Rapid phenotypic and genotypic change in a laboratory schistosome population

22

Genomic analysis has revealed extensive contamination among laboratory-maintained 23 microbes including malaria parasites, Mycobacterium tuberculosis and Salmonella spp. Here, we provide 24 direct evidence for recent contamination of a laboratory schistosome parasite population, and we 25 investigate its genomic consequences. The Brazilian Schistosoma mansoni population SmBRE has several 26 distinctive phenotypes, showing poor infectivity, reduced sporocysts number, low levels of cercarial 27 shedding and low virulence in the intermediate snail host, and low worm burden and low fecundity in 28 the vertebrate rodent host. In 2021 we observed a rapid change in SmBRE parasite phenotypes, with a 29 ~10x increase in cercarial production and ~4x increase in worm burden. 30

To determine the underlying genomic cause of these changes, we sequenced pools of SmBRE 31 adults collected during parasite maintenance between 2015 and 2023. We also sequenced another 32 parasite population (SmLE) maintained alongside SmBRE without phenotypic changes. 33

While SmLE allele frequencies remained stable over the eight-year period, we observed sudden 34 changes in allele frequency across the genome in SmBRE between July 2021 and February 2023 , 35 consistent with expectations of laboratory contamination. (i) SmLE-specific alleles rose in the SmBRE 36 population from 0 to 41-46% across the genome between September and October 2021, documenting 37 the timing and magnitude of the contamination event. (ii) After contamination , strong selection ( s = 38 ~0.23) dr ove replacement of low fitness SmBRE with high fitness SmLE alleles. (iii) A llele frequency 39 changed rapidly across the whole genome , except for a region on chromosome 4 where SmBRE alleles 40 remained at high frequency. 41 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint

We were able to detect contamination in this case because SmBRE shows distinctive 42 phenotypes. However, this would likely have been missed with phenotypically similar parasites. These 43

provide a cautionary tale about the importance of tracking the identity of parasite populations, 44 but also showcase a simple approach to monitor changes within populations using molecular profiling 45 of pooled population samples to characterize fixed single nucleotide polymorphisms. We also show that 46 genetic drift results in continuous change even in the absence of contamination, causing parasites 47 maintained in different labs (or sampled from the same lab at different times) to diverge. 48 49 50 KEY WORDS: Schistosoma mansoni, parasite, laboratory populations, contamination, SmBRE, SmLE, 51 population genomics, pool-sequencing. 52 53 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint

54 Laboratory research with pathogen populations or cell lines requires rigorous safeguards to prevent 55 contamination and to ensure repeatability of results from different laboratories. Nevertheless, a growing 56 body of literature suggests that contamination (or mislabeling) of laboratory pathogens is surprisingly 57 common. For example, phylogenetic studies of laboratory adapted malaria parasite lines reveal 58 widespread evidence for these issues [1–3]. Contamination from positive control samples have resulted 59 in extensive false positive diagnoses in hospital diagnostic laboratories working with Mycobacterium 60 tuberculosis, Salmonella spp. and enterococci [4–7]. Finally, methods like isozyme analysis, HLA identity 61 testing, and DNA fingerprinting have exposed misidentification of lymphoma, hematopoietic, and 62 ovarian carcinoma cell lines as a result of cross-contamination [8–10]. In many cases, the contamination 63 may go unnoticed, particularly when no change is observed in pathogen phenotypes or when changes 64 are subtle. As a result, the National Institutes for Health (NIH) and other funding agencies now require 65 provision of protocols for validating the identity of the pathogens under study. 66 A second process – rapid evolution – can also result in genomic and phenotypic change in 67 pathogen populations over a short time period [11]. Rapid evolution of microbial populations in response 68 to drug pressure, or to avoid immune attack, is ubiquitous. Evolution can also be surprisingly rapid in 69 helminth parasites such as schistosomes. For example, selection for drug resistance [12,13] or cercarial 70 shedding number [14] can substantially alter parasite phenotypes in <10 generations. 71 The lifecycle of the schistosome parasites can be maintained in the laboratory using freshwater 72 snail intermediate hosts and rodent s as definitive host s. Our laboratory maintains several populations 73 of Schistosoma mansoni including two parasite populations originating from Brazil, SmLE and SmBRE. 74 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint We have previously investigated the se two populations in great detail , and we have reported striking 75 differences in virulence, sporocyst growth, cercaria l shedding, and immunopathology between the m 76 [15–18]. SmBRE exhibited lower fitness than SmLE for multiple life history traits in both the intermediate 77 and definitive host. However, we noticed a drastic change in phenotypes typical for the SmBRE 78 population starting in 2021. Over time, we noticed increased snail infectivity, higher cercarial shedding, 79 and increased worm burden in SmBRE, while SmLE phenotypes remained relatively unchanged. These 80 observations led us to speculate that the changes observed in the low fitness SmBRE parasites could 81 have resulted from two processes: (i) laboratory contamination with the more efficient SmLE population 82 or (ii) selection of de novo mutations within the SmBRE population leading to increased fitness. 83 To evaluate these alternative scenarios, w e sequenced pools of male and female worms from 84 SmBRE and SmLE parasites collected at 10 time intervals over a seven-year period (2016 -2023). We 85 monitored allele frequency changes across the genome over time, both within and between the SmBRE 86 and SmLE populations, to answer the following questions: (i) h ow stable are allele frequencies in 87 laboratory schistosome populations ? (ii) Do phenotypic changes in SmBRE reflect selection of de novo 88 mutations or laboratory contamination? (i ii) If contamination occurred, what can we learn about the 89 dynamics of genomic changes following admixture? (iv) Can we develop molecular approaches to verify 90 laboratory schistosome populations and detect contamination? 91 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint

92 Ethics statement 93 This study was performed in accordance with the Guide for the Care and Use of Laboratory Animals of 94 the National Institutes of Health. The protocol was approved by the Institutional Animal Care and Use 95 Committee of Texas Biomedical Research Institute (permit number: 1419-MA). 96 97 Parasite lifecycle maintenance and recovery of Schistosoma mansoni worms 98 The S. mansoni lifecycle spans approximately 75 days (30 days development within snails and 45 days in 99 hamsters). To safeguard against the loss of parasite populations, we establish duplicate cohorts of 100 hamster infections ~3 -4 weeks apart. Many of the same shedding snails are used to infect the two 101 cohorts of hamsters. Hence, the parallel populations of each line form a single population, and some of 102 our adult worm pools are collected one month apart. 103 104 To recover adult worms, we perfused infected Golden Syrian hamsters used for schistosome life 105 cycle maintenance as previously described [19]. Briefly, we euthanized each hamster with a solution of 106 1 ml o f phenobarbital (Fatal Plus) + 10% heparin and dissected the animal to expose the liver. We 107 disrupted the hepatic portal vein using a needle and perfused the heart and the liver for around 1 minute 108 each with a perfusion solution (193 nM of NaCl / 1mM EDTA) at a flow rate of 40 ml/minute using a 109 peristaltic pump . After perfusion, we rinsed the intestine with normal saline and collected worms 110 trapped in the intestine. All expelled worms were collected in a fine mesh sieve and rinsed with normal 111 saline solution (154 nM of NaCl, pH 7.5). We then transferred the collected S. mansoni worms to a petri 112 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint dish for counting and separation by sex. The worms were stored in 1.5 ml microcentrifuge tubes, flash-113 frozen in liquid nitrogen, and preserved at -80 °C until gDNA extraction. 114 115 Cercarial shedding 116 We used datasets from Le Clec’h et al. [17] from 2015 and performed a similar infection experiment to 117 measure cercarial production of SmBRE in 2023. Briefly, we exposed 240 BgBRE snails to a single SmBRE 118 miracidium in 24-well plates overnight. We then transferred the exposed snails to trays for 4 weeks. At 119 four weeks post -exposure, each snail was individually placed in a well of a 24 well -plate in 1 mL 120 freshwater and kept under artificial light for 2 h to induce cercarial shedding. For each well with cercariae, 121 we sampled three 10 µL (for the high shedder parasites) or 100 µL (for the low shedder parasites) aliquots 122 and added 20 µl of 20× normal saline. We then counted the immobilized cercariae in triplicate under a 123 microscope. We multiplied the mean of the triplicated measurement by the dilution factor to determine 124 the number of cercariae produced by each infected snail. We monitored c ercarial production weekly 125 from week 4 to 7 post -exposure in SmBRE-infected snails. To track cercarial production of individual 126 snails throughout the 4-week patent period, we isolated each infected snail in a uniquely labeled 100 mL 127 glass beaker filled with ~50 mL freshwater at the first shedding. All snails were fed ad libitum with fresh 128 lettuce and kept in the dark in the 26-28°C temperature-controlled room. 129 130 gDNA extraction, gDNA Library preparation and sequencing 131 We extracted gDNA from 27 to 100 single-sex worms per pool (Table 1) with the DNeasy Blood & Tissue 132 Kit (Qiagen, Germantown, MD, USA), following the manufacturer protocol. We ground the worms in 180 133 μl of ATL buffer using a sterile micro pestle and added 2 0 μl of proteinase K before incubation at 56°C 134 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint for 2h. gDNA was eluted in 75 µL of elution buffer. We quantified extracted gDNA using Qubit dsDNA BR 135 Assay Kit (Invitrogen, Carlsbad, CA, USA) and performed library preparation using the KAPA Hyperplus 136 Kit (Roche, Indianapolis, IN, USA) with 400 ng of input material. We used the manufacturer’s instructions 137 with the following modifications for our library construction: enzymatic fragmentation time: 20 minutes, 138 library amplification: six PCR cycles, library size selection: a first size cut at 0.6X (30 µl beads), and a 139 second size cut at 0.8X (10 µl beads). The library sizes were assessed using TapeStation 4200 D1000 140 ScreenTape (Agilent, Santa Clara, CA, USA), and all libraries were quantified using the KAPA Library 141 Quantification Kit (Roche, Indianapolis, IN, USA). Pooled libraries were submitted to Admera Health and 142 sequenced to high read depth on a NovaSeq X Plus platform (Illumina) with 150 bp paired-end reads. 143 144 Computational environment 145 We used conda v23.1.0 to manage environments and download packages required for the analysis. Data 146 processing was performed in R 4.2.0 using tidyverse v1.3.2, and figures were generated with ggplot 147 v3.4.2. 148 149 Genotyping 150 We used trim_galore v0.6.7 [20] (-q 28 --illumina --max_n 1 --clip_R1 7 --clip_R2 7) for adapter and 151 quality trimming before mapping the sequences to v ersion 10 of the S. mansoni reference genome 152 (Wellcome Sanger Institute, BioProject PRJEA36577) with BWA v 0.7.17-r118 [21] and the default 153 parameters. We used GATK v4.3.0.0 [22] for further processing of the sequences. First, we removed all 154 optical/PCR duplicates with MarkDuplicates. Next, we used HaplotypeCaller and GenotypeGVCFs to call 155 single nucleotide variants (SNV) on a contig -by-contig basis. These were aggregated for each pooled 156 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint sample and further consolidated into a comprehensive VCF file encompassing all sequences. Quality 157 filtering was performed using VariantFiltration with recommended parameters (FS > 60.0, SOR > 3.0, MQ 158 < 40.0, MQRankSum < -12.5, ReadPosRankSum < -8.0, QD < 2.0). Additionally, we used VCFtools v0.1.16 159 [23] for refining, specifically excluding non -biallelic sites with quality < 15 and read depth < 10, along 160 with sites and individuals with a genotyping rate < 50%. 161 We measured selection coefficient ( s) at each SNP loc us by fitting a linear model between the 162 natural log of the allele ratio (freq[allele1]/freq[allele2]) against generation time (measured as the 163 number of 75-day parasite life cycles). The raw s values were smoothed by computing the running 164 medians to remove noise. 165 166 FST statistics 167 We calculated FST with popoolation2 [24], a pipeline designed for analysis of pooled samples. Briefly, we 168 used samtools v1.9 [25] mpileup to generate a joint bam file containing sequences from two different 169 samples to make comparisons across time or between populations. Next, we converted the file to a 170 suitable input file for popoolation 2 with mpileup2sync.jar, keeping only bases with a minimum quality 171 of 20 . F inally, we calculated F ST with fst-sliding.pl and the following parameters: “ --suppress-172 noninformative”, “--min-count 6”, “--min-coverage 50”, “--max-coverage 200”, “--min-covered-fraction 173 1”, “--window-size 1”, “--step-size 1”, and the relevant pool sizes with “--pool-size.” We then calculated 174 mean FST in 20 kb windows using a custom function in R and added the smoothing line using the locfit 175

from the locfit v1.5-9.8 package. 176 To calculate FST for SmLE specific variants, we modified the parameters above to “--min-coverage 177 10” and “--max-coverage 6000” and overlapped the resulting files with known variant loci. 178 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint Statistical analysis 179 We performed all statistical analyses with the rstatix v0.7.2 package [26]. For normally distributed data 180 (Shapiro test, p > 0.05), we performed parametric Student's t-test to compare time points. Otherwise, 181 we used non-parametric Wilcoxon rank-sum tests. We adjusted p-values for multiple comparisons using 182 the Benjamini–Hochberg method when needed and considered these significant when p < 0.05 [27]. 183 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint

184 Phenotypic differences between SmBRE parasites from 2015 and 2023 185 Starting in 2021, we observed an increase in cercarial shedding from infected snails and in worm burden 186 from infected hamsters within the SmBRE population during lifecycle maintenance. As we had previously 187 characterized different SmBRE life history traits, including cercarial shedding in 2015 [17], we repeated 188 this experiment with SmBRE parasites collected in 2023 and quantified cercarial shedding in snails 4-7 189 weeks post-infection. SmBRE parasites produced 5-17x more cercariae in 2023 than the ones from 2015 190 (Figure 1A; Week 4: W = 512, p < 0.001; Week 5: W = 16.5, p < 0.001; Week 6: W = 68, p < 0.001; Week 191 7: W = 9.5, p < 0.001). 192 We u sed our life cycle maintenance records to quantify changes in worm burden in SmBRE 193 infected hamsters in 2015 and 2023. W e normalized worm burden by accounting for variation in the 194 number of cercariae used for hamster infections. We collected almost four times more worms from 195 SmBRE infected hamsters in 2023 compared to their 2015 counterparts (Figure 1A; t (7.89) = -3.55, p = 196 0.008). 197 198 Differentiation between SmBRE and SmLE over time 199 We used F ST to measure the differentiation between SmBRE and SmLE over time. Genetic markers 200 showed consistent high differentiation (average FST = 0. 24) across the autosomes (chr 1 -7) and sex 201 chromosome (chr) Z between 2016 and September 2021 (Figure 2). We observed a drastic reduction in 202 genetic differentiation (FST reduced from 0. 27 to 0.11) between September and October 2021. After 203 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint October 2021, there was a progressive genome wide reduction in FST reaching 0.03 by the last sampling 204 date (February 2023). 205 To determine whether SmBRE or SmLE populations were changing over time , we calculated FST 206 between the earliest time point sampled ( 2016) and pooled samples from each time point for both 207 SmBRE and SmLE. This information is plotted across the genome in Additional File 1: Figures S1 and 208 Additional File 2: Figure S2 and summarized in Figure 3. This analysis indicates a unidirectional change, 209 stemming from the contamination of SmBRE with SmLE. Across the genome, SmLE parasites showed 210 minor differentiation, with average F ST rising from 0.014 in 2016 to 0. 022 in 2023. Meanwhile, we 211 observed a rapid change in SmBRE occurring between September and October 2021, when average F ST 212 suddenly surged from 0.014 to 0.079. From this point on, differentiation intensified, reaching 0.167 by 213 2023. This significant shift occurred over two years, equivalent to approximately nine 75-day parasite 214 generations. 215 216 Rapid allele frequency change across the SmBRE genome 217 To more precisely examine the dynamics of this cont amination event, w e identified 96,778 ancestry 218 informative SNPs that were present in SmLE pools at a frequency of 100% but completely absent in 219 SmBRE pools until September 2021. We then plotted the mean allele frequencies of these SmLE specific 220 loci in all sequenced SmBRE pools (Figure 4A). We saw a consistent jump in mean allele frequency of 221 SmLE specific alleles on each autosome (chr 1-7) and the Z sex chromosome from 0 to 41-46% between 222 September and October 2021, pinpointing when the contamination event occurred and revealing the 223 size of the initial contamination event. 224 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint We also identified 217,657 SmBRE specific variants that were at fixation in SmBRE and absent 225 from SmLE prior to Sept ember 2021. These remained undetected in SmLE after September 2021, 226 demonstrating that contamination was unidirectional from SmLE to SmBRE. A summary of SmLE and 227 SmBRE specific SNPs is shown in Table 2, and detailed information for each SNP is listed in Additional File 228 3: Tables S1 (SmBRE) and Additional File: Table S2 (SmLE). 229 230 Patterns of selection across the genome 231 We would expect allele frequencies of SmLE alleles to remain at the same level in subsequent 232 generations, assuming that most introduced SNPs are selectively neutral. However, we observed a 233 steady increase in the frequency of SmLE specific alleles , which reached 77-90% by February 2023. The 234 average patterns of change are extremely similar across the genome (Figure 4A), with the exception of 235 chr 4 where we observed slower change. 236 To investigate allele frequency change across the genome after the initial contamination event, 237 we calculated selection coefficients (s) for SmLE specific SNPs. Figure 4B shows average changes in allele 238 frequency of SmLE across the genome (plotted as the natural log of the genotype ratio) against time (in 239 parasite generations) and reveals a good fit to a linear model, with a slope of 0.23, demonstrating strong 240 selection towards SmLE alleles across the genome. We then calculated selection coefficients for 241 individual SmLE specific SNPs and plotted these across the genome (Figure 4C). Selection coefficients for 242 SmLE specific alleles average s = 0.23 across the whole genome as expected, but there are peaks where 243 s = 0.41 on chr 5, and s = 0.37 on the Z chr. There is a 1.55 Mb region of particular inter est on chr 4, 244 where s < 0.06, and frequencies of SmBRE alleles showed minimal change following the initial admixture 245 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint event. This was the only genome region where selection for SmLE alleles was weak (s between 0.03 and 246 0.06). This region contains 11 genes (Additional File 5: Table S3). 247 248 Changes in allele frequency in SmLE parasite pools 249 The seven-year longitudinal series of SmLE samples provides an opportunity to examine stability of allele 250 frequencies over time in the absence of contamination. There were 706,496 SNPs segregating within our 251 SmLE populations. Variant SNPs were defined as those showing genetic variation (MAF > 0.05) in at least 252 one of the time periods sampled. While some of these show large changes in allele frequencies over the 253 7-year dataset (Figure 5A), the majority remain stable over time, as shown by FST comparisons of 2016 254 pools with 2023 pools (Figure 5B) . Similarly, allele frequency changed by 0.16 in males and 0.17 in 255 females on average between 2016 and 2023 (Figure 5C). However, 0.31% of segregating SNPs showed 256 allele frequency change of > 0.8, while 0.08% spread to fixation. 257 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint

258 Using pooled sequencing analyses, we demonstrate that the drastic increase in SmLE -specific alleles in 259 the SmBRE population, resulted from a unidirectional contamination event , with SmLE taking over the 260 SmBRE population except for a singular region on chr 4. We speculate that mixing cercariae or miracidia 261 during life cycle maintenance was the cause of this contamination event, as we performed this task for 262 both populations at the same time. 263 264 Dynamics of a laboratory contamination event 265 Size of initial contamination event 266 We observed a 40-46% change in the frequency of SmLE specific markers in the SmBRE population in a 267 single generation. The change is of the same magnitude across the autosomes and the Z chr. This 268 indicates that 40-46% of worms analyzed from October 2021 resulted from infection with SmLE rather 269 than SmBRE cercariae. The actual proportion of SmLE cercariae in the infecting pools was likely much 270 lower than 40-46%, because SmLE shows 1.8 fold higher establishment rate than SmBRE [28]. Assuming 271 that contamination occurred during the cercariae stage, we therefore speculate that the contaminating 272 fraction was ~22.2 – 25.6%. 273 Genome replacement of SmBRE by SmLE alleles 274 After contamination, we might expect that allele frequencies from each parent would remain relatively 275 stable in the absence of selection. However, o ur analyses show a systematic genome-wide increase in 276 SmLE specific alleles over time . Selection is extremely strong (s = 0.23) averaged across the whole 277 genome. This is comparable to selection for artemisinin resistance in P. falciparum [29]. To further put 278 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint this in perspective, the estimated mean selection coefficient in humans is ~ 0.001, targeting only 1% of 279 the genome [30]. We have previously determined quantitative trait loci ( QTLs) on chr 1, 3, and 5 that 280 underlie high cercarial shedding rates in SmLE; these were identified through genetic crosses with SmBRE 281 [16]. We predicted that these regions would show a rapid increase in SmLE specific alleles, but that other 282 genome regions would remain unchanged. Instead, we see a consistent increase in proportion of SmLE 283 specific SNPs across the genome in the admixed population . The genome -wide changes observed 284 suggest limited mating between SmLE and SmBRE worms within admixed populations. Such assortative 285 mating may occur due to differences in establishment rate of mature worms in the blood vessels . We 286 speculate that SmLE establishes in the portal venous system before SmBRE, and that SmLE males and 287 females are already paired and producing eggs prior to emergence of mature SmBRE adults. As a 288 consequence, S mLE eggs are overrepresented in the liver eggs that are harvested to found the next 289 generation, leading to the genome -wide replacement of SmBRE with SmLE alleles. We note that 290 fecundity is also three times greater in SmLE than SmBRE females [15]. This will further accelerate 291 replacement of SmBRE alleles by SmLE and contributes to the high genome wide selection (s = 0.23) for 292 SmLE alleles. 293 Variation in strength of selection across the genome 294 Some regions of the genome show higher or lower selection coefficients than the genome wide average 295 of 0.23. This suggest s that some mating between SmBRE and SmLE occurs, and that some genome 296 regions show much stronger selection. Of particular interest is the region on chr 4. This is the only part 297 of the genome in which SmBRE alleles remain at high frequency after the initial admixture event (Table 298 S3). The chr 4 region contains 1 1 genes, including a Leishmanolysin -like peptidase (Smp_127030). This 299 class of metalloprotease-encoding genes impact infection rates in both snail and vertebrate host [31,32]. 300 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint We also observe four genome regions ( chr 2, 5, 7, and Z) showing particularly high selection 301 coefficients indicating extremely strong selection for SmLE alleles (Table S3). These regions do not 302 correspond to the QTLs determining cercarial production in previous genetic crosses between SmBRE 303 and SmLE [16]. 304 305 Genetic drift in SmLE parasites 306 We saw no evidence for contamination in SmLE. The seven -year longitudinal data set from this 307 population provides a valuable opportunity to examine allele frequency change due to genetic drift. We 308 observed a subset of SNPs present in SmLE pools exhibiting high allele frequency changes over the seven-309 year period. We have previously determined that laboratory schistosome populations retain abundant 310 genetic variation (Jutzeler et al., unpublished observations). In the SmLE parasite pools examined here , 311 there are 706,496 SNPs with allele frequency > 5%. SNPs changed in allele frequency on average by 0.16 312 between 2016 and 2023. However, variance was high and a subset (0.31%) of segregating SNPs changed 313 in frequency by > 0.8 between 2016-2023. The effective population size Ne in laboratory maintained S. 314 mansoni populations is relatively small (53-264 in SmLE, Jutzeler et al. unpublished observations). While 315 some of the change in allele frequencies may be driven by selection, t he pattern observed is broadly 316 consistent with genetic drift and results in gradual change in the SmLE population over the years. These 317

illustrate how parasite populations maintained in different laboratories, or sampled from the 318 same laboratory over time, may differ in allele frequency. Hence, the reproducibility of experiments may 319 potentially be affected simply by the divergence of the schistosome populations. 320 321 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint Pooled sequencing for validating schistosome populations and identifying contamination 322 Developing a simple approach to characterize laboratory schistosome populations is challenging because 323 these populations show abundant genetic variation (Jut zeler et al ., unpublished observations ). 324 Sequencing pools of parasites provides a simple and relatively inexpensive solu tion, because we can 325 profile SNVs that are fixed within populations. These SNVs should remain relatively stable indicators of 326 population identity baring contamination, or mutation, which is expected to be extremely rare. Here, we 327 share a list of population specific SNPs (Table S1 and S2) to help with the identification and validation of 328 SmBRE and S mLE parasite populations. Expanding these lists to include other commonly used 329 schistosome parasite populations would provide an important resource for verifying the identity of these 330 populations and detecting potential contamination. 331 332 Implications for schistosome research 333 How commonly does contamination occur in laboratory schistosome populations? In addition to the 334 event documented in this paper, we have also retrospectively discovered a contamination of the SmHR 335 parasite population, which was fixed for the SmSULT-OR ∆142 mutation responsible for oxamniquine 336 resistance (Winka Le Clec’h and Frederic Chevalier, unpublished observations). We received the SmHR 337 population in 2016 but found that the SmSULT-OR ∆142 mutation was no longer at 100% frequency , 338 most likely as a result of contamination. We therefore used marker-assisted selection to “purify” this 339 population (now named SmOR) by conducting single miracidium infections and established hamster 340 infections with cercariae that were fixed for the ∆142 deletion. Hence, there are a minimum of two 341 known contamination events in laboratory schistosome populations. 342 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint Schistosomes are typically maintained by laboratory passage through its hosts , because 343 cryopreservation, while possible, is quite inefficient [33]. As a result, even if such contamination events 344 occur extremely rarely, they can cause irreversible changes to the genetic makeup of laboratory parasite 345 populations. Moreover, these changes may go undetected if they don’t alter specific phenotypes. The 346

observed in SmLE, where no contamination occurred, also demonstrate how genetic drift within 347 parasite populations can lead to gradual change in allele frequencies. Characterizing pooled population 348 samples using fixed SNP profiles of pooled parasites, as described here, will be a powerful tool to verify 349 parasite identity and determine the extent of contamination and the magnitude of change resulting from 350 genetic drift in laboratory parasite populations. 351 How does the contamination event documented here impact interpretation of prior experiments 352 using SmBRE? We recently used SmBRE and other parasite populations, to investigate the contribution 353 of parasite and host genotype on immunopathology in the mouse host [15]. The cercariae used for 354 rodent infections in this experiment were obtained from snails infected in July 2021 , prior to the 355 contamination event. Hence, this experiment was unaffected. We also examined genetic variation in five 356 distinct S. mansoni populations (Jutzeler et al., unpublished observations ). This work was conducted 357 after the contamination event, but we replaced the SmBRE parasites used initially with -80°C-preserved 358 SmBRE worms collected prior to contamination to avoid this issue. 359 We note that the snail intermediate hosts used for maintaining schistosome populations in the 360 laboratory are also maintained as continuously breeding colonies and cannot currently be 361 cryopreserved. Like schistosomes, t hese snail colonies are maintained as genetically variable, sexually 362 reproducing populations, and contamination between co-maintained colonies is a potential issue. We 363 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint suggest that profiles of fixed SNPs could also provide a valuable approach to detecting contamination 364 and maintaining integrity of laboratory snail populations. 365 366

367 This study demonstrates a significant contamination event between the SmBRE and SmLE parasite 368 populations, leading to a notable increase in SmLE-specific alleles within the SmBRE population. The 369 potential for genetic drift within these populations, as evidenced by the gradual changes in allele 370 frequencies in the SmLE population, further underscores the necessity for tools to validate the identity 371 of laboratory-maintained schistosome populations. 372 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint Supplementary information 373 Additional file 1: Figure S1. Differentiation of SmBRE parasites between 2016 and all following time 374 points. Dot plot showing smoothed average FST across the whole genome calculated in 20 kb windows. 375 The solid lines indicate FST after smoothing with a local regression model as calculated by the locfit R 376 package. 377 Additional file 2: Figure S 2. Differentiation of SmLE parasites between 2016 and all following time 378 points. Dot plot showing smoothed average FST across the whole genome calculated in 20 kb windows. 379 The solid lines indicate FST after smoothing with a local regression model as calculated by the locfit R 380 package. 381 Additional file 3: Table S1. List of SmBRE specific variants. The reference alleles are those shown at each 382 position listed in version 10 of the S. mansoni reference genome (Wellcome Sanger Institute, BioProject 383 PRJEA36577). 384 Additional file 4: Table S2. List of SmLE specific variants. The reference alleles are those shown at each 385 position listed in version 10 of the S. mansoni reference genome (Wellcome Sanger Institute, BioProject 386 PRJEA36577). 387 Additional file 5: Table S3. Genes under selection. This table lists the genes and corresponding gene 388 ontology (GO) terms as identified by WormBase’s BioMart v0.7 [34]. 389 390 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint Declarations 391 Competing interests 392 The authors declare that they have no competing interests. 393 394 Funding 395 This research was supported by a Graduate Research in Immunology Program training grant NIH T32 396 AI138944 (KSJ), and NIH R21 AI171601-02 (FDC, WL), and R01 AI133749, R01 AI166049 (TJCA), and was 397 conducted in facilities constructed with support from Research Facilities Improvement Program grant 398 [C06 RR013556] from the National Center for Research Resources. SNPRC research at Texas Biomedical 399 Research Institute is supported by grant [P51 OD011133 ] from the Office of Research Infrastructure 400 Programs, NIH. 401 Availability of data and materials 402 The datasets supporting the conclusions of this article and all codes used for data analysis and generation 403 of figures (1 -5, S1 -S2) are available at https://github.com/kathrinsjutzeler/BRE-LE-contamination and 404 Zenodo 10.5281/zenodo.13136643. Sequencing data is available on NCBI short read archive (SRA), under 405 BioProject PRJNA1090435 (accession numbers: SAMN40565564 to SAMN40565601, Table 1). 406 407 Authors’ contributions 408 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint KSJ and TJCA designed and planned the experiments. WL, FDC infected snails and counted cercariae. WL, 409 FDC, MM and RD maintained parasites and collected pools of adult worms. KSJ performed experimental 410 and molecular work and analyzed data. RNP and XL provided guidance on data analysis. KSJ and TJCA 411 drafted the manuscript. All authors read and approved the final manuscript. 412 413

414 We thank Evelien Bunnik, Elizabeth Leadbetter, Robin Leach, and P’ng Loke for insightful comments and 415 suggestions on this work. 416 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint

417 1. Mu J, Awadalla P, Duan J, McGee KM, Joy DA, McVean GAT, et al. Recombination hotspots and 418 population structure in Plasmodium falciparum. PLoS Biol. 2005;3:e335. 419 2. Nair S, Nkhoma S, Nosten F, Mayxay M, French N, Whitworth J, et al. Genetic changes during 420 laboratory propagation: copy number At the reticulocyte-binding protein 1 locus of Plasmodium 421 falciparum. Mol Biochem Parasitol. 2010;172:145–8. 422 3. Neafsey DE, Schaffner SF, Volkman SK, Park D, Montgomery P, Milner DA, et al. Genome-wide SNP 423 genotyping highlights the role of natural selection in Plasmodium falciparum population divergence. 424 Genome Biol. 2008;9:R171. 425 4. Jasmer RM, Roemer M, Hamilton J, Bunter J, Braden CR, Shinnick TM, et al. A prospective, 426 multicenter study of laboratory cross-contamination of Mycobacterium tuberculosis cultures. Emerg 427 Infect Dis. 2002;8:1260–3. 428 5. De Lappe N, Connor JO, Doran G, Devane G, Cormican M. Role of subtyping in detecting Salmonella 429 cross contamination in the laboratory. BMC Microbiol. 2009;9:155. 430 6. de Boer AS, Blommerde B, de Haas PEW, Sebek MMGG, Lambregts-van Weezenbeek KSB, Dessens 431 M, et al. False-positive mycobacterium tuberculosis cultures in 44 laboratories in The Netherlands 432 (1993 to 2000): incidence, risk factors, and consequences. J Clin Microbiol. 2002;40:4004–9. 433 7. Katz KC, McGeer A, Low DE, Willey BM. Laboratory contamination of specimens with quality control 434 strains of vancomycin-resistant enterococci in Ontario. J Clin Microbiol. 2002;40:2686–8. 435 8. Liscovitch M, Ravid D. A case study in misidentification of cancer cell lines: MCF-7/AdrR cells (re-436 designated NCI/ADR-RES) are derived from OVCAR-8 human ovarian carcinoma cells. Cancer Lett. 437 2007;245:350–2. 438 9. Drexler HG, Dirks WG, MacLeod RA. False human hematopoietic cell lines: cross-contaminations and 439 misinterpretations. Leukemia. 1999;13:1601–7. 440 10. Drexler HG, MacLeod RA, Dirks WG. Cross-contamination: HS-Sultan is not a myeloma but a Burkitt 441 lymphoma cell line. Blood. 2001;98:3495–6. 442 11. Messer PW, Petrov DA. Population genomics of rapid adaptation by soft selective sweeps. Trends 443 Ecol Evol. 2013;28:659–69. 444 12. Couto FFB, Coelho PMZ, Araújo N, Kusel JR, Katz N, Jannotti-Passos LK, et al. Schistosoma mansoni: 445 a method for inducing resistance to praziquantel using infected Biomphalaria glabrata snails. Mem Inst 446 Oswaldo Cruz. 2011;106:153–7. 447 13. Rogers SH, Bueding E. Hycanthone resistance: development in Schistosoma mansoni. Science. 448 1971;172:1057–8. 449 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint 14. Gower CM, Webster JP. Fitness of indirectly transmitted pathogens: restraint and constraint. 450 Evolution. 2004;58:1178–84. 451 15. Jutzeler KS, Le Clec’h W, Chevalier FD, Anderson TJC. Contribution of parasite and host genotype to 452 immunopathology of schistosome infections. Parasit Vectors. 2024;17:203. 453 16. Le Clec’h W, Chevalier FD, McDew-White M, Menon V, Arya G-A, Anderson TJC. Genetic 454 architecture of transmission stage production and virulence in schistosome parasites. Virulence. 455 2021;12:1508–26. 456 17. Le Clec’h W, Diaz R, Chevalier F, McDew-White M, Anderson T. Striking differences in virulence, 457 transmission and sporocyst growth dynamics between two schistosome populations. Parasites & 458 Vectors. 2019;12:485. 459 18. Le Clec’h W, Chevalier FD, Jutzeler K, Anderson TJC. No evidence for schistosome parasite fitness 460 trade-offs in the intermediate and definitive host. Parasites Vectors. 2023;16:132. 461 19. Tucker MS, Karunaratne LB, Lewis FA, Freitas TC, Liang Y. Schistosomiasis. Current Protocols in 462 Immunology [Internet]. 2013 [cited 2020 Nov 10];103. Available from: 463 https://onlinelibrary.wiley.com/doi/abs/10.1002/0471142735.im1901s103 464 20. Krueger F, James F, Ewels P, Afyounian E, Weinstein M, Schuster-Boeckler B. TrimGalore [Internet]. 465 Available from: https://github.com/FelixKrueger/TrimGalore 466 21. Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. 467 Bioinformatics. 2009;25:1754–60. 468 22. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis 469 Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 470 2010;20:1297–303. 471 23. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and 472 VCFtools. Bioinformatics. 2011;27:2156–8. 473 24. Kofler R, Orozco-terWengel P, De Maio N, Pandey RV, Nolte V, Futschik A, et al. PoPoolation: a 474 toolbox for population genetic analysis of next generation sequencing data from pooled individuals. 475 PLoS One. 2011;6:e15925. 476 25. Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools 477 and BCFtools. GigaScience. 2021;10:giab008. 478 26. Kassambara A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests [Internet]. 2023. Available 479 from: 480 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint 27. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach 481 to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57:289–482 300. 483 28. Jutzeler KS, Le Clec’h W, Chevalier FD, Anderson TJC. Contribution of parasite and host genotype to 484 immunopathology of schistosome infections [Internet]. Microbiology; 2024 Jan. Available from: 485 http://biorxiv.org/lookup/doi/10.1101/2024.01.12.574230 486 29. Li X, Kumar S, McDew-White M, Haile M, Cheeseman IH, Emrich S, et al. Genetic mapping of fitness 487 determinants across the malaria parasite Plasmodium falciparum life cycle. PLoS Genet. 488 2019;15:e1008453. 489 30. Zeng J, Xue A, Jiang L, Lloyd-Jones LR, Wu Y, Wang H, et al. Widespread signatures of natural 490 selection across human complex traits and functional genomic categories. Nat Commun. 2021;12:1164. 491 31. Hambrook JR, Hanington PC. A cercarial invadolysin interferes with the host immune response and 492 facilitates infection establishment of Schistosoma mansoni. PLoS Pathog. 2023;19:e1010884. 493 32. Hambrook JR, Kaboré AL, Pila EA, Hanington PC. A metalloprotease produced by larval Schistosoma 494 mansoni facilitates infection establishment and maintenance in the snail host by interfering with 495 immune cell function. PLoS Pathog. 2018;14:e1007393. 496 33. Stirewalt M, Cousin CE, Lewis FA, Leefe JL. Cryopreservation of Schistosomules of Schistosoma 497 Mansoni in Quantity *. The American Journal of Tropical Medicine and Hygiene. 1984;33:116–24. 498 34. Consortium WP. WormBase ParaSite BioMart [Internet]. Available from: 499 https://parasite.wormbase.org/biomart/martview/91ea287e9ed5f190f9da26ae4d9a9ba3 500 501 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint Figure legends 502 Figure 1: Phenotypic differences between SmBRE and SmLE. (A) Boxplots showing cercarial shedding 503 from infected snails, measured in 2015 (data from Le Clec’h et al., 2019) and 2023 over four weeks of 504 the patent period (4 -7 weeks post snail infection). Statistical comparisons done between years using a 505 Wilcoxon rank sum test and adjusted for multiple comparisons (Benjamini -Hochberg). (B) Boxplots 506 showing worm burden normalized by the number of cercariae used for hamster infection in 2015 and 507 2023. Statistical comparison between years done with Student’s t-test. * P < 0.05, ** P < 0.01, *** P < 508 0.001, **** P < 0.0001. 509 Figure 2: Differentiation between SmBRE and SmLE across time between 2016 and 2023. Dot plot 510 showing smoothed a verage FST across the whole genome calculated in 20 kb windows. The solid lines 511 indicate FST after smoothing with a local regression model as calculated by the locfit R package. 512 Figure 3: Differentiation in SmBRE and SmLE across time in comparison to 2016. Line plot showing 513 average FST across the genome for each time point in comparison to pools sampled in 2016. 514 Figure 4: SmLE-specific allele frequencies in SmBRE pools. (A) Line plot showing mean allele frequency 515 of SmLE specific variants per chromosome and across time. (B) Natural log of the genotype ratio plotted 516 against sexual life cycles. The selection coefficient was estimated as the slope of the least -squares fit. 517 The genotype ratio was calculated as the average genome wide frequency of SmBRE alleles/average 518 genome wide frequency of SmLE alleles at each time point after the initial contamination event. (C) 519 Selection coefficient (s) for individual SNPs across the whole genome. A local regression smooth line is 520 shown in red. 521 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint Figure 5: Observed differentiation in SmLE parasites over time. (A) Line plot showing allele frequency 522 change over time in specific variants in the SmLE population. Variants are labeled by chromosome and 523 position. (B) Histogram illustrating the distribution of FST values from the comparison of 657,592 524 variants in female pools and 661,996 variants in male pools from 2016 with those from 2023. (C) 525 Distribution of allele frequency change in the same variants between 2016 and 2023. 526 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint Table 1 - Sample information SampleID Population Collection Date Pool size Sex Mean coverage Coverage > 10X Coverage < 1X Accession1 BRE110916_m SmBRE 11/09/16 64 males 49.5 96.3% 1.9% SAMN40565564 BRE110916_f SmBRE 11/09/16 71 females 52.3 96.7% 2.3% SAMN40565565 LE110216_m SmLE 11/02/16 77 males 48.8 96.4% 1.9% SAMN40565566 LE110216_f SmLE 11/02/16 67 females 55.4 97.8% 1.4% SAMN40565567 BRE062718_m SmBRE 06/27/18 98 males 69.4 96.8% 2.0% SAMN40565568 BRE062718_f SmBRE 06/27/18 78 females 57.3 97.6% 1.5% SAMN40565569 LE062118_m SmLE 06/21/18 86 males 61.5 96.4% 2.2% SAMN40565570 LE062118_f SmLE 06/21/18 87 females 50.0 96.6% 2.7% SAMN40565571 BRE051420_m SmBRE 5/14/2020 95 males 62.8 96.2% 1.7% SAMN40565572 BRE051420_f SmBRE 5/14/2020 76 females 88.0 97.8% 1.2% SAMN40565573 LE051420_m SmLE 5/14/2020 32 males 61.6 96.6% 1.3% SAMN40565574 LE051420_f SmLE 5/14/2020 30 females 51.6 97.5% 1.6% SAMN40565575 BRE112320_m SmBRE 11/23/2020 103 males 60.0 96.0% 2.1% SAMN40565576 BRE112320_f SmBRE 11/23/2020 90 females 56.7 95.7% 3.3% SAMN40565577 LE112320_m SmLE 11/23/2020 68 males 187.7 97.3% 1.2% SAMN40565578 LE112320_f SmLE 11/23/2020 106 females 56.3 97.4% 1.8% SAMN40565579 BRE070521_m SmBRE 7/5/2021 100 males 65.7 96.4% 2.2% SAMN40565580 BRE070521_f SmBRE 7/5/2021 57 females 62.9 97.6% 1.3% SAMN40565581 LE070521_m SmLE 7/5/2021 75 males 53.7 95.2% 3.1% SAMN40565582 LE070521_f SmLE 7/5/2021 73 females 54.1 96.7% 2.5% SAMN40565583 BRE122121_m SmBRE 12/21/2021 101 males 66.9 97.7% 1.5% SAMN40565584 BRE122121_f SmBRE 12/21/2021 83 females 53.3 98.3% 1.1% SAMN40565585 LE122121_m SmLE 12/21/2021 101 males 83.9 97.5% 1.8% SAMN40565586 LE122121_f SmLE 12/21/2021 104 females 68.1 97.9% 1.3% SAMN40565587 BRE070522_m SmBRE 7/5/2022 93 males 76.2 97.6% 1.8% SAMN40565588 LE070522_m SmLE 7/5/2022 64 males 69.4 96.6% 1.9% SAMN40565589 LE070522_f SmLE 7/5/2022 51 females 61.8 97.0% 2.2% SAMN40565590 BRE021523_m SmBRE 2/15/2023 96 males 93.3 97.5% 1.7% SAMN40565591 BRE021523_f SmBRE 2/15/2023 96 females 54.6 97.6% 1.9% SAMN40565592 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint LE021523_m SmLE 2/15/2023 106 males 66.8 97.7% 1.7% SAMN40565593 LE021523_f SmLE 2/15/2023 33 females 55.9 94.5% 4.8% SAMN40565594 BRE092921_m SmBRE 9/29/2021 100 males 90.8 96.8% 2.1% SAMN40565595 BRE092921_f SmBRE 9/29/2021 94 females 66.1 96.8% 2.1% SAMN40565596 LE092921_m SmLE 9/29/2021 27 males 71.1 96.3% 1.7% SAMN40565597 LE092921_f SmLE 9/29/2021 100 females 66.5 97.6% 1.6% SAMN40565598 BRE102621_m SmBRE 10/26/2021 100 males 79.5 97.2% 1.0% SAMN40565599 BRE102621_f SmBRE 10/26/2021 54 females 98.0 98.5% 1.0% SAMN40565600 LE102621_m SmLE 10/26/2021 60 males 93.7 97.0% 1.9% SAMN40565601 1All accession numbers are in bioproject PRJNA1090435 527 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint Table 2 - Summary of SmBRE and SmLE Specific SNVs Chromosome Count SmBRE Count SmLE 1 43,670 16,403 2 32,175 11,041 3 21,569 14,968 4 24,752 8,741 5 19,219 8,205 6 13,028 14,019 7 12,242 7,240 Z 50,999 16,159 MITO 3 2 Total 217,657 96,778 528 529 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted August 8, 2024. ; https://doi.org/10.1101/2024.08.06.606850doi: bioRxiv preprint