Engines of change: Transposable element mutation rates are high and variable within Daphnia magna

Eddie K H Ho; Emily S Bellis; Jaclyn Calkins; Jeffrey R Adrion; Leigh C Latta IV; Sarah Schaack

doi:10.1371/journal.pgen.1009827

. 2021 Nov 1;17(11):e1009827. doi: 10.1371/journal.pgen.1009827

Engines of change: Transposable element mutation rates are high and variable within Daphnia magna

Eddie K H Ho ^1,^#, Emily S Bellis ^1,^2,^#, Jaclyn Calkins ^1,³, Jeffrey R Adrion ⁴, Leigh C Latta IV ^1,⁵, Sarah Schaack ^1,^*

Editor: Lindi Wahl⁶

PMCID: PMC8594854 PMID: 34723969

Abstract

Transposable elements (TEs) represent a major portion of most eukaryotic genomes, yet little is known about their mutation rates or how their activity is shaped by other evolutionary forces. Here, we compare short- and long-term patterns of genome-wide mutation accumulation (MA) of TEs among 9 genotypes from three populations of Daphnia magna from across a latitudinal gradient. While the overall proportion of the genome comprised of TEs is highly similar among genotypes from Finland, Germany, and Israel, populations are distinguishable based on patterns of insertion site polymorphism. Our direct rate estimates indicate TE movement is highly variable (net rates ranging from -11.98 to 12.79 x 10⁻⁵ per copy per generation among genotypes), differing both among populations and TE families. Although gains outnumber losses when selection is minimized, both types of events appear to be highly deleterious based on their low frequency in control lines where propagation is not limited to random, single-progeny descent. With rate estimates 4 orders of magnitude higher than base substitutions, TEs clearly represent a highly mutagenic force in the genome. Quantifying patterns of intra- and interspecific variation in TE mobility with and without selection provides insight into a powerful mechanism generating genetic variation in the genome.

Author summary

Transposable elements (TEs) are a significant portion of most eukaryotic genomes, yet our understanding of their rates of mobility and their patterns of accumulation remain very limited. Here, we estimate genome-wide rates of gain and loss of TEs in Daphnia magna, a well-studied model organism in ecology, and compare these rates of mutation to the long-term accumulation of TEs in the genome. Rates vary remarkably among genotypes and populations, and between different types of TEs within the same lineage. Despite this variation, over long time periods, TE content in the genome is extremely similar across genotypes within the species. We compare our results to the few estimates available from other taxa, and argue that TEs are an important source of mutagenesis in the genome worthy of further investigation.

Introduction

It is now known that transposable elements (TEs) make up a significant proportion of the genome in most eukaryotes, and in some cases even represent the majority of the sequence (e.g., [1–3]). Although commonly referred to as ‘junk DNA’ or genomic ‘parasites’, and therefore masked (or removed) in genomic analyses in favor of focusing on genic regions [4], the importance of TEs is gaining wider appreciation and the repetitive landscape of the genome is no longer ignored [5, 6]. Notably, there are now many high profile examples of TEs performing functional roles in the host genome (e.g., [7]) and recent work has cited their role in numerous biological processes, such as adaptation and speciation (e.g., [8–10]). The potential influence of TEs at the genomic level, whether structural (e.g., [11]), direct (e.g., contributing new coding or regulatory sequences; [12]), or indirect (e.g., changing the epigenomic landscape of the host genome; [13, 14]), is now known to be significant [15].

Because TEs are mobile and far outnumber ‘regular’ protein-coding genes in most eukaryotic genomes, elucidating their patterns of replication, transposition, and excision/deletion is a major task that spans subdisciplines from molecular biology to population genetics [16, 17]. Understanding the dynamics of TE proliferation includes knowing how TEs jump between lineages (horizontal transfer of TEs [HTT]; [18, 19]), differential success among TE families in various host lineages (e.g., [20]), and how TEs “die” or go extinct, or are resurrected (e.g., [21]). Indeed, the idea that genomes are like habitats and that TEs are like individuals (and TE families like species) has gained popularity as a way of characterizing the complexities of TE activity in different host genomes (e.g., [22]). Furthermore, the notion that TEs and their host genomes co-evolve is now widely acknowledged [23]. On average, the effects of new TE insertions, like all spontaneous mutations, are thought to be deleterious, although there are longstanding debates about whether the majority of these negative effects are direct (e.g., interrupting genes) or indirect (e.g., increasing the risk of ectopic recombination) [24]. More broadly, the outcomes of TE activity in host genomes is increasingly a target of investigation and is known to range from beneficial to neutral to deleterious [25].

Ultimately, the TE content observed in a lineage is the net product of the intrinsic mutational properties of the TEs, combined with the host genome’s cellular and genomic defense system, which is then acted upon (over evolutionary time scales) by population genetic factors such as the strength of selection and genetic drift. An important question is to what degree the genetic variation generated by TEs is altered or retained in natural populations. If selection can operate efficiently, TEs should not accumulate to high copy number, unless their mutation rates are very high. On the other hand, if effective population sizes or recombination rates are low, selection may not act efficiently, and TEs could accumulate even with low rates of gain [26, 27]. Comparing TE dynamics in the laboratory versus in natural populations can reveal the relative roles of mutation, selection, and drift. Furthermore, quantifying TE dynamics among closely-related lineages reveals how the mutational process and/or evolutionary constraints vary within and between genotypes, populations, and species due to host differences. Finally, contrasting the rate and spectra of TE mutations with other types of more well-studied mutations (e.g., base substitutions) or mutational processes that might affect their spread (e.g., gene conversion) is critical for understanding how, and how fast, genetic variation is generated.

Here, we compare patterns of TE activity over short time periods using a mutation accumulation (MA) experiment, where selection is minimized, to patterns of long-term accumulation by comparing TE content among genotypes from multiple populations and between congeners using Daphnia. Daphnia are an excellent model organism for studying TEs (and mutations, more broadly) because they can reproduce asexually, removing the complicating influence of meiosis and sex on proliferation, and have been shown to have high mutation rates for other categories of mutation [28–30]. Daphnia are aquatic microcrustaceans (Order: Cladocera) often used in ecological and toxicological studies, but which have more recently become the focus of evolutionary and genomic research [31]. In this study, we quantify the TE profiles of 9 starting genotypes sampled from three populations of D. magna across a latitudinal gradient (Finland, Germany, and Israel; S1 Table). We use those same genotypes to perform a multi-year MA experiment to directly estimate rates of gains and losses for all known TEs. We also compare our results to the congener, D. pulex, for which some similar data are available, and to mutation rates for other types of mutation that have been measured in D. magna previously [29]. While both D. magna and D. pulex appear extremely similar in morphology, physiology, behavior, distribution, and life-history, they do differ in genome size (D. magna > D. pulex by ~30%; [32, 33]) and mutation rate (D. magna > D. pulex; [28, 29, 34]).

Patterns of long-term TE accumulation can be measured in several ways: abundance and diversity of TEs present in the genome, insertion site polymorphism among lineages, and mean pairwise divergences (MPDs) of copies of each TE family, where lower values are assumed to represent more recent activity because copies will not have diverged yet due to the accumulation of point mutations. Direct observations of TE movement in real-time using MA experiments represent the gold standard for accurate rate estimates, but have been rarely used to quantify rates of TE movement (reviewed in [35]). In MA experiments, descendent lineages are propagated via single-progeny descent from a known ancestor to minimize natural selection and lines are sequenced to count the number of events per copy per generation and calculate rates. Importantly, while there are two kinds of events that can be scored for a particular TE copy—gains and losses—there are a number of ways by which these two events can occur, even in asexually-reproducing lineages. New TE copies can result from insertions (transposition or retrotransposition), duplication events, polyploidization, DNA repair, gene conversion events, and/or ectopic recombination. Similarly, loss of a TE can be due to excision (although not all elements are capable of excision [e.g., Class 1 retroelements]), deletions (if a TE was present in a deleted region), gene conversion, or ectopic recombination events. In the vast majority of cases, the exact mechanism of gain or loss is not known, nor is the degree to which the host genome has co-evolved molecular mechanisms to suppress even active TE families. Furthermore, the likelihood of gain and loss via these different mechanisms may vary, for example among sites that are initially unoccupied, heterozygous, or homozygous for a TE (Fig 1), and thus we predict rates of TE activity to vary among TE families based on a number of factors (including TE type, mechanisms of mobility, copy number, and/or the time since the TE first entered the host genome). Ultimately, our goals are to measure TE mutation rates and determine to what extent they vary across lineages, compare TE dynamics over the short (with and without selection) and long time scales, and determine if rates of TE movement correlate with more frequently measured mutation rates, such as base substitution mutation rates.

Fig 1 — Different mechanisms can explain the categories of loss and gain of TEs at a given locus (0→1, 1→2, 1→0, and 2→1) that occur in asexually-reproducing, diploid organisms like *Daphnia magna*. For each type of gain or loss, check marks indicate the qualitative, relative likelihood of a given mechanism and X marks indicate a particular mechanism cannot produce that type of gain or loss.

Results

To quantify the long-term patterns of TE accumulation, we surveyed the whole genome of 9 genotypes of D. magna from three populations and characterized the TE content using three metrics: 1) overall abundance and diversity, 2) insertion site polymorphism, and 3) mean pairwise divergence among copies in each family or superfamily. To quantify short-term patterns of mobility, we directly estimated TE mutation rates (gains and losses; Fig 1) based on events observed during a multi-year mutation accumulation (MA) experiment initiated from each of the same 9 genotypes. In these experiments, descendant lines are either propagated via single-progeny descent (to minimize selection) or maintained at large population sizes (selection is not minimized). We examine intra- and interspecific variation by comparing our results from D. magna collected from populations along a latitudinal gradient (Finland, Germany, and Israel; Fig 2A) to the congener, D. pulex, wherever possible. D. magna and D. pulex assemblies were of similarly good quality, possessing N50 of approximately 1 Mb and containing greater than 77% of the complete genes from the Arthropod reference gene set (S2 Table). Lastly, we compare TE mutation rates from D. magna to base substitution mutation and gene conversion rates estimated in the same lineages to see if patterns of TE rate variation covary with other mutational processes.

Characterizing TE content in Daphnia

The relative abundance of TEs across the nine D. magna genotypes is similar (Fig 2B and Tables 1 and S3 and S4). In Daphnia, LTR retrotransposons are the most common type of TEs, with the Gypsy superfamily being the most abundant (Table 1). All other categories of TEs (DNA transposons, Long and Short Interspersed Nuclear Elements [LINEs and SINEs], and rolling circle [RCs]) constitute less than 2% of the genome, although DNA transposons are still highly diverse with 18 different families represented (S5 Table). Although abundance is consistent within D. magna when comparing across genotypes, overall abundance and the abundance of individual families differed between D. magna and its congener, D. pulex (D. pulex > D. magna; t₈ = -14.2, P < 0.0001; Fig 2B and S6 Table), with 7 and 19 families of DNA transposons being specific to D. magna and D. pulex, respectively.

Table 1. Abundance of TE types by family or superfamily for Daphnia magna (averaged across nine genotypes) and D. pulex (PA42 [PRJNA307976]).

TE Type	Family or Superfamily	Percent of assembly		Active in D. magna MA lines?
TE Type	Family or Superfamily	D. magna	D. pulex	Active in D. magna MA lines?
DNA	Academ-1	0.05	0.01	Y
	CMC-EnSpm	0.15	0.23	Y
	Dada	0.00	0.08	N
	hAT	0.02	0.00	N
	hAT-Ac	0.38	0.29	Y
	hAT-Charlie	0.01	0.00	N
	hAT-hATm	0.04	0.03	N
	hAT-Tip100	0.03	0.00	N
	IS3EU	0.00	0.05	N
	Kolobok-H	0.00	0.07	N
	Merlin	0.06	0.00	N
	MULE	0.00	0.02	N
	MULE-F	0.00	0.05	N
	MULE-MuDR	0.03	0.15	N
	P	0.08	0.09	Y
	P-Fungi	0.04	0.00	N
	PIF-Harbinger	0.05	0.05	N
	PIF-ISL2EU	0.04	0.03	Y
	PiggyBac	0.00	0.05	N
	Sola-1	0.00	0.11	N
	Sola-2	0.02	0.03	N
	Sola-3	0.00	0.06	N
	TcMar-Fot1	0.04	0.08	N
	TcMar-Tc1	0.07	0.02	N
	TcMar-Tigger	0.00	0.05	N
	Zator	0.00	0.01	N
	Zisupton	0.04	0.00	N
	Unclassified	0.01	0.35	N
	Total	1.16	1.89
LINE	I	0.22	0.05	Y
	I-Jockey	0.03	0.05	N
	L1	0.00	0.03	N
	L1-Tx1	0.11	0.21	N
	L2	0.00	0.30	N
	Penelope	0.02	0.09	Y
	R1	0.02	0.11	N
	R1-LOA	0.00	0.05	N
	R2-NeSL	0.10	0.16	N
	Rex-Babar	0.00	0.02	N
	Tad1	0.00	0.04	N
	Total	0.51	1.10
LTR	Copia	0.35	0.69	N
	DIRS	0.25	0.28	Y
	ERV1	0.00	0.04	N
	ERVK	0.00	0.10	N
	Gypsy	2.12	1.84	Y
	Ngaro	0.08	0.04	N
	Pao	0.94	1.12	Y
	Unclassified	0.05	0.02	N
	Total	3.79	4.12
SINE	5S-Deu-L2	0.00	0.14	N
	ID	0.02	0.02	N
	tRNA-Core-RTE	0.00	0.05	N
	tRNA-V-CR1	0.02	0.00	N
	Unclassified	0.40	1.09	N
	Total	0.43	1.30
RC	Helitron	0.05	0.11	N
Retroposon	L1-dep	0.00	0.03	N
TOTAL		5.94	8.82

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*Abundance estimates based on RepeatMasker method (see Methods for details).

It is important to note, TE abundance can be measured in two ways—repeat masking a genome assembly with a TE library or mapping short reads to a TE library to use depth of coverage as an estimate of abundance. In fact, estimates of overall TE content in D. magna differ by more than a factor of two using these two methods (6% using repeat masking and 16% using read mapping; S4 Table), which is likely because repeat masking is more sensitive to the quality of the assembly. We recommend using a read-mapping approach for accuracy, however repeat masking is more common and provides the opportunity for inter- as well as intraspecific comparisons here; see S2 Text, S2 Text, and S5 Table for estimates using both methods.

Variation in TE activity over long time periods

Despite the consistency in terms of abundance, we quantified TE insertion polymorphisms (TIPs) among the 9 genotypes of D. magna sampled and were able to clearly distinguish genotypes based on their population-of-origin using principal components analysis (PCA; Fig 2C), regardless of reference genome used (S1 Fig). Depending on the assembly used as reference (see S2 Text), we identified between 1442 and 1903 TE sites, of which 13% to 16% were polymorphic across the 9 genotypes (S7 Table) and which, by k-means clustering, always revealed non-overlapping clusters corresponding to their population-of-origin (S8 Table). On average, we find 19% of TIPs are specific to a single genotype (i.e., singletons) and an additional 29% of TIPs are specific to a single population (Figs 2D and S2 and S3, and S9 and S10 Tables). Whether a particular position in the genome is occupied by a TE is determined by events at multiple levels: the chromosome level (e.g., gains/losses due to insertions, deletions, or gene conversion events,) and/or at the individual/population level (e.g., frequency of sexual reproduction or the strength of selection against new insertions). An additional interpretation of an excess of singletons is that the TE family is, or has recently been, active.

Another indicator of recent activity is low levels of mean pairwise divergence (MPD) among copies belonging to a given TE family because new copies have not yet accumulated point mutations. The range of MPDs across TE families was 15–31%, with SINE elements having the lowest values (S4–S15 Figs). Surprisingly, we observed higher MPDs in TE families that were currently active in our MA experiments (~21% for active families and ~19% for inactive; S2 Text and S11 Table). An alternative explanation for high MPDs is a higher base substitution mutation rate, which has been reported for D. magna (greater than D. pulex; [29]). While we observed interspecific differences in MPDs across TE families between the two species, they were not consistently higher in D. magna as one would predict (S12 Table), nor did they correlate with known intraspecific variation in base substitution mutation rates within this species (S16 Fig; ρ = -0.66, t₇ = -2.3, P = 0.055).

Estimated rates of TE loss and gain using mutation accumulation experiments

We used mutation accumulation (MA) experiments initiated from each of the 9 genotypes of D. magna from each of the three populations to estimate overall (Tables 2 and S13) and family-specific TE mutation rates (Table 3). Using whole genome sequence (WGS) data from the MA lines, we detected 67 gain and 28 loss mutations; S14 Table shows the location and read support for each event. Rates of gain across MA lines ranged from 0 to 22.6 x 10⁻⁵ per copy per generation with a mean rate of 1.39 x 10⁻⁵ /copy/gen (95% CI: 0.41 x 10⁻⁵–2.66 x 10⁻⁵) and loss rates ranged from 0 to 31.8 x 10⁻⁵ /copy/gen with a mean of 1.70 x 10⁻⁵ /copy/gen (95% CI: 0.53 x 10⁻⁵–3.23 x 10⁻⁵; S15 Table). Looking across genotypes, averaging across rates for all TE families (with non-zero copy numbers in all genotypes), it is clear that some genotypes have a bias towards gains while others exhibit mainly losses (Fig 3A and S16 Table). To test for a population effect, we fit a binomial mixed effects model (Fig 3B; for gains, χ² = 5.9, df = 2, p = 0.0514 and losses χ² = 12.1, df = 2, p = 0.0024). Post-hoc Tukey HSD tests reveals that Israel genotypes had lower gain rates than Finland genotypes (p = 0.039) and Germany genotypes had greater losses than Israel genotypes (p = 0.0005; S17 Table). In addition to the gain and loss rates, we also calculated a net mutation rate for each genotype (S16 Table) and for each active TE family (S18 Table). These rates range from negative (e.g., P elements in genotype IB are decreasing at a rate of -7.44 x 10⁻⁴ per copy per generation) to positive (e.g., Penelope elements increasing at a rate of 9.06 x 10⁻⁴ /copy/gen in IC), and can even vary for the same TE family among genotypes (e.g., -2.22 and 4.06 x 10⁻⁴ /copy/gen for Gypsy elements in GA and FC, respectively).

Table 2. Number of events and mean TE mutation rates (per copy per generation, including 95% confidence intervals [CI]) for gains and losses based on whole genome sequence data from 66 Daphnia magna mutation accumulation and extant control lines descended from 9 starting genotypes collected from Finland, Germany, and Israel.

	Mutation Accumulation Lines				Extant Control Lines
	Mutation rate (× 10⁻⁵) per copy/ per generation				Mutation rate (× 10⁻⁵) per copy/ per generation
	Number of events	Mean	Lower CI	Upper CI	Number of events	Mean	Lower CI	Upper CI
Gain (all kinds)	67	1.39	0.41	2.66	2	0.002	0	0.005
0-->1 gain	62	1.17	0.23	2.42	1	0.001	0	0.002
1-->2 gain	5	0.22	0	0.63	1	0.001	0.000	0.004
Loss (all kinds)	28	1.7	0.53	3.23	17	0.23	0.064	0.46
2-->1 loss	2	0.04	0	0.09	9	0.11	0.02945	0.21
1-->0 loss	26	1.67	0.46	3.21	8	0.12	0.004	0.33
Total	95	3.09	1.37	5.14	19	0.23	0.064	0.47

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*Confidence intervals estimated by bootstrapping across MA lines 10000 times.

Table 3. Number of events and rates (plus 95% confidence intervals [CI]) of gain and loss (per copy per generation) for each TE superfamily in which events were observed averaged across all MA lines.

Gains and losses based on whole genome sequence data from Daphnia magna mutation accumulation lines descended from 9 starting genotypes collected from Finland, Germany, and Israel.

Type	Family/ Superfamily	Gains				Losses
Type	Family/ Superfamily	Number of events	Mean Rate (× 10⁻⁵)	Lower CI	Upper CI	Number of events	Mean Rate (×10⁻⁵)	Lower CI	Upper CI
DNA	Academ-1	1	4.9	0.0	14.6	0	-	-	-
	CMC-EnSpm	0	-	-	-	1	0.3	0.0	1.0
	hAT-Ac	1	3.6	0.0	10.9	3	5.9	0.0	14.7
	P	0	-	-	-	1	9.0	0.0	27.1
	PIF-ISL2EU	1	10.1	0.0	30.3	1	3.3	0.0	10.0
LINE	I	0	-	-	-	1	4.4	0.0	13.1
LINE	Penelope	1	11.0	0.0	32.9	0	-	-	-
LTR	DIRS	0	-	-	-	2	10.2	0.0	27.2
	Gypsy	59	7.9	3.5	13.4	11	6.1	1.0	13.1
	Pao	4	0.8	0.1	1.8	8	7.2	1.2	15.6

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*Confidence intervals estimated by bootstrapping across MA lines 10000 times.

Fig 3 — Gain and loss rates (per copy per generation) for each (A) genotype and (B) population of D. *magna* averaged across all TE families. Mean rates for MA lines from Finland, Germany and Israel represented in gold, green and blue, respectively, and are provided, along with estimates of 95% CI, in S17 Table. In (A), gain rates for FB, IA and loss rate for FB are zero.

When selection was not minimized (i.e., in the extant control lineages maintained in large populations in parallel to the MA lines), we only detected 2 gain and 17 loss mutations (Tables 2, S19, and S20). Fitting binomial mixed-effects models, we found that EC lines had significantly lower gain rates (χ² = 27.9, df = 1, P < 0.001) and significantly lower loss rates (χ² = 10.5, df = 1, P = 0.0012) compared to MA lines, revealing the deleterious effect of TE activity. Furthermore, gain rates in MA lines were 695x higher than in EC lines, compared to loss rates which were only 7.4x higher in MA lines, suggesting that TE gains are much more deleterious than losses (Table 2).

Validation methods

Rather than perform PCR validation to gauge the sensitivity of our methods, given that each event was of an unknown length, we performed simulations to estimate the false discovery and false omission rate (FDR and FOR) for the four cases of TE events that can occur (Fig 1 and S21 Table). FDRs were relatively low (< 3%) for all four types of mutations (S2 Text and S21 Table), and neither FDRs or FORs varied greatly for TEs of different lengths or for different mutational events (S22 Table). Mutation rates for each type of event in the MA and EC lines adjusted for FDRs can be found in S13 Table. Notably, the fact that the four cases of events are not equally likely (most gains were novel (0 → 1 [n = 62/67]) and most losses were at previously heterozygous sites (1 → 0 [n = 26/28]) is potentially revealing about what proximal mechanisms explain the bulk of TE proliferation and loss (see Discussion). It is important to note, our rate estimates for TE activity likely represent a lower bound. This is, in part, because our analyses focus only on those TEs that could be classified as belonging to one of the five major groups of known TEs (rates for all TEs, classified and unknown, are presented in S23 and S24 Tables).

TE mutation rates are not correlated with other types of mutation rates

Overall, TE mutation rates in D. magna vary intraspecifically among genotypes (Fig 3A) mirroring the high levels of intraspecific variation observed in other mutation rate estimates for this species (see [28, 29]). In terms of frequency per site, TE mutations are intermediate among the other types of mutation examined so far in D. magna, (i.e., microsatellite mutation rates are much higher (~10⁻²) and nuclear and mtDNA base substitution rates are much lower (~10⁻⁸ and ~10⁻⁷, respectively), on a per site per generation basis). As expected, we observe more events in higher copy number families (S17A Fig). We looked at the relationship between rates of TE gain and loss (and net rates) and the proportion of the genome that is TEs in each genotype and found no correlation (S25 Table), nor do TE rates correlate with base substitution mutation rates (Fig 4A and S25 Table). The only correlation with other mutational processes is between TE mutation rates and gene conversion rates (when plotting only rates for TE events that are likely to be caused by gene conversions [1→0 TE losses and 1→2 TE gains]; ρ = 0.83, t⁷ = 3.91, P = 0.0058; Fig 4B), although even this predicted correlation is driven largely by one genotype (GB) with high estimates for both rates.

Fig 4 — (A) Base substitution rates (per bp per generation) plotted against TE mutation rates (per copy per generation). Circles represent the sum of all TE gains and losses, triangles represent only 0→1 TE gains. (B) Gene conversion rates are plotted against TE events that could be caused by gene conversion (the sum of 1→0 TE losses and 1→2 TE gain rates; shown as squares). Points in gold, green, and blue represent rate estimates for genotypes collected from Finland, Germany and Israel, respectively. Base substitution and gene conversion rates are from [29].

Discussion

Our analyses of TE profiles aim to quantify the levels of intra- and interspecific variation in TE content and mutation rates with and without selection, in order to better understand the mutagenic role of TEs genome-wide over short and long time scales. There are a number of challenges when comparing TE content between lineages or across studies, as differences in repeat content, sequencing technologies, assembly algorithms, software, and pipelines can make standardizing results difficult [36, 37]. In addition, TEs that cannot be classified into any of the major known categories of mobile elements, which are not uncommon, cannot be included in the calculations of family-, class-, or superfamily-specific rates (but see S23 and S24 Tables for rates including ‘unknown’ TEs; [38]). Furthermore, even if a completely annotated TE library exists, the most commonly used methods for quantifying repeat content in the genome (RepeatMasker [39] versus read-mapping approaches) provide very different estimates of the TE content because the former method relies heavily on assembly quality (see S2 Text). Similarly, our method for measuring TE mutation rates (TEFLoN; [25]) depends on being able to map reads that span gain and loss events, meaning read depth or length can alter the false positive and false negative rates. While we are able to gauge the sensitivity of our methods using simulations, our ability to characterize TEs and detect their movement is likely to continue to improve with technological and bioinformatic advances (S21 Table).

Previous work on Daphnia TEs (e.g., [27, 40, 41]) utilized their unique reproductive mode (typically, cyclical parthenogenesis [asexual reproduction with occasional bouts of sex], but with the repeated evolution of obligate asexuality) to explore an early and frequently posed question about how TEs proliferate via sex [42]. These studies and those in other species that can reproduce with and without sex have painted a complex picture: some TEs exhibit different patterns of proliferation among sexuals and asexuals (e.g., in D. pulex [43]), but this is not always the case (e.g., in yeast [[44] reanalysis of data from [45]]). Even though most Daphnia can reproduce sexually, they can be propagated in the lab exclusively via asexually-produced clonal offspring, allowing us to estimate rates of TE gain and loss without the complicating influence of sex, unlike TE studies in Drosophila (reviewed in [46]). Although the lineages in this study were reared without sex during the MA experiment, the 9 starting genotypes of D. magna originally collected from Finland, Germany, and Israel (Fig 2A) have, historically, experienced quite varied environmental regimes (S1 Table), likely impacting the frequency of sexual reproduction in the past and/or influencing effective population sizes. The differences in mean temperatures, temperature ranges, light exposure, and drought conditions across the latitudinal gradient surveyed here helps provide a glimpse of the intraspecific variation in mutation rates typically overlooked by most studies estimating mutation rates for only one or a few genotypes. It is known, for example, that Finnish genotypes experience freezing temperatures and yearly dry downs, whereas German genotypes experience only freezing temperatures and genotypes from Israel experience only seasonal dry downs [47]. These ecological differences, paired with different rates of recombination [48, 49], could result in a historical selection regime tolerant of different mutation rates if, for example, frequent population bottlenecks in Finnish rock pools maximize drift relative to selection. Ultimately, our quantification of accumulated TE content (over long time periods) and rates of TE movement (over short time periods) in the Daphnia genome will help disentangle the mutational input provided by TEs from the evolutionary forces that subsequently shape the repetitive portion of the genome.

Long-term patterns of TE accumulation do not correspond to short-term mutation rates

Overall, TE content, in terms of abundance, is very similar across genotypes from the three populations sampled for this study (Fig 2B). Elements from the Gypsy superfamily of LTRs (Class 1) are the most numerous, as has been reported in the congener, D. pulex (Rho et al. 2010), which has more TEs overall than D. magna (Table 1) even though D. magna has a larger genome (as measured by flow cytometry, D. magna = 0.30 pg and D. pulex = 0.23 pg [33]). Despite these similarities in patterns of TE abundance, patterns of insertion site polymorphism (differences among individuals in terms of which specific sites are occupied by TEs of a given family) make all three populations readily distinguishable (Fig 2C and S8 Table), which begs the question—how much do mutation rates for TEs differ intraspecifically in Daphnia?

Based on over 100 observed events in our multi-year MA experiments, we were able to estimate rates of gain and loss for each type of TE mutation (Table 2). Rates of gain and loss in D. magna are similar (1.4 and 1.7 x 10⁻⁵ per copy per generation, respectively; Table 2), but they vary widely among genotypes and populations (Fig 3A and 3B) and among TE families (Table 3). The majority of the gains observed are novel gains (0 → 1 gains; Fig 1), most likely resulting from insertions of TEs either excised from elsewhere in the genome (in the case of cut-and-paste elements) or retrotransposed (in the case of Class I elements, such as Gypsy), rather than 1 → 2 gains which can result from homolog-dependent DNA repair [50]. The majority of loss events were at positions that were initially heterozygous (1 → 0), again a pattern expected based on mechanism since both DNA repair and gene conversion events could “reconstitute” a TE lost due to excision or deletion at an ancestrally homozygous site. A genome-wide assay of TE mutation rates in Drosophila showed insertions far outnumber deletions, but in flies the per copy per generation rates differ significantly, with insertions higher (~10⁻⁹) than deletion rates (~10⁻¹⁰), and much lower rates overall compared to those observed here [25].

Little is known about intraspecific variation in TE mutation rates in other animal species, even though there have been several large-scale studies of their polymorphism (e.g., [51–53]). Among D. magna genotypes, rates ranged from a high gain bias in one genotype from Finland (FA; 5.3 x 10⁻⁵ per copy per generation) to a deletion bias in one genotype from Germany (GB; -5.5 x 10⁻⁵ per copy per generation; S16 Table), with the highest number of events overall occurring in a single genotype (FC; S16 Table) in a single family (Gypsy; n = 51; S18 Table). Looking across families of TEs, populations are distinct in their rates, with Finland exhibiting higher rates of gain overall, Germany exhibiting high rates of loss overall, and Israel exhibiting gains and losses with almost equal frequency resulting in the lowest net rates overall (Fig 3B and S16 and S17 Tables). Thus, while genotype-specific rates of mutation surely introduce variable levels of TE-related genetic variation in these lineages, evolutionary forces acting at the population-level likely explain the consistent overall abundance of TEs (Fig 2B) and distinctive patterns of insertion site polymorphism (Fig 2C).

Ultimately, the lack of correspondence between the variable mutation rates and the consistent patterns of TE accumulation across the 9 genotypes suggests natural selection may prevent TEs from over-running the genome long-term. Evidence in support of selection against TE activity from this study was our observation of much lower rates in control lines (where lineages were maintained in large population sizes) compared to MA lines (where selection is minimized by propagating lines via single-progeny descent), suggesting that TE mutations, especially gains, are highly deleterious (Table 2). Early papers on rates of TE activity posited that high copy number families might even evolve lower transposition rates because of the deleterious effects of TE insertions (much like parasites evolve to be less virulent; [54]), however the relationship we observe between per copy per generation rates of mutation and abundance in the genome observed is weak (S17A Fig), with no clear downward trend even for high copy number families (S17B Fig) or with rates of gain (S17C Fig).

Looking specifically at the most abundant family with the most mutation events, Gypsy, we see rates of gain and loss can vary greatly among genotypes (Fig 5A) and, in this case, the variation is reflected in the long-term patterns of insertion site polymorphism (Fig 5B) and abundance (Fig 5C). While the patterns reflect a mixture of active and inactive elements, some population-specific trends, which have been reported previously for Gypsy elements [55], are notable. Specifically, high rates of gain in Finnish genotypes could explain a non-significant trend in terms of singletons (excess in Finland [n = 13] compared to Germany [n = 5] or Israel [n = 7]; G = 4.01, df = 2, P = 0.13) or the higher percent abundance of Gypsy elements in Finnish clones compared to Germany (Fig 5C). Future studies with additional genotypes and populations and a longer mutation accumulation experiment will be necessary to determine if the patterns of TE accumulation reflect the mutational variation, as suggested by the data for this large TE family, or if evolutionary forces mute the variation introduced by TE movement, as observed when looking across all families of elements.

Fig 5 — (A) Mean gain and loss rates (per copy per generation) for each genotype. Gain rates for FB, IA, IB and loss rates for FB, IB are zero. (B) Colored bars indicate the number of genotype-specific polymorphic sites (singletons; x = 1) or population-specific sites (when x = 2 and x = 3), grey bars represent sites where elements are shared across populations (reference genome used for this analysis was FASC). (C) Percent abundance in the genome for each genotype estimated using RepeatMasker.

Rates of TE gain and loss do not correlate with other mutation rates

Base substitution mutation rates (bsMRs) are the most frequently estimated, and are used broadly in models and discussion of the mutation rate in evolutionary biology. Although they are the most commonly studied, bsMRs are not necessarily representative of mutation rates for other categories of mutation, nor are they likely to generate they greatest amount of genetic variation [56]. Microsatellites are known to be highly mutable (reviewed in [57]) and the average genome-wide rates of mutation at these loci in these genotypes of D. magna are several orders of magnitude higher (~10⁻²; [28]) than the TE mutation rates we report here (~10⁻⁵). The bsMRs we reported for D. magna were the highest and most variable direct estimates reported in animals so far using an MA approach (~10⁻⁷ and ~10⁻⁹ for the mtDNA and nucleus, respectively; [29]), but are also several orders of magnitude lower than the overall TE mutation rates we report. Evolutionary theory aimed at explaining how mutation rates evolve does not specify mutation types, however, thus we would expect that lineages with relatively high rates of mutation in one category would have high mutation rates for other types of mutation as well. The data do not support this prediction, as there is no correlation between TE mutation rates and bsMRs across the 9 genotypes (Fig 4A and S25 Table). Rates of gene conversion, however, do positively correlate with TE rates when based on those events that can be produced by gene conversion as predicted (Figs 1 and 4B and S25 Table).

While there is no positive linear correlation among mutation rates for different types of mutations (comparing TEs and base substitutions) across all 9 genotypes (Fig 4A), it is interesting to note that, in our MA experiment, genotypes from Finland have the highest rates of TE gain (and gains are more deleterious than losses), the highest rates of microsatellite deletions [28], the highest rates of base substitution among the three populations assayed [29], and the highest rates of mutations causing structural variation (e.g., insertions and deletions) [30]. These commonalities among our direct estimates based on rearing animals in a common laboratory environment point to the historical selection regime due to population genetic constraints or the frequency of recombination, rather than mutagens in the atmosphere, as an explanation for higher rates of deleterious mutation in the Finnish genotypes. Alternatively, this pattern could result if selection on DNA repair mechanisms, as opposed to the mechanisms causing mutations, is more influential. In contrast, genotypes from Israel consistently exhibit the lowest net rates of TE mutation, microsatellite mutation, and base substitutions of the three populations assayed.

Conclusions

Few direct estimates of TE mutation rates have been published outside of classic model organisms in genetics and our own species (e.g., from Drosophila [58], Arabidopsis [59], and human [60]), however adding to this list and quantifying levels of intraspecific rate variation is key for understanding how rates evolve. Furthermore, investigating the correspondence between TE mutation rates and long-term patterns of accumulation is essential for understanding genome evolution and finding solutions to long-standing puzzles, such as the C-value paradox [61, 62]. Finally, differences in rates among categories of mutations or genomic compartments (e.g., [63, 64]) pose a challenge to evolutionary theory, and require that we expand our investigation of mutation rates beyond base substitution rates in the nuclear genome [65]. Our study shows rates of TE mutation are high, variable, and uncorrelated with rates for other categories of mutation, making them important engines of change generating genetic variation worthy of further investigation. Future work aimed at understanding the causes and consequences of mutation rate variation within populations and species, the heritability and evolvability of mutation rates for different types of mutation, and the significance of the mobilome for generating genetic variation are necessary to improve our understanding of how mutation rates evolve over time and space.

Methods

Study system

The D. magna genotypes used in this experiment were provided by Dieter Ebert and are part of a collection of samples from across the species range. Genotypes were selected from populations along a latitudinal gradient (Finland, Germany, and Israel) in order to sample individuals originating from a broad range of environments. Different maximum and mean temperatures and photoperiods (S1 Table), both of which can also result in fluctuating habitat sizes [66], are represented along the gradient.

Experimental design

Three genotypes from each of three populations (Finland, Germany, and Israel) were used to initiate laboratory stocks. From these lab stocks, starting controls (SCs) were selected (immediate descendants of which were frozen and sequenced) for each of the 9 genotypes. From the SCs, mutation accumulation (MA) lines (n = 5–12 per genotype; total of 66) and large population controls (extant controls [ECs]; n = 2 per genotype; total of 18) were initiated and propagated in parallel. Tissue from each line (MAs and ECs) was frozen after the mutation accumulation period; the average number of generations across MA lines was 12 and the experiment ran for approximately 30 months in total (S26 Table; see S1 Text for additional details).

The MA and EC lines from each genotype were maintained as single individuals or large populations in 250 mL beakers containing 175–200 mL or 3.5 L jars containing 3 L of Aachener Daphnien Medium (ADaM [67]), respectively. All lines were maintained under a constant photoperiod (16L:8D) and temperature (18°C), and fed the unicellular green alga Scenedesmus obliquus (2–3 times per week ad libitum). While selection is permitted to act in the large population ECs, the single-progeny descent used to propagate the MA lines maximizes chance and minimizes selection, and thus allows for the accumulation of mutations. The experimental protocols used here have been described previously [28, 29].

DNA extraction and sequencing

At the end of the mutation accumulation period, the 9 SCs, 66 MA lines, and 18 ECs were sequenced (Illumina) to assess the TE content in the original genotypes (SCs), to quantify TE mutation rates (MA lines), and to compare to laboratory-reared lines where selection is not minimized (ECs). Five asexually-produced clonal individuals from each SC line, all derived MA lines, and the extant control lines were flash frozen for DNA extractions (see S1 Text for details). Libraries were used to generate approximately 50x depth of coverage genome-wide for each sample. Paired-reads from SC lines were then used to construct reference-guided assembles for each of the 9 genotypes (see S2 and S17 Tables for genome assembly statistics and S1 Text for assembly methods).

Characterizing TE content

A custom D. magna TE consensus library was created from a concatenated file of the 9 reference-guided assemblies from the SC for each genotype using RepeatModeler v1.0.11 [68] and used to mask each assembly using the slow search setting of RepeatMasker v4.1.0 [39]. We clustered elements in the TE library that exhibited ≥ 98% nucleotide identity over their full length to a longer sequence in the library using cd-hit-est v4.8.1 [69], yielding a non-redundant TE library containing full and partial TE copies (S1 Data). The non-redundant TE library was then used to determine the abundance, length, percent occupancy, insertion site polymorphism, and pairwise divergence for all categorized TEs in each assembly (see S1 Text for details), and in some cases analyses were performed using both categorized and ‘unknown’ TEs. To compare TE abundance and diversity to the congener D. pulex, we utilized the publicly available reference assembly PA42 (https://www.ncbi.nlm.nih.gov/bioproject/307976). The quality of our D. magna assemblies were similar to that of the D. pulex assembly (S2 Table).

TE mutation rate estimation in MA lines

We used TEFLoN v0.4 [25] to identify active TEs in the MA lines (see S1 Text for details). There are two types of TE gain mutations (0→1 and 1→2) and two types of TE loss mutations (2→1 and 1→0) that can be observed based on whether the ancestor (SC) was homozygous, heterozygous or lacked a TE (an “absence allele”) at a given site relative to the status in the descendant MA line (e.g., if the SC was heterozygous and experienced a gain, it would be classified as a 1→2 gain event in the MA line; Fig 1). Our ability to detect these different events is not uniform, however, thus we used a series of filtering steps and simulations to assess the support for each observed event and to assess the sensitivity of our methods (see S1 Text). Family-specific mutation rates for each of the four mutation types were calculated using N_m / (N_SC*G), where N_m represents that number of sites that experienced a particular mutation event, N_SC represents the initial copy number of that TE family in the SC line, and G represents the number of MA generations. For a full description of our estimates of our false discovery and false omission rates and our simulations, see the S1 Text.

Statistical analyses

Statistical analyses were performed in R [70]. Family-specific TE mutation rates for a particular genotype was estimated by averaging across MA lines. Rates of a particular mutation type (0→1 gain, 1→2 gain, 1→0 loss, 2→1 loss) of an MA line were estimated by averaging that rate across all TE families. Rates of a particular mutation type for a genotype were estimated by averaging that rate across MA lines. Confidence intervals for mutation rates were estimated by bootstrapping across MA lines 10000 times. Details on all statistical test are included in S1 Text and all code for data processing and analysis is available at https://github.com/EddieKHHo/DaphiaMagna_MA_TE.

Supporting information

S1 Text. Supplementary methods.

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S2 Text. Supplementary results.

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S1 Data. TE library constructed by RepeatModeler using the 9 refernece assemblies of Daphnia magna.

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S1 Table. Collection data for the 9 starting genotypes of Daphnia magna (FASC, FBSC, FCSC, GASC, GBSC, GCSC, IASC, IBSC, ICSC) used in this study.

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S2 Table. Assembly statistics for the 9 starting genotypes of Daphnia magna collected originally from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, GCSC), and Israel (IASC, IBSC, and ICSC) and one genotype of Daphnia pulex (PA42 version 4.1) for which sequence data were publicly available (https://www.ncbi.nlm.nih.gov/bioproject/307976).

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S3 Table. ANOVA results for the log TE percent abundance between nine genotypes of D. magna.

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S4 Table. TE content for each starting genotype of Daphnia magna collected originally from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, GCSC), and Israel (IASC, IBSC, and ICSC) compared to D. pulex, including amount (megabases [Mb]), percent of assembly, and number of elements according to RepeatMasker results.

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S5 Table. Mean proportional abundance of each TE family or superfamily in Daphnia magna (averaged across 9 starting genotypes originally collected from Finland, Germany, and Israel) using two different methods (read mapping to a repeat library and repeat masking with a repeat library).

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S6 Table. ANOVA results for the log TE percent abundance between D. magna and D. pulex.

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S7 Table. Number of sites and percent polymorphism (means, in bold) for each TE superfamily in each of the genomes sequenced from the 9 starting genotypes of Daphnia magna collected originally from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, GCSC), and Israel (IASC, IBSC, and ICSC).

Analyses were also performed using each possible reference genome using all sites that passed filters for each (regular font).

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S8 Table. K-means clustering of principal component axes from a Principal Component Analysis of TIPs identified in analyses using each of the nine genotypes of Daphnia magna as reference assemblies.

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S9 Table. Proportion of singleton and population-specific sites among three genotypes of Daphnia magna each from Finland, Germany, and Israel for all TE families combined.

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S10 Table. Polymorphism levels and proportion of population-specific sites among three genotypes of Daphnia magna each from Finland, Germany, and Israel for the seven most abundant TE superfamilies.

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S11 Table. Abundance and mean pairwise divergence of active and inactive TE superfamilies in the 9 starting genotypes of Daphnia magna collected originally from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, GCSC), and Israel (IASC, IBSC, and ICSC), where active families are those found to exhibit new mutations in the mutation accumulation experiment conducted as part of this study.

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S12 Table. Mean pairwise divergence of TE superfamilies shared by Daphnia magna (averaged across nine genotypes) and D. pulex (PA42 [PRJNA307976]).

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S13 Table. Mean and adjusted rates for each type of mutation for MA annd EC lines of each D. magna genotype.

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S14 Table. List of all TE mutation events in MA and EC lines of D. magna.

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S15 Table. Mutation count and rate for each MA and EC line descending from the 9 starting genotypes of Daphnia magna collected originally from Finland (FA, FB, and FC), Germany (GA, GB, GC), and Israel (IA, IB, and IC).

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S16 Table. Post-hoc Tukey HSD tests of binomial mixed effect models on the effect of population on gain and loss rates for D. magna MA lines from Finland, Germany and Israel.

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S17 Table. Estimates of mean gain, loss, total and net rates averaged across TE families and for only Gypsy elements based on MA lines derived from 9 starting genotypes of Daphnia magna collected originally from Finland (FA, FB, and FC), Germany (GA, GB, GC), and Israel (IA, IB, and IC).

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S18 Table. Estimates of mean gain, loss and net rates (per copy per generation) for each TE superfamily averaged across MA lines derived from 9 starting genotypes of Daphnia magna collected originally from Finland (FA, FB, and FC), Germany (GA, GB, GC), and Israel (IA, IB, and IC).

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S19 Table. Estimates of mean gain and loss rates (per copy per generation) averaged across TE families in extant control lines derived from 9 starting genotypes of Daphnia magna collected originally from Finland (FA, FB, and FC), Germany (GA, GB, GC), and Israel (IA, IB, and IC).

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S20 Table. Number of events and mean rates of gain and loss (per copy per generation) for each TE superfamily in extant control lines derived from 9 starting genotypes of Daphnia magna collected originally from Finland (FA, FB, and FC), Germany (GA, GB, GC), and Israel (IA, IB, and IC).

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S21 Table. False discovery and false omission rates for each type of TE mutation across all simulations.

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S22 Table. False discovery and false omission rates for each type of TE mutation for simulations with different TE minimum lengths.

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S23 Table. Count of gains and losses when using TE libraries with and without unknown repeats to estimate mutation rates in MA lines derived from 9 starting genotypes of Daphnia magna collected originally from Finland (FA, FB, and FC), Germany (GA, GB, GC), and Israel (IA, IB, and IC).

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S24 Table. Mutation rate estimates for the analyses performed with and without unknown repeats in the repeat library using whole genome sequence data from MA lines derived from 9 starting genotypes of Daphnia magna collected originally from Finland (FA, FB, and FC), Germany (GA, GB, GC), and Israel (IA, IB, and IC).

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S25 Table. Correlations between proportions of the genome comprised of TEs and TE mutation rates.

Proportions (top) of different TE types in the genome estimated using the read-mapping approach as they correlated with rates of gain, loss, and net rates for TE mutations. Mutation rates (bottom) for other mutation types as they correlate with TE mutation rates (different subsets shown). Base substitution rates (per nucleotide per generation), gene conversion rates (per heterozygous site per generation) and microsatellite mutation rates (absolute value of the mutation rate per copy per generation and net copy number change per copy per generation) are from Ho et al. (2019, 2020).

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S26 Table. Number of generations and statistics for paired-end sequencing reads generated from each starting control (SC), mutation accumulation (MA, and extant control (EC) line sequenced from each the 9 starting genotypes of Daphnia magna collected originally from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, GCSC), and Israel (IASC, IBSC, and ICSC).

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S27 Table. Analysis of genetic relatedness among three populations of Daphnia magna based on pairwise genetic distances from single nucleotide variants.

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S28 Table. Pearson correlations of TE insertion rates against median depth of coverage for MA lines of each genotype of Daphnia magna.

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S29 Table. Presence and absence of TE for each starting genotype of Daphnia magna at polymorphic TE sites.

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S1 Fig. Principal Component Analysis based on the presence/absence of TEs when using each of the nine reference assemblies.

Variance explained by principal components 1 and 2 are displayed on the axes. The reference assembly used is indicated on the top of each plot. Genotypes from Finland, Germany and Israel are colored in gold, green, blue, respectively.

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S2 Fig. Number of polymorphic TE sites occupied across the 9 genotypes.

The left bar represents the number of singletons (sites occupied in only one genotype) for each population (gold, green and blue for Finland, Germany and Israel, respectively). Colored portions of bars in x = 2 and x = 3 represent sites occupied in 2 and 3 genotypes, respectively, when from the same population. Grey portions of each bar represent the number of sites that were occupied in ≥2 genotypes that were not population-specific. The reference assembly used is indicated on the top of each plot.

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S3 Fig. Proportion of singletons TEs in each population for analyses using different reference genomes.

Gold, green and blue represents singletons specific to genotypes in Finland, Germany and Israel, respectively. The proportion of singletons belonging to each population was not significantly different when using different reference genomes (χ² = 8.8, df = 16, P = 0.92).

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S4 Fig. Pairwise divergence of TEs in the D. magna FASC assembly for TE families that are active within D. magna MA lines.

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S5 Fig. Pairwise divergence of TEs in the D. pulex reference genome (PA42) for families that are active within D. magna MA lines.

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S6 Fig. Pairwise divergence of DNA/Academ-1 for all nine reference genomes of D. magna originally collected from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, and GCSC), and Israel (IASC, IBSC, and ICSC).

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S7 Fig. Pairwise divergence of DNA/CMC-EnSpm for all nine reference genomes of D. magna originally collected from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, and GCSC), and Israel (IASC, IBSC, and ICSC).

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S8 Fig. Pairwise divergence of DNA/hAT-Ac for all nine reference genomes of D. magna originally collected from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, and GCSC), and Israel (IASC, IBSC, and ICSC).

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S9 Fig. Pairwise divergence of DNA/P for all nine reference genomes of D. magna originally collected from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, and GCSC), and Israel (IASC, IBSC, and ICSC).

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S10 Fig. Pairwise divergence of DNA/PIF-ISL2EU for all nine reference genomes of D. magna originally collected from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, and GCSC), and Israel (IASC, IBSC, and ICSC).

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S11 Fig. Pairwise divergence of LINE/I for all nine reference genomes of D. magna originally collected from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, and GCSC), and Israel (IASC, IBSC, and ICSC).

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S12 Fig. Pairwise divergence of LINE/Penelope for all nine reference genomes of D. magna originally collected from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, and GCSC), and Israel (IASC, IBSC, and ICSC).

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S13 Fig. Pairwise divergence of LTR/DIRS for all nine reference genomes of D. magna originally collected from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, and GCSC), and Israel (IASC, IBSC, and ICSC).

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S14 Fig. Pairwise divergence of LTR/Gypsy for all nine reference genomes of D. magna originally collected from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, and GCSC), and Israel (IASC, IBSC, and ICSC).

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S15 Fig. Pairwise divergence of LTR/Pao for all nine reference genomes of D. magna originally collected from Finland (FASC, FBSC, and FCSC), Germany (GASC, GBSC, and GCSC), and Israel (IASC, IBSC, and ICSC).

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S16 Fig. Mean divergence of TE copies plotted against mean base substitution rates for each starting genotype.

Divergence averaged across all TE families, only active TE families, and only inactive TE families are plotted as filles squares, filled circles, and empty circles, respectively.

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S17 Fig. Relationship between TE content and mutation rates.

Percent abundance (log scale) of each TE family averaged across genotypes in D. magna plotted against (A) number of mutation events for each TE family and (B) gain and loss rates for each active TE family. Gain rates for DNA/CMC-EnSpm, DNA/P, LTR/DIRS, LINE/I and loss rates for DNA/Academ-1, LINE/Penelope are not shown because there were zero mutation events. (C) Percent of the genome occupied by TEs for each assembly plotted against the TE gain (black), loss (white) and net (grey) rates averaged across all families and MA lines. Percent abundance of TEs was estimated using the read mapping approach.

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Acknowledgments

We would like to thank Maia J. Benner, Dana Howe, Dee Denver, Dieter Ebert, Peter Fields, and Jeremy Coate for supplying animals, technical assistance, resources/support, and helpful feedback. The map in Fig 2A was made with BioRender.com.

Data Availability

WGS data have been deposited at NCBI (PRJNA658680) and all code is available online (https://github.com/EddieKHHo/DaphiaMagna_MA_TE). The TE library used is available as S1 Data.

Funding Statement

This work was supported by awards from the National Institute of General Medical Sciences of the National Institutes of Health (GM132861) and National Science Foundation (MCB-1150213) to SS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Genet. doi: 10.1371/journal.pgen.1009827.r001

Decision Letter 0

Lindi Wahl, Kirsten Bomblies

7 Sep 2021

Dear Dr Schaack,

Thank you very much for submitting your Research Article entitled 'Engines of change: Transposable element mutation rates are high and variable within Daphnia magna' to PLOS Genetics.

The manuscript was fully evaluated at the editorial level and by three independent peer reviewers. The editors and all three reviewers were enthusiastic about your contribution, but the reviewers have identified some concerns that we ask you address in a revised manuscript.

We therefore ask you to modify the manuscript according to the review recommendations. Of these, clarification regarding some statistical issues (bootstrap versus mixed model), and further details regarding genome assembly are essential. I would also re-iterate that headings within the results section would aid the reader and really help people grasp the "take-home" messages of your study. While we highlight these three points, all three reviewers had excellent (although relatively minor) comments and suggestions; please address each of their comments in the revision.

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Please let us know if you have any questions while making these revisions.

Yours sincerely,

Lindi Wahl

Associate Editor

PLOS Genetics

Kirsten Bomblies

Section Editor: Evolution

PLOS Genetics

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: In this manuscript, Ho, Bellis, et al. investigate transposable element variation and mutation rates in different genotypes of Daphnia magna. The authors explain the different ways TE copy number may change in Daphnia, which is useful to the reader because of the unique aspect of asexuality. The authors find that mutation rates are variable between lines, and interestingly several lines show a directional bias. They also propagated populations of the same genotypes where selection occurred and show that transposable element mobilization is constrained. Overall, the manuscript’s data support the authors’ conclusions which represent a strong contribution to the field. However, the manuscript could be improved in clarity and organization. There are also a few details missing from the manuscript, and discrepancies, which need to be clarified for full confidence in the paper’s findings and conclusions.

1. The results section was challenging to read as many results were listed and it was difficult to extract take-home messages. I suggest further subdividing the results section, and making subsection titles more informative and summarizing the results. For example, the authors may consider subheadings similar to these:

Line 167:

Characterizing TE content in Daphnia

Line 187:

Variation in TE activity on a long-term scale

Line 214:

Estimated rates of TE loss and gain using mutation accumulation lines

Line 236:

TE activity is under selective constraint

Line 245:

Validation of methods for detecting TE insertion mutations

Line 259:

TE mutation rates are not correlated with other types of mutation

2. The authors use simulations to estimate false positive and false negative rates. They mention in the discussion that read depth can alter the false positive and negative rates, however it seems this was not factored into the simulations. Please clarify if there was a correlation between depth and number of insertions detected. Why was 50x depth used for the simulations and not the empirical depth, which will vary between different lines? I could not find any mention of the empirical depth in the manuscript or supplement.

3. Confidence intervals are overlapping for TE losses between ECs and MA lines in Table 2. Why are the bootstrap results presented when a mixed effect model was referred to in the main text? Why is the mixed effect model more suitable than the bootstrap, as the two methods result in a discrepancy in significance for TE loss rates?

4. Please include a clarification of a couple details about the EC lines. How was the number of generations estimated and what is the confidence of this estimate (e.g. there may be overlapping generations)? Please briefly explain the limitations of your sampling approach of the EC lines, as you may not be capturing variation within the population.

5. L251 – please clarify that you are referring to the empirical data, and FDR/FOR didn’t greatly vary between the mutation types

6. Why were the EC rates not adjusted in the table S9? Please include explanation in the simulations section and/or the table legend.

7. L281-283: Each unknown element identified by RepeatModeler may represent a family, and in theory could have a family-specific mutation rate. I think you should say class or superfamily -level specific rates.

8. Please try to reword the sentence on lines 401-403 to increase clarity.

9. In comparison of D. pulex and D. magna TE content, why is the RepeatMasker method used when you state it is a poor method for comparing different assemblies especially those of different qualities?

Reviewer #2: This is a very well-written article that sheds light not only on the abundance and diversity of transposable elements (TEs) in populations of Daphnia magna across a latitudinal gradient and how this compares with that of the closely related species D. pulex, but also on estimating mutation rates with and without selection across populations. Determining whether transposition rates vary across populations is a fundamental and still open question in the field, and the results presented in this manuscript do contribute to answer it.

I have a few suggestions for the authors to consider.

One of the metrics used to estimate overall abundance is affected by the quality of the assemblies, as mentioned by the authors. The authors apparently used previous available assemblies. Would the authors consider adding a few lines about the quality of the assemblies? Is it comparable across genomes? Are these assemblies based on long-reads? how the variation in assembly impacts the abundance estimates? Part of this analysis is currently in supplemental material, I think it deserves a brief mention in the results section as well. Along the same lines, how does the assemblies of D. magna and D. pulex compare?

Line 175. Maybe mention what does “t8” stand for?

Line 205 Do you mean “higher MPD in TE families that were currently active”?

Line 482. Was redundancy not remove from the TE library? How can this affect the annotations?

Line 529. I would encourage the authors to upload their TE library to a public repository such as Dfam so that it is more easily accessible to potential users rather than providing (or additional to providing it) as a supplemental data file.

Figure 1. Why losses from 1 to 0 cannot be due to cut and paste transposition?

Figure 2. Increase contrast of the map so that sampling collections are more conspicuous.

Figure 3. Are the gain and loss rates of FB zero? Maybe mention it in the legend if that is the case. Same for figure 5A, FB an IB.

Typos in lines 115, 550

Reviewer #3: This paper quantifies the transposable element activity in a 30-month mutation-accumulation experiment involving 9 genotypes of Daphnia magna. The results are compared to large populations of the same genotypes, in which selection is allowed to act. A total of 95 mutation events are recorded in the MA lines, 70 of which involved gypsy-elements. The mutation rates vary widely between lines and are much lower in the large control populations. I find this an interesting and well-presented study.

I have only minor comments:

line 65-68. Inducing structural variation in the genome might be added to this list.

line 196-199. I am not sure I see this distinction (you use “or”). Both types of processes operate at the same time, I would think.

line 205. I think this should be “higher”. Lower is what you expect.

line 302. Chen and Zhang reanalysed Bast et al. 2019, not 2016.

line 404-405. This is an important point, worth emphasising. It extends to TE family differences.

line 418-422. Perhaps selection is acting on DNA repair mechanisms?

line 427-428. I find it difficult to get a sense of how the Daphnia TE mutation rates compare to these other organisms, except Drosophila, which is mentioned on line 347).

line 477. Please mention briefly the sequencing technique (Illumina short reads) and assembly method (reference-guided) here.

line 512. Sentence truncated.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: No: Code is accessible on the Github page, however I was unable to access data at the accession PRJNA658680 at NCBI. Please ensure the sequencing data is publicly available before full acceptance.

Reviewer #2: No: Some will be provided upon acceptance

Reviewer #3: Yes

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

PLoS Genet. 2021 Nov 1;17(11):e1009827. doi: 10.1371/journal.pgen.1009827.r002

Author response to Decision Letter 0

15 Sep 2021

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PLoS Genet. doi: 10.1371/journal.pgen.1009827.r003

Decision Letter 1

Lindi Wahl, Kirsten Bomblies

16 Sep 2021

Dear Dr Schaack,

We are pleased to inform you that your manuscript entitled "Engines of change: Transposable element mutation rates are high and variable within Daphnia magna" has been editorially accepted for publication in PLOS Genetics. Congratulations! Thanks for your care in putting the revisions together, which all seem clear and reasonable. This is a strong contribution and we're glad that the review process made it even stronger.

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Associate Editor

PLOS Genetics

Kirsten Bomblies

Section Editor: Evolution

PLOS Genetics

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PLoS Genet. doi: 10.1371/journal.pgen.1009827.r004

Acceptance letter

Lindi Wahl, Kirsten Bomblies

21 Oct 2021

PGENETICS-D-21-01012R1

Engines of change: Transposable element mutation rates are high and variable within </i>Daphnia magna</i>

Dear Dr Schaack,

We are pleased to inform you that your manuscript entitled "Engines of change: Transposable element mutation rates are high and variable within </i>Daphnia magna</i>" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript.

Soon after your final files are uploaded, unless you have opted out or your manuscript is a front-matter piece, the early version of your manuscript will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting PLOS Genetics and open-access publishing. We are looking forward to publishing your work!

With kind regards,

Amy Kiss

PLOS Genetics

On behalf of:

The PLOS Genetics Team

Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom

plosgenetics@plos.org | +44 (0) 1223-442823

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Text. Supplementary methods.

(DOCX)

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S2 Text. Supplementary results.

(DOCX)

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S1 Data. TE library constructed by RepeatModeler using the 9 refernece assemblies of Daphnia magna.

(FASTA)

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S1 Table. Collection data for the 9 starting genotypes of Daphnia magna (FASC, FBSC, FCSC, GASC, GBSC, GCSC, IASC, IBSC, ICSC) used in this study.

(XLSX)

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S3 Table. ANOVA results for the log TE percent abundance between nine genotypes of D. magna.

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S6 Table. ANOVA results for the log TE percent abundance between D. magna and D. pulex.

(XLSX)

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Analyses were also performed using each possible reference genome using all sites that passed filters for each (regular font).

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S9 Table. Proportion of singleton and population-specific sites among three genotypes of Daphnia magna each from Finland, Germany, and Israel for all TE families combined.

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S12 Table. Mean pairwise divergence of TE superfamilies shared by Daphnia magna (averaged across nine genotypes) and D. pulex (PA42 [PRJNA307976]).

(XLSX)

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S13 Table. Mean and adjusted rates for each type of mutation for MA annd EC lines of each D. magna genotype.

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S14 Table. List of all TE mutation events in MA and EC lines of D. magna.

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S16 Table. Post-hoc Tukey HSD tests of binomial mixed effect models on the effect of population on gain and loss rates for D. magna MA lines from Finland, Germany and Israel.

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S21 Table. False discovery and false omission rates for each type of TE mutation across all simulations.

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S22 Table. False discovery and false omission rates for each type of TE mutation for simulations with different TE minimum lengths.

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S25 Table. Correlations between proportions of the genome comprised of TEs and TE mutation rates.

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S27 Table. Analysis of genetic relatedness among three populations of Daphnia magna based on pairwise genetic distances from single nucleotide variants.

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S28 Table. Pearson correlations of TE insertion rates against median depth of coverage for MA lines of each genotype of Daphnia magna.

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S29 Table. Presence and absence of TE for each starting genotype of Daphnia magna at polymorphic TE sites.

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S1 Fig. Principal Component Analysis based on the presence/absence of TEs when using each of the nine reference assemblies.

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S2 Fig. Number of polymorphic TE sites occupied across the 9 genotypes.

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S3 Fig. Proportion of singletons TEs in each population for analyses using different reference genomes.

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S4 Fig. Pairwise divergence of TEs in the D. magna FASC assembly for TE families that are active within D. magna MA lines.

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S5 Fig. Pairwise divergence of TEs in the D. pulex reference genome (PA42) for families that are active within D. magna MA lines.

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S16 Fig. Mean divergence of TE copies plotted against mean base substitution rates for each starting genotype.

Divergence averaged across all TE families, only active TE families, and only inactive TE families are plotted as filles squares, filled circles, and empty circles, respectively.

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S17 Fig. Relationship between TE content and mutation rates.

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Attachment

Submitted filename: 090921_CoverLetter_RtR.pdf

Click here for additional data file.^{(178.4KB, pdf)}

Data Availability Statement

WGS data have been deposited at NCBI (PRJNA658680) and all code is available online (https://github.com/EddieKHHo/DaphiaMagna_MA_TE). The TE library used is available as S1 Data.

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PERMALINK

Engines of change: Transposable element mutation rates are high and variable within Daphnia magna

Eddie K H Ho

Emily S Bellis

Jaclyn Calkins

Jeffrey R Adrion

Leigh C Latta IV

Sarah Schaack

Roles

Abstract

Author summary

Introduction

Fig 1. Categories of loss and gain for TE copies.

Results

Fig 2. TE profiles for the 9 starting genotypes of Daphnia magna.

Characterizing TE content in Daphnia

Table 1. Abundance of TE types by family or superfamily for Daphnia magna (averaged across nine genotypes) and D. pulex (PA42 [PRJNA307976]).

Variation in TE activity over long time periods

Estimated rates of TE loss and gain using mutation accumulation experiments

Table 3. Number of events and rates (plus 95% confidence intervals [CI]) of gain and loss (per copy per generation) for each TE superfamily in which events were observed averaged across all MA lines.

Fig 3. TE gain and loss rates for D. magna MA lines.

Validation methods

TE mutation rates are not correlated with other types of mutation rates

Fig 4. The relationship between TE, base substitution, and gene conversion mutation rates in D. magna MA lines.

Discussion

Long-term patterns of TE accumulation do not correspond to short-term mutation rates

Fig 5. Rates of insertion site polymorphism and abundance for Gypsy family TEs in 9 genotypes of D. magna from Finland (gold), Germany (green), or Israel (blue).

Rates of TE gain and loss do not correlate with other mutation rates

Conclusions

Methods

Study system

Experimental design

DNA extraction and sequencing

Characterizing TE content

TE mutation rate estimation in MA lines

Statistical analyses

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Lindi Wahl

Kirsten Bomblies

Roles

Author response to Decision Letter 0

Decision Letter 1

Lindi Wahl

Kirsten Bomblies

Roles

Acceptance letter

Lindi Wahl

Kirsten Bomblies

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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