic observations of a representative aggregate formed in a co-culture of wt and Venus (YFP) strains. Red fluorescence represents chlorophyll and yellow fluorescence represents mVenus. E, Sugar detection in a representative ECM (arrow), within a saz1 aggregate, left panel brightfield, right panel chlorophyll (red), and ConA-FITC fluorescence (green, arrow). Death quantification in wt and saz1, after (F) heat shock (50°C—4 min, 24 h), (G) GSNO (1 mM, 24 h), and (H) rose bengal (4 µM, 8 h) treatments. In this figure, error bars indicate ±sem and for t test: *P ≤ 0.05 or **P ≤ 0.01. Scale bars represents 50 µm (D) and 100 µm (A, E).

The culture medium is sufficient to induce aggregation

Growing saz1 with the mVenus strain that does not spontaneously aggregate induces the formation of heterogenous aggregates (Figure 2D), suggesting a potential role of the culture medium in transmitting an aggregation signal. To test this hypothesis, we removed the saz1 cells from the culture medium by centrifugation and filtration. Wild-type cells were then grown in this culture medium and the formation of aggregates was quantified. Remarkably, the culture medium of the mutant was able to induce a strong aggregation of wild-type cells (Figure 3A). There is therefore probably one or several molecules in the culture medium capable of inducing aggregation. To investigate the nature of this molecule, we subjected the culture medium to different treatments. After boiling for 2 min, a treatment that denatures proteins, the culture medium was no longer able to induce aggregation (Figure 3A). Filtration of the culture medium revealed that only the fraction containing molecules above 30 kDa was capable of inducing aggregation (Figure 3A). These results are consistent with the hypothesis that one or several proteins in the culture medium might induce aggregation. To test whether the culture medium resulting from stress-induced aggregation could also induce the formation of aggregates, we induced aggregation using heat shock, as this treatment does not require incorporation of foreign molecules into the culture medium. This culture medium was just as capable as the medium of the saz1 mutant to trigger aggregation of non-stressed cells (Figure 3B).

Figure 3.

Figure 3

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Culture medium and aggregation. A, Medium from wt (CC4425) and saz1 cultures were harvested and used to inoculate new wt cultures in 24-well plates. Other cultures were inoculated in wt (gray) or saz1 (red) medium that were boiled for 2 min or fractionated using a 30-kDa filter. For each culture, aggregates were detected and their surface area measured. Values represent the average of total surface area of four independent wells. B, Wt cells were grown in 24-well plates in control (gray), or heat shock (HS, purple) medium. Values represent the average of total surface area of the aggregates detected in four independent wells. For A and B error bars indicates ±sem and for the t test *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001; or ****P ≤ 0.0001. We analyzed the protein content of the culture medium in the wt in comparison to six different saz mutants. The results are expressed as a ratio of the different mutants compared with the wt and were classified according to whether the proteins are under-represented (C) or over-represented (D) in six, five, four, three, or two mutants. In the scale bars of (C) and (D) the different colors represent the log2 ratio between the corresponding mutant and the wt strain. E, Function of the proteins deregulated in five or six mutants according to the number of proteins represented.

The aggregation secretome

As the culture medium is sufficient to induce aggregation in Chlamydomonas, possibly by way of proteins, we performed a quantitative proteomic analysis by comparing the culture media of six of the saz mutants having the most pronounced phenotypes, with that of the wild type. For each strain, five biological replicates were prepared and analyzed by tandem mass spectrometry. In our seven samples, we identified a total of 763 proteins in the culture medium (Supplemental Data Set S1). The PredAlgo algorithm (Tardif et al., 2012) predicts that 44.7% of these proteins are secreted, 14.2% are chloroplastic, 5.8% are mitochondrial while 36.3% of them have no prediction. Consistent with the fact that the culture medium was analyzed, a majority of the identified proteins are predicted to be secreted. The proteins whose content is altered in several mutants in comparison to the wild type were classified into different categories, depending on whether their expression is altered in six, five, four, three, or two mutants (Figure 3, C and D). To identify common regulators of aggregation, we focused on the proteins whose content is significantly deregulated in six but also in five mutants (131 proteins), to avoid missing interesting candidates. We classified the proteins into different categories according to their annotation on the JGI Phytozome v13.0 website (https://phytozome-next.jgi.doe.gov), and their descriptions in the literature (Figure 3E).

In this list, pherophorins hold an especially important place (16/131). These proteins are essential constituents of the ECM in V.carteri, a multicellular organism that belongs, like Chlamydomonas species, to the order of Volvocales (Hallmann, 2006). Among the most represented protein classes, we also found proteases, including the subtilisin VLE which is absent in saz1 as expected, and interestingly downregulated in the medium of four other saz mutants. We also detected several matrix metalloproteinases (MMPs), such as gametolysins which are known to degrade the cell wall during mating in Chlamydomonas (Kinoshita et al., 1992; Abe et al., 2004), but also to contribute to the destruction of the ECM in V.carteri (Nishimura et al., 2017) and animals (Zaragoza et al., 2002). Other proteins related to the ECM were also detected, such as VSPs (vegetative, SP rich) (Waffenschmidt et al., 1993), lysyl oxidases (LOX) containing scavenger receptor cysteine-rich domains, which are necessary for ECM formation in animals (Laczko and Csiszar, 2020). We also found many sugar-related proteins, mainly involved in catabolism (e.g. glucan 1,3-beta-glucosidase or beta-galactosidase), glycosylation, or sugar binding (lectins). Interestingly, we found four ephrin A/B receptor-like proteins that are known in animals to mediate cell–cell adhesion (Niethamer and Bush, 2019). Two of them share similarities with mastigonemes which have been proposed to play a role in adhesion through an interaction with the transient receptor potential cation channel polycystic kidney disease 2 (PKD2) (Liu et al., 2020). The latter mediates flagellar agglutination during mating (Huang et al., 2007). Ephrin-related genes have also recently been identified during the formation of multicellular structures in response to exposure to a predator (Bernardes et al., 2021). Altogether this suggests that adhesion mechanisms could also have a role in the assembly of multicellular structures. Finally, we found several lipases that are downregulated in saz mutants, suggesting that lipids may be important for the aggregation process (Figure 3E). The entire list of the 131 proteins is shown in Supplemental Data Set S1.

Transcriptomic analysis of the saz mutants

We completed our systemic approach by analyzing the transcriptome of the same six saz mutants in comparison with the wild type, using mRNA Illumina High-Sequencing technology. During this analysis, a total of 16,829 transcripts were detected and quantified (Supplemental Data Set S2). As we did for our proteomics analysis, we focused on genes deregulated in six but also in five mutants, to avoid missing important genes that would not be affected in one mutant (Figure 4, A and B). This subset corresponds to the 249 genes significantly up- or down-regulated at least four-fold in at least five saz mutants (Supplemental Data Set S2). Strikingly, many genes belong to the main families identified during our proteomic analysis including several genes encoding pherophorins, proteases, lipases, as well as proteins linked to the ECM (Figure 4C). We also identified several genes encoding lectins that bind specifically to sugars. Beyond these families also found in our secretome, this transcriptomic analysis uncovered additional gene families that may be involved in the intracellular signaling controlling aggregation. Sixteen genes encode kinases, which are key players in cellular signaling. Interestingly, six genes related to cyclic guanosine monophosphate (cGMP), which can act as a second messenger controlling kinase activities (Kim and Sharma, 2021), are downregulated in saz mutants, including four guanylate cyclases. Other interesting genes with a potential role in sensing or signaling include several ion channels, receptors, or transporters (Figure 4C).

Figure 4.

Figure 4

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Transcriptomic analysis of the saz mutants. We analyzed the transcriptome of the wt in comparison to six different saz mutants. The results are expressed as a ratio of the different mutants compared with the wt, we classified them according to whether the genes are repressed (A) or induced (B) in six, five, four, three, or two mutants. In the scale bars of (A) and (B) the different colors represent the log2 ratio between the corresponding mutant and the wt strain. C, We analyzed the function of the genes that are deregulated in five or six mutants and we divided them into different categories according to the number of genes represented. D, Cross-analysis of the proteomic and the transcriptomic data. We compiled the results obtained during our proteomics and transcriptomics studies for the 747 proteins whose gene expression could also be quantified. Each of the points reflects the ratio between a mutant and the wt strain. When a protein has been found in a mutant and not in the wt or vice versa, an infinite value is assigned to it (−inf or +inf, in gray rectangles). When the regulation is associated at the protein and gene expression level, the dots are in a blue rectangle, if the regulation is only observed at the protein level the dots are in a white rectangle, if the regulation is only found at the gene expression level the dots are in a purple rectangle and if the gene expression and proteomic regulations go in opposite directions the dots are in a yellow rectangle. We have highlighted the example of pherophorins (PHC), which appear as blue dots, and MMPs, which appear as pink dots.

Cross-analysis of the transcriptomic and proteomic analyses

Of the 763 proteins identified in the aggregation secretome, 747 of the corresponding mRNA were quantified in the transcriptomics study. We studied the regulation of these 747 proteins in comparison to the expression of the corresponding genes, in the six saz mutants compared with the wild type (Figure 4D). Strikingly, we observed that the variation in the amount of protein in the extracellular compartment is most often not accompanied by a variation in the expression of the corresponding gene. Indeed, 56.9% of proteins are present in different quantities in the extracellular compartment without being affected in the expression of the corresponding gene (Figure 4D, white rectangles), whereas only in 36.9% of cases the same behavior is observed at the gene and protein levels (Figure 4D, blue rectangles). In very few cases (3.7%), we observe a deregulation of gene expression without the corresponding protein being affected (Figure 4D, purple rectangles) and in even rarer cases (2.5%), the regulations at the level of gene expression and the corresponding protein go in opposite directions (Figure 4D, yellow rectangles). These results indicate that other mechanisms than transcription are most often responsible for the changes in protein abundance in the culture medium such as changes in mRNA or protein stability, in translation, or subcellular localization. To illustrate this phenomenon in further details, we focused on the pherophorins and the MMPs that were detected in the extracellular compartment (Figure 4D, blue and pink dots, respectively). For many members of these two families, we find both under-expressed and over-expressed representatives, illustrating the complexity of the signaling pathways induced during aggregation and that these protein families could contain members that are pro-aggregative as well as anti-aggregative.

Identification of regulators of aggregation

Our multiomic analysis allowed identification of candidates potentially involved in the control of aggregation. To assess this possible function, we analyzed insertion mutants of the CLiP library (Li et al., 2019), for some of the genes of the main categories identified. We selected several pherophorins, MMPs, and VSPs from our proteomic or transcriptomic analyses. Among the selected mutants, several exhibit a phenotype related to aggregation, which we have characterized in more detail. For each mutant strain, we confirmed the presence of the insertion sequence at the expected locus identified by the CLiP library (Supplemental Figure S5). The ability of each mutant to aggregate in response to stress was investigated. Three mutants affected in pherophorin genes (PHC30, PHC41, and PHC50), which are all up regulated in saz mutants, are no longer able to aggregate in response to rose bengal (Figure 5A). This suggests that the corresponding proteins have a pro-aggregation role. Conversely, several mutants spontaneously form aggregates, indicating that the corresponding proteins probably have an anti-aggregation role (Figure 5B). For all these mutants, the anti-aggregation role is consistent with the downregulation of the corresponding protein (PHC28, PHC35, MMP32, VSP4) or mRNA (PHC28, PHC35) revealed by our multiomic analysis (Figure 5B).

Figure 5.

Figure 5

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Identification of regulators of aggregation and the putative role of the ECM. From our multi-omics analysis, we have selected and characterized CLiP mutants of genes of potential interest, and strains exhibiting an aggregation-related phenotype are presented here. These strains were grown in 24-well plates and aggregates were detected and their surface area measured. For each condition, values represent the average of total surface area of four biological replicates. Some of the mutants we analyzed are no longer able to form aggregates in response to rose bengal (A), other mutants spontaneously form aggregates under standard culture conditions (B). When a gene or protein is down- or up-regulated in saz mutants, the symbols (−) or (+) are indicated under its name. For A and B four replicates from independent cultures were used, error bars indicate ±sem and for the t test *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001. C, In this model, we propose that long periods of stress would have induced and stabilized multicellular structures during evolution, through the action of PHC and MMP. The diversification of these proteins would have allowed the appearance of the first multicellular organisms, such as V. carteri, notably through the stabilization of an appropriate ECM.

Discussion

Several responses to abiotic stress have been reported in Chlamydomonas including acclimation, palmelloid formation, and programmed cell death (de Carpentier et al., 2019). Here, we report the characterization of a mechanism of abiotic stress response leading to the formation of large aggregates visible to the naked eye that can comprise several thousand cells (Figure 1A). This phenomenon appears as a general stress response since aggregation is induced and can confer tolerance to stresses of different nature. The mode of aggregate formation is not simply the result of the non-separation of cells after division (clonal mode, palmelloids), but rather a complex process that may involve communication between cells, a change in their ability to interact and a sugar-rich ECM (Figure 1, B and C). We show here that the formation of aggregates is an efficient way for Chlamydomonas to survive abiotic stress and that the size of aggregates corresponds with the capacity to survive under stress conditions (Figure 1, D and E).

To understand how this complex process is genetically controlled, we have identified a family of mutants exhibiting a spontaneous aggregation phenotype, the saz mutants. A large-scale multiomic analysis of six of these mutants revealed the existence of a common genetic program and the importance of the protein composition of the extracellular compartment to implement a collective behavior leading to the formation of aggregates in response to stress. Spontaneous aggregation of saz mutants appears comparable to stress-induced aggregation including the same mode of aggregate formation, a sugar-rich ECM (Figure 2, D and E) and increased tolerance to stress (Figure 2, D and E). The saz mutant collection therefore constitutes an invaluable tool for the understanding of this phenomenon.

In the present study, we further characterized the saz1 mutant and showed that inactivation of the Cre01.g049950 gene induces a spontaneous aggregation phenotype (Figure 2, A and C). This gene has already been described in the literature as VLE (Matsuda et al., 1995) or SPO (Kubo et al., 2009). Because of its expression profile, its spatio-temporal localization, and its biochemical function, it was suggested that VLE was responsible for sporangial cell wall degradation during cell division (Matsuda et al., 1995; Kubo et al., 2009). However, this hypothesis has never been validated by the characterization of a VLE mutant. If the sole function of VLE was to release cells during division, a culture of the vle mutant would likely consist only of cells in sporangia or possibly in larger multicellular structures resulting from a clonal formation process. By contrast, for the four vle mutants analyzed, we observed both free cells, as well as large aggregates (Figure 2A), which we showed to be heterogeneous and therefore not the result of a clonal formation process (Figure 2D). This would suggest that not only VLE, but several enzymes are capable of releasing cells during division, and that VLE has at least one other function in controlling aggregation in Chlamydomonas. Other authors have suggested that since this protein is a homolog of the prohormone convertases (PC), that are involved in the neuropeptide machinery in animals, VLE could also contribute in Chlamydomonas to the production of signal peptides (Luxmi et al., 2018), that could allow cells to communicate with each other. Finally, VLE could activate gametolysins (Luxmi et al., 2018), these proteins are MMPs responsible for cell wall removal during mating and need to be activated by a still unknown serine protease (Snell et al., 1989). Therefore VLE, which was the only serine endoprotease identified in the mating secretome (Luxmi et al., 2018), was proposed to be responsible for the cleavage activation of gametolysins during this process. MMPs are also known to control the degradation of the ECM in plants (Marino and Funk, 2012) and animals (Zaragoza et al., 2002; Fanjul-Fernández et al., 2010). The possible MMP activation function of VLE could explain how it contributes to the control of aggregation in Chlamydomonas, as ECMs are present in all the aggregates we analyzed (Figure 5C). Another argument in favor of the importance of MMPs in the control of the aggregation process is that several of them are deregulated in saz mutants, at the gene and/or the protein level (Figure 4D). To investigate the potential involvement of the selected MMPs, the corresponding mutants were characterized. A mutant of MMP32 exhibits a spontaneous aggregation phenotype, giving it an anti-aggregation role in this process (Figure 5B). Beyond MMPs which are known to target ECM proteins (Fanjul-Fernández et al., 2010; Marino and Funk, 2012), we identified other protein controlling ECM formation in other organisms. Pherophorins are a family of extracellular hydroxyproline-rich proteins, whose function remains poorly known in Chlamydomonas. In V.carteri, a multicellular green alga belonging like Chlamydomonas to the order of Volvocales, pherophorins are known to be necessary for the formation of the ECM (Hallmann, 2006). A pherophorin was also proposed to participate in the signaling process leading to sexual reproduction in V.carteri (Sumper et al., 1993). In both our transcriptomic and proteomic analyses, we found many pherophorins which are over- or under-expressed in saz mutants. The role of phrerophorins in relation to aggregation in Chlamydomonas appears complex, since while some pherophorins are present in all saz mutants and absent in the wild type, which one would expect from a protein necessary for the formation of the ECM, the opposite situation is also true and even more frequent (Figure 4D). There would thus exist pherophorins having a pro-aggregative or anti-aggregative role. This hypothesis has been confirmed by our reverse genetic analysis, which shows that when pherophorins are absent in saz mutants and present in the wild type (PHC28, PHC35), the corresponding mutants spontaneously aggregate, whereas when they are absent in the mutants and present in the wild type (PHC30, PHC41, PHC50), their mutants are no longer able to aggregate in response to stress (Figure 5A). Pherophorins have A and B globular domains similar to plant lectins, suggesting that beside being glycosylated, they may also have sugar-binding functions (Hallmann, 2006), such as an interaction with the sugar-rich ECM we detected in aggregates (Figures 1C and 2E). Finally, we analyzed a family of extracellular proteins, the VSPs which are hydroxyproline and Serine–Proline (SP)-rich proteins in Chlamydomonas enriched during the vegetative part of its cycle (Waffenschmidt et al., 1993). Several VSPs (Hallmann and Kirk, 2000) were also found to be deregulated. These proteins are homologs of ISG (inversion-specific glycoprotein) that is essential for ECM formation and cell orientation in V.carteri (Hallmann and Kirk, 2000). Three VSP proteins (VSP4, VSP6, and VSP7) are downregulated in the culture medium of all saz mutants (Supplemental Data Set S2). We further show here that an insertion in the VSP4 gene induces the formation of aggregates (Figure 5B) suggesting an anti-aggregative function for VSP4.

The protein content of Chlamydomonas medium has already been analyzed in the context of saline stress inducing the formation of palmelloids (Khona et al., 2016). Cross-analysis of these results with ours shows that the formation of palmelloids and large aggregates involves specific sets of proteins. Indeed, even if MMPs or pherophorins are found in the secretome during palmelloid formation, their identities are distinct from those identified in the medium of aggregates. Moreover, among the 62 proteins identified during the formation or dislocation of palmelloids, only one (MMP3) is common to our list of 131 proteins of interest, showing that the formation of aggregates involves a protein network clearly specific to this process. Another recent study aimed at identifying genes specifically expressed during the formation of groups of Chlamydomonas cells after exposure to a predator showed in a fascinating way that even in this process families of genes encoding adhesion proteins such as ephrins or ECM molecules such as pherophorins could be involved (Bernardes et al., 2021). Although the precise identity of these proteins differs from those we found, this confirms the importance of these families in collective behaviors in response to stress in Chlamydomonas and the thoroughness of their regulation.

In conclusion, we describe in this study the characterization of a spectacular mode of adaptation of Chlamydomonas in response to stress which involves the formation of multicellular structures, clearly distinct from the palmelloids described so far, that can comprise several thousand cells. We show that this process is fundamental, since within these aggregates the cells can survive environmental stresses. The mechanisms involved in the tolerance to stress of the cells included in the aggregates could first of all be simply physical, the cells within this structure being isolated from the toxic environment. But it is also reasonable to think that the structure of the aggregates favors contact between the cells and possibly exchanges of protective molecules that would favor their survival. An example of the existence of exchanges of protective molecules in Chlamydomonas has been shown during programmed cell death, where the death of a part of the cells leads to the enrichment of the culture medium in a still undetermined protective signal (de Carpentier et al., 2019).

VLE plays a particularly important role in aggregation in Chlamydomonas, since its absence leads to aggregation in saz1 and it is downregulated in the extracellular compartment of four other saz mutants. We detected within the aggregates a sugar-rich ECM which, based on the results of our multiomics and reverse genetic analyses, appears to be central to the formation of the aggregates. This discovery opens up avenues of research into the poorly understood role of the ECM in Chlamydomonas. During aggregation, the formation of an ECM would allow to link the cells to each other to form large multicellular structures, whereas in palmelloids, the ECM surrounds the cells and is bounded by a cell wall. We hypothesize that an intracellular signaling pathway partly involving the regulation of gene expression leads to an equilibrium in the extracellular compartment between pro- and anti-aggregative proteins such as pherophorins or MMPs, to control the establishment of protective multicellular structures. Comparison of the genomes of Chlamydomonas and V.carteri revealed a high degree of similarity between the gene families present, but among the protein families that diverged the most in Volvox appeared the MMPs and the pherophorins (Prochnik et al., 2010; Hanschen et al., 2016). This led to the suggestion that the increase in the number and diversity of these proteins could be at the origin of the establishment of an ECM making the transition to multicellularity possible (Nishii and Miller, 2010; Olson and Nedelcu, 2016), since the formation of an ECM is a necessary step in this process (Hallmann, 2006). The results we present here could therefore constitute the missing link between stress response, ECM, aggregation, and transition to multicellularity (Figure 5C). Our discovery allows to explain how, during evolution, long periods of stress could have induced and stabilized the expression of MMPs and pherophorins, allowing the formation of aggregates containing an ECM, likely to have evolved, through the diversification of pherophorins, toward a structure such as the one known in V.carteri. These aggregates would have been even more advantageous during stress periods of evolution, since we have shown that they constitute a very efficient survival strategy (Figure 5C). This scenario is all the more likely since the hypothesis classically formulated for the emergence of multicellularity is the formation of an intermediate stage of aggregation (Olson, 2013). The order of Volvocales is particularly admirably adapted to study this phenomenon, given that it extends over a relatively short evolutionary distance, from the unicellular Chlamydomonas to the multicellular V.carteri.

Materials and methods

Strains, media, and growth conditions

The Chlamydomonas (C.reinhardtii), D66 strain (CC-4425) (Schnell and Lefebvre, 1993), was used as a wild type for this study in addition to the wild-type strain of the CLiP library CMJ030 (CC-4533) (Li et al., 2019), when necessary. The list of the CLiP mutants used in this study is shown in Supplemental Table S1. Cells were grown in liquid cultures mixotrophically in Tris acetate phosphate (TAP) medium (Gorman and Levine, 1965) on a rotary shaker (120 rpm) under continuous light (40–60 µmol photons/m2s) at 25°C. Aggregation assay and stress treatments were done in 24-well plates in a 1-mL volume at a concentration of 4–8 × 106 cells/mL. Rose bengal (330,000) and paraquat (856,177) were purchased from Sigma-Aldrich (Saint-Louis, USA). GSNO was synthesized as described in Tagliani et al. (2021).

Aggregate area and cell death image analysis

Multi-well plates were imaged with the Perfection V800 scanner (Epson, Suwa, Japan) and micrograph were taken using the Axio Observer (Zeiss, Oberkochen, Germany) microscope. The software used were Fiji/ImageJ 1.52a (Schindelin et al., 2012) and R Studio 1.2.5 (tidyverse, ggpubr, and PlotsOfData) (Wickham, 2016; Postma and Goedhart, 2019). The particle areas were determined from 24-well plate scans using a Fiji macro (github.com/fdecarpentier/ParticleWell). The quantification of cell death was done following the previously published method (de Carpentier et al., 2020), using a final concentration of 0.2% w/v Evans Blue (E2129, Sigma-Aldrich). The percentage of dead cells was calculated on a minimum of 100 individuals. To quantify the area and viability of each particle, we created a Fiji macro (github.com/fdecarpentier/PlantDeath) based on the CIELAB color space.

mVenus plasmid construction

Unless otherwise specified, the enzymes were bought from New England BioLabs (Ipswich, USA). The level one plasmid pCM1-034 was constructed by cloning the parts PPSAD (pCM0-016), mVenus (pCM0-086), and TPSAD (pCM0-114) (Lauersen et al., 2015; Crozet et al., 2018) in the acceptor plasmid pICH47732 (Weber et al., 2011) by a Golden Gate reaction. pCM1-034 was then assembled with the blasticidin resistance transcription unit pCM1-029 (de Carpentier et al., 2020) and the empty linker pICH50881 in the level M acceptor plasmid pAGM8031 (Weber et al., 2011) yielding pCMM-23.

Chlamydomonas transformation and generation of saz mutants

i72 cassette conferring paromomycin resistance was excised from pSL72 plasmid (Pollock et al., 2004) using XhoI and EcoRI, isolated on agarose gel and purified (Macherey-Nagel NucleoSpin Gel and PCR Clean-up Kit). Transformants were generated using electroporation as previously described (Pollock et al., 2017) and selected on agar plates containing paromomycin at a concentration of 20 mg/L. The transformants were then grown in 96-well plates from which were selected the saz mutants that aggregated spontaneously.

For the generation of the mVenus strain, pCMM-23 was electroporated in D66 as previously described (Onishi and Pringle, 2016). Transformants were selected agar plates using with 50 mg/L blasticidin (de Carpentier et al., 2020). After 6 days of growth, the plates were scanned with a Typhoon FLA 9500 laser scanner (GE, Healthcare) at 473 nm excitation with the filters BPG1 for YFP and LPG for chlorophyll, to select the mVenus fluorescent strain.

Insert localization in saz1 and CLiP mutants

Genomic DNA purification method was adapted from Danon and Gallois (1998). The insertion locus of i72 cassette was determined by RESDA-PCR (Restriction Enzyme Site-Directed Amplification PCR) (González-Ballester et al., 2005). PCR were performed with Quick-Load Taq 2× Master Mix (New England BioLabs) according to the manufacturer recommendations with the primer displayed in Supplemental Table S1. We used the NucleoSpin Gel and PCR Clean-up Kit (Macherey-Nagel) for band purification and sequencing was performed by Eurofins Genomics.

Medium purification and protein quantitation by tandem mass spectrometry

Culture medium was harvested after 5 days of culture in TAP medium and cleared of cells by centrifugation (2,300 × g, 5 min) and 0.45 µm filtration. For quantitative proteomics, 50 mL of the medium was concentrated 125 times using Amicon centrifugal filter unit (10 kDa MWCO, 4,000 × g, 4°C). The concentrated medium was centrifugated (10 min, 17,000 × g, 4°C) to pellet the insoluble debris. Twenty micrograms of proteins were denatured in the presence of Laemmli buffer and separated by SDS-PAGE. After a short migration (<0.5 cm) and Coomassie blue staining, gel pieces containing extracellular proteins were excised and subjected to trypsin digestion as previously described (Marchand et al., 2010). Peptide mixtures were prepared in 60 µL of solvent A (0.1% [v/v] formic acid in 3% [v/v] acetonitrile). For each strain, five replicates from five independent cultures were prepared which were hereafter analyzed as technical duplicates. Mass spectrometry analyses were performed on a Q-Exactive Plus hybrid quadripole-orbitrap mass spectrometer (Thermo Fisher, San José, CA, USA) coupled to an Easy 1000 reverse phase nano-flow LC system (Proxeon) using the Easy nano-electrospray ion source (Thermo Fisher). Five microliters of peptide mixtures were loaded onto an Acclaim PepMap precolumn (75 µm × 2 cm, 3 µm, 100 Å; Thermo Scientific) equilibrated in solvent A and separated at a constant flow rate of 250 nL/min on a PepMap RSLC C18 Easy-Spray column (75 µm × 50 cm, 2 µm, 100 Å; Thermo Scientific) with a 90-min gradient (0%–20% B solvent [0.1% {v/v} formic acid in acetonitrile] in 70 min and 20%–32% B solvent in 20 min). Data acquisition was performed as described in Pérez-Pérez et al. (2017). Raw Orbitrap data were processed with MaxQuant 1.5.6.5 using the Andromeda search engine (Tyanova et al., 2016) against the Chlamydomonas database (V.5.6) (Blaby et al., 2014; Gallaher et al., 2018) and the MaxQuant contaminants database. Mass tolerance was set to 10 ppm for the parent ion mass and 20 mDa for fragments, and up to two missed cleavages per peptide were allowed. Peptides were identified and quantified using the “match between run” setting and an false discovery rate (FDR) below 0.01. The intensity of proteins with at least two unique peptides were quantified with the MaxLFQ method (Tyanova et al., 2016). Proteins detected in at least three biological replicates that were analyzed with a Wilcoxon–Mann–Whitney non-parametrical test. Proteins were regarded as differentially expressed than the wild type for adjusted P-values < 0.05 and log2 (fold change) >1.

RNA analysis

Total RNA of Chlamydomonas was extracted from 10 mL cultures at 5–6 × 106 cells/mL according to the protocol described in Cavaiuolo et al. (2017). RNAs were treated with DNase I (New England BioLabs) according to the manufacturer recommendations. RNA quality was checked with the TapeStation System (Agilent, Santa Clara, USA), three replicates from three independent cultures per strain with a RINe (RNA integrity number equivalent) above 5.5 were selected. Library preparation and sequencing were performed by the iGenSeq genotyping and sequencing core facility (ICM Institute—Hôpital Pitié-Salpêtrière AP-HP, Paris, France) using KAPA HyperPrep Kits (Roche, Basel, Switzerland) and the NovaSeq 6000 sequencer (Illumina, San Diego, USA). Paired-end reads were mapped against the Chlamydomonas genome v.5.6 using Bowtie2 v2.3.4.3 (Langmead and Salzberg, 2012; Blaby et al., 2014). with at least 30 base-mean mapped reads. Normalization and differential analysis were performed according to DESeq2 v1.30.0 (Love et al., 2014). Genes were regarded as differentially expressed than the wild type for adjusted P-values <0.05 and log2 (fold change) > 2 or < −2. For quantitative real-time PCR analysis, RNA was treated with RNase-Free DNase (New England Biolabs) and reverse-transcribed following manufacturer’s recommendations (The ProtoScript Taq RT-PCR Kit, New England Biolabs). Relative mRNA abundance was calculated using the comparative delta-Ct method and normalized to the corresponding RACK1 (Cre06.g278222) gene levels.

Accession numbers

Sequence data from this article can be found in the Phytozome database under accession numbers:_VLE: Cre01.g049950, MMP32: Cre14.g625850, PHC28: Cre17.g717950, PHC30: Cre09.g404201, PHC35: Cre05.g238687, PHC41: Cre12.g506750, PHC50: Cre06.g292249, and VSP4: Cre09.g391801.

Supplemental data

The following materials are available in the online version of this article.

Supplemental Figure S1. Evolution of the surface of the aggregates over time.

Supplemental Figure S2. Aggregation in response to stress in wild-type strains.

Supplemental Figure S3. Quantification of cell death 24 h after treatment with heat shock.

Supplemental Figure S4. Characterization of the different insertional mutants for the VLE gene.

Supplemental Figure S5. Genotyping of the CLiP mutants used in this study.

Supplemental Table S1. List of primers used in our study.

Supplemental Data Set S1. List of proteins detected in the extra cellular compartment of the different strains.

Supplemental Data Set S2. List of genes detected in our RNA-Seq analysis in the different strains.

Supplementary Material

kiac321_Supplementary_Data
Click here for additional data file. (6.3MB, zip)

Acknowledgments

We acknowledge Marion Hamon of the mass spectrometry platform of the Institut de Biologie Physico-Chimique for the mass spectrometer running and data collection. We thank Oliver Caspari for his help in the fluorescent protein expression, and Lionel Bénard and Marina Cavaiuolo for their highly valuable advice about RNA extraction. Finally, we thank Nicolas D. Boisset, Théo Le Moigne, and Julien Henri for stimulating discussions and suggestions.

Funding

This work was supported by CNRS, Sorbonne Université, and Université Paris-Saclay, by Agence Nationale de la Recherche Grant 17-CE05-0001 CalvinDesign and by LABEX DYNAMO (ANR-LABX-011) and EQUIPEX CACSICE (ANR-11-EQPX-0008) for the funding of the IBPC Proteomic Platform (PPI).

Conflict of interest statement. None declared.

Contributor Information

Félix de Carpentier, Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, 75005 Paris, France; Institut de Biologie Paris-Seine, UMR 7238, CNRS, Sorbonne Université, 75005 Paris, France; Université Paris-Saclay, 91190 Saint-Aubin, France.

Alexandre Maes, Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, 75005 Paris, France.

Christophe H Marchand, Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, 75005 Paris, France; Institut de Biologie Paris-Seine, UMR 7238, CNRS, Sorbonne Université, 75005 Paris, France.

Céline Chung, Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, 75005 Paris, France.

Cyrielle Durand, Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, 75005 Paris, France.

Pierre Crozet, Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, 75005 Paris, France; Institut de Biologie Paris-Seine, UMR 7238, CNRS, Sorbonne Université, 75005 Paris, France; Polytech-Sorbonne, Sorbonne Université, 75005 Paris, France.

Stéphane D Lemaire, Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, 75005 Paris, France; Institut de Biologie Paris-Seine, UMR 7238, CNRS, Sorbonne Université, 75005 Paris, France.

Antoine Danon, Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, 75005 Paris, France; Institut de Biologie Paris-Seine, UMR 7238, CNRS, Sorbonne Université, 75005 Paris, France.

 

A.D. and F.d.C. designed the study. A.D., F.d.C., and S.D.L. wrote the manuscript. A.D., F.d.C., A.M., C.H.M., C.C. and C.D. performed the experiments and analyzed the data. P.C. assisted with modular cloning experiments.

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plphys/pages/general-instructions) is: Antoine Danon (antoine.danon@upmc.fr).

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