Mutations in ErbB2 accumulating in the male germline measured by error-corrected sequencing

Mutations in ErbB2 accumulating in the male germline measured by error-corrected sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mutations in ErbB2 accumulating in the male germline measured by error-corrected sequencing Irene Tiermann-Boege, Atena Yasari, Monika Heinzl, Theresa Mair, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4887284/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mutations in the male germline are a driving force behind rare genetic diseases. Driver mutations enjoying a selective advantage expand to mutant clusters within the aged testis, and are thus overrepresented in sperm with age. Other kinds of driver mutations, occurring pre-pubescently, are the focus of recent attention given their high occurrence independent of age. Here, we investigate the gene ErbB2 with error-corrected-sequencing, and find a high rate of missense mutations, including recurrent ones, observed mainly in the tyrosine kinase domain with likely functional consequences, as we verified for a subset with biophysical methods. While these mutations increased with age, we found no evidence that they originate from mutational clusters in the aged-testis, and young donors also showed an accumulation of driver mutations-- suggesting that the mutational enrichment is not exclusive to the sexually mature germline, but can occur earlier during germline development forming evenly distributed micro-mosaics stable in size. Biological sciences/Evolution/Evolutionary genetics Biological sciences/Genetics/Evolutionary biology Duplex sequencing ultra-rare mutation de novo mutations driver mutations germline mutagenesis receptor tyrosine kinase ErbB2 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Driver or selfish mutations, well known in cancer, induce higher rates of self-propagation in the cells that carry them, resulting in sub-clonal expansion events that grow larger with age 1–4 , and thus can increase within an organism due to positive selection 1,3–5 , following similar rules as species 6 . New or de novo mutations (DNMs) events are rarer in the germline than in somatic tissue, possibly due to more robust DNA repair mechanisms 7 . But once germline DNMs occur, they might also behave like driver mutations and lead to sub-clonal expansions, as is well-known to occur in the mature male germline for mutations in a handful of genes in the receptor tyrosine kinase (RTK) pathway like FGFR2, FGFR3, HRAS, PTPN11, KRAS, RET, BRAF, CBL, MAPK1, MAPK2, and RAF1 8–18 . These DNMs (also known as selfish 19 , RAMP 20 or paternal-age effect (PAE) 21 mutations are often missense and gain-of-function changes associated with rare congenital disorders. Until recently, these driver mutations have been hypothesized to expand exclusively in the sexually mature male germline. Specifically, they result in a modified functionality of the protein usually associated with a hyperactivation of the RTK signaling pathway 18,22–29 . This hyperactivation of the RTK signaling might confer a selective advantage to spermatogonial stem cells (SrAp) by, e.g., changes in the symmetric cell division patterns 8,14,16,18,30,31 and reviewed in 20,32 . Consequently, driver mutations accumulate with the ongoing cell-divisions of the mature male germline and form focal mutation pockets observed in the dissected aged testis 8,10–14,16−18,30,31 , and are enriched in the sperm of older donors 8,12–18,33−35 . Correspondingly, the risk of a germline-dominant genetic disorder in the offspring increases with paternal age 19–21,32 . However, it has been suggested that driver mutations might accumulate earlier, before post-pubertal spermatogenesis 18 . This early accumulation would be expected to form germline micro-mosaics, which could result in a high mutational load in sperm that is independent of the age of the donor 18,36 , as was also described for DNMs from the same parent shared among siblings in pedigrees 37,38 . Consequently, the risk of recurrence in siblings or the incidence in the general population might be higher for driver mutations already present as micro-mosaics at a young age. This is particularly worrisome, since most RTK driver mutations are associated with rare genetic disorders or cancer. Consequently, it is imperative to understand this type of mutagenesis and the expansion patterns of driver mutations in the male germline. One gene of particular interest is erb-b2 receptor tyrosine kinase ( ErbB2 ), a member of the epidermal growth factor receptor family, which also includes EGFR , ERBB3 , and EBBB4 , and regulates RTK signaling. Mutations in ErbB2 have been identified in numerous somatic tumors of the breast, the ovary, the lung, the large intestine, and the prostate 39,40 and have been also implicated in the embryonal carcinoma of testes and advanced testicular teratomas 39 . In the mature testis, ErbB2 is broadly expressed in spermatogonia, early spermatocytes, elongating/elongated spermatids, Sertoli cells, and Leydig cells 41,42 and potentially regulates signaling in mitosis and the onset of meiosis of germ cells and spermiogenesis 39 . Similar to other RTK mutations, missense substitutions in ErbB2 might have a selective advantage and expand in the male germline. Here we investigated mutations in ErbB2 accumulating in sperm DNA of differently aged-donors with error-corrected sequencing (ecSeq), also known as duplex sequencing (DS). This approach allowed us to collect a large number of DNMs, compare substitution frequencies and mutation types in the coding regions of ErbB2 , and detect instances of positive selection. We also investigated the expansion patterns in aged dissected testes and the effect of selected mutations on the signaling of the receptor to better understand functional changes associated with these substitutions. Results Targeted error-corrected sequencing to detect mutations DS or ecSeq has the lowest reported error rate for Illumina sequencing, achieved by using a double barcode strategy that assembles information from both strands of the original DNA molecule 43,44 and reviewed in 45 . Consequently, DNA lesions or PCR mistakes can be distinguished from true mutations 46 , and this method is thus considered one of the advanced approaches for measuring low-frequency mutations. However, nicks in the DNA resulting from random DNA shearing with high energy bursts of sonication combined with error-prone repair of the templates before adaptor ligation might still be sources of artifacts. Specifically, the removal of nicks by strand extension and repair of protruding 3’DNA ends during the library preparation eliminates the original duplex sequence information necessary to distinguish real mutation from DNA lesions 47 . For this reason, we implemented experimental ‘repair steps’ during the library preparation. Specifically, we used dideoxy bases (ddBTPs) that block nick extensions and Mung Bean blunt ending to reduce errors derived from DNA synthesis by the enzyme mix used before adapter ligation, as described in 47 . In addition, we treated the DNA with enzymes (Fpg and UNG) to remove the most common DNA lesions (oxo-G and non-methylated cytosine deamination, respectively). Figure 1 shows a lower average substitution frequency for the 17 repaired libraries (Supplementary Table S1 ) than for the 25 unrepaired ones (Supplementary Table S2), with comparable substitution frequencies among individual libraries (Supplementary Figure S1 ). Five of the donors were the same for the repaired and unrepaired libraries, and the others were matched in terms of age, but donors differed in terms of sperm quality (50% vs 100% normospermic donors, respectively). For the repaired libraries, we observed a reduction of C > A/G > T transversions, a common substitution for oxo-G lesions (Supplementary Figure S2). The cosine similarity value comparing the mutational spectra with exonic variants in COSMIC or gnomAD was also more similar for repaired libraries than unrepaired ones (0.85 vs 0.71-COSMIC or 0.79 vs 0.64-gnomAD, respectively). We suspect that unrepaired libraries had more artifacts that were reduced in the ‘repaired’ libraries by the nick- and end treatments and the enzymatic removal of lesions. We also used a lower sonication energy (a potential source of nicks) for the repaired libraries. Based on these results, data from unrepaired libraries were not used further except if specified in individual analyses. To further validate the ecSeq methodology, we measured the overall substitution frequency of a control human genomic region in 8 different donors (7 of these donors were also analyzed for the exonic ErbB2 regions) with the data shown in Supplementary Table S3. For these controls, we also used the ´repaired library preparation’ strategy. The control region was a subset (~ 10 kb; similar in size as the ErbB2 region) of a mutagenic test used in other projects 48,49 that comprise different genomic sequences, each 0.5kb in size (total 20kb), with no known evidence of positive/negative selection or a functional role, and designed to represent a balanced sequence context in terms of GC content, genic/non-genic, coding/non-coding regions. Using the ‘repaired’ approach, we measured in these controls a substitution frequency of 5.8x10 − 7 (Fig. 1 A). A comparable substitution frequency of 1.9-5.5x10 − 7 was reported for a human airway cell line 49 and a human lymphoblastoid cell line 50 targeting the 20kb control targets. The three-fold lower substitution frequency measured in the negative controls compared to the ErbB2 region (5.8x10 − 7 vs. 1.5x10 − 6 , respectively) suggests that two-thirds of the DNMs are exclusive to a process within ErbB2 , but we cannot unambiguously exclude a contribution from artifacts or contaminating somatic cells. Contaminating somatic cells, in particular, may increase the substitution frequency, given their 10- to 100-fold higher mutation rates compared to germline cells 7,47 . In normospermic individuals, non-sperm cells typically range between 1–10% in the semen but vary with factors like fertility, age, health, and lifestyle, and also potentially show higher proportions in cases of infertility 51,52 . In sperm donors that were examined for both the exonic ErbB2 targets and controls, the difference in substitution frequency between ErbB2 and control regions was also three-fold (Supplementary Fig. 1D). We also implemented a series of filtering steps at the bioinformatic level. These included the assignment of a tier classification (tier 1.1. to 7) by the Variant-Analyzer (VAR-A) that verified the reliability of the variant call based on evidence like mate information, family size, and errors within a family 53 . We further carried out a haplotype analysis and identified variants that co-occurred with another rare variant or rare haplotype (Supplementary Figure S3). These rare haplotypes were identified in multiple libraries (both within and among repaired libraries), were usually short, ≤ 10 base pairs, and were tagged sometimes by supplementary alignments (part of the read mapped to a different region of the genome). Since it is ambiguous if these variants were exclusive to ErbB2 , we removed these variants from further analyses. We also removed variants within a short tandem repeat (STR), given the problematic and correct variant annotation at these sites. In total, we measured in the repaired libraries 493 substitutions (high-quality tier of 1.1 to 2.5) in the exonic regions of ErbB2 (4557 bp) (Fig. 1 B). Of those, 486 substitutions were classified as de novo occurring in different donors (in at least two libraries); and 441 were unique substitutions. Those variants observed in multiple donors were labelled as recurrent (Fig. 1 ; Supplementary Table S4). Of the 42 recurrent variants in the repaired libraries, 2 variants co-occurred in more than 2 donors, and 40 variants occurred in two different donors. Of the recurrent variants, 17 were also observed in the unrepaired libraries (Fig. 1 B). No significant differences in age, age category, diagnosis, millions of sperms (MS), Volume (ml), or MS/ml were measured between the recurrent and non-recurrent variants based on the Mann-Whitney-U test (p-value < 0.05; Supplementary Table S5-S6). A higher number of substitutions was observed in older donors We analyzed libraries from three age classes: six from younger donors (19 to 30 years old), six libraries from middle-aged donors (31 to 45 years old), and the remaining five donors from an older category (46 to 63 years old); all healthy individuals, mainly from European ancestry. The average coverage depth of the repaired libraries was ~ 5000x (max ~ 7000x). We observed that the substitution frequency increased with the age group (Fig. 2 A) and was significantly higher for the middle-/ and older-age groups compared to the younger group. This difference between age groups was not as pronounced with the recurrent data set, probably because of the reduced power of a smaller sample size. We further analyzed the mutational spectra, mutational signatures, and transcriptional bias of the observed substitutions. When considering all the data, the most frequent substitution types were non-CpG transversions and non-CpG transitions (Fig. 2 B). The mutational spectra also reflected a high number of G > T / C > A or (S > W) transversions and G > A / C > T or (S > W) transitions. Based on the cosine similarity value, the substitutions in ErbB2 were more similar to variants reported in tumors for ErbB2 (COSMIC) than to variants captured in the general population (gnomAD), as shown in Fig. 2 C (0.85 vs. 0.79, respectively). The mutational signature comparing the variants also in the context of the 5′ and 3′ adjacent base reflected that most substitutions occurred in the context of an S-C-R trinucleotide, and this signature overlapped by ~ 52% with SBS4 (associated with tobacco smoking), ~ 22% with SBS30 (deficiency in base excision repair due to inactivating mutations in NTHL1), ~ 15% with the SBS1 pattern (spontaneous or enzymatic deamination) and ~ 11% with SBS5 (unknown etiology; Supplementary Figure S4). We have no knowledge if donors had a particular disease background or smoking history. In contrast, when considering only recurrent variants, we observed mainly S > W transitions, mostly at CpG sites (Fig. 2 B), consistent with a higher rate of 5meC > T mutations. Also, for this dataset, the mutational spectra of recurrent variants were also more similar to COSMIC than to gnomAD (0.81 vs. 0.77, respectively). Curiously, the mutational spectra of recurrent variants was also similar to another driver gene ( FGFR3 ) characterized by ecSeq in the male germline 34 (cosine similarity between ErbB2 and FGFR3 of 0.89, CI = 0.89–0.99). The mutational signature for recurrent variants showed the most frequent substitutions in the context of V-C-G sites with ~ 26% overlap of variants explained by the SBS1 pattern (spontaneous deamination of 5-methylcytosine), ~ 28% by SBS5, and ~ 46% by SBS87 (thiopurine chemotherapy treatment; Supplementary Figure S5). When analyzing which substitutions accumulated more often in the genic region, we found that the strong to weak transversions (C > A) and transitions (C > T) and the pyrimidine to purine transversions (T > A) occurred more often in the transcribed strand than in the un-transcribed strand (Supplementary Figure S6). Enrichment of recurrent substitutions in the extracellular and protein kinase domain Driver mutations in the male germline might also be associated with a functional change or a change in the signaling activity of the receptor 19,20,32 . To explore this possibility, we examined where the substitutions occurred within the ErbB2 protein domains. Figure 3 A shows that when considering all the data, most of the substitutions occurred in the transmembrane domain and flanking regions, with one-third of the substitutions predicted to result in a deleterious change based on the SIFT scores 54,55 , which is one of the most commonly used algorithms for predicting the effect of a substitution on protein function (Supplementary Table S1 ). A small fraction was associated with cancer (COSMIC). Variants that occurred more than once independently (i.e., in different donors) appear especially likely to be responsible for clonal expansion compared to all the exonic ErbB2 DNMs. First, most (34 out of 42) of the recurrent variants were missense substitutions, 6 were synonymous, and 2 were classified as stop-gain mutations (Table 1 ). Second, many of these recurrent mutations are likely to be deleterious. Approximately one-fifth of these recurrent substitutions were reported as somatic cancer-promoting mutations in COSMIC (compared to 8% considering all variants), and half were reported in gnomAD and are likely viable; 10 have never been reported before. Furthermore, ~ 50% of these recurrent substitutions are highly likely to be deleterious, according to their CADD score 56 (Table 1 and Supplementary Table S4). Third, when classifying the substitutions per domain, two-thirds of the substitutions occurred in the protein kinase domain, PKD (Fig. 3 A). Variants categorized with a disorder by ClinVar or reported in COSMIC also mainly accumulate in the PKD of ErbB2 (Fig. 3 B-C), as also was observed for the recurrent DNMs (Fig. 3 D) with mutation frequencies or variant allele frequencies (VAF) ~ 2x10 − 4 (Fig. 3 E); with the highest average VAF (5.2 x10 − 4 ) measured for c.C2442T (p.R814R), a silent mutation in the kinase domain (see Table 1 and Supplementary Table S4). This enrichment in the kinase domain of pathogenic-, tumor-related variants and recurrent DNMs in sperm suggests that this domain is a target for mutations likely with clonal expansion consequences in ErbB2. Evidence for positive selection: passenger versus driver mutations In order to investigate whether the variants enriched in sperm are driven by positive selection, we performed a version of a d N / d S analysis, which compares the non-synonymous vs. synonymous substitutions adjusting for local sequence context 60 . A d N / d S ratio close to the reference value of one ( 1 ) indicates no selection, below the reference value implies negative selection, and above the reference indicates positive selection 60,61 . Table 2 shows that ErbB2 substitutions have a signature of positive selection with a d N / d S ratio of 1.5, significantly larger than one. Curiously, this ratio was increased considerably when considering only the recurrent substitutions ( d N / d S = 3.7), further supporting that recurrent mutations are a subset of variants enriched for targets of positive selection. For both data sets, the strongest indication for positive selection was in the protein kinase domain (Table 2 ). Mutations in all age groups, also showed signatures of positive selection, with similar values observed for younger, middle and older groups, slightly increasing with age when considering all the data (Table 2 ). Given the smaller sample size of recurrent variants, the d N / d S estimates for these mutations have large confidence intervals, but all categories analyzed show statistical evidence for positive selection. Table 2 Estimates of the d N / d S ratio following 60 calculated in total, or per protein domain (EC: extracellular domain, TMD + flanking regions, PKD: protein kinase domain, C-terminal tail) and per age group for de novo exonic ErbB2 variants (n = 486 mutations for the repaired libraries, or n = 457 nonsynonymous and synonymous variants, or n = 87 recurrent mutations; n = 83 nonsynonymous and synonymous). Variants were retrieved from Supplementary Table S1 and S4, respectively omitting variants resulting in stop codons. The age category was defined as younger sperm donors than 30-years old, middle age are donors between 30–45 years old and older group to donors older than 45. This partitioning ensured similar sample sizes between groups. de novo de novo recurrent n nonsyn n synon dN / dS p value n nonsyn n synon dN / dS p value All 326 131 1.5 2E-10 * 71 12 3.7 0E + 00 * Domain EC 163 65 1.4 2E-05 * 30 4 4.6 2E-11 * TMD 26 14 1.1 5E-01 4 - - - PKD 68 19 2.1 4E-08 * 27 2 7.5 3E-15 * C-terminal 69 33 1.3 7E-02 10 6 1.2 6E-01 Age Class Younger 85 38 1.3 3E-02 * 16 1 8.5 2E-10 * Middle 156 60 1.5 7E-06 * 34 6 3.9 1E-10 * Older 85 33 1.6 3E-05 * 21 5 2.5 3E-04 * Spatial distribution of selected variants in the male gonad In order to gain further insights into the accumulation of ErbB2 mutations in the germline of sexually mature males, we screened the spatial distribution of three variants in a post-mortem testis of two different donors, a 70- and a 73-year-old. In particular, we examined c.428G > A (p.R143Q), c.2033G > A (p.R678Q), and c.2524G > A (p.V842I), which were selected based on the association of the variant with a cancer phenotype, different clinical significances (pathogenic to likely benign), high predicted deleteriousness (CADD score) 56 , COSMIC reports, as well as differences in embryonic viability of these variants based on gnomAD reports (Fig. 4 A). Furthermore, p.R678Q is the fourth most common mutation in ErbB2 reported in COSMIC, and p.V8421 is reported as an activating mutation 62 . Further, based on our ecSeq data, these variants occurred in sperm at VAFs ~ 10 − 4 in at least 3 different donors when also considering unrepaired libraries (Fig. 4 B). Note that we also observed different nucleotide substitutions at the same codons rendering alternative missense or silent substitutions reported in Supplementary Table S1 and S2. We followed the testis micro-dissection technique 13,14,16,30 in combination with digital droplet PCR (ddPCR), as described previously 18 . In short, each testis was divided into 6 slices and each slice was further partitioned into 32 pieces. The extracted DNA of four adjacent testis pieces was pooled rendering 8 pools per slice, or in total 48 pools per testis, as shown in Fig. 5 A (for details see 18 ). For each pool, we screened 270,000-300,000 genomes. With this input, some samples yielded 1–3 mutants, implying VAFs of ~ 10 − 5 (Supplementary Table S7). In 15–30% of samples, we did not detect any mutations (Supplementary Figure S7). Note that at input levels of 300,000 genomes, the chance of finding zero mutations is 60% (based on a Poisson distribution with λ = 0.5); thus, failure to observe positive counts at this depth can still be consistent with mutations present at VAFs lower than the detection threshold. We validated the ddPCR measurements by screening the flushed epididymis (mostly sperm of the testis donor) and a piece of scrotum skin, both expected to have different mutation frequencies than individual testis pieces. In addition, we screened the same sperm donors as with ecSeq (Supplementary Figure S8; Table S8) to compare the measurements between the two methodologies. For all three variants, the skin measurements (proxy of a negative control) rendered lower VAFs (significant except for p.V821I) than the epididymis (proxy of a positive control). Furthermore, we found a good congruence between VAFs of donor-matched sperm data measured with ecSeq and ddPCR. This suggests the ddPCR method is appropriate to screen expansion clusters, if following the sub-clonal expansions observed in FGFR3 14 and FGFR2 13 reaching VAFs as high as 8x10 -4 and 2.9x10 -2 , respectively. None of the three ErbB2 sites reached VAFs larger than ~ 4x10 -5 (maxVAF, Fig. 5 B and Supplementary Figure S9). It is possible that the clusters were diluted by the pooling strategy, but the analysis of individual testis pieces within two selected pools showed that this was unlikely (Fig. 5 B and Supplementary Table S7). The ‘hotness’ of focal mutation pockets was assessed by the ratio Max/Med that compared the highest VAF (maxVAF) to the remaining pools (median VAF) and ranged for both testis and the three different variants between ~ 3- to 4-fold. For comparative purposes, we included data on two mutations known to form sub-clonal clusters in the aging testis that were also collected with the same dissection scheme. Specifically, we selected c.1138G > A (p.G380A) and c.755C > G (p.S252W) of the FGFR3 or FGFR2 gene associated with achondroplasia 14 or Apert syndrome 30 , respectively. Figure 5 C shows that these two variants form distinct clusters in the testis with VAFs one- to three- orders of magnitude greater than the median VAF measured in the remaining testis (Max/Med). In conclusion, we did not observe as large differences in VAFs among pools for the ErbB2 variants in any of the two testis (Fig. 5 B and Supplementary Figure S9) and have no support that the selected ErbB2 variants accumulate in the sexually mature gonad with age. Analysis of signaling activity of selected ErbB2 variants with biophysical methods Some kinase domain variants show increased recruitment of downstream adaptor proteins We also investigated the changes in activation of the three focal ErbB2 protein variants (p.R143Q, p.R678Q, and p.V842I) at the cellular level. For this purpose, we investigated the recruitment of downstream adaptor proteins using total internal reflection fluorescence (TIRF) microscopy that analyses receptor-adaptor interactions at the cell membrane of live cells 63 , as have been previously done for FGFR3 18,29 and EGFR 64 . Cells co-expressing both a cytosolic downstream adapter protein (Grb2 or Shc1 fused to monomeric red fluorescent protein, mRFP) and one ErbB2 variant (tagged with monomeric green fluorescent protein mGFP) were seeded on micrometer-scaled antibody-patterned surfaces (Fig. 6 A). Here, the ErbB2 is arranged at the plasma membrane following the micropattern on the surface. If the receptor is activated, the downstream signaling adaptor proteins (Grb2 or Shc1) are recruited by ErbB2 kinase activity to the receptor-enriched micropatterns (Fig. 6 B). To quantify the receptor activation state, we used the respective fluorescence signal intensities within and outside the antibody-patterned regions: the degree of ErbB2 activation is expected to be proportional to the level of Grb2/Shc1 co-recruitment to the active receptor variants, which is in turn reflected in the normalized mRFP-contrast value (see Supplementary Methods). False-positive TIRF signals due to the deformation of the plasma membrane on top of the patterned antibody surfaces was excluded, since control cells co-transfected with Lact-C2-RFP (RFP fused with C2 domain of bovine lactadherin), showed no patterning of RFP signal due to homogenous membrane distribution in the central regions of GFP-ErbB2 patterned cells, as also was shown in detail in 65 and Supplementary Figure S11. With this approach, we measured the activation of our focal ErbB2 variants, compared to wild type ErbB2, two negative controls [-C; a kinase dead variant (p.K753M) 66 and a truncated mutant lacking the intercellular domain (ΔIC)], and two positive controls (+ C) [constitutively active variants p.V659E and p.G778D 66–69 ]. The signaling activity normalized to the wild type is shown for all eight analyzed variants, for both the Grb2 and Shc1 adapter protein co-recruitment (Fig. 6 C-D, Supplementary Figure S10 and Supplementary Table S9 and S10). None of the negative controls co-recruited Grb2. The negative control results for Shc1 were mixed: ΔIC had a reduced interaction with Shc1, but that of p.K753M(-C) remained unchanged, consistent with Shc1 having a different binding site, as also reported by 70 . Both positive controls (p.V659E and p.G778D) showed a ~ 1.6-fold and ~ 1.2-fold increased recruitment of Grb2 and a ~ 1.2-fold and ~ 1.5-fold increased recruitment for Shc1, respectively. Of the three focal variants, p.R678Q, the re-occurring variant in cancer patients, did not show increased activity, while both p.R143Q and p.V842I reported a significantly higher receptor activity than wild-type, of ~ 1.4–1.8 fold, respectively, similar to that of the constitutively active positive controls. Discussion Roughly 80% of the new germline mutations passed down through generations can be traced back to the paternal lineage 71,72 . Therefore, investigating mutations that occur in the male germline during a lifetime can yield valuable insights into human diseases, their potential impact on future generations, and evolutionary processes 7,37,73 . Identifying these mutations has been challenging due to their low frequencies. Using ecSeq, our study examined the occurrence of ErbB2 DNMs in the male germline, resulting in an extensive dataset of mutations enriched in sperm DNA from donors of varying ages. This facilitated the characterization of mutational distribution, spectra and signatures specific to ErbB2 , thereby furthering our understanding on driver mutations and their expansion in the male germline. In addition, this large dataset had sufficient power for testing the preferential accumulation of non-synonymous versus synonymous substitutions ( d N / d S analysis) that identified positive selection to explain the enrichment of missense ErbB2 mutations observed in sperm DNA. The data also revealed that mutations accumulate even at a young age, likely existing as micro-mosaics within mutation pockets that remain relatively small and constant over time with no large expansion measured in the aged testis, a novel finding for driver mutations. Functional analyses using biophysical methods further demonstrated that selected ErbB2 mutations hyperactivate the RTK pathway, leading to increased downstream signaling, which likely contributes to the accumulation of ErbB2 mutations in the male germline. Our findings provide relevant insights into the enrichment of mutations or micro-mosaicism in the male germline, impacting the transmission and recurrence of ErbB2-associated disorders independently of age. Origin of ErbB2 mutations Germline DNMs can arise at various stages, including early embryogenesis, primordial germ cell differentiation, pre-pubertal or post-pubertal spermatogenesis and adulthood, ultimately populating the sexually mature testis (reviewed in 37,74 ). Some mutations may be driven by selection, while others may change through the stochastic process of genetic drift 75,76 resulting in different proportions of mosaics, as described in hematopoiesis 77,78 . DNMs linked to selection get enriched by one lineage of cells carrying the mutation being favored over another producing more daughter cells 4,79 . This results in the expansion of DNMs with age, with strong driver mutations leading to selective sweeps in the male germline. However, neutral or passenger DNMs, lost or fixed, also reduce the number of cell lineages and the variation or heterogeneity of sub-populations, as was observed for white blood cell DNA with age 45 . Consequently, neutral random drift might be misinterpreted as a selective sweep 76 . The chance of encountering any particular DNM at a given specific site is very small, with a frequency of ~ 10 − 8 on average in the human genome. The exonic ErbB2 DNMs had a VAF 4–5 orders of magnitude larger, with individual donors showing frequencies as high as ~ 5x10 -4 . How can such a high VAF be explained? Furthermore, how does this explanation fit the observation that 10% of the exonic ErbB2 DNMs occurred independently in multiple donors, particularly missense mutations occurring at both CpG and non-CpG sites? An explanation based solely on a "hypermutable site" concept (with CpG transitions having only one order of magnitude higher frequencies compared to non-CpG transitions) is unsatisfactory. Random drift cannot explain these high frequencies either. The probability of encountering the same high-frequency mutation occurring independently in different donors by random chance or drift is exceedingly small. A more plausible scenario is that these mutations arise infrequently, but cause a functional change coupled with a growth advantage and undergo clonal expansion within the male gonad, leading to a relative enrichment of mutant spermatogonia or their sperm equivalents, characteristic of driver mutations. Further, our large dataset had sufficient power for testing the preferential accumulation of non-synonymous versus synonymous substitutions (d N /d S analysis) and identified positive selection as an explanation for the enrichment of missense ErbB2 mutations in sperm DNA. This trend was more robust when considering recurrent mutations. It is notable that recurrent mutations were mainly C > T substitutions at CpG sites, in contrast to the complete dataset that was enriched for non-CpG transitions and transversions. A similar difference was observed between recurrent mutations (sibling-shared mutations) derived from the maternal lineage or paternal lineage 80 , the latter being more similar to recurrent ErbB2 mutations. The observed recurrent substitutions were mainly missense substitutions placed in the kinase domain, and described as deleterious by different predictors (CADD, SIFT and PolyPhen scores). These mutations might be linked to changes in signaling activity, as was shown for selected mutations with RTK signal activation by receptor-adaptor interactions at the cell membrane of live cells (micropatterns combined with TIRF) for two of the selected mutants (p.R143Q, p.V842I) that exhibited elevated recruited Shc and Grb2 compared to WT ErbB2. Intriguingly, the downstream signaling resembled the WT for the R678Q mutant. These results are in agreement with previous findings 62 . In light of these results, we hypothesize that the ErbB2 variants captured with ecSeq in sperm DNA are enriched in the male germline by positive selection. Are ErbB2 mutations exclusive to the sexually mature germline? The ongoing cell-divisions in the sexually mature male-gonad might lead to the accumulation of driver mutations with age. For decades it has been hypothesized that the expansion of driver mutations occurs exclusively in the sexually mature male germline (adulthood), explaining the larger mutation pockets in aged testes but absent in the gametes of young donors 8,16,20,30 . The mutation arises rarely but expands clonally in the sexually mature testis, leading to a relative enrichment of mutant spermatogonia or sperm equivalents. Critical for the formation of these clusters is that all of the descendants stay in close proximity to the initial mutant cell (reviewed in 20,32 ). However, the expansion of driver mutations might not be limited to post-pubertal spermatogenesis and adulthood, but occur already at earlier stages, including early embryogenesis, primordial germ cell differentiation, or pre-pubertal spermatogenesis. Recent work observed increased VAF frequencies also in young sperm donors in FGFR3 18,34 . In particular, missense mutations were observed at levels of 0.01 − 0.005% in FGFR3 and in some cases the frequencies did not increase with the donor´s age 18 . Particularly, FGFR3 , a well-studied driver gene, the studied gain-of-function variants with promiscuous activation (ligand-independent) showed two distinct mutational behaviors: one that grows to larger sub-clonal clusters in the sexually mature gonad and increases in frequency in sperm with age. The other likely occurs pre-puberty, forming stable niches that stay constant in size (as also described here for ErbB2) challenging the long-standing hypotheses that driver or selfish mutations originate exclusively in the sexually mature male germline and keep growing with time 18 . Accumulation of variants before post-pubertal spermatogenesis was also reported in family pedigrees for neutral mutations with DNMs being shared among siblings and coming from the same parent 37 , but not present in the parent’s somatic cells and with no evidence for a dependency on parental age 38 . Also, for other species including reptiles, birds, and mammals it was reported that mutations accumulated not just during spermatogenic cycles post-puberty, but also during earlier developmental phases 81 . The hypothesis that mutations can accumulate in the germline pre-puberty would also align with the expansion patterns observed for ErbB2 . We found evidence for positive selection (d N /d S analysis) in all three age categories (younger, middle and older donor groups), mainly with variants in the extracellular- and protein kinase domain indicating likely functional consequences. Our testis dissection study also suggests that some ErbB2 mutations rather establish stable niches that remain constant in size for the three selected ErbB2 variants (c.428G > A, c.2033G > A, and c.2524G > A). This stability in size may be attributed to some variants being tolerated only at low levels, as often observed in highly activating rasopathies forming mosaics in the skin 82 . A similar behavior was reported for selected FGFR3 activating mutations that also showed increased frequencies in young donors that formed rather small mutation pockets in the testis 18 and for early developmental neutral clones that remained temporally stable across serial samples and age groups, with no changes in size (or frequency) in the stem cell niches 36 . This challenges also the dogma that driver mutations expand into large clusters with time in the male germline. In conclusion, while age-associated driver mutations are more prevalent in offspring from fathers of advanced age, the risk of recurrence in siblings or the incidence in the general population might be higher for germline mosaics than for age-associated mutations; albeit this might strongly depend on the selective advantage conferred by the mutation. This is particularly worrisome, since different ErbB2 activating mutations might have early- or late-onset effects associated with a clinical phenotype that ranges from a rare genetic disorder, cancer or tumor resistance to certain protein tyrosine kinase inhibitors. Materials and Methods Sample collection, preparation and DNA extraction Sperm samples from anonymous donors, who been abstinent for > 3 days, were collected in the Kinderwunsch Klinik, MedCampus IV, Kepler Universitätsklinikum, Linz following the protocol approved by the ethics commission of Upper Austria (Approval F1-11). Donors were mainly between 19 and 63 years old and mostly of European ancestry. Two Snap-frozen, post-mortem testes from 73 year-old (ID: NRD#ND 10354) and a 70 year-old (ID: NRD#ND 10225) donors were collected from the National Disease Research Interchange (NDRI, Philadelphia, PA). None of the donors had chronic infections, diabetes, chemotherapy, or radiation or antecedents of alcohol, tobacco, or drug abuse. The DNA from sperm was extracted from fresh semen samples, following the protocol as described in 18,83 . Testis dissection was followed as previously described 8,13,14,16–18,30 . Details of sperm and testis DNA extraction can be found in SM Methods. The information about the different sperm donors including the WHO classification for human semen characteristics 84 , as well as the library protocol used is listed in Supplementary Table S11. Library preparation Library preparation followed the protocol by 34 with adaptations from 5,47 , outlined in Supplementary Table S12 and Supplementary Materials. We used two strategies: repaired libraries underwent treatment with USER enzyme (NEB, M5505S) and Fpg-Glycosylase (M0240S, NEB), along with a blunting step using Mung bean nuclease (NEB, M0250S) and ddBTPs (Merck/Sigma Aldrich, GE27-2045-01) for nick sealing as detailed in Supplementary Methods and Supplementary Table S12. For unrepaired libraries, we used focused ultrasonication (Covaris M220 instrument) followed by size selection, end repair, and A-tailing. Adapter ligation employed DS_Hairpin_U adaptors with 12 random nucleotides, ligated using the NEBNext Ultra II end repair/dA-tailing module (NEB) and the NEBNext Ultra II ligation module for both library types. The USER enzyme digest (NEB) was used to open the adapter loop. Adapters were synthesized as previously described 34 and specified in Supplementary Methods. Amplification, using variable input DNA and PCR conditions involved 12 or 6 cycles of single primer extension followed by 2 PCR cycles. The reaction was carried out in 1x Kapa HiFi Reaction Mix (Roche) followed by DNA cleanup with 1.2x volumes of Sera-Mag Select beads (Cytiva) according to manufacturer’s instructions. Input DNA, PCR conditions, and reaction volumes are described in the Supplemental Methods and Supplementary Table S13. Primer sequences and oligonucleotides are shown in Supplementary Table S14. In both strategies, after initial amplification, 120 bp biotinylated oligonucleotide probes were employed for two rounds of targeted capture followed with further PCR cycles for sufficient target enrichment, as described 34,85 . Details, including the number of cycles for each capture PCR and the sequence of biotinylated oligonucleotide probes, are provided in Supplementary Tables S13, S14, S15, and S16 and Supplemental methods. Sequencing was performed using the MiSeq Reagent v3 600 cycles kit at the VBCF NGS Unit, Vienna, Austria, or primarily on the HiSeq X 150 PE platform at Macrogen, Seoul, South Korea Duplex Sequencing data Analysis and variant filtering The raw sequencing data were analyzed with the Du Novo package on the Galaxy platform ( https://usegalaxy.org/u/jku-itb-lab/w/galaxy-workflow-ErbB2 , Supplementary Figure S12, a duplex sequencing (DS) pipeline that assembles reads sharing the same barcode into families before the alignment to the human genome assembly hg38 86 . For details on the pipeline, see SM Methods. We also removed the first and last 15 nucleotides of the duplex consensus sequence (DCS) as potential artifacts deriving from error-prone end-repair. Additionally, we used the Variant Analyzer tools (VAR-A) 53 to classify the confidence in the identified alternate alleles by a tier-based system, as described in Supplementary Table S17. We filtered all intronic variants and SNPs from the analysis, and only variants in high-quality tiers (tiers 1.1–2.5) were kept. We also removed variants that co-occurred with another rare variant or rare haplotype and within STRs. This list of variants is denoted as “all variants”. Moreover, “recurrent variants” represent variants that happen in at least two libraries. Finally, the variants were annotated with wANNOVAR 87 and the Variant Effector Predictor (VEP) 88 . The deleteriousness of a variant was described with the CADD 89 and SIFT scores. Additionally, the association with a cancer type was extracted from COSMIC, and ErbB2 variants (human genome assembly hg38) of transcript ENST00000269571.5 were extracted from gnomAD v3.1.2 ( https://gnomad.broadinstitute.org ) and Cosmic V96 ( https://cancer.sanger.ac.uk ). Substitution frequency The substitution frequency is calculated as the number of de novo events per number of sequenced nucleotides, which is estimated as the mean coverage multiplied by the targeted region (exonic size). If a mutation is happening in different libraries, it is also counted multiple times. Mutational spectra We categorized the mutational spectra after the (un)transcribed strand. The mutation spectra are estimated with the relative unique counts, where each variant is counted only once regardless of the number of occurrences in the libraries, to compare it to the variant counts from public databases. Only substitutions of the ErbB2 gene (transcript ENST00000269571.9) and within the negative control regions (human genome assembly hg38) were extracted from the gnomAD v3.1.2 90 and COSMIC v96 91 databases. Cosine similarity The similarity between a query and reference mutational spectra is measured with the cosine similarity. First, a distribution of cosine similarities is generated by randomly sampling \(\:n\) mutations (number of mutations of the query spectra) from the reference spectra and each of the samples is compared to the original reference spectra (1,000 iterations). Second, the observed cosine similarity can be compared to the 95% confidence intervals of the bootstrapped reference samples. This approach is followed as in 47 . Mutational signature The mutational signatures are built from the trinucleotide context (5’ and 3’ neighboring nucleotide of the variant) with the tools SigProfilerMatrixGenerator and SigProfilerPlotting 92 and compared to the COSMIC signatures v3.3 93 with SigProfilerExtractor 34 . The ratio of non-synonymous and synonymous variants The d N/ d S ratio was estimated as described in 60 , using the dNdScv package v. 0.1.0 in R v. 4.3.1, and the human reference sequence v. GRChg38.14 and annotation for the ErbB2 region v.GR38.111 from ENSEMBL ( https://www.ensembl.org/ ). In this analysis, the dN/dS estimates considered the nearby sequence context, but not genome-wide epigenetic covariates of mutation rate 94 . Droplet Digital PCR (ddPCR) The fractional abundance (FA) of mutations was evaluated by ddPCR (BioRad) 95 as described also in 18 . The Site-specific mutation detection assays were designed using the online platform of BioRad ( https://www.bio-rad.com/digital-assays ) listed in Supplementary Table S18. Each ddPCR reaction contained 10 µl of 10x SuperMix for Probes (no dUTP), 6.7 µl of nucleic acid-free water, 2 µl of genomic DNA (~ 125 ng/µl; ~36,000 genomes/µl), 1 µl of probes (900M of each probe and 100M of each primer), and 0.3 µl of restriction enzyme (MseI;10U/µl) and incubated at room temperature for 15 minutes to enhance target template availability. The digested mix, along with 70 µl of droplet generation oil, was loaded into a cartridge covered with a gasket to then form the droplets in the droplet generator (BioRad). About 43 µl of the droplet solution was transferred to a ddPCR 96-well plate and sealed in the PX1 PCR Plate Sealer (BioRad) followed by PCR at 95°C for 10 minutes, 40 cycles of 94°C for 30 seconds, 53–56°C (see Supplementary Table S18) for 1 minute, and a final step of 98°C for 10 minutes, with a ramp rate of 2°C/sec and a lid temperature of 105°C. The end-point PCR plate underwent data analysis using QuantaSoft Analysis Pro Software (version 1.7.4; Bio-Rad Laboratories Inc.). The threshold between positive and negative droplets was manually adjusted well by well or across an entire plate based on the fluorescence amplitude (fluorescence intensity of positive and negative droplet clusters and/or histogram plots) for each specific probe, and we reported here the Poisson-corrected data points. Site -Direct Mutagenesis (SDM) To assess the functional impact of selected mutations, site-specific single-nucleotide mutations were introduced into the mGFP-ErbB2 expression plasmid 96 using a site-directed mutagenesis strategy with Phusion HS II high-fidelity polymerase as detailed in (see Supplementary Methods and Supplementary Table S19) and also described in 18,29 . The designed oligonucleotide pairs, including a primer with the desired mutation and another with a silent mutation, both featuring 3' PTO bonds for enhanced specificity, were used for amplification. The resulting PCR products, confirming the correct length via gel electrophoresis, underwent ligation without purification, followed by DpnI digestion and transformation into E. coli NEB 10-beta. Ampicillin-resistant clones were screened by colony PCR, and positive clones were cultured overnight, subjected to plasmid miniprep, and sequenced to verify the introduced mutations. Live cell micropatterning experiments Preparation of protein micropatterned surfaces by large-area microcontact printing 65 , total internal reflection fluorescence microscopy 64 , and image analysis 97 was carried out as previously reported, and is described in the Supplementary Methods and Supplementary Figure S11. Data Access The raw sequencing data generated in this study have been submitted to the NCBI BioProject database ( https://www.ncbi.nlm.nih.gov/bioproject/ ) under accession number PRJNA1052412. Declarations Disclosure Declaration The authors declare no competing interests. Acknowledgements We would like to thank Václav Brož for his help in the testis DNA extraction and measurements with ddPCR and Philipp Hermann for the advice on statistical testing. Funding This research was funded in whole or in part by the Austrian Science Fund (FWF) I.H. (FWFW1250) and I.T.-B. (P30867000; and SFB F-8809-B, FWF), the European Regional Development Fund I.T.-B., (REGGEN ATCZ207), the FH Upper Austria Center of Excellence for Technological Innovation in Medicine (TIMed CENTER), the “Dissertationsprogramm der Fachhochschule OÖ 2022” for T.K. and the Upper Austria (Austrian Research Promotion Agency (FFG) grant P.L. (895967), and the ERC CoG grant TE-INVASION for AJB. For open access purposes, the authors applied a CC BY public copyright license to any author-accepted manuscript version arising from this submission. Authors' contributions T.M., A.Y, T.K., I.H. performed the experiments. M.H., S.M., P.L., A.J.B. and I.T.-B. analyzed the data, I.T.-B. conceived the project and provided funding. A.Y., M.H., T.M., T.K., P.L., A.J.B., and I.T.-B. wrote the manuscript. All authors read and approved the final manuscript. Conflict of interests The authors declare that they have no conflicts of interest with the contents of this article. 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Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Mol Biol Evol 3 , 418-26 (1986). Hindson, B.J. et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Analytical chemistry 83 , 8604-8610 (2011). Szabo, A., Szollosi, J. & Nagy, P. Principles of Resonance Energy Transfer. Curr Protoc 2 , e625 (2022). Hager, R., Muller, U., Ollinger, N., Weghuber, J. & Lanzerstorfer, P. Subcellular Dynamic Immunopatterning of Cytosolic Protein Complexes on Microstructured Polymer Substrates. ACS Sens 6 , 4076-4088 (2021). Table Table 1 is available in the Supplementary Files section Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTableS1allrepaired.xlsx Supplementary Table S1 SupplementaryTableS2allunrepaired.xlsx Supplementary Table S2 SupplementaryTableS3negcontrols.xlsx Supplementary Table S3 SupplementaryTableS4recurrentfinal.xlsx Supplementary Table S4 SupplementaryTableS5CORRdonorinfo.xlsx Supplementary Table S5 SupplementaryTableS6CORRrecnon.xlsx Supplementary Table S6 SupplementaryTableS7ddPCRtestis.xlsx Supplementary Table S7 SupplementaryTableS8ddPCRvsDS.xlsx Supplementary Table S8 SupplementaryTableS9TIRFGrb21.xlsx Supplementary Table S9 SupplementaryTableS10TIRFShc.xlsx Supplementary Table S10 SupplementaryTableS11donorinfoRandNRlibAtena.xlsx Supplementary Table S11 SupplementaryTableS12libprot.xlsx Supplementary Table S12 SupplementaryTableS13PCRconditions.xlsx Supplementary Table S13 SupplementaryTableS14primerprobes.xlsx Supplementary Table S14 SupplementaryTableS15captureprobes.xlsx Supplementary Table S15 SupplementaryTableS16captureprobesctrl.xlsx Supplementary Table S16 SupplementaryTableS17tierclass.xlsx Supplementary Table S17 SupplementaryTableS18ddPCR.xlsx Supplementary Table S18 SupplementaryTableS19SDM.xlsx Supplementary Table S19 SupplementaryMethodsandFigures.docx Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4887284","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":339834994,"identity":"bce951cb-f77a-4c7e-a936-b2047e8f90bc","order_by":0,"name":"Irene Tiermann-Boege","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACNijNA6Eq2AyI1WIA1XIGrIWxgQjLoGYztjEQ1sInffjYgw8Mf2T4xQ4/e1w4j8/Y4ADz8wd4HcaXlm44A+gwydlp5sYzt7GZGRxgM8RrCxsPj5k0D1CLwe0EM2nebWw2BgcYCGnh/yb9B6jF/nb6N2neOSAt7B8J2cImDQoxA+kcoC0NIIfxELKFzUyyx8CYR+J2Tpk0zzE2Y8nDPIUz8GmR72F+JvGjQs6ef3b6NmmemmOGfcfbN3zApwUCEFF+jIGBmbB6FFBDovpRMApGwSgYCQAAaGs5WWFise8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-3621-7020","institution":"Johannes Kepler University","correspondingAuthor":true,"prefix":"","firstName":"Irene","middleName":"","lastName":"Tiermann-Boege","suffix":""},{"id":339834995,"identity":"c428c432-fd2a-4510-838f-deae617a3d76","order_by":1,"name":"Atena Yasari","email":"","orcid":"","institution":"Johannes Kepler University","correspondingAuthor":false,"prefix":"","firstName":"Atena","middleName":"","lastName":"Yasari","suffix":""},{"id":339834996,"identity":"b09c8edc-0549-4b88-9256-3a9c6ccd29b2","order_by":2,"name":"Monika Heinzl","email":"","orcid":"","institution":"Johannes Kepler University","correspondingAuthor":false,"prefix":"","firstName":"Monika","middleName":"","lastName":"Heinzl","suffix":""},{"id":339834997,"identity":"bf1c779d-147e-4ecd-bab4-599a32b31787","order_by":3,"name":"Theresa Mair","email":"","orcid":"","institution":"Johannes Kepler University","correspondingAuthor":false,"prefix":"","firstName":"Theresa","middleName":"","lastName":"Mair","suffix":""},{"id":339834998,"identity":"8a7d571c-08ca-4953-b015-0cfe63d816e2","order_by":4,"name":"Tina Kariminian","email":"","orcid":"","institution":"University of Applied Sciences Upper Austria","correspondingAuthor":false,"prefix":"","firstName":"Tina","middleName":"","lastName":"Kariminian","suffix":""},{"id":339834999,"identity":"320ac7cf-1e2f-44d9-b24b-aa3b11c54625","order_by":5,"name":"Shehab Moukbel Ali Aldawla","email":"","orcid":"","institution":"Johannes Kepler University","correspondingAuthor":false,"prefix":"","firstName":"Shehab","middleName":"Moukbel Ali","lastName":"Aldawla","suffix":""},{"id":339835000,"identity":"3f726988-1df8-4782-a4de-2c2a0b4b7be6","order_by":6,"name":"Ingrid Hartl","email":"","orcid":"","institution":"Johannes Kepler University","correspondingAuthor":false,"prefix":"","firstName":"Ingrid","middleName":"","lastName":"Hartl","suffix":""},{"id":339835001,"identity":"a71e6b18-e7d7-42df-b012-b53bd796ce21","order_by":7,"name":"Peter Lanzerstorfer","email":"","orcid":"","institution":"University of Applied Sciences Upper Austria","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Lanzerstorfer","suffix":""},{"id":339835002,"identity":"41407536-aec1-4c30-a16d-dee15a9ceda1","order_by":8,"name":"Andrea Betancourt","email":"","orcid":"","institution":"University of Liverpool","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Betancourt","suffix":""}],"badges":[],"createdAt":"2024-08-09 13:05:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4887284/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4887284/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64071742,"identity":"4b6d35b0-037f-4861-8c8e-4d7292015942","added_by":"auto","created_at":"2024-09-06 07:24:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72381,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eOverall substitution frequencies observed in repaired libraries (17 libraries), unrepaired libraries (25 libraries), and negative controls (8 libraries). Included are high-quality variants (tier 1.1.-2.5) listed in Supplementary Tables S1-4, respectively. For these estimates, we considered \u003cem\u003ede novo\u003c/em\u003evariant counts defined as the sum of all variants observed in the different donors but counted only once if occurring within the same donor. Note that each of the repaired libraries represents a different donor. For pairwise testing, we used the Chi-square test with Bonferroni–Holm correction, and only significant differences are shown. (∗) P-value \u0026lt; 0.05; (∗∗∗∗) P-value \u0026lt; 0.0001 \u003cstrong\u003e(B)\u003c/strong\u003eNumber of substitutions captured in the different library protocols classified based on the substitution type (unique), the substitution counted in different donors (\u003cem\u003ede novo\u003c/em\u003e), or all substitutions occurring within the same or different donors (allele count-AC). For the repaired and unrepaired libraries, we excluded intronic variants. We excluded SNPs (VAF ~50%) for all libraries, or variants occurring in more than 40% of the libraries or as a rare haplotypes. The number of sequenced base pairs was estimated by the average coverage in exonic regions in repaired/unrepaired libraries or targeted regions for the negative controls. ‘Recurrent’ represents variants observed in at least two donors. Of the 42 unique variants found to recur in the repaired libraries, 17 were observed also in the unrepaired libraries (intersection).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/d72c470822e17b1b986566da.png"},{"id":64072589,"identity":"223abc84-df25-468c-8d24-c2b36465fdf2","added_by":"auto","created_at":"2024-09-06 07:40:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67632,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of substitutions in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eErbB2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. (A) \u003c/strong\u003eSubstitution frequencies of all- (n=486) and recurrent (n=87)-exonic variants counted in different donors (de novo) classified into different age categories (younger, middle-age, older) in the repaired libraries. \u003cstrong\u003e(B) \u003c/strong\u003eSubstitution frequency of all- and recurrent-exonic variants classified into substitution types. Included are high-quality variants (tier 1.1.-2.5) listed in Supplementary Tables S1 and S4. \u003cstrong\u003e(C)\u003c/strong\u003eMutational substitution spectra categorized after the un-transcribed strand. \u003cstrong\u003e(D)\u003c/strong\u003e signature within the context of the two flanking bases of all- (n=441) and recurrent-unique variants (n=42) of the repaired libraries compared to the spectra extracted from COSMIC and gnomAD. For pairwise testing, the Chi-square test with Bonferroni–Holm correction was used, and only significant differences are shown in A and B. (∗) P-value \u0026lt; 0.05; (∗∗∗∗) P-value \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/22d169a7b0e1cca53234b3e3.png"},{"id":64071764,"identity":"97437b1f-4b32-414a-8548-3226df29b847","added_by":"auto","created_at":"2024-09-06 07:24:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":208154,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of substitutions in different protein domains of ErbB2 (A)\u003c/strong\u003e Substitution frequencies split by domain, deleteriousness based on the SIFT score \u003csup\u003e55\u003c/sup\u003e, and correlation with cancer-based on COSMIC of de novo variants found in the repaired (486) and recurrent repaired (87) data set detected in all 17 repaired libraries (variants listed in Tables S1 and S4) \u003cstrong\u003e(B-D)\u003c/strong\u003e Distribution of pathogenic/likely pathogenic missense substitutions in \u003cem\u003eErbB2\u003c/em\u003e correlated with germline or somatic cancers reported in ClinVar with pathogenic variants in red and likely pathogenic variants in orange paired with reference data from the Exome Aggregation Consortium (ExAC) of gnomAD v3.1. access Jan2024 \u003csup\u003e57\u003c/sup\u003e\u003cstrong\u003e \u003c/strong\u003edisplayed on the right y-axis \u003cstrong\u003e(B)\u003c/strong\u003e, the number of reported exonic somatic amino acid substitutions in \u003cem\u003eErbB2\u003c/em\u003e associated with tumors retrieved from the COSMIC v96 database \u003csup\u003e58\u003c/sup\u003e \u003cstrong\u003e(C)\u003c/strong\u003e, and the recurrent exonic mutations (42) with their respective VAF measured in 17 different sperm DNA libraries with numbers displayed representing randomly selected donors with the recurrent variant \u003cstrong\u003e(D).\u003c/strong\u003e Plots were prepared by protein paint \u003csup\u003e59\u003c/sup\u003e with the size of the ball proportional to the mutation hits/counts and the color in C-D denoting the substitution type: missense, non-sense, or silent mutation (blue, orange, green, respectively). \u003cstrong\u003e(E)\u003c/strong\u003e Lines represent the variant allele frequency (VAF) of the 42 recurrent exonic \u003cem\u003eErbB2 \u003c/em\u003evariants scaled to the left y-axis. Note that variants occurring in multiple donors were plotted using the mean VAF. Variants also reported in COSMIC are shown in with red lines and in gnomAD with blue lines. The grey area represents the median DCS coverage with the DCS counts displayed at the right y-axis. \u003cstrong\u003e(F)\u003c/strong\u003e ErbB2 Protein Domains. (I) Domain I, (II) Domain I, (III) Domain III, (IV) Domain IV, (TMD) transmembrane domain, Juxtamembrane domain (JM), and protein kinase domain of \u003cem\u003eErbB2\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/60c58561d3e39559cbb57231.png"},{"id":64072590,"identity":"3352a9f7-5ed0-4707-8b01-80d8122110fa","added_by":"auto","created_at":"2024-09-06 07:40:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e \u003cem\u003eErbB2\u003c/em\u003e variants explored for the testis dissection analysis. Listed are the target site of the coding sequence position, AA: Amino Acid. CADD score \u003csup\u003e56\u003c/sup\u003e GRCh38-v1.6 and deleteriousness. VAF: Variant Allele Frequency. All gnomAD information was retrieved from v3.1. \u003csup\u003e57\u003c/sup\u003e. COSMIC data was based on version 94 \u003csup\u003e58\u003c/sup\u003e. The data was called based on transcript ENST00000269571 \u003cstrong\u003eB.\u003c/strong\u003e Relative position of the three selected variants within the \u003cem\u003eErbB2\u003c/em\u003e domains and the recurrence of variants in both unrepaired and repaired libraries (Supplementary Table S1 \u0026amp; S2). Each dot represents one mutation count that is color-coded based on the age of the sperm donor. (I) Domain I, (II) Domain I, (III) Domain III, (IV) Domain IV, (TMD) transmembrane domain, Juxtamembrane domain (JM), and protein kinase domain (PKD) of ErbB2.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/e81d62d960f83c9f6c6eca0a.png"},{"id":64071766,"identity":"6ee79bd7-6a31-419d-a5c3-f8a0a19e49b0","added_by":"auto","created_at":"2024-09-06 07:24:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":163065,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutational screening of testis DNA with droplet digital PCR (ddPCR). A.\u003c/strong\u003e Testis cutting scheme strategy: The testis was cut in half and divided further into 6 slices. Each slice was dissected into 32 pieces, and the DNA of four adjacent pieces was pooled and analyzed with ddPCR (8 pools per slice). The curved line above the slice denotes the epididymis for orientation purposes. \u003cstrong\u003eB.\u003c/strong\u003e VAFs of the three \u003cem\u003eErbB2\u003c/em\u003e variants measured in a post-mortem testis of a 70-year-old-, Caucasian donor (ID: ND10225)\u003cstrong\u003e. \u003c/strong\u003eVAFs for a second donor (ND10354) are shown in Supplementary Figure S9.\u003cstrong\u003e \u003c/strong\u003eDescriptive statistics of the maximum and median VAF in the 48 pools (data in Supplementary Table S7)\u003cstrong\u003e.\u003c/strong\u003e The ratio Max/Med represents the ‘hotness’ of the mutation cluster and compares the highest VAF (maxVAF) to the remaining pools (median VAF). \u003cstrong\u003eC.\u003c/strong\u003e Data of canonical driver mutations expanding in the aging male gonad documented for \u003cem\u003eFGFR2\u003c/em\u003e and \u003cem\u003eFGFR3\u003c/em\u003e: Variant c.755C\u0026gt;G screened in testis 374-2 of an unaffected 62-year-old-donor \u003csup\u003e13\u003c/sup\u003e and c.1138G\u0026gt;A of an unaffected 80-year-old donor (ID 57650) \u003csup\u003e14\u003c/sup\u003e. The color scale of the VAF’s magnitude is representative for all the panels.\u0026nbsp;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/f19f60bcb1944ce1d2a0436c.png"},{"id":64071756,"identity":"4e54abff-5c36-449f-9ae9-4656230df087","added_by":"auto","created_at":"2024-09-06 07:24:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":231843,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional signaling consequences of ErbB2 mutations.\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003e Schematic representation of the micropatterning assay. Cells are transiently transfected with fluorescently labelled bait (mGFP-ErbB2) and prey (Grb2-mRFP, Shc-mRFP) molecules and grown on anti-mGFP antibody patterned surfaces. Co-localization of the respective adapter protein to mGFP\u003cem\u003e-\u003c/em\u003eErbB2 enriched areas reports on the activation state of the receptor. TIRF microscopy is used to specifically detect membrane-proximal fluorescent proteins and to reduce cytosolic background signals. \u003cstrong\u003eB. \u003c/strong\u003eRepresentative TIRF images of Hela cells transiently co-expressing mGFP-ErbB2 (WT) and Grb2-mRFP grown on anti-mGFP antibody patterns. \u003cstrong\u003eC.\u003c/strong\u003eSchematic presentation of fluorescent ErbB2 fusion proteins and variants used for the activation analysis. The position of the variants is indicated at their approximate location in the protein domains with their respective amino acid substitutions. \u003cstrong\u003eD \u003c/strong\u003eQuantitation of bait-normalized fluorescence contrast of Grb2 and Shc adapter protein co-patterning, respectively. Data were normalized to the WT control and represent the mean ± SEM (n \u0026gt; 45 cells measured on at least three different days). All measurements were carried out in the non-liganded state, as there is no active and specific ligand known for the ErbB2. Only significant differences between WT and the respective variants are indicated. The \u003cem\u003eP\u003c/em\u003e-value annotations are represented as follows: p≤0.05 (*), p≤0.01 (**), and p≤0.001 (***).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/962d4c26c877d6ab85732d2d.png"},{"id":64073134,"identity":"cbb5379d-f034-4081-aaae-e794e15d9f94","added_by":"auto","created_at":"2024-09-06 07:56:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1893995,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/ee5c8d1c-5046-4b8b-bc27-f0fe1e724747.pdf"},{"id":64071746,"identity":"741aaa0a-7a1f-4ae7-8cfe-a045e0ff1b10","added_by":"auto","created_at":"2024-09-06 07:24:37","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":82048,"visible":true,"origin":"","legend":"Supplementary Table S1","description":"","filename":"SupplementaryTableS1allrepaired.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/62af1ca3e51fadc917f8e731.xlsx"},{"id":64072225,"identity":"e7190fa0-125d-4cd9-858d-58b291a9a93b","added_by":"auto","created_at":"2024-09-06 07:32:39","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":270492,"visible":true,"origin":"","legend":"Supplementary Table S2","description":"","filename":"SupplementaryTableS2allunrepaired.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/3fa1d167dece129f46592b7b.xlsx"},{"id":64072215,"identity":"465ee264-5958-48b8-8437-23f209e779fb","added_by":"auto","created_at":"2024-09-06 07:32:37","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":31281,"visible":true,"origin":"","legend":"Supplementary Table S3","description":"","filename":"SupplementaryTableS3negcontrols.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/d7927251ae788c6bd7fb302c.xlsx"},{"id":64071747,"identity":"adad46be-2a60-4c9b-a75e-1dc22e0d5e2d","added_by":"auto","created_at":"2024-09-06 07:24:37","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":23820,"visible":true,"origin":"","legend":"Supplementary Table S4","description":"","filename":"SupplementaryTableS4recurrentfinal.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/dc6948d0ecc218ead840367b.xlsx"},{"id":64071754,"identity":"b076b3e8-b01d-483c-b0a8-64cce65aace6","added_by":"auto","created_at":"2024-09-06 07:24:38","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":47891,"visible":true,"origin":"","legend":"Supplementary Table S5","description":"","filename":"SupplementaryTableS5CORRdonorinfo.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/6fc8f7d691be1069d080e4b6.xlsx"},{"id":64071749,"identity":"3127e9ba-0af0-4bae-a98c-79c33cd825ba","added_by":"auto","created_at":"2024-09-06 07:24:38","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":12660,"visible":true,"origin":"","legend":"Supplementary Table S6","description":"","filename":"SupplementaryTableS6CORRrecnon.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/5fe8cc6fce94e7a725b902ee.xlsx"},{"id":64071748,"identity":"99e9992d-c1e1-4716-a799-15dcd4ca8004","added_by":"auto","created_at":"2024-09-06 07:24:38","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":22559,"visible":true,"origin":"","legend":"Supplementary Table S7","description":"","filename":"SupplementaryTableS7ddPCRtestis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/70f839d014cb34c91221f94f.xlsx"},{"id":64071758,"identity":"33f3034d-ccb5-4cdc-9e96-7c081a5e32ac","added_by":"auto","created_at":"2024-09-06 07:24:39","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":21564,"visible":true,"origin":"","legend":"Supplementary Table S8","description":"","filename":"SupplementaryTableS8ddPCRvsDS.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/318476e173e62e3298487878.xlsx"},{"id":64071755,"identity":"23996a98-ef3d-4bcd-95c0-6a0cbe73fb48","added_by":"auto","created_at":"2024-09-06 07:24:39","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":43914,"visible":true,"origin":"","legend":"Supplementary Table S9","description":"","filename":"SupplementaryTableS9TIRFGrb21.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/e2ceda6077f30c1adfa5f9ef.xlsx"},{"id":64071751,"identity":"d2724435-b3e2-493b-9f66-04cb8578339a","added_by":"auto","created_at":"2024-09-06 07:24:38","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":49060,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S10\u003c/p\u003e","description":"","filename":"SupplementaryTableS10TIRFShc.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/68e1d2df4c8049dd4fb298d1.xlsx"},{"id":64071753,"identity":"7907761a-8f95-466b-9c0f-226e0014d8b1","added_by":"auto","created_at":"2024-09-06 07:24:38","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":12989,"visible":true,"origin":"","legend":"Supplementary Table S11","description":"","filename":"SupplementaryTableS11donorinfoRandNRlibAtena.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/2d512585f4bcb1ed5a925058.xlsx"},{"id":64071752,"identity":"af7c2b06-05fc-4a4f-8f10-e5566a0b524c","added_by":"auto","created_at":"2024-09-06 07:24:38","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":19544,"visible":true,"origin":"","legend":"Supplementary Table S12","description":"","filename":"SupplementaryTableS12libprot.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/8d51d19bfb77908c1937ed54.xlsx"},{"id":64071761,"identity":"3f2d8cf6-06bc-4487-90cc-ab3cc5ecff6d","added_by":"auto","created_at":"2024-09-06 07:24:40","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":12385,"visible":true,"origin":"","legend":"Supplementary Table S13","description":"","filename":"SupplementaryTableS13PCRconditions.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/c093309967fca98a1f092b5e.xlsx"},{"id":64071757,"identity":"75238efb-9e7e-42b3-82c8-d8695b14f06d","added_by":"auto","created_at":"2024-09-06 07:24:39","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":10978,"visible":true,"origin":"","legend":"Supplementary Table S14","description":"","filename":"SupplementaryTableS14primerprobes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/d47dee7108ce1db0ade0d4a0.xlsx"},{"id":64071770,"identity":"15d870ce-b7c5-4c56-bd1d-68ac15b1ece3","added_by":"auto","created_at":"2024-09-06 07:24:42","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":27434,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S15\u003c/p\u003e","description":"","filename":"SupplementaryTableS15captureprobes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/f9264a9cb6b146bf2a97b74d.xlsx"},{"id":64071763,"identity":"7eb7eeb9-c406-44e0-8798-f7ff8b1ea702","added_by":"auto","created_at":"2024-09-06 07:24:40","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":27521,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S16\u003c/p\u003e","description":"","filename":"SupplementaryTableS16captureprobesctrl.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/5afb3e4d687481820631c264.xlsx"},{"id":64071762,"identity":"1432d79b-4c2f-449d-99c0-483e63fdaee7","added_by":"auto","created_at":"2024-09-06 07:24:40","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":10932,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S17\u003c/p\u003e","description":"","filename":"SupplementaryTableS17tierclass.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/89fe17dd5f916e7a095c31aa.xlsx"},{"id":64072226,"identity":"13a1d823-c15c-4741-93a2-e2d727c2dc15","added_by":"auto","created_at":"2024-09-06 07:32:40","extension":"xlsx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":10675,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S18\u003c/p\u003e","description":"","filename":"SupplementaryTableS18ddPCR.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/eb34789a3ae6c8f190b5fe29.xlsx"},{"id":64072228,"identity":"2c7a05c7-1c72-4f4d-bb7d-3ef95237c666","added_by":"auto","created_at":"2024-09-06 07:32:41","extension":"xlsx","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":12333,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S19\u003c/p\u003e","description":"","filename":"SupplementaryTableS19SDM.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/068edd663b7a7675181f902a.xlsx"},{"id":64071767,"identity":"b583a413-2352-4deb-b38e-6a8fe7b0cad8","added_by":"auto","created_at":"2024-09-06 07:24:41","extension":"docx","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":4798458,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethodsandFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/12203c108f0eb8437ed07da0.docx"},{"id":64071768,"identity":"a7cbd690-13bb-4740-a62a-16cd308361ef","added_by":"auto","created_at":"2024-09-06 07:24:41","extension":"docx","order_by":21,"title":"","display":"","copyAsset":false,"role":"supplement","size":25725,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4887284/v1/ea97a31bfe99c785c302a0e1.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Mutations in ErbB2 accumulating in the male germline measured by error-corrected sequencing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDriver or selfish mutations, well known in cancer, induce higher rates of self-propagation in the cells that carry them, resulting in sub-clonal expansion events that grow larger with age \u003csup\u003e1\u0026ndash;4\u003c/sup\u003e, and thus can increase within an organism due to positive selection \u003csup\u003e1,3\u0026ndash;5\u003c/sup\u003e, following similar rules as species \u003csup\u003e6\u003c/sup\u003e. New or \u003cem\u003ede novo\u003c/em\u003e mutations (DNMs) events are rarer in the germline than in somatic tissue, possibly due to more robust DNA repair mechanisms \u003csup\u003e7\u003c/sup\u003e. But once germline DNMs occur, they might also behave like driver mutations and lead to sub-clonal expansions, as is well-known to occur in the mature male germline for mutations in a handful of genes in the receptor tyrosine kinase (RTK) pathway like FGFR2, FGFR3, HRAS, PTPN11, KRAS, RET, BRAF, CBL, MAPK1, MAPK2, and RAF1 \u003csup\u003e8\u0026ndash;18\u003c/sup\u003e. These DNMs (also known as selfish \u003csup\u003e19\u003c/sup\u003e, RAMP \u003csup\u003e20\u003c/sup\u003e or paternal-age effect (PAE) \u003csup\u003e21\u003c/sup\u003e mutations are often missense and gain-of-function changes associated with rare congenital disorders.\u003c/p\u003e \u003cp\u003eUntil recently, these driver mutations have been hypothesized to expand exclusively in the sexually mature male germline. Specifically, they result in a modified functionality of the protein usually associated with a hyperactivation of the RTK signaling pathway \u003csup\u003e18,22\u0026ndash;29\u003c/sup\u003e. This hyperactivation of the RTK signaling might confer a selective advantage to spermatogonial stem cells (SrAp) by, e.g., changes in the symmetric cell division patterns \u003csup\u003e8,14,16,18,30,31\u003c/sup\u003e and reviewed in \u003csup\u003e20,32\u003c/sup\u003e. Consequently, driver mutations accumulate with the ongoing cell-divisions of the mature male germline and form focal mutation pockets observed in the dissected aged testis \u003csup\u003e8,10\u0026ndash;14,16\u0026minus;18,30,31\u003c/sup\u003e, and are enriched in the sperm of older donors \u003csup\u003e8,12\u0026ndash;18,33\u0026minus;35\u003c/sup\u003e. Correspondingly, the risk of a germline-dominant genetic disorder in the offspring increases with paternal age \u003csup\u003e19\u0026ndash;21,32\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, it has been suggested that driver mutations might accumulate earlier, before post-pubertal spermatogenesis \u003csup\u003e18\u003c/sup\u003e. This early accumulation would be expected to form germline micro-mosaics, which could result in a high mutational load in sperm that is independent of the age of the donor \u003csup\u003e18,36\u003c/sup\u003e, as was also described for DNMs from the same parent shared among siblings in pedigrees \u003csup\u003e37,38\u003c/sup\u003e. Consequently, the risk of recurrence in siblings or the incidence in the general population might be higher for driver mutations already present as micro-mosaics at a young age. This is particularly worrisome, since most RTK driver mutations are associated with rare genetic disorders or cancer. Consequently, it is imperative to understand this type of mutagenesis and the expansion patterns of driver mutations in the male germline.\u003c/p\u003e \u003cp\u003eOne gene of particular interest is erb-b2 receptor tyrosine kinase (\u003cem\u003eErbB2\u003c/em\u003e), a member of the epidermal growth factor receptor family, which also includes \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e, and \u003cem\u003eEBBB4\u003c/em\u003e, and regulates RTK signaling. Mutations in \u003cem\u003eErbB2\u003c/em\u003e have been identified in numerous somatic tumors of the breast, the ovary, the lung, the large intestine, and the prostate \u003csup\u003e39,40\u003c/sup\u003e and have been also implicated in the embryonal carcinoma of testes and advanced testicular teratomas \u003csup\u003e39\u003c/sup\u003e. In the mature testis, \u003cem\u003eErbB2\u003c/em\u003e is broadly expressed in spermatogonia, early spermatocytes, elongating/elongated spermatids, Sertoli cells, and Leydig cells \u003csup\u003e41,42\u003c/sup\u003e and potentially regulates signaling in mitosis and the onset of meiosis of germ cells and spermiogenesis \u003csup\u003e39\u003c/sup\u003e. Similar to other RTK mutations, missense substitutions in \u003cem\u003eErbB2\u003c/em\u003e might have a selective advantage and expand in the male germline.\u003c/p\u003e \u003cp\u003eHere we investigated mutations in \u003cem\u003eErbB2\u003c/em\u003e accumulating in sperm DNA of differently aged-donors with error-corrected sequencing (ecSeq), also known as duplex sequencing (DS). This approach allowed us to collect a large number of DNMs, compare substitution frequencies and mutation types in the coding regions of \u003cem\u003eErbB2\u003c/em\u003e, and detect instances of positive selection. We also investigated the expansion patterns in aged dissected testes and the effect of selected mutations on the signaling of the receptor to better understand functional changes associated with these substitutions.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eTargeted error-corrected sequencing to detect mutations\u003c/h2\u003e\n \u003cp\u003eDS or ecSeq has the lowest reported error rate for Illumina sequencing, achieved by using a double barcode strategy that assembles information from both strands of the original DNA molecule \u003csup\u003e43,44\u003c/sup\u003e and reviewed in \u003csup\u003e45\u003c/sup\u003e. Consequently, DNA lesions or PCR mistakes can be distinguished from true mutations \u003csup\u003e46\u003c/sup\u003e, and this method is thus considered one of the advanced approaches for measuring low-frequency mutations. However, nicks in the DNA resulting from random DNA shearing with high energy bursts of sonication combined with error-prone repair of the templates before adaptor ligation might still be sources of artifacts. Specifically, the removal of nicks by strand extension and repair of protruding 3\u0026rsquo;DNA ends during the library preparation eliminates the original duplex sequence information necessary to distinguish real mutation from DNA lesions \u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eFor this reason, we implemented experimental \u0026lsquo;repair steps\u0026rsquo; during the library preparation. Specifically, we used dideoxy bases (ddBTPs) that block nick extensions and Mung Bean blunt ending to reduce errors derived from DNA synthesis by the enzyme mix used before adapter ligation, as described in \u003csup\u003e47\u003c/sup\u003e. In addition, we treated the DNA with enzymes (Fpg and UNG) to remove the most common DNA lesions (oxo-G and non-methylated cytosine deamination, respectively). Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows a lower average substitution frequency for the 17 repaired libraries (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e) than for the 25 unrepaired ones (Supplementary Table S2), with comparable substitution frequencies among individual libraries (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Five of the donors were the same for the repaired and unrepaired libraries, and the others were matched in terms of age, but donors differed in terms of sperm quality (50% vs 100% normospermic donors, respectively).\u003c/p\u003e\n \u003cp\u003eFor the repaired libraries, we observed a reduction of C\u0026thinsp;\u0026gt;\u0026thinsp;A/G\u0026thinsp;\u0026gt;\u0026thinsp;T transversions, a common substitution for oxo-G lesions (Supplementary Figure S2). The cosine similarity value comparing the mutational spectra with exonic variants in COSMIC or gnomAD was also more similar for repaired libraries than unrepaired ones (0.85 vs 0.71-COSMIC or 0.79 vs 0.64-gnomAD, respectively). We suspect that unrepaired libraries had more artifacts that were reduced in the \u0026lsquo;repaired\u0026rsquo; libraries by the nick- and end treatments and the enzymatic removal of lesions. We also used a lower sonication energy (a potential source of nicks) for the repaired libraries. Based on these results, data from unrepaired libraries were not used further except if specified in individual analyses.\u003c/p\u003e\n \u003cp\u003eTo further validate the ecSeq methodology, we measured the overall substitution frequency of a control human genomic region in 8 different donors (7 of these donors were also analyzed for the exonic \u003cem\u003eErbB2\u003c/em\u003e regions) with the data shown in Supplementary Table S3. For these controls, we also used the \u0026acute;repaired library preparation\u0026rsquo; strategy. The control region was a subset (~\u0026thinsp;10 kb; similar in size as the \u003cem\u003eErbB2\u003c/em\u003e region) of a mutagenic test used in other projects \u003csup\u003e48,49\u003c/sup\u003e that comprise different genomic sequences, each 0.5kb in size (total 20kb), with no known evidence of positive/negative selection or a functional role, and designed to represent a balanced sequence context in terms of GC content, genic/non-genic, coding/non-coding regions.\u003c/p\u003e\n \u003cp\u003eUsing the \u0026lsquo;repaired\u0026rsquo; approach, we measured in these controls a substitution frequency of 5.8x10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). A comparable substitution frequency of 1.9-5.5x10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e was reported for a human airway cell line \u003csup\u003e49\u003c/sup\u003e and a human lymphoblastoid cell line \u003csup\u003e50\u003c/sup\u003e targeting the 20kb control targets.\u003c/p\u003e\n \u003cp\u003eThe three-fold lower substitution frequency measured in the negative controls compared to the \u003cem\u003eErbB2\u003c/em\u003e region (5.8x10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e vs. 1.5x10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, respectively) suggests that two-thirds of the DNMs are exclusive to a process within \u003cem\u003eErbB2\u003c/em\u003e, but we cannot unambiguously exclude a contribution from artifacts or contaminating somatic cells. Contaminating somatic cells, in particular, may increase the substitution frequency, given their 10- to 100-fold higher mutation rates compared to germline cells \u003csup\u003e7,47\u003c/sup\u003e. In normospermic individuals, non-sperm cells typically range between 1\u0026ndash;10% in the semen but vary with factors like fertility, age, health, and lifestyle, and also potentially show higher proportions in cases of infertility \u003csup\u003e51,52\u003c/sup\u003e. In sperm donors that were examined for both the exonic \u003cem\u003eErbB2\u003c/em\u003e targets and controls, the difference in substitution frequency between \u003cem\u003eErbB2\u003c/em\u003e and control regions was also three-fold (Supplementary Fig. 1D).\u003c/p\u003e\n \u003cp\u003eWe also implemented a series of filtering steps at the bioinformatic level. These included the assignment of a tier classification (tier 1.1. to 7) by the Variant-Analyzer (VAR-A) that verified the reliability of the variant call based on evidence like mate information, family size, and errors within a family \u003csup\u003e53\u003c/sup\u003e. We further carried out a haplotype analysis and identified variants that co-occurred with another rare variant or rare haplotype (Supplementary Figure S3). These rare haplotypes were identified in multiple libraries (both within and among repaired libraries), were usually short, \u0026le; 10 base pairs, and were tagged sometimes by supplementary alignments (part of the read mapped to a different region of the genome). Since it is ambiguous if these variants were exclusive to \u003cem\u003eErbB2\u003c/em\u003e, we removed these variants from further analyses. We also removed variants within a short tandem repeat (STR), given the problematic and correct variant annotation at these sites.\u003c/p\u003e\n \u003cp\u003eIn total, we measured in the repaired libraries 493 substitutions (high-quality tier of 1.1 to 2.5) in the exonic regions of \u003cem\u003eErbB2\u003c/em\u003e (4557 bp) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). Of those, 486 substitutions were classified as de novo occurring in different donors (in at least two libraries); and 441 were unique substitutions. Those variants observed in multiple donors were labelled as recurrent (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table S4). Of the 42 recurrent variants in the repaired libraries, 2 variants co-occurred in more than 2 donors, and 40 variants occurred in two different donors. Of the recurrent variants, 17 were also observed in the unrepaired libraries (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). No significant differences in age, age category, diagnosis, millions of sperms (MS), Volume (ml), or MS/ml were measured between the recurrent and non-recurrent variants based on the Mann-Whitney-U test (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Supplementary Table S5-S6).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eA higher number of substitutions was observed in older donors\u003c/h2\u003e\n \u003cp\u003eWe analyzed libraries from three age classes: six from younger donors (19 to 30 years old), six libraries from middle-aged donors (31 to 45 years old), and the remaining five donors from an older category (46 to 63 years old); all healthy individuals, mainly from European ancestry. The average coverage depth of the repaired libraries was ~\u0026thinsp;5000x (max\u0026thinsp;~\u0026thinsp;7000x). We observed that the substitution frequency increased with the age group (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA) and was significantly higher for the middle-/ and older-age groups compared to the younger group. This difference between age groups was not as pronounced with the recurrent data set, probably because of the reduced power of a smaller sample size.\u003c/p\u003e\n \u003cp\u003eWe further analyzed the mutational spectra, mutational signatures, and transcriptional bias of the observed substitutions. When considering all the data, the most frequent substitution types were non-CpG transversions and non-CpG transitions (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). The mutational spectra also reflected a high number of G\u0026thinsp;\u0026gt;\u0026thinsp;T / C\u0026thinsp;\u0026gt;\u0026thinsp;A or (S\u0026thinsp;\u0026gt;\u0026thinsp;W) transversions and G\u0026thinsp;\u0026gt;\u0026thinsp;A / C\u0026thinsp;\u0026gt;\u0026thinsp;T or (S\u0026thinsp;\u0026gt;\u0026thinsp;W) transitions. Based on the cosine similarity value, the substitutions in \u003cem\u003eErbB2\u003c/em\u003e were more similar to variants reported in tumors for \u003cem\u003eErbB2\u003c/em\u003e (COSMIC) than to variants captured in the general population (gnomAD), as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC (0.85 vs. 0.79, respectively). The mutational signature comparing the variants also in the context of the 5\u0026prime; and 3\u0026prime; adjacent base reflected that most substitutions occurred in the context of an S-C-R trinucleotide, and this signature overlapped by ~\u0026thinsp;52% with SBS4 (associated with tobacco smoking), ~\u0026thinsp;22% with SBS30 (deficiency in base excision repair due to inactivating mutations in NTHL1), ~\u0026thinsp;15% with the SBS1 pattern (spontaneous or enzymatic deamination) and ~\u0026thinsp;11% with SBS5 (unknown etiology; Supplementary Figure S4). We have no knowledge if donors had a particular disease background or smoking history.\u003c/p\u003e\n \u003cp\u003eIn contrast, when considering only recurrent variants, we observed mainly S\u0026thinsp;\u0026gt;\u0026thinsp;W transitions, mostly at CpG sites (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB), consistent with a higher rate of 5meC\u0026thinsp;\u0026gt;\u0026thinsp;T mutations. Also, for this dataset, the mutational spectra of recurrent variants were also more similar to COSMIC than to gnomAD (0.81 vs. 0.77, respectively). Curiously, the mutational spectra of recurrent variants was also similar to another driver gene (\u003cem\u003eFGFR3\u003c/em\u003e) characterized by ecSeq in the male germline \u003csup\u003e34\u003c/sup\u003e (cosine similarity between \u003cem\u003eErbB2\u003c/em\u003e and \u003cem\u003eFGFR3\u003c/em\u003e of 0.89, CI\u0026thinsp;=\u0026thinsp;0.89\u0026ndash;0.99).\u003c/p\u003e\n \u003cp\u003eThe mutational signature for recurrent variants showed the most frequent substitutions in the context of V-C-G sites with ~\u0026thinsp;26% overlap of variants explained by the SBS1 pattern (spontaneous deamination of 5-methylcytosine), ~\u0026thinsp;28% by SBS5, and ~\u0026thinsp;46% by SBS87 (thiopurine chemotherapy treatment; Supplementary Figure S5). When analyzing which substitutions accumulated more often in the genic region, we found that the strong to weak transversions (C\u0026thinsp;\u0026gt;\u0026thinsp;A) and transitions (C\u0026thinsp;\u0026gt;\u0026thinsp;T) and the pyrimidine to purine transversions (T\u0026thinsp;\u0026gt;\u0026thinsp;A) occurred more often in the transcribed strand than in the un-transcribed strand (Supplementary Figure S6).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eEnrichment of recurrent substitutions in the extracellular and protein kinase domain\u003c/h2\u003e\n \u003cp\u003eDriver mutations in the male germline might also be associated with a functional change or a change in the signaling activity of the receptor \u003csup\u003e19,20,32\u003c/sup\u003e. To explore this possibility, we examined where the substitutions occurred within the ErbB2 protein domains. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA shows that when considering all the data, most of the substitutions occurred in the transmembrane domain and flanking regions, with one-third of the substitutions predicted to result in a deleterious change based on the SIFT scores \u003csup\u003e54,55\u003c/sup\u003e, which is one of the most commonly used algorithms for predicting the effect of a substitution on protein function (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). A small fraction was associated with cancer (COSMIC).\u003c/p\u003e\n \u003cp\u003eVariants that occurred more than once independently (i.e., in different donors) appear especially likely to be responsible for clonal expansion compared to all the exonic \u003cem\u003eErbB2\u003c/em\u003e DNMs. First, most (34 out of 42) of the recurrent variants were missense substitutions, 6 were synonymous, and 2 were classified as stop-gain mutations (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Second, many of these recurrent mutations are likely to be deleterious. Approximately one-fifth of these recurrent substitutions were reported as somatic cancer-promoting mutations in COSMIC (compared to 8% considering all variants), and half were reported in gnomAD and are likely viable; 10 have never been reported before. Furthermore, ~\u0026thinsp;50% of these recurrent substitutions are highly likely to be deleterious, according to their CADD score \u003csup\u003e56\u003c/sup\u003e (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table S4). Third, when classifying the substitutions per domain, two-thirds of the substitutions occurred in the protein kinase domain, PKD (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eVariants categorized with a disorder by ClinVar or reported in COSMIC also mainly accumulate in the PKD of ErbB2 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB-C), as also was observed for the recurrent DNMs (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD) with mutation frequencies or variant allele frequencies (VAF)\u0026thinsp;~\u0026thinsp;2x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE); with the highest average VAF (5.2 x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) measured for c.C2442T (p.R814R), a silent mutation in the kinase domain (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table S4). This enrichment in the kinase domain of pathogenic-, tumor-related variants and recurrent DNMs in sperm suggests that this domain is a target for mutations likely with clonal expansion consequences in ErbB2.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eEvidence for positive selection: passenger versus driver mutations\u003c/h2\u003e\n \u003cp\u003eIn order to investigate whether the variants enriched in sperm are driven by positive selection, we performed a version of a \u003cem\u003ed\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e/\u003cem\u003ed\u003c/em\u003e\u003csub\u003eS\u003c/sub\u003e analysis, which compares the non-synonymous vs. synonymous substitutions adjusting for local sequence context \u003csup\u003e60\u003c/sup\u003e. A \u003cem\u003ed\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e/\u003cem\u003ed\u003c/em\u003e\u003csub\u003eS\u003c/sub\u003e ratio close to the reference value of one (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) indicates no selection, below the reference value implies negative selection, and above the reference indicates positive selection \u003csup\u003e60,61\u003c/sup\u003e. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows that \u003cem\u003eErbB2\u003c/em\u003e substitutions have a signature of positive selection with a \u003cem\u003ed\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e/\u003cem\u003ed\u003c/em\u003e\u003csub\u003eS\u003c/sub\u003e ratio of 1.5, significantly larger than one. Curiously, this ratio was increased considerably when considering only the recurrent substitutions (\u003cem\u003ed\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e/\u003cem\u003ed\u003c/em\u003e\u003csub\u003eS\u003c/sub\u003e = 3.7), further supporting that recurrent mutations are a subset of variants enriched for targets of positive selection. For both data sets, the strongest indication for positive selection was in the protein kinase domain (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eMutations in all age groups, also showed signatures of positive selection, with similar values observed for younger, middle and older groups, slightly increasing with age when considering all the data (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Given the smaller sample size of recurrent variants, the \u003cem\u003ed\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e/\u003cem\u003ed\u003c/em\u003e\u003csub\u003eS\u003c/sub\u003e estimates for these mutations have large confidence intervals, but all categories analyzed show statistical evidence for positive selection.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimates of the \u003cem\u003ed\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e/\u003cem\u003ed\u003c/em\u003e\u003csub\u003eS\u003c/sub\u003e ratio following \u003csup\u003e60\u003c/sup\u003e calculated in total, or per protein domain (EC: extracellular domain, TMD\u0026thinsp;+\u0026thinsp;flanking regions, PKD: protein kinase domain, C-terminal tail) and per age group for de novo exonic \u003cem\u003eErbB2\u003c/em\u003e variants (n\u0026thinsp;=\u0026thinsp;486 mutations for the repaired libraries, or n\u0026thinsp;=\u0026thinsp;457 nonsynonymous and synonymous variants, or n\u0026thinsp;=\u0026thinsp;87 recurrent mutations; n\u0026thinsp;=\u0026thinsp;83 nonsynonymous and synonymous). Variants were retrieved from Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e and S4, respectively omitting variants resulting in stop codons. The age category was defined as younger sperm donors than 30-years old, middle age are donors between 30\u0026ndash;45 years old and older group to donors older than 45. This partitioning ensured similar sample sizes between groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cem\u003ede novo\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cem\u003ede novo\u003c/em\u003e recurrent\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en nonsyn\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en synon\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003edN\u003c/em\u003e/\u003cem\u003edS\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en nonsyn\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en synon\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003edN\u003c/em\u003e/\u003cem\u003edS\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDomain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-terminal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYounger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOlder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eSpatial distribution of selected variants in the male gonad\u003c/h2\u003e\n \u003cp\u003eIn order to gain further insights into the accumulation of \u003cem\u003eErbB2\u003c/em\u003e mutations in the germline of sexually mature males, we screened the spatial distribution of three variants in a post-mortem testis of two different donors, a 70- and a 73-year-old. In particular, we examined c.428G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.R143Q), c.2033G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.R678Q), and c.2524G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.V842I), which were selected based on the association of the variant with a cancer phenotype, different clinical significances (pathogenic to likely benign), high predicted deleteriousness (CADD score) \u003csup\u003e56\u003c/sup\u003e, COSMIC reports, as well as differences in embryonic viability of these variants based on gnomAD reports (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Furthermore, p.R678Q is the fourth most common mutation in \u003cem\u003eErbB2\u003c/em\u003e reported in COSMIC, and p.V8421 is reported as an activating mutation \u003csup\u003e62\u003c/sup\u003e. Further, based on our ecSeq data, these variants occurred in sperm at VAFs\u0026thinsp;~\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e in at least 3 different donors when also considering unrepaired libraries (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). Note that we also observed different nucleotide substitutions at the same codons rendering alternative missense or silent substitutions reported in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2.\u003c/p\u003e\n \u003cp\u003eWe followed the testis micro-dissection technique \u003csup\u003e13,14,16,30\u003c/sup\u003e in combination with digital droplet PCR (ddPCR), as described previously \u003csup\u003e18\u003c/sup\u003e. In short, each testis was divided into 6 slices and each slice was further partitioned into 32 pieces. The extracted DNA of four adjacent testis pieces was pooled rendering 8 pools per slice, or in total 48 pools per testis, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA (for details see \u003csup\u003e18\u003c/sup\u003e). For each pool, we screened 270,000-300,000 genomes. With this input, some samples yielded 1\u0026ndash;3 mutants, implying VAFs of ~\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e (Supplementary Table S7). In 15\u0026ndash;30% of samples, we did not detect any mutations (Supplementary Figure S7). Note that at input levels of 300,000 genomes, the chance of finding zero mutations is 60% (based on a Poisson distribution with \u0026lambda;\u0026thinsp;=\u0026thinsp;0.5); thus, failure to observe positive counts at this depth can still be consistent with mutations present at VAFs lower than the detection threshold.\u003c/p\u003e\n \u003cp\u003eWe validated the ddPCR measurements by screening the flushed epididymis (mostly sperm of the testis donor) and a piece of scrotum skin, both expected to have different mutation frequencies than individual testis pieces. In addition, we screened the same sperm donors as with ecSeq (Supplementary Figure S8; Table S8) to compare the measurements between the two methodologies. For all three variants, the skin measurements (proxy of a negative control) rendered lower VAFs (significant except for p.V821I) than the epididymis (proxy of a positive control). Furthermore, we found a good congruence between VAFs of donor-matched sperm data measured with ecSeq and ddPCR. This suggests the ddPCR method is appropriate to screen expansion clusters, if following the sub-clonal expansions observed in \u003cem\u003eFGFR3\u003c/em\u003e \u003csup\u003e14\u003c/sup\u003e and \u003cem\u003eFGFR2\u003c/em\u003e \u003csup\u003e13\u003c/sup\u003e reaching VAFs as high as 8x10\u003csup\u003e-4\u003c/sup\u003e and 2.9x10\u003csup\u003e-2\u003c/sup\u003e, respectively.\u003c/p\u003e\n \u003cp\u003eNone of the three \u003cem\u003eErbB2\u003c/em\u003e sites reached VAFs larger than ~\u0026thinsp;4x10\u003csup\u003e-5\u003c/sup\u003e (maxVAF, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB and Supplementary Figure S9). It is possible that the clusters were diluted by the pooling strategy, but the analysis of individual testis pieces within two selected pools showed that this was unlikely (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB and Supplementary Table S7). The \u0026lsquo;hotness\u0026rsquo; of focal mutation pockets was assessed by the ratio Max/Med that compared the highest VAF (maxVAF) to the remaining pools (median VAF) and ranged for both testis and the three different variants between ~\u0026thinsp;3- to 4-fold. For comparative purposes, we included data on two mutations known to form sub-clonal clusters in the aging testis that were also collected with the same dissection scheme. Specifically, we selected c.1138G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.G380A) and c.755C\u0026thinsp;\u0026gt;\u0026thinsp;G (p.S252W) of the \u003cem\u003eFGFR3\u003c/em\u003e or \u003cem\u003eFGFR2\u003c/em\u003e gene associated with achondroplasia \u003csup\u003e14\u003c/sup\u003e or Apert syndrome \u003csup\u003e30\u003c/sup\u003e, respectively. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC shows that these two variants form distinct clusters in the testis with VAFs one- to three- orders of magnitude greater than the median VAF measured in the remaining testis (Max/Med). In conclusion, we did not observe as large differences in VAFs among pools for the \u003cem\u003eErbB2\u003c/em\u003e variants in any of the two testis (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB and Supplementary Figure S9) and have no support that the selected \u003cem\u003eErbB2\u003c/em\u003e variants accumulate in the sexually mature gonad with age.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis of signaling activity of selected ErbB2 variants with biophysical methods\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003eSome kinase domain variants show increased recruitment of downstream adaptor proteins\u003c/h2\u003e\n \u003cp\u003eWe also investigated the changes in activation of the three focal \u003cem\u003eErbB2\u003c/em\u003e protein variants (p.R143Q, p.R678Q, and p.V842I) at the cellular level. For this purpose, we investigated the recruitment of downstream adaptor proteins using total internal reflection fluorescence (TIRF) microscopy that analyses receptor-adaptor interactions at the cell membrane of live cells \u003csup\u003e63\u003c/sup\u003e, as have been previously done for FGFR3 \u003csup\u003e18,29\u003c/sup\u003e and EGFR \u003csup\u003e64\u003c/sup\u003e. Cells co-expressing both a cytosolic downstream adapter protein (Grb2 or Shc1 fused to monomeric red fluorescent protein, mRFP) and one ErbB2 variant (tagged with monomeric green fluorescent protein mGFP) were seeded on micrometer-scaled antibody-patterned surfaces (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Here, the ErbB2 is arranged at the plasma membrane following the micropattern on the surface. If the receptor is activated, the downstream signaling adaptor proteins (Grb2 or Shc1) are recruited by ErbB2 kinase activity to the receptor-enriched micropatterns (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). To quantify the receptor activation state, we used the respective fluorescence signal intensities within and outside the antibody-patterned regions: the degree of ErbB2 activation is expected to be proportional to the level of Grb2/Shc1 co-recruitment to the active receptor variants, which is in turn reflected in the normalized mRFP-contrast value (see Supplementary Methods). False-positive TIRF signals due to the deformation of the plasma membrane on top of the patterned antibody surfaces was excluded, since control cells co-transfected with Lact-C2-RFP (RFP fused with C2 domain of bovine lactadherin), showed no patterning of RFP signal due to homogenous membrane distribution in the central regions of GFP-ErbB2 patterned cells, as also was shown in detail in \u003csup\u003e65\u003c/sup\u003e and Supplementary Figure S11.\u003c/p\u003e\n \u003cp\u003eWith this approach, we measured the activation of our focal ErbB2 variants, compared to wild type ErbB2, two negative controls [-C; a kinase dead variant (p.K753M) \u003csup\u003e66\u003c/sup\u003e and a truncated mutant lacking the intercellular domain (\u0026Delta;IC)], and two positive controls (+\u0026thinsp;C) [constitutively active variants p.V659E and p.G778D \u003csup\u003e66\u0026ndash;69\u003c/sup\u003e]. The signaling activity normalized to the wild type is shown for all eight analyzed variants, for both the Grb2 and Shc1 adapter protein co-recruitment (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC-D, Supplementary Figure S10 and Supplementary Table S9 and S10). None of the negative controls co-recruited Grb2. The negative control results for Shc1 were mixed: \u0026Delta;IC had a reduced interaction with Shc1, but that of p.K753M(-C) remained unchanged, consistent with Shc1 having a different binding site, as also reported by \u003csup\u003e70\u003c/sup\u003e. Both positive controls (p.V659E and p.G778D) showed a\u0026thinsp;~\u0026thinsp;1.6-fold and ~\u0026thinsp;1.2-fold increased recruitment of Grb2 and a\u0026thinsp;~\u0026thinsp;1.2-fold and ~\u0026thinsp;1.5-fold increased recruitment for Shc1, respectively. Of the three focal variants, p.R678Q, the re-occurring variant in cancer patients, did not show increased activity, while both p.R143Q and p.V842I reported a significantly higher receptor activity than wild-type, of ~\u0026thinsp;1.4\u0026ndash;1.8 fold, respectively, similar to that of the constitutively active positive controls.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRoughly 80% of the new germline mutations passed down through generations can be traced back to the paternal lineage \u003csup\u003e71,72\u003c/sup\u003e. Therefore, investigating mutations that occur in the male germline during a lifetime can yield valuable insights into human diseases, their potential impact on future generations, and evolutionary processes \u003csup\u003e7,37,73\u003c/sup\u003e. Identifying these mutations has been challenging due to their low frequencies.\u003c/p\u003e \u003cp\u003eUsing ecSeq, our study examined the occurrence of \u003cem\u003eErbB2\u003c/em\u003e DNMs in the male germline, resulting in an extensive dataset of mutations enriched in sperm DNA from donors of varying ages. This facilitated the characterization of mutational distribution, spectra and signatures specific to \u003cem\u003eErbB2\u003c/em\u003e, thereby furthering our understanding on driver mutations and their expansion in the male germline. In addition, this large dataset had sufficient power for testing the preferential accumulation of non-synonymous versus synonymous substitutions (\u003cem\u003ed\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e/\u003cem\u003ed\u003c/em\u003e\u003csub\u003eS\u003c/sub\u003e analysis) that identified positive selection to explain the enrichment of missense \u003cem\u003eErbB2\u003c/em\u003e mutations observed in sperm DNA. The data also revealed that mutations accumulate even at a young age, likely existing as micro-mosaics within mutation pockets that remain relatively small and constant over time with no large expansion measured in the aged testis, a novel finding for driver mutations. Functional analyses using biophysical methods further demonstrated that selected ErbB2 mutations hyperactivate the RTK pathway, leading to increased downstream signaling, which likely contributes to the accumulation of ErbB2 mutations in the male germline. Our findings provide relevant insights into the enrichment of mutations or micro-mosaicism in the male germline, impacting the transmission and recurrence of ErbB2-associated disorders independently of age.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOrigin of\u003c/b\u003e \u003cb\u003eErbB2\u003c/b\u003e \u003cb\u003emutations\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGermline DNMs can arise at various stages, including early embryogenesis, primordial germ cell differentiation, pre-pubertal or post-pubertal spermatogenesis and adulthood, ultimately populating the sexually mature testis (reviewed in \u003csup\u003e37,74\u003c/sup\u003e). Some mutations may be driven by selection, while others may change through the stochastic process of genetic drift \u003csup\u003e75,76\u003c/sup\u003e resulting in different proportions of mosaics, as described in hematopoiesis \u003csup\u003e77,78\u003c/sup\u003e. DNMs linked to selection get enriched by one lineage of cells carrying the mutation being favored over another producing more daughter cells \u003csup\u003e4,79\u003c/sup\u003e. This results in the expansion of DNMs with age, with strong driver mutations leading to selective sweeps in the male germline. However, neutral or passenger DNMs, lost or fixed, also reduce the number of cell lineages and the variation or heterogeneity of sub-populations, as was observed for white blood cell DNA with age \u003csup\u003e45\u003c/sup\u003e. Consequently, neutral random drift might be misinterpreted as a selective sweep \u003csup\u003e76\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe chance of encountering any particular DNM at a given specific site is very small, with a frequency of ~\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e on average in the human genome. The exonic ErbB2 DNMs had a VAF 4\u0026ndash;5 orders of magnitude larger, with individual donors showing frequencies as high as ~\u0026thinsp;5x10\u003csup\u003e-4\u003c/sup\u003e. How can such a high VAF be explained? Furthermore, how does this explanation fit the observation that 10% of the exonic \u003cem\u003eErbB2\u003c/em\u003e DNMs occurred independently in multiple donors, particularly missense mutations occurring at both CpG and non-CpG sites? An explanation based solely on a \"hypermutable site\" concept (with CpG transitions having only one order of magnitude higher frequencies compared to non-CpG transitions) is unsatisfactory. Random drift cannot explain these high frequencies either. The probability of encountering the same high-frequency mutation occurring independently in different donors by random chance or drift is exceedingly small. A more plausible scenario is that these mutations arise infrequently, but cause a functional change coupled with a growth advantage and undergo clonal expansion within the male gonad, leading to a relative enrichment of mutant spermatogonia or their sperm equivalents, characteristic of driver mutations.\u003c/p\u003e \u003cp\u003eFurther, our large dataset had sufficient power for testing the preferential accumulation of non-synonymous versus synonymous substitutions (d\u003csub\u003eN\u003c/sub\u003e/d\u003csub\u003eS\u003c/sub\u003e analysis) and identified positive selection as an explanation for the enrichment of missense ErbB2 mutations in sperm DNA. This trend was more robust when considering recurrent mutations. It is notable that recurrent mutations were mainly C\u0026thinsp;\u0026gt;\u0026thinsp;T substitutions at CpG sites, in contrast to the complete dataset that was enriched for non-CpG transitions and transversions. A similar difference was observed between recurrent mutations (sibling-shared mutations) derived from the maternal lineage or paternal lineage \u003csup\u003e80\u003c/sup\u003e, the latter being more similar to recurrent \u003cem\u003eErbB2\u003c/em\u003e mutations.\u003c/p\u003e \u003cp\u003eThe observed recurrent substitutions were mainly missense substitutions placed in the kinase domain, and described as deleterious by different predictors (CADD, SIFT and PolyPhen scores). These mutations might be linked to changes in signaling activity, as was shown for selected mutations with RTK signal activation by receptor-adaptor interactions at the cell membrane of live cells (micropatterns combined with TIRF) for two of the selected mutants (p.R143Q, p.V842I) that exhibited elevated recruited Shc and Grb2 compared to WT ErbB2. Intriguingly, the downstream signaling resembled the WT for the R678Q mutant. These results are in agreement with previous findings \u003csup\u003e62\u003c/sup\u003e. In light of these results, we hypothesize that the \u003cem\u003eErbB2\u003c/em\u003e variants captured with ecSeq in sperm DNA are enriched in the male germline by positive selection.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAre ErbB2 mutations exclusive to the sexually mature germline?\u003c/h2\u003e \u003cp\u003eThe ongoing cell-divisions in the sexually mature male-gonad might lead to the accumulation of driver mutations with age. For decades it has been hypothesized that the expansion of driver mutations occurs exclusively in the sexually mature male germline (adulthood), explaining the larger mutation pockets in aged testes but absent in the gametes of young donors \u003csup\u003e8,16,20,30\u003c/sup\u003e. The mutation arises rarely but expands clonally in the sexually mature testis, leading to a relative enrichment of mutant spermatogonia or sperm equivalents. Critical for the formation of these clusters is that all of the descendants stay in close proximity to the initial mutant cell (reviewed in \u003csup\u003e20,32\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eHowever, the expansion of driver mutations might not be limited to post-pubertal spermatogenesis and adulthood, but occur already at earlier stages, including early embryogenesis, primordial germ cell differentiation, or pre-pubertal spermatogenesis. Recent work observed increased VAF frequencies also in young sperm donors in \u003cem\u003eFGFR3\u003c/em\u003e \u003csup\u003e18,34\u003c/sup\u003e. In particular, missense mutations were observed at levels of 0.01\u0026thinsp;\u0026minus;\u0026thinsp;0.005% in FGFR3 and in some cases the frequencies did not increase with the donor\u0026acute;s age \u003csup\u003e18\u003c/sup\u003e. Particularly, \u003cem\u003eFGFR3\u003c/em\u003e, a well-studied driver gene, the studied gain-of-function variants with promiscuous activation (ligand-independent) showed two distinct mutational behaviors: one that grows to larger sub-clonal clusters in the sexually mature gonad and increases in frequency in sperm with age. The other likely occurs pre-puberty, forming stable niches that stay constant in size (as also described here for ErbB2) challenging the long-standing hypotheses that driver or selfish mutations originate exclusively in the sexually mature male germline and keep growing with time \u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccumulation of variants before post-pubertal spermatogenesis was also reported in family pedigrees for neutral mutations with DNMs being shared among siblings and coming from the same parent \u003csup\u003e37\u003c/sup\u003e, but not present in the parent\u0026rsquo;s somatic cells and with no evidence for a dependency on parental age \u003csup\u003e38\u003c/sup\u003e. Also, for other species including reptiles, birds, and mammals it was reported that mutations accumulated not just during spermatogenic cycles post-puberty, but also during earlier developmental phases \u003csup\u003e81\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe hypothesis that mutations can accumulate in the germline pre-puberty would also align with the expansion patterns observed for \u003cem\u003eErbB2\u003c/em\u003e. We found evidence for positive selection (d\u003csub\u003eN\u003c/sub\u003e/d\u003csub\u003eS\u003c/sub\u003e analysis) in all three age categories (younger, middle and older donor groups), mainly with variants in the extracellular- and protein kinase domain indicating likely functional consequences.\u003c/p\u003e \u003cp\u003eOur testis dissection study also suggests that some \u003cem\u003eErbB2\u003c/em\u003e mutations rather establish stable niches that remain constant in size for the three selected ErbB2 variants (c.428G\u0026thinsp;\u0026gt;\u0026thinsp;A, c.2033G\u0026thinsp;\u0026gt;\u0026thinsp;A, and c.2524G\u0026thinsp;\u0026gt;\u0026thinsp;A). This stability in size may be attributed to some variants being tolerated only at low levels, as often observed in highly activating rasopathies forming mosaics in the skin \u003csup\u003e82\u003c/sup\u003e. A similar behavior was reported for selected \u003cem\u003eFGFR3\u003c/em\u003e activating mutations that also showed increased frequencies in young donors that formed rather small mutation pockets in the testis \u003csup\u003e18\u003c/sup\u003e and for early developmental neutral clones that remained temporally stable across serial samples and age groups, with no changes in size (or frequency) in the stem cell niches \u003csup\u003e36\u003c/sup\u003e. This challenges also the dogma that driver mutations expand into large clusters with time in the male germline.\u003c/p\u003e \u003cp\u003eIn conclusion, while age-associated driver mutations are more prevalent in offspring from fathers of advanced age, the risk of recurrence in siblings or the incidence in the general population might be higher for germline mosaics than for age-associated mutations; albeit this might strongly depend on the selective advantage conferred by the mutation. This is particularly worrisome, since different ErbB2 activating mutations might have early- or late-onset effects associated with a clinical phenotype that ranges from a rare genetic disorder, cancer or tumor resistance to certain protein tyrosine kinase inhibitors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eSample collection, preparation and DNA extraction\u003c/h2\u003e\n \u003cp\u003eSperm samples from anonymous donors, who been abstinent for \u0026gt;\u0026thinsp;3 days, were collected in the Kinderwunsch Klinik, MedCampus IV, Kepler Universit\u0026auml;tsklinikum, Linz following the protocol approved by the ethics commission of Upper Austria (Approval F1-11). Donors were mainly between 19 and 63 years old and mostly of European ancestry. Two Snap-frozen, post-mortem testes from 73 year-old (ID: NRD#ND 10354) and a 70 year-old (ID: NRD#ND 10225) donors were collected from the National Disease Research Interchange (NDRI, Philadelphia, PA). None of the donors had chronic infections, diabetes, chemotherapy, or radiation or antecedents of alcohol, tobacco, or drug abuse. The DNA from sperm was extracted from fresh semen samples, following the protocol as described in \u003csup\u003e18,83\u003c/sup\u003e. Testis dissection was followed as previously described \u003csup\u003e8,13,14,16\u0026ndash;18,30\u003c/sup\u003e. Details of sperm and testis DNA extraction can be found in SM Methods. The information about the different sperm donors including the WHO classification for human semen characteristics \u003csup\u003e84\u003c/sup\u003e, as well as the library protocol used is listed in Supplementary Table S11.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eLibrary preparation\u003c/h2\u003e\n \u003cp\u003eLibrary preparation followed the protocol by \u003csup\u003e34\u003c/sup\u003e with adaptations from \u003csup\u003e5,47\u003c/sup\u003e, outlined in Supplementary Table S12 and Supplementary Materials. We used two strategies: repaired libraries underwent treatment with USER enzyme (NEB, M5505S) and Fpg-Glycosylase (M0240S, NEB), along with a blunting step using Mung bean nuclease (NEB, M0250S) and ddBTPs (Merck/Sigma Aldrich, GE27-2045-01) for nick sealing as detailed in Supplementary Methods and Supplementary Table S12. For unrepaired libraries, we used focused ultrasonication (Covaris M220 instrument) followed by size selection, end repair, and A-tailing.\u003c/p\u003e\n \u003cp\u003eAdapter ligation employed DS_Hairpin_U adaptors with 12 random nucleotides, ligated using the NEBNext Ultra II end repair/dA-tailing module (NEB) and the NEBNext Ultra II ligation module for both library types. The USER enzyme digest (NEB) was used to open the adapter loop. Adapters were synthesized as previously described \u003csup\u003e34\u003c/sup\u003e and specified in Supplementary Methods. Amplification, using variable input DNA and PCR conditions involved 12 or 6 cycles of single primer extension followed by 2 PCR cycles. The reaction was carried out in 1x Kapa HiFi Reaction Mix (Roche) followed by DNA cleanup with 1.2x volumes of Sera-Mag Select beads (Cytiva) according to manufacturer\u0026rsquo;s instructions. Input DNA, PCR conditions, and reaction volumes are described in the Supplemental Methods and Supplementary Table S13. Primer sequences and oligonucleotides are shown in Supplementary Table S14. In both strategies, after initial amplification, 120 bp biotinylated oligonucleotide probes were employed for two rounds of targeted capture followed with further PCR cycles for sufficient target enrichment, as described \u003csup\u003e34,85\u003c/sup\u003e. Details, including the number of cycles for each capture PCR and the sequence of biotinylated oligonucleotide probes, are provided in Supplementary Tables S13, S14, S15, and S16 and Supplemental methods. Sequencing was performed using the MiSeq Reagent v3 600 cycles kit at the VBCF NGS Unit, Vienna, Austria, or primarily on the HiSeq X 150 PE platform at Macrogen, Seoul, South Korea\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eDuplex Sequencing data Analysis and variant filtering\u003c/h2\u003e\n \u003cp\u003eThe raw sequencing data were analyzed with the Du Novo package on the Galaxy platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://usegalaxy.org/u/jku-itb-lab/w/galaxy-workflow-ErbB2\u003c/span\u003e\u003c/span\u003e, Supplementary Figure S12, a duplex sequencing (DS) pipeline that assembles reads sharing the same barcode into families before the alignment to the human genome assembly hg38 \u003csup\u003e86\u003c/sup\u003e. For details on the pipeline, see SM Methods. We also removed the first and last 15 nucleotides of the duplex consensus sequence (DCS) as potential artifacts deriving from error-prone end-repair. Additionally, we used the Variant Analyzer tools (VAR-A) \u003csup\u003e53\u003c/sup\u003e to classify the confidence in the identified alternate alleles by a tier-based system, as described in Supplementary Table S17. We filtered all intronic variants and SNPs from the analysis, and only variants in high-quality tiers (tiers 1.1\u0026ndash;2.5) were kept. We also removed variants that co-occurred with another rare variant or rare haplotype and within STRs. This list of variants is denoted as \u0026ldquo;all variants\u0026rdquo;. Moreover, \u0026ldquo;recurrent variants\u0026rdquo; represent variants that happen in at least two libraries.\u003c/p\u003e\n \u003cp\u003eFinally, the variants were annotated with wANNOVAR \u003csup\u003e87\u003c/sup\u003e and the Variant Effector Predictor (VEP) \u003csup\u003e88\u003c/sup\u003e. The deleteriousness of a variant was described with the CADD \u003csup\u003e89\u003c/sup\u003e and SIFT scores. Additionally, the association with a cancer type was extracted from COSMIC, and \u003cem\u003eErbB2\u003c/em\u003e variants (human genome assembly hg38) of transcript ENST00000269571.5 were extracted from gnomAD v3.1.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gnomad.broadinstitute.org\u003c/span\u003e\u003c/span\u003e) and Cosmic V96 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancer.sanger.ac.uk\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eSubstitution frequency\u003c/h2\u003e\n \u003cp\u003eThe substitution frequency is calculated as the number of de novo events per number of sequenced nucleotides, which is estimated as the mean coverage multiplied by the targeted region (exonic size). If a mutation is happening in different libraries, it is also counted multiple times.\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eMutational spectra\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003cp\u003eWe categorized the mutational spectra after the (un)transcribed strand. The mutation spectra are estimated with the relative unique counts, where each variant is counted only once regardless of the number of occurrences in the libraries, to compare it to the variant counts from public databases. Only substitutions of the \u003cem\u003eErbB2\u003c/em\u003e gene (transcript ENST00000269571.9) and within the negative control regions (human genome assembly hg38) were extracted from the gnomAD v3.1.2 \u003csup\u003e90\u003c/sup\u003e and COSMIC v96 \u003csup\u003e91\u003c/sup\u003e databases.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eCosine similarity\u003c/h2\u003e\n \u003cp\u003eThe similarity between a query and reference mutational spectra is measured with the cosine similarity. First, a distribution of cosine similarities is generated by randomly sampling \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e mutations (number of mutations of the query spectra) from the reference spectra and each of the samples is compared to the original reference spectra (1,000 iterations). Second, the observed cosine similarity can be compared to the 95% confidence intervals of the bootstrapped reference samples. This approach is followed as in \u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eMutational signature\u003c/h2\u003e\n \u003cp\u003eThe mutational signatures are built from the trinucleotide context (5\u0026rsquo; and 3\u0026rsquo; neighboring nucleotide of the variant) with the tools SigProfilerMatrixGenerator and SigProfilerPlotting \u003csup\u003e92\u003c/sup\u003eand compared to the COSMIC signatures v3.3 \u003csup\u003e93\u003c/sup\u003e with SigProfilerExtractor \u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eThe ratio of non-synonymous and synonymous variants\u003c/h2\u003e\n \u003cp\u003eThe \u003cem\u003ed\u003c/em\u003eN/\u003cem\u003ed\u003c/em\u003eS ratio was estimated as described in \u003csup\u003e60\u003c/sup\u003e, using the dNdScv package v. 0.1.0 in R v. 4.3.1, and the human reference sequence v. GRChg38.14 and annotation for the ErbB2 region v.GR38.111 from ENSEMBL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ensembl.org/\u003c/span\u003e\u003c/span\u003e). In this analysis, the dN/dS estimates considered the nearby sequence context, but not genome-wide epigenetic covariates of mutation rate \u003csup\u003e94\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eDroplet Digital PCR (ddPCR)\u003c/h2\u003e\n \u003cp\u003eThe fractional abundance (FA) of mutations was evaluated by ddPCR (BioRad) \u003csup\u003e95\u003c/sup\u003e as described also in \u003csup\u003e18\u003c/sup\u003e. The Site-specific mutation detection assays were designed using the online platform of BioRad (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bio-rad.com/digital-assays\u003c/span\u003e\u003c/span\u003e) listed in Supplementary Table S18. Each ddPCR reaction contained 10 \u0026micro;l of 10x SuperMix for Probes (no dUTP), 6.7 \u0026micro;l of nucleic acid-free water, 2 \u0026micro;l of genomic DNA (~\u0026thinsp;125 ng/\u0026micro;l; ~36,000 genomes/\u0026micro;l), 1 \u0026micro;l of probes (900M of each probe and 100M of each primer), and 0.3 \u0026micro;l of restriction enzyme (MseI;10U/\u0026micro;l) and incubated at room temperature for 15 minutes to enhance target template availability. The digested mix, along with 70 \u0026micro;l of droplet generation oil, was loaded into a cartridge covered with a gasket to then form the droplets in the droplet generator (BioRad). About 43 \u0026micro;l of the droplet solution was transferred to a ddPCR 96-well plate and sealed in the PX1 PCR Plate Sealer (BioRad) followed by PCR at 95\u0026deg;C for 10 minutes, 40 cycles of 94\u0026deg;C for 30 seconds, 53\u0026ndash;56\u0026deg;C (see Supplementary Table S18) for 1 minute, and a final step of 98\u0026deg;C for 10 minutes, with a ramp rate of 2\u0026deg;C/sec and a lid temperature of 105\u0026deg;C. The end-point PCR plate underwent data analysis using QuantaSoft Analysis Pro Software (version 1.7.4; Bio-Rad Laboratories Inc.). The threshold between positive and negative droplets was manually adjusted well by well or across an entire plate based on the fluorescence amplitude (fluorescence intensity of positive and negative droplet clusters and/or histogram plots) for each specific probe, and we reported here the Poisson-corrected data points.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eSite -Direct Mutagenesis (SDM)\u003c/h2\u003e\n \u003cp\u003eTo assess the functional impact of selected mutations, site-specific single-nucleotide mutations were introduced into the mGFP-ErbB2 expression plasmid \u003csup\u003e96\u003c/sup\u003e using a site-directed mutagenesis strategy with Phusion HS II high-fidelity polymerase as detailed in (see Supplementary Methods and Supplementary Table S19) and also described in \u003csup\u003e18,29\u003c/sup\u003e. The designed oligonucleotide pairs, including a primer with the desired mutation and another with a silent mutation, both featuring 3\u0026apos; PTO bonds for enhanced specificity, were used for amplification. The resulting PCR products, confirming the correct length via gel electrophoresis, underwent ligation without purification, followed by DpnI digestion and transformation into E. coli NEB 10-beta. Ampicillin-resistant clones were screened by colony PCR, and positive clones were cultured overnight, subjected to plasmid miniprep, and sequenced to verify the introduced mutations.\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003eLive cell micropatterning experiments\u003c/h2\u003e\n \u003cp\u003ePreparation of protein micropatterned surfaces by large-area microcontact printing \u003csup\u003e65\u003c/sup\u003e, total internal reflection fluorescence microscopy \u003csup\u003e64\u003c/sup\u003e, and image analysis \u003csup\u003e97\u003c/sup\u003e was carried out as previously reported, and is described in the Supplementary Methods and Supplementary Figure S11.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eData Access\u003c/h2\u003e\n \u003cp\u003eThe raw sequencing data generated in this study have been submitted to the NCBI BioProject database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/bioproject/\u003c/span\u003e\u003c/span\u003e) under accession number PRJNA1052412.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eDisclosure Declaration\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to thank V\u0026aacute;clav Brož for his help in the testis DNA extraction and measurements with ddPCR and Philipp Hermann for the advice on statistical testing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded in whole or in part by the Austrian Science Fund (FWF) I.H. (FWFW1250) and I.T.-B. (P30867000; and SFB F-8809-B, FWF), the European Regional Development Fund I.T.-B., (REGGEN ATCZ207), the FH Upper Austria Center of Excellence for Technological Innovation in Medicine (TIMed CENTER), the \u0026ldquo;Dissertationsprogramm der Fachhochschule O\u0026Ouml; 2022\u0026rdquo; for T.K. and the Upper Austria (Austrian Research Promotion Agency (FFG) grant P.L. (895967), and the ERC CoG grant TE-INVASION for AJB. For open access purposes, the authors applied a CC BY public copyright license to any author-accepted manuscript version arising from this submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.M., A.Y, T.K., I.H. performed the experiments. M.H., S.M., P.L., A.J.B. and I.T.-B. analyzed the data, I.T.-B. conceived the project and provided funding. A.Y., M.H., T.M., T.K., P.L., A.J.B., and I.T.-B. wrote the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eConflict of interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest with the contents of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJamal-Hanjani, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Tracking the Evolution of Non-Small-Cell Lung Cancer. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e376\u003c/strong\u003e, 2109-2121 (2017).\u003c/li\u003e\n \u003cli\u003eTurajlic, S.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e173\u003c/strong\u003e, 595-610 e11 (2018).\u003c/li\u003e\n \u003cli\u003eYates, L.R.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Subclonal diversification of primary breast cancer revealed by multiregion sequencing. \u003cem\u003eNat Med\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 751-9 (2015).\u003c/li\u003e\n \u003cli\u003eTurajlic, S., Sottoriva, A., Graham, T. \u0026amp; Swanton, C. 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Driver mutations enjoying a selective advantage expand to mutant clusters within the aged testis, and are thus overrepresented in sperm with age. \u0026nbsp;Other kinds of driver mutations, occurring pre-pubescently, are the focus of recent attention given their high occurrence independent of age. \u0026nbsp;Here, we investigate the gene ErbB2 with error-corrected-sequencing, and find a high rate of missense mutations, including recurrent ones, observed mainly in the tyrosine kinase domain with likely functional consequences, as we verified for a subset with biophysical methods. \u0026nbsp;While these mutations increased with age, we found no evidence that they originate from mutational clusters in the aged-testis, and young donors also showed an accumulation of driver mutations-- suggesting that the mutational enrichment is not exclusive to the sexually mature germline, but can occur earlier during germline development forming evenly distributed micro-mosaics stable in size.\u003c/p\u003e","manuscriptTitle":"Mutations in ErbB2 accumulating in the male germline measured by error-corrected sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-06 07:24:32","doi":"10.21203/rs.3.rs-4887284/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d0fabba3-3f55-4eb3-a251-9183abb1038e","owner":[],"postedDate":"September 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36004058,"name":"Biological sciences/Evolution/Evolutionary genetics"},{"id":36004059,"name":"Biological sciences/Genetics/Evolutionary biology"}],"tags":[],"updatedAt":"2024-09-06T07:24:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-06 07:24:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4887284","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4887284","identity":"rs-4887284","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}