Sphaeroforma species exhibit large variation in sedimentation rates

Similar to other ichthyosporeans, Sphaeroforma species proliferate through continuous rounds of nuclear divisions without cytokinesis to form a multinucleated coenocyte [34,35,45,46]. Sphaeroforma coenocytes then undergo a coordinated cellularization process leading to the formation of a transient multicellular life stage resembling an epithelium [32]. This layer of cells then detaches and cell-walled newborn cells are released to the environment (Fig 1B) [32]. The entire life cycle prior to cellularization occurs in highly spherical (multinucleated) cells.

Extensive literature has documented a positive correlation between cell size and sedimentation rate, including during the life cycle of marine phytoplankton (Fig 1C) [37–41].This is consistent with Stoke’s law, which shows that the relationship between a spherical particle’s terminal sedimentation rate v in a fluid and its radius R should be determined by where c represents the scaled ratio of gravitational to viscosity constants, and p_p is the difference between the particle’s and the fluid’s densities (see Methods). Therefore, even small shifts in a particle’s radius will lead to pronounced (i.e., quadratic) changes in sedimentation rate. Similarly, for particles sitting near the buoyancy threshold, small changes in density can lead to proportionally large changes in settling rate (Fig 1D).

Due to the nature of the coenocytic life cycle (Fig 1B), which is associated with an increase in the number of nuclei and coenocyte volume, we expected to observe an increase in cellular sedimentation rates over time [44,47,48].To better understand this relationship, throughout this study, we conducted overlapping experiments characterizing cell volume and sedimentation rates of Sphaeroforma species for cultures growing at 17°C for 72 hours. For certain replicates of this core dataset, we included measurements of various genetic variants, the temperature dependence of phenotypes, fitness, as well as the speed of nuclear duplication. Overall, the measurements we report are highly reproducible with >95% variance explained for replicate measurements across phenotypes (S1A Fig and S1–S4 Data) (see Methods). This reflected a high heritability of the different phenotypes in a given environment.

To begin, we measured the sedimentation rate of 5 different Sphaeroforma species that have been isolated from different habitats either free-living or host-derived, namely S. arctica, S. sirkka, S. napiecek, S. gastrica, and S. nootakensis, using a quantitative sedimentation rate assay based on changes in optical density over time [32,45,49–52]. According to these estimates, cell sedimentation rates varied greatly, from between 0.4 to 125 μm per second (i.e., up to 0.45 meters per day) (Fig 1E). This broad variability over the life cycle and among Sphaeroforma species suggested that appreciable changes in size and/or cellular density should accompany different stages of the life cycle.

The transient multicellular life stage of S. arctica is associated with an increase in sedimentation rate

To better understand the cellular basis of sedimentation rate variation, we focused on S. arctica, the most studied Sphaeroforma species to date [32,34,50]. Using fixed cell imaging, we observed that synchronized cultures of S. arctica undergo a complete life cycle in about 48 hours and can reach up to 256 nuclei per coenocyte before undergoing cellularization and releasing newborn cells (Fig 1F). Prior to this release, all cellular division occurs in highly spherical mother coenocytes. Consistent with previous results [34], nuclear division cycles were periodic during coenocytic growth and occurred on average every 9 to 10 hours, as measured indirectly from changes in DNA content (Fig 1G). Average cell volume increased throughout the coenocytic cycle to reach its maximum value at 36 hours prior to the release of newborn cells (Fig 1H). Similarly, the sedimentation rate increased up to >300-fold the initial value after 36 hours (Fig 1I). Altogether, we observe that every cycle of nuclear division is associated with a significant increase in nuclear content, volume, and sedimentation rate with a distinct peak just prior to cell release. As the transient multicellular life stage of S. arctica occurs during the latest stage of cellularization and ahead of cell release [32], our results suggest that it is tightly associated with an increased sedimentation rate.

Experimental evolution of fast-settling mutants

Given how cell size and division across the cell cycle are regulated, we reasoned that these variable traits should also be heritable and, hence, evolvable. To test this, we conducted an evolution experiment to generate mutants with increased sedimentation rates [15,16,18]. Briefly, 10 independent cultures of S. arctica (S1 to S10) originating from the same ancestral clone (AN) (Fig 2A) were diluted in fresh MB medium at 17°C (Fig 1C). Selection was performed every 24 hours by allowing the cultures to sediment in tubes for 2 minutes before transferring and propagating the fastest-settling cells in fresh medium (Fig 2A). EE was continued for 56 transfers (8 weeks) or about 364 nuclear generations, and a frozen stock was conserved every 7 transfers (1 week) (see Methods) (Fig 2A, S1 Table). Most lineages exhibited a distinct clumping phenotype to varying degrees across all evolved populations (Fig 2B). This phenotype has never been observed in any Sphaeroforma culture, despite variation of culturing conditions across labs, and the hundreds of routine passages without selection, thus suggesting that the appearance of such phenotype is linked to our selection regime. The clumps of cells were not maintained by ionic forces or protein-dependent interactions, however could be separated by mild sonication without leading to cell lysis (S2B Fig). To assess when sedimentation rates increased during the selection process for each evolved population, we synchronized cultures using mild sonication before dilution in fresh medium and allowed them to undergo a complete life cycle before measuring the sedimentation rate (Fig 2C). We observed that populations S1, S4, and S9 had the highest sedimentation rates at the end of the evolution experiment (Fig 2C). We observed a dramatic increase in sedimentation rate already after 14 transfers for population S1 (approximately 91 generations) (Fig 2C). To assess and compare the variability in sedimentation rates among evolved lineages, we isolated and characterized a single clone from each evolved culture. Our results show that evolved clones all settled significantly faster than the common ancestor but that there was stark variation in sedimentation rates at the end of the EE (Fig 2D). Isolates from lineages S1, S4, and S9 settled the fastest upon sedimentation (S1 Video), while clones from lineages S2, S3, S5, and S7 were intermediate, and lineages S6, S8, and S10 settled the slowest (Fig 2D). Taken together, our results show that we can rapidly and routinely evolve fast-settling mutants in S. arctica using EE but that the outcome is not uniform across lineages.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 2. Rapid evolution of fast-settling and clumpy mutants.

(A) Schematic representation of the EE design using sedimentation as a selective pressure. All 10 replicate populations (S1-S10) were isolated from the same ancestral culture (AN) and were subject to 56 transfers over 8 weeks of selection. (B) Representative images of the ancestral culture (AN) and 3 evolved mutants (S1, S5, and S6) with varying clumping capability after 5 days of growth. Bar, 50 μm. (C) Sedimentation rates of S. arctica evolved populations per number of transfers shows rapid emergence of fast-settling phenotypes, particularly in S1. (D) Sedimentation rates of single clones of S. arctica evolved mutants after 56 rounds of selection displays vast variations in sedimentation rate. Associated with S1 Video. (E) Head-to-head competitions between the clumpy evolved isolates S01 and S03 with the ancestral strain (AN) show that sedimentation rate is adaptive in the conditions of our evolution experiment. The rate of change of the frequency of the clumpy phenotype per day in log 2 units was determined by least-squares regression. Error bars represent the 95% confidence interval based on 500 calculations of this statistic obtained by resamplings of the observed binomial count frequencies for each data point. The data underlying this graph may be found in S4 Data; see Methods. (F) Clump size distribution of evolved clones shows that fast-settling mutants (S1, S4, and S9) show bigger clump size (n > 800 measurements per time point). The data underlying this figure may be found at https://doi.org/10.6084/m9.figshare.18319232.v1. EE, experimental evolution.

https://doi.org/10.1371/journal.pbio.3001551.g002

To assess whether fast sedimentation is adaptive in our EE setup, we independently competed 2 fast-sedimenting isolates, S01 and S03, against their common ancestor (AN). For this relative fitness assay, we mixed monocultures of either S01 or S03 with AN at an approximately 1:1 ratio and subjected them to the same selective regime as during EE. Parallel cultures of each 1:1 mix grown and maintained without sedimentation selection served as experimental control (see Methods). Our results show that the proportion of clumpy S01 and S03 cells both significantly increased over time (a log2 selection coefficient of 0.23 and 0.13 per day for S01 and S03, respectively, p < 1e-7), suggesting that this trait was adaptive in our experimental setup (Figs 2E, S1A, and S2D; S4 Data). In contrast, S01 and S03 cells decreased in frequency relative to the AN strain in the control growth experiment without sedimentation (a log2 selection coefficient of −0.13 and −0.15 per day for S01 and S03, respectively, p < 1e-7), suggesting that the heritable changes that these strains bore were deleterious in normal laboratory propagation conditions (Figs 2E and S2D).

We next sought to characterize the cellular mechanisms giving rise to variation in cellular sedimentation rates in evolved clones. Using quantitative microscopy, we show that clump perimeter and average number of cells observed per clump correlated highly with sedimentation rates (Figs 2E, S2C, and S2E). Indeed, across all experiments reported in this study, we found that using Stokes’ law, a single globally fitted density parameter and our size measurements, could explain the majority of variance in sedimentation rate (R² = 0.69, RMSE (as a proportion of the range in observations) = 18%).

Fast-sedimenting mutants form clonal clumps

Earlier, we defined 3 distinct developmental stages of S. arctica life cycle: (i) cell growth with an increase in coenocyte volume; (ii) cellularization, which coincides with actomyosin network formation and plasma membrane invaginations; and (iii) release of newborn cells (Fig 1B) [32]. A key developmental movement named “flip,” defined by an abrupt internal morphological change in the coenocyte, can be used as a reference point to characterize life cycle stages. Prior to this event (pre-flip), actomyosin-dependent plasma membrane invaginations occur, while afterwards (post-flip), the cell wall is formed around individual cells prior to their release from the coenocyte [32]. Using time-lapse microscopy at 12°C, we observed that clump formation in fast-settling mutants coincides with cell release (Fig 3A, S2 Video).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 3. Clumps are formed by incomplete cell–cell separation in fast-settling mutants.

(A) Time-lapse images of the life cycle of S. arctica ancestral strain (AN) and the fast-settling mutants (S1, S4, and S9) at 12°C show that clumps are observed concomitantly with release of newborn cells. Associated with S2 Video. Bar, 50 μm. (B) Plasma membrane staining using FM4-64 show that plasma membrane invaginations during cellularization seems to occur normally in fast-settling mutants. Associated with S3 Video. Bar, 50 μm. (C) Cell wall staining using calcofluor-white indicates that cells are still separated by a cell wall inside fast-settling clumps, suggesting that clumps are formed post-flip. Bar, 50 μm. (D) Number of cells detaching per clumps at cell release and for the following 3 hours, measured from time-lapse movies (n = 50 coenocytes at cell release per strain). (E) Coenocyte volume at flip, measured from time-lapse movies, shows that S4 and S9 are significantly smaller when compared to AN and S1 (n = 50 coenocytes at flip per strain). The data underlying this figure may be found at https://doi.org/10.6084/m9.figshare.18319232.v1.

https://doi.org/10.1371/journal.pbio.3001551.g003

Importantly, no new cell aggregation processes were detected after cell release, suggesting that all clumps formed either prior to or concomitantly with the release of newborn cells (S2 Video). To examine whether clumps formed due to defects at the level of either plasma membrane or cell wall, we stained plasma membranes using FM4-64 and cell walls using calcofluor-white. We found that the process of plasma membrane invaginations during cellularization appears to be unchanged prior to flip (Fig 3B, S3 Video) and that all newborn cells, even in the clumps, have a distinctive cell wall surrounding them (Figs 3C and S3A). These results show that clumps are formed post-flip in fast-settling mutants.

As no cells appear to aggregate after release of newborn cells, our results suggest that clumps are maintained together in a clonal form. To further confirm this, we sonicated both the ancestor (AN) and S1 clumps and stained them separately with 2 distinct fluorescent dyes prior to mixing them in a 1:1 ratio (S3B Fig). After 1 complete life cycle, we observed that out of 198 clumps, 183 contained only evolved S1 derivates, whereas ancestral cells only randomly and sporadically formed clumps under the conditions of the experiment (a total of 12 small clumps). Only 3 of the clumps (approximately 1.5%) contained cells of both colors (S3B Fig), and each only contained a single AN cell trapped inside a smaller S1 clump. In a control experiment with two differentially stained S1 cultures, we observed an almost identically small number (i.e., approximately 1.7%) of mixed clumps (S3B Fig); hence, it appears that mixes of cells happen only sporadically at low frequencies by random association, irrespective of genotype. Altogether, our results suggest that the evolved clump phenotype is not a result of spontaneous cell aggregation and instead arises from incomplete detachment between cells.

We next examined the kinetics of cellularization and the process by which cells propagate as part of clumps. We counted the number of cells detaching from clumps at cell release and observed variable detachment across all 3 fast-settling mutants. The clumpiest isolate, population S1, was the least prone to cell detachment (Fig 3A and 3D, S2 and S3 Videos). Intriguingly, despite exhibiting similar sedimentation rates, S4 and S9 clumps showed a higher detachment frequency than S1 (Fig 3A and 3D). Image analysis of time-lapse movies also showed that life stage durations varied among fast-settling mutants at 12°C, with S1, S4, and S9 initiating cellularization and undergoing flip, respectively, 5.5, 7.5, and 10.5 hours earlier than the ancestor (S3C and S3D Fig). Post-flip duration also varied significantly among mutants, with S1 and S9 requiring more time to release cells compared to S4 (S3C and S3D Fig). While many aspects of the replication cycle dynamics were variable for these mutants, the duration of cellularization was fairly invariant (approximately 9 hours) (S3C and S3D Fig). Finally, measurements of coenocyte volume show that S4 and S9 coenocytes undergo flip at substantially smaller volumes (approximately 1.8 and approximately 3.3 times smaller, respectively) compared to AN or S1 (Fig 3E). These results show that, despite their shared capacity to clump and similar sedimentation rates, fast-settling mutants exhibit significant variability in their life cycle dynamics, with S4 and S9 mutants initiating cellularization earlier, dispersing from clumps with a higher frequency, and undergoing flip and cell release at smaller coenocyte volumes compared to the S1 mutant.

Increased nuclear number-to-volume ratio leads to faster sedimentation

Above, we observed that S4 and S9 mutants can sediment as fast as S1 despite their smaller coenocyte volumes, suggesting an alternative regulation mechanism of sedimentation rate. Across all experiments reported in this study, we found that approximately 31% of the variance in observations of sedimentation rate could not be explained by cell size only, suggesting that cellular density might also contribute to this variation [37,40,41]. From Stoke’s law, we calculated that excess cellular density, i.e., cellular density minus that of distilled water (1,000 kg/m³), might vary between 40 and 300 kg/m³ for S. arctica wild-type and evolved clones across their life cycle—the upper limits between the densities of pure protein and pure cellulose. Values reached approximately 650 kg/m³ for wild S. nootakensis (Snoo) soon after cellularization, approaching the excess density of pure nucleic acid (Fig 1D). During cell cycle stages prior to cellularization, when cells were most spherical (<36 hours), excess density varied from 40 to 200 kg/m³ across Sphaeroforma isolates.

To better characterize the relationship between sedimentation rate, cell cycle, and size, we performed higher resolution measurements of sedimentation rates over the complete life cycle of the ancestor and all 3 fast-settling mutants at 12°C and 17°C. Consistent with their capacity to form clumps, we observed that the sedimentation rate of all fast-settling mutants increases during growth but, unlike the ancestor, does not recover after cell release to their original levels (Figs 4A and S4A). Interestingly, we noticed that individual S4 and S9 coenocytes sediment faster (approximately 2.5× and approximately 1.6×, respectively) than S1 or AN even before clump formation (24 to 36 hours time points) (Figs 4A and S4A). Such increase in sedimentation rate was not due to a rise in cell size or change in cell shape as both S4 and S9 exhibit smaller cell perimeters throughout the cell cycle (Figs 4B, 4C, S4B, and S4C). Rather, excess cellular density estimations show that both S4 and S9, even prior to cell release and clump formation, tend to be on average 3× denser when compared to the ancestor (Figs 4D and S4D). Altogether, these results show that both cell size and cell density contribute to sedimentation rate variation in S. arctica.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 4. Sedimentation rates variation in fast-settling mutants is associated with variation in cell size and cellular density.

(A) Sedimentation rates of S. arctica AN and evolved mutants during the life cycle at 17°C. Every trace represents an independent experiment. (B) Average perimeter measured from fixed cells every 12 hours over a complete life cycle of 72 hours at 17°C shows that fast-settling mutants increase their size upon cell release. Every trace represents an independent experiment (n > 180 measurements per time point for each independent experiment). (C) Average perimeter of fast-settling cells and clumps at 24 and 60 hours, respectively, show that S4 and S9 cells and clumps have a smaller size when compared to S1. Every square represents an independent experiment, and the white circle represents the median (n > 180 coenocytes per time point for each independent experiment). (D) Excess cellular density of fast-settling individual coenocytes (before cellularization) and clumps (after cell release) at 24 hours and 60 hours, respectively, show that S4 and S9 single cells are denser when compared to S1 and AN. Every square represents an independent experiment, and the white circle represents the median. (E) Quantification of mean DNA content per time point for fast-settling mutants grown in MB at 17°C. Every trace represents an independent experiment (n > 400 coenocytes per time point for each independent experiment). (F) Nuclear doubling time, calculated by linear regression of mean nuclear content at time points from 0 hour to 24 hours. Every square represents an independent experiment, and the white circle represents the median (n > 400 coenocytes per time point for each independent experiment). (G) Boxplots of cell volume measurements of DAPI-stained fixed cells. For 1-, 4-, 16-, and 64-nuclei cells. Cells with 1 nucleus represent newborn cells at the end of the experiment (n > 80 coenocytes per DNA content). (H) Boxplots of nuclear number-to-volume ratio of DAPI-stained cells show significant increase for S4 and S9 fast-settling mutants. Every square represents an independent experiment, and the white circle represents the median (n > 600 coenocytes per strain). (I-K) Temporal transcript abundance of genes mutated in fast-settling phenotype across the native life cycle of S. arctica. The data underlying these graphs may be found in S5 Table. The data underlying this figure may be found at https://doi.org/10.6084/m9.figshare.18319232.v1.

https://doi.org/10.1371/journal.pbio.3001551.g004

As cell size and nuclear division cycles are decoupled in S. arctica [34], we reasoned that increased cell density in S4 and S9 could be caused by an acceleration of nuclear divisions leading to a rise in the number of nuclei per volume. Using DAPI staining to label nuclear DNA, we observed that S4 and S9 undergo nuclear duplication faster (approximately 2 hours) than both AN and S1 (Figs 4E, 4F, and S4E–S4H). By carefully examining the volumes of coenocytes containing the same number of nuclei at the single-cell level, we show that for the same nuclear content, S4 and S9 tend to be 30% to 45% smaller in volume when compared to the ancestor (Figs 4G and S4I). Consequently, both S4 and S9 exhibited the highest number of nuclei per volume (nuclear number-to-volume ratio) (Figs 4H and S4J). Taken together, these results argue that cell density can contribute an appreciable amount to cellular sedimentation rates (up to approximately 50 μm/s) and that mechanistically this could arise by faster nuclear doubling times relative to cell size.

Evolved genetic variation correlating with fast sedimentation

Up to now, our results suggest that S. arctica mutants evolved faster sedimentation using 2 strategies: (i) clump formation; and (ii) increased nuclear number-to-volume ratio. We found that sedimentation rate variation was highly heritable, persisting for 780 generations of passaging for all 10 isolates without selection for sedimentation phenotype, suggesting that the phenotypes have a genetic basis. To test this, we resequenced the whole genomes of both the ancestral clone (AN) and 1 evolved clone per lineage (S1 to S10) obtained at the conclusion of the evolution experiment (Week 8) with high (>30-fold) coverage of the very large genome size of S. arctica, at 143 Mbp. Following very careful variant filtering (S2 and S3 Tables), we identified a total of only 26 independently evolved variants with an average of 2.6 mutations per clone (range 1 to 5 per clone) (S4 Table). Of the 26 variants, 24 (approximately 92.3%) were SNPs (11 coding, and 13 intergenic or intronic), and 2 were insertions (1 coding, 1 intergenic) (S4 Table). Beside the fact that 2 of these variants are identical SNPs at the same position (Sarc4_g3900) and have independently evolved in 2 different backgrounds (S4 Table), no other form of genetic parallelism was detected. While it is very likely that some of these mutations hitchhiked in the background of beneficial driver mutations, we found that the coding SNP variants were clearly skewed toward nonsynonymous changes (9:2), with a cumulative dN:dS ratio of 1.32. This indicates the general presence of positive selection and, hence, adaptative evolution driving this molecular pattern. Based on COG assignments (S6 Table), 4 of the changes are orthologous to genes implicated in signal transduction, and 2 are related to genes with functions in DNA binding or chromosome condensation.

In the absence of molecular genetic tools and functional knowledge of many of the mutated gene targets, we set out to better understand how the distinct genetic variants could have influenced sedimentation rates and clump formation by examining the predicted expression dynamics of mutated genes across the cell cycle. The data were derived from a recently published time-resolved transcriptomics dataset of the S. arctica life cycle [32]. Of the mutated genes, 9 showed no expression during the native life cycle, whereas 12 displayed dynamical expression during cellularization, and the remaining 5 genes were more or less stably expressed (Figs 4I–4K and S4K, S5 Table). We also annotated all mutation-associated genes based on a recent comprehensive orthology search [53] (S6 Table).

The fastest-settling and clumpiest mutant isolated from the population S1 bore a synonymous mutation of a homolog of increased sodium tolerance 1 (Ist1) superfamily and as such likely impacts gene expression rather than gene function. This gene shows a dynamic expression during cellularization and codes for a conserved protein involved in multivesicular body (MVB) protein sorting (Fig 4I, S6 Table) [54,55]. In humans, hIST1, also known as KIAA0174, is a regulator of the endosomal sorting complex required for transport (ESCRT) pathway and has been shown to be essential for cytokinesis in mammalian cells [56]. Similarly, Ist1 orthologs in both budding and fission yeasts play a role in MVB sorting pathway and, when deleted, exhibit a multiseptated phenotype consistent with a role in cytokinesis and cell separation [57,58].

In the clone derived from population S4, we observed 5 distinct mutations, 2 nonsynonymous, 1 intergenic, and 2 intronic SNPs. Among the 2 nonsynonymous SNPs, one causes a E90G change in a homolog of human Kanadaptin (SLC4A1AP), which may play a role in signal transduction [59,60]. Among the noncoding SNPs, 1 mutation is found in an intron of the 7-dehydrocholesterol reductase (DHCR7), expressed during cellularization and known to be key in the cholesterol biosynthesis pathway [61,62], and the second intronic mutation codes for a STE20-like kinase (SLK), which plays numerous roles in cell cycle signaling and actin cytoskeleton regulation (Fig 4J) [63–66].

Finally, among the 5 mutations discovered in the clone derived from population S9, 2 mutations are in transcription factors that are continually expressed during the cell cycle: an intronic SNP in a basic helix–loop–helix (bHLH) transcription factor, and the sole nonsynonymous SNP leading to A923V change in a gene predicted to encode a nucleotide binding C2H2 Zn finger domain [67]. A third mutation was found in an intron of the highly and dynamically expressed homolog of the regulator of chromosome condensation 1 (RCC1; Fig 4K) [68–70]. RCC1 is a chromatin-associated protein implicated in several processes including nuclear formation, mRNA splicing, and DNA replication [69,71–74]. Thus, it may contribute to the accelerated nuclear duplication cycle observed in S9 by impacting cell cycle progression. Altogether, the mutations identified in both S4 and S9 may affect both cellularization and cell separation.

Among the variants detected in evolved clones with intermediate-settling phenotype, we highlight an intergenic mutation 118 bp downstream of Dynamin-1 known to be essential for cytokinesis across different taxa [75–77], and a nonsynonymous mutation in a protein similar to Fibrillin-2 (Sarc4_g7365T), which is an extracellular matrix (ECM) glycoprotein essential for the formation of elastic fibers in animals (S4K Fig) [78–80]. Altogether, our results across all isolates suggest that a large mutational target affects cellular sedimentation and multicellularity.

Sedimentation rate variation across Sphaeroforma species is driven by cell size and density

Lastly, we examined whether the variation in cell sedimentation observed across different Sphaeroforma species (Fig 1E) could also be explained by clumping or increased nuclear number-to-volume ratio. To do so, we investigated the life cycle dynamics, coenocyte volume, and nuclear duplication time among distinct Sphaeroforma sister species. To date, 6 different Sphaeroforma species have been isolated either in a free-living form or derived from different marine hosts: S. arctica, S. sirkka, S. napiecek, S. tapetis, S. gastrica, and S. nootakensis (Fig 5A) [32,46,49–52]. Using previously established growth methods for S. arctica combined with live and fixed imaging, we first observed that all sister species but S. tapetis show a synchronized coenocytic life cycle (Figs 5B and S5A, S4 Video). Similar to abovementioned results with fast-settling mutants, sedimentation rate variations (Fig 1E) could be explained by variations in both cell size and cellular density. Indeed, we first observed that both S. gastrica and S. nootakensis occasionally form clumps, exhibit a lower frequency of cell detachment, and thus have an increased cellular density after cell release compared to the other Sphaeroforma species (Figs 1C, 5B, and 5C, S4 Video). Additionally, despite their increased sedimentation rate (Fig 1C), we found that all sister species exhibited approximately 20% to 45% smaller coenocyte size prior to cell release when compared to S. arctica, which reveals an increased cellular density (Figs 5D, S5B, and S5D, S4 Video). Similar to the fast-settling mutants S4 and S9 above, the increase in cellular density was associated with an acceleration of the nuclear division cycles and the subsequent rise in the nuclear number-to-volume ratio (Figs 5D–5F and S5D–S5F). Notably, S. sirkka and S. napiecek, both previously isolated as free-living [51,52], exhibit an increase in nuclear number-to-volume ratio but no ability to form clumps. Altogether, our results show that, similarly to experimentally evolved strains, fast sedimentation variation could occur by both clump formation and/or increase in the nuclear number-to-volume ratio for Sphaeroforma species (Fig 5H) and thus might represent in itself a widespread and highly variable phenotype in the marine habitat.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 5. Sedimentation rate variation across Sphaeroforma species is associated with clumping and increased nuclear number-to-volume ratio.

(A) A cladogram [49], representing the position of all Sphaeroforma sister species used in the study and their observed phenotypes relative to S. arctica. Cells exhibit either variation in clumpiness and/or nuclear number-to-volume ratio (N/C), which is regulated by the doubling time relative to volume. (B) Representative images of different Sphaeroforma sp. at cell release. Arrowheads indicate the formation of clumps. Associated with S4 Video. Bar, 50 μm. (C) Number of cells detaching at cell release, measured from time-lapse movies, show significant differences among the different sister species. (n > 48 coenocytes per Sphaeroforma sp.). (D) Excess cellular density of Sphaeroforma sp. cells (before cellularization) and clumps (after cell release) at 36 and 72 hours, respectively. Every square represents an independent experiment, and the white circle represents the median. (E) Quantification of mean DNA content per time point for Sphaeroforma sp. grown in MB at 17°C. Every trace represents an independent experiment (n > 300 coenocytes/time point for each independent experiment). (F) Nuclear doubling time, calculated by linear regression of mean nuclear content at time points from 0 hour to 24 hours at 17°C. Every square represents an independent experiment, and the white circle represents the median. (n > 300 coenocytes per time point for each independent experiment). (G) Boxplots of nuclear number-to-volume ratio of DAPI-stained cells at 17°C for Sphaeroforma sister species. Every square represents an independent experiment, and the white circle represents the median. (n > 300 coenocytes per strain). (H) Scheme illustrating a landscape of how phenotypic and genotypic plasticity varies over the physical dimensions of size and cellular density. Gray axis shows the cellular traits affecting these physical dimensions. Colors represent how the different Sphaeroforma species investigated in this study behave across their life cycle. Black = S. arctica (AN), Blue = S. arctica (S1 mutant), Pink = S. arctica (S4 and S9 mutants, S. gastrica, S. nootakensis), Green = S. sirkka, S. napiecek. Thick lines represent the typical cellular behavior. Thin and dotted lines indicate that clumping cells may restart the process at any position. The data underlying this figure may be found at https://doi.org/10.6084/m9.figshare.18319232.v1.

https://doi.org/10.1371/journal.pbio.3001551.g005

Culture conditions

All Sphaeroforma sp. cultures were grown and synchronized as described previously for S. arctica [32,34]⁠. Briefly, saturated cultures in MB (Difco BD, NJ, USA; 37.4 g/L) were diluted into fresh medium at low density (1:250 dilution of the saturated culture) and grown in rectangular canted neck cell culture flask with vented cap (Falcon; ref: 353108) at either 17°C or 12°C, resulting in a synchronously growing culture. Saturated culture of Sphaeroforma sp. are obtained after 3 weeks of growth in MB.

Experimental evolution

Ten replicate population (S1 to S10) of genetically identical S. arctica (AN) were first diluted 250-fold in 5 ml of MB and grown in rectangular canted neck cell culture flask with vented cap (Falcon; ref: 353108) at 17°C. Cells were grown at 17°C rather than the previously used 12°C in order to increase growth rates and accelerate evolutionary outcome. Every 24 hours, the entire population was transferred into a 15-ml falcon tube and allowed to sediment for 2 minutes on the bench. A 5-ml pipette was then positioned vertically and used to collect 500 μl of cell culture from the bottom of the falcon tube. The cells, still vertically positioned in the pipette, were then allowed to sediment once more for 15 seconds before the transfer of a single drop, equivalent to 20 μl, into 5 ml of fresh MB (approximately 250× dilution). Every 7 transfers, a frozen fossil was conserved by adding 10% of DMSO to 1 ml of culture and preserved at −80°C. Single clones of each replicate population (S1 to S10) were obtained at the end of week 8 by serial dilutions. Since S. arctica grows as a coenocyte, a temporal generation is not defined by a complete coenocytic cycle but is equivalent to the doubling of the number of nuclei. We estimated the nuclear doubling time by measuring the number of cells and the number of nuclei per cell for each transfer separately (S1 Table). Briefly, the entire EE experiment comprised approximately 364 generations, in which all populations underwent a total of 28 complete coenocytic cycles. Each coenocytic cycle included two 24 hours subpassages and comprised a total of approximately 13 doublings (S2A Fig, S1 Table). Importantly, the effective population size was kept in a very narrow range across subpassages (at approximately 10⁵) and thus over the entire experiment (S2A Fig, S1 Table). Therefore, in evolutionary terms, the population size was consistently high enough to favor of natural selection over random evolution throughout the course of the experiment. This assumption was reconfirmed genetically by deriving a dN:dS ratio >1 from sequencing data (see main text).

Sedimentation rate measurements

Sedimentation rate was measured for Sphaeroforma sp. every 12 hours for a total of 72 hours unless indicated otherwise. To ensure reproducibility and homogeneous results, saturated Sphaeroforma sp. cultures were sonicated prior to the dilution in fresh MB media (250-fold dilution) using a Branson 450 Digital Sonifier (3 pulses of 15 seconds, 10% amplitude). For each measurement, obtained either from different stages of the cell cycle or from different Sphaeroforma species, 1 ml of cell culture was added into a disposable plastic spectrophotometer cuvette (semi-micro, 1.5 ml) and homogenized by vortex. Optical density (OD₆₀₀) was measured using an Eppendorf Biophotometer (Model #1631) at T = 0, corresponding to the first time point after placing the cuvette in the spectrophotometer. The OD₆₀₀ was then continuously measured every 30 seconds for 3 minutes while cells were slowly sedimenting in the cuvette. To ensure that OD₆₀₀ measurements stayed within the detection limits of the spectrophotometer, early life stages (T0-T48) were not diluted in the cuvette, whereas later life stages (T60-T72) were diluted 1/100 in fresh MB media.

For assessing clump dissociation in S2B Fig, AN or S1 cultures were incubated for 2 hours at 37°C in MB, artificial sea water (ASW) (Instant Ocean, 36 g/L) with different salt concentrations (18 g/L for 0.5X and 72 g/L for 2X) to assess any effect of electrostatic forces, phosphate-buffered saline (PBS) 1X (Sigma-Aldrich) with either Proteinase K at 200 μg/mL final concentration (New England Biolabs, Ipswich, MA, USA) to assess for any protein-dependent effect or sonication using a Branson 450 Digital Sonifier (3 pulses of 15 seconds, 10% amplitude). Only sonication resulted in dissociation of the clumps and a reduction in sedimentation rates.

Maximum likelihood estimation of sedimentation velocity and cellular density based on OD600 sedimentation rate assay

To briefly summarize, we related our OD600 A.U. sedimentation rate measurements and radius measurements (S1 Data) to previously published datasets [37] and [91] (S2 and S3 Data) to gain an estimate of our sedimentation rate measurements in metric units, which allowed maximum likelihood estimation of cellular densities based on these estimates (S1 Data).

We started by estimating the average radius of cells from perimeter measurements as

We estimated sedimentation velocity in our measurements by assuming OD600 (OD) changes at a constant proportional rate of change with respect to time t = 0 and t = 1 by

This yielded a rate of change in arbitrary distance units per second, which we next sought to relate to metric distance units. For this, we turned to the datasets from [40], compiled along with data from other studies by [37] in his Appendix Table 1 (our S2 Data). In this table, 39 observations included joint values of salt percentages (or seawater density) for measurement, temperature, cell diameters, and sedimentation rate measurements for various phytoplankton isolates.

For each of these observations, we calculated seawater density in experimental assays as SC + salinity where salinity constant SC = 35.16504/35 and the salinity is the salt content in g·l⁻¹ [91]. We estimated the specific gravity or density of media used in each sedimentation rate measurement based upon the dataset published by [91] (S3 Data), using a second-order polynomial function of their density measurements as a function of salinity and temperature using R’s lm() function. The error of this estimate is extremely low (<4e-3 kg·m⁻³) and was not propagated downstream.

We next calculated the density p_p of cells across observations based on sedimentation velocity V in m·s, cell radius R in m, and media density p_f by rearrangement of the terminal velocity equation where dynamic viscosity μ = 0.00109 Pa·s and gravitational acceleration g = 9.780 m·s². This yielded a median phytoplankton excess density (p_p- 1,000) of 139 kg·m⁻³ with a range of 30 to 1,300 kg·m⁻³. Some excess density estimates exceeding protein (220 kg·m^-3) and cellulose (500 kg·m⁻³) have previously been suggested to arise by calcide in diatoms [92]. We concluded that this method of estimating cell density yielded similar values to those published by, for example, [92] and [40]. For downstream analyses, we excluded obvious outliers, including measurements of samples with densities exceeding that of cellulose, and measurements of D. rex, a large diatom, which, in the Smayda dataset, exhibited extraordinarily low sedimentation rates given their size.

To estimate the sedimentation velocities of our dataset, we assumed that our data (S1 Data) would fall within the typical measurement in the Smayda dataset (S2 Data). For the outlier-excluded subset of the Smayda dataset, we calculated an expected sedimentation velocity for what we would measure in our experimental setup based on the specific gravity of the seawater formulation we used in our measurements (37.4 g/l or a solvent density of 1,028.9 kg·m⁻³). We then used least-squares minimization to estimate 2 parameters: a scalar S of the AU velocity measurements, dODdt, which could best match our dataset with these expected velocities, and an average density parameter p_{p_hat}, which could predict these values and the Smayda dataset’s values based on Stokes’ law. For this, we minimized the loss function where V₀ is either the seawater density–corrected velocity from Smayda or the dODdt parameter we calculated above, and H is the one-hot binary scalar in [0,1] corresponding to whether the i’th data point in our N observations was from Smayda’s or our dataset, respectively. We used R’s optim() function with the “L-BFGS-B” method with initial parameters values of S = 0.03 and p_{p_hat} = 100 and repeated this fit for 500 10-fold (10% out-of-bag) bootstrap samples of individual observations across our full dataset and Smayda’s to gain an estimate of the error on the parameters.

S and p_phat fits were positively correlated across testing/training folds (Pearson’s ρ = 0.97); however, mean values were fairly limited, with S = 0.028 ± 0.002 and p_{p_hat} = 78 ± 6.4 kg·m⁻³ (with error equal to the standard deviation of estimates across 500 folds). These narrow estimates indicated that the fits were reasonably well defined by the underlying dataset. The means and standard deviations across predictions of out-of-bag samples are what we have reported as means and standard error in S1 Data and propagated along with interreplicate and batch error reported in the figures and text.

Percentage of clumps measurements

To measure percentage of clumps across all mutants and transfers in S2C Fig, we first sonicated saturated S. arctica cultures using a Branson 450 Digital Sonifier (3 pulses of 15 seconds, 10% amplitude). Cells were then diluted in fresh MB (1:250 dilution), and cell concentration was then measured using a hemocytometer. Approximately 20 cells were then transferred per well in a 96-well plate. Cells were then monitored every 24 hours using an inverted optical microscope, and the percentage of clumps observed after cellularization was measured manually using a tally counter. This experiment was performed 3 independent times, and error bars are standard deviations.

Head-to-head competition

To perform the fitness experiment (head-to-head competition), we used saturated and sonicated cultures of the ancestor (AN), S01 and S03 evolved isolates. Following the measurement of the cell concentration using a neubauer chamber, we independently mixed both S01 and S03 with the AN at an approximately 1:1 ratio and subjected them to either the same selective regime as the evolution experiment (see above) or a control culture that was simply maintained in exponential growth without sedimentation selection. The experiment was maintained for 72 hours with passages every 24 hours. To quantify the outcome of the experiment, every 24 hours for 72 hours (7.5 to 12.5 generations), after dilution of the competition culture, we counted the number of clumpy and nonclumpy cells at each passage. For this, a fraction of the cultures was sonicated and highly diluted to obtain fewer than 10 cells per well of a 96-well plate. These cells were allowed to undergo a full life cycle in order to count by microscopy the proportion of cells, which bore the clumpy phenotype. The high penetrance of the clumpy phenotype in these clones therefore allowed not only a quantification of the clumpy phenotype itself in competition, but also the measurement of overall clonal fitness of these derived isolates.

Each data point for the fitness assay was a count of cells with an associated binomial error. In Figs 2E and S2D, this error is represented as 95% confidence intervals calculated using the binom.logit() function from the R package “binom” (Sundar Dorai-Raj (2014). binom: Binomial Confidence Intervals for Several Parameterizations. R package version 1.1–1. https://CRAN.R-project.org/package=binom).

The fitness measurements began from highly synchronized cultures, and, therefore, reporting a per-generation fitness value was clouded by a sudden jump in cellular counts that occurred at 48 hours. Therefore, for simplicity and given the clear time-dependent changes in clumpy phenotype frequencies, we report fitness in terms of log2 fractional change per unit time (“log2 selection coefficient” in days). To obtain these numbers, we first used R’s rbinom() function to resample the dataset 500 times, with each resampling using each sample’s original measurement of clumpy cell count, total cell count, and proportion clumpy. For each resampling, after calculating the new value for proportion clumpy, we logit2 transformed the data and rescaled to the initial measurement with the following equation: where p is the proportion of the population that was clumpy, the tx underscore is each time point, and the t0 underscore is the time point of the initial proportion measurement in units days. The slope of the least-squares fit for this quantity with respect to time was obtained by R’s lm() function, yielding the per day change for each resampled biological replicate (the “log2 selection coefficient”). The mean and standard deviation of this coefficient across all resampled replicates were taken as the fitness point estimate and standard error, respectively, and the 95% confidence intervals for Fig 2D is that standard error times 1.96.

Heritability measurements

To obtain a coarse understanding of how heritable our phenotypes were, we used a statistical genetic approach to quantify the heritability of traits, or what proportion of the variance in phenotype is due to an individual’s inherited state. Briefly, the total Variance of a measured phenotype V_P(Total) can be variance partitioned between Environment, Genetics (or other inherited factors), and technical error or stochastic events (epsilon):

The different partitioning terms in V_P(T) can be expressed as a proportion of V_P(T):

And, therefore, we can refer to the proportion of variance due to genetics as pV_P(G).

In the lab, we eliminated environmental variance with tightly controlled experimental conditions such as temperature and the number of hours a measurement was taken after cell cycle synchronization. This allowed us to define heritability therefore as the environment-controlled genotypic variance:

For this, we determined H for DNA content, perimeter length, sedimentation rate, and fitness phenotypes by calculating the variance explained by mean phenotype values within distinct genotype/environment combinations (S1A Fig). The results show that H exceeds 95% across phenotypes, and across the entire dataset, H exceeded 99% of the total phenotypic variance (ANOVA F = 1,118 on 252 and 735 DF, p = 0). This means that for a typical individual genotype in a given environment, we could predict its average phenotypic measurement with >97% accuracy.

Microscopy

Microscopy of live and fixed cells was performed using a Zeiss Axio Observer Z.1 Epifluorescence inverted microscope equipped with Colibri LED illumination system and an Axiocam 503 mono camera. An EC Plan-Neofluar 40x/0.75 air objective was used for images of fixed cells, and an N-ACHROPLAN 20x/0.45na Ph2 air objective was used for all live imaging, unless indicated otherwise.

Cell fixation and staining

Throughout this study, saturated Sphaeroforma cultures were mildly sonicated prior to diluting them 250X in fresh marine broth to initiate a synchronized culture. To assess for any temperature dependency, cultures were grown at both 17°C and 12°C, and measurements were conducted every 12 hours for a duration of 72 hours. For every time point, cells were fixed using 4% formaldehyde and 250 mM sorbitol for 30 minutes before being washed twice with PBS. For nuclei staining, cells were centrifuged at 1,000 rpm for 3 minutes after fixation and washed again 3 times with PBS before adding DAPI at a final concentration of 5 μg/mL to 5 μl of concentrated sample. DAPI-stained samples were imaged to measure DNA content and coenocyte size. It is important to note that results obtained from fast-settling mutants prior to cell release correspond to measurements of unicellular coenocytes (24 hours for 17°C and 36 hours for 12°C), whereas results collected after cell release correspond to measurements of multicelled clumps (48 hours for 17°C and 72 hours for 12°C). For cell wall staining, cells were incubated with Calcofluor-white (Sigma-Aldrich) at a final concentration of 5 μg/ml from a 200X stock solution prior to fixation. Cells were then fixed as previously mentioned and concentrated before being disposed between slide and coverslip.

Live cell imaging

For live cell imaging, saturated cultures were diluted 250× in fresh MB medium inside a μ-Slide 4- or 8-well slide (Ibidi) at time 0. To ensure oxygenation during the whole period of the experiment, the cover was removed. To maintain the temperature at 17 or 12°C, we used a P-Lab Tek (Pecon GmbH) Heating/Cooling system connected to a Lauda Ecoline E100 circulating water bath. To reduce light toxicity, we used a 495-nm Long Pass Filter (FGL495M- ThorLabs). For plasma membrane live staining (Fig 3B, S3 Video), FM4-64 (Invitrogen) at a final concentration of 10 μM from a 100× DMSO-diluted stock solution was added at time 0 unless indicated otherwise in figure legends. For cytoplasmic staining of cells in S3B Fig, cells were either stained with CellTrace CFSE Cell Proliferation Kit (Thermofisher) or CellTrace Calcein Red-Orange (Thermofisher).

Image analysis

Image analysis was done using ImageJ software (version 1.52) [93]⁠. For nuclear content distribution across Sphaeroforma sp.’s life cycle, fixed and DAPI-stained coenocytes were imaged, and the number of nuclei per coenocyte was counted using the ObjectJ plugin in imageJ. To compute nuclear duplication times, log2 of geometric mean of DNA content was calculated as: where fi is the fraction of cells and xi the DNA content (number of nuclei per cell) (ploidy) of each i-th DNA content bin. Nuclear doubling times were computed as linear regression of log2 of geometric mean of DNA content versus time. Note that for S. tapetis, nuclear doubling times could not be computed due to the asynchrony in growth. For measurements of cell volume in live and fixed cells, we used the oval selection tool to draw the contour of each cell and measured cell perimeter. As cells are spherical, we computed cell volume as V = 4/3πr³ where r is the cell radius. For measurements of clumps perimeter, we transformed the images into binaries to ensure later segmentation. We then used the particle analysis function in ImageJ with a circularity parameter set to 0.15 to 1 to measure cell perimeter. For nuclear number-to-volume ratios, the number of nuclei was divided by the coenocyte volume measured as previously described for fixed cells. All figures were assembled with Illustrator CC 2020 (Adobe). Several figures were generated using ggplot2 in R version 4.0.5 [94].

Sphaeroforma arctica genome sequencing and assembly

Genomic DNA was extracted for the ancestral strain (AN) and a single clonal isolate from each evolved population (S1-S10) using QIAamp DNA Blood Midi Kit (Qiagen) following the manufacturer’s recommendations from 50 mL culture incubated at 17°C for 5 days in 75 cm² flasks. The Qubit (Invitrogen) quantification ranged between 3 and 13 μg of genomic DNA in total. All of the subsequent steps were performed by the CRG Genomics Unit (Barcelona): Sequencing libraries were prepared from the pure high molecular weight DNA using TruSeq DNA HT Library Preparation kit (Illumina HiSeq Sequencing v4 Chemistry). A paired-end library with a target insert size of approximately 500 bp was sequenced on an Illumina HiSeq2500 platform in paired-end mode, with read lengths of 125 bp. The resulting paired raw read files were demultiplexed by the sequencing facility and data stored in 2 separate, gzip-compressed FASTQ files of equal sizes. Genome sequencing data have been deposited in NCBI SRA under the BioProject accession PRJNA693121.

Bioinformatic analyses of the genomes