Garrido et al. Clin Epigenet (2021) 13:6 https://doi.org/10.1186/s13148-020-00995-2
RESEARCH Open Access Sperm DNA methylation epimutation biomarker for paternal ofspring autism susceptibility Nicolás Garrido1, Fabio Cruz1, Rocio Rivera Egea1, Carlos Simon2,3, Ingrid Sadler‑Riggleman4, Daniel Beck4, Eric Nilsson4, Millissia Ben Maamar4 and Michael K. Skinner4*
Abstract Background: Autism spectrum disorder (ASD) has increased over tenfold over the past several decades and appears predominantly associated with paternal transmission. Although genetics is anticipated to be a component of ASD eti‑ ology, environmental epigenetics is now also thought to be an important factor. Epigenetic alterations, such as DNA methylation, have been correlated with ASD. The current study was designed to identify a DNA methylation signature in sperm as a potential biomarker to identify paternal ofspring autism susceptibility. Methods and results: Sperm samples were obtained from fathers that have children with or without autism, and the sperm then assessed for alterations in DNA methylation. A genome-wide analysis (> 90%) for diferential DNA methylation regions (DMRs) was used to identify DMRs in the sperm of fathers (n 13) with autistic children in com‑ parison with those (n 13) without ASD children. The 805 DMR genomic features= such as chromosomal location, CpG density and length of =the DMRs were characterized. Genes associated with the DMRs were identifed and found to be linked to previously known ASD genes, as well as other neurobiology-related genes. The potential sperm DMR bio‑ markers/diagnostic was validated with blinded test sets (n 8–10) of individuals with an approximately 90% accuracy. = Conclusions: Observations demonstrate a highly signifcant set of 805 DMRs in sperm that can potentially act as a biomarker for paternal ofspring autism susceptibility. Ancestral or early-life paternal exposures that alter germline epigenetics are anticipated to be a molecular component of ASD etiology. Keywords: Autism, Sperm, Epigenetics, Diagnostic, Generational, Father, Ofspring
Introduction diagnosis and current awareness have played a role in this Autism spectrum disorder (ASD) is a complex neurologi- increase, particularly in the frst couple decades (1975– cal disorder involving defcits in communication, social 2000), the increase in the last two decades is thought to behaviors and stereotypic movements [1, 2]. Te preva- be due to environmental and molecular factors [2–4]. lence of ASD in 1975 was reported as 1 in 5000 and then Tis is supported by twin studies and numerous environ- in 2009 as 1 in 110 [3]. Te American Centers for Disease mental studies. Genetic studies using genome-wide asso- Control and Prevention reported a 1 in 88 prevalence ciation studies (GWAS) have identifed multiple genetic in 2012 and then a 1 in 68 in 2014. Although improved mutations, but they are correlated with only a small percentage of the autism patients [5]. A recent study
*Correspondence: [email protected] identifed sperm genetic alterations associated with of- 4 Center for Reproductive Biology, School of Biological Sciences, spring autism [6]. Combining genetic mutations and Washington State University, Pullman, WA 99164‑4236, USA altered epigenetics appear to improve associations [7]. Full list of author information is available at the end of the article Many specifc toxicants and factors have been suggested
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to be involved, but generally more extensive analysis is epigenetic analysis is less common. Sperm DNA meth- required [8]. Environmental factors are now believed ylation diagnostics have been proposed for the use in to be involved in the etiology of autism. A number of assisted reproduction [18]. Te availability of a sperm molecular alterations in the genome have been correlated DNA methylation biomarker for ofspring autism sus- to the neurobiology of ASD [2]; however, the specifc ceptibility would allow improved clinical management environmental factors, molecular processes and etiology and early treatment options to be considered. An epi- of autism remain to be fully elucidated. genome-wide association study for DNA methylation Although there are both paternal and maternal trans- alterations in sperm from fathers with or without autistic mission of ASD, the prevalence of paternal transmission children was used to identify potential sperm epigenetic is higher in most populations. One of the main fac- alterations as a biomarker for paternal ofspring autism tors proposed to be involved is paternal age [9], with an susceptibility. increased percentage risk of 28% between 40–49 years and nearly 70% when greater than 50 years of age [4]. Results Increased paternal age has been associated with epi- Paternal males with children afected by autism (case) genetic DNA methylation alterations in sperm [10], or without (control) were recruited, and paternal sperm including specifc genes associated with autism [11, 12]. samples were collected at the Andrology Laboratory of Paternal age-associated DNA methylation alterations IVIRMA Clinic in Valencia, Spain. Te sperm sample have been shown to impact ofspring health and disease was collected upon enrollment. Tirty-six patients were susceptibility [13, 14]. Terefore, the current study con- enrolled, which included thirteen in the control group, trolled for age at conception and sample collection for the thirteen in the autism case group, and eight or ten for comparison. In addition to paternal age efects, ancestral the blinded test groups. Te diferences (mean ± SD) and early-life exposures to toxicants, abnormal nutrition between the semen analysis for both control and case and stress can also impact sperm DNA methylation to group are shown in Table 1. Observations from the potentially afect disease susceptibility of ofspring [15]. groups showed no signifcant diference in age, fathers Te current study was designed to examine the father’s age at pregnancy, fathers age upon sperm collection, sperm epigenetics (DNA methylation) in families with sperm volume, concentration, or sperm concentration or without autistic children. Te hypothesis examined is between the groups. Progressive sperm motility was that a father’s specifc sperm DNA methylation altera- greater in the autism case group, with no diference in tions will correlate with ofspring autism susceptibility. non-progressive sperm motility, as shown in Table 1a. Epigenetics is defned as “molecular factors and pro- Te motile percentage was higher in the control group, cesses around DNA that regulate genome activity inde- and no diference was observed in the total motile sperm pendent of DNA sequence and are mitotically stable.” count. One of the control samples, IVI 14, had a very Te molecular factors and processes currently known high sperm count of 396.62 million that was outside two are DNA methylation, histone modifcations, chromatin standard deviations of the mean (2 ± SD), so the analysis structural changes, noncoding RNA and RNA methyla- was redone without this sample. When the IVI 14 sam- tion [15]. When the epigenetic alterations become pro- ple was not used in the analysis, the total sperm number grammed in the germ cells (sperm or egg), they have was increased in the autism case group (p < 0.02), and the potential to promote in subsequent generations the the total motile sperm count (time) was increased in the epigenetic transgenerational inheritance of disease and autism case group (p < 0.017), as well as the progressive phenotypic alterations [15]. Environmental factors that spermatozoa (%) (p < 0.019) and immotile % (p < 0.019) promote these early-life epigenetic alterations have the parameters. In addition to the case and control male age ability to promote epigenetic inheritance to subsequent and sperm analysis parameters, Table 1a, all the blinded generations and dramatically increase disease suscep- test set males, Table 1b, c, age and sperm parameters tibility and prevalence [15–17]. Te current study was were analyzed and found to also be within the mean ± SD designed to use an epigenome-wide association study of the case and control samples presented (Additional approach and develop a potential paternal sperm bio- fle 2: Figure S1). Terefore, the blinded test set of indi- marker for ofspring autism susceptibility. viduals was appropriate comparisons with the same clini- Te use of specifc sperm epigenetic (DNA meth- cal parameters. ylation) alterations (i.e., biomarkers) could be used for a Te participant demographics and clinical informa- fathers (i.e., paternal) ofspring autism susceptibility, and tion were similar between the case and control popula- applications in an assisted reproduction setting could tion participants. Te ethnicity of all the fathers was be considered. Although genetic tests are common in Caucasian. No major comorbidities were observed assisted reproduction and preimplantation diagnostics, within either the control or case populations. Te date Garrido et al. Clin Epigenet (2021) 13:6 Page 3 of 13 ± 63 ± 71 50.4 1.03 62.4 5.38 117.94 21.81 98.96 30.46 1.43 21.17 3.51 10.63 218.14 44.35 84.29 240 43.96 152.25 136.5 194.58 15.69 7 78.54 89.76 23.94 105.91 Total Total motile sperm count (time) NS 49 94 ± 14 ± 12 48 81 45 57 39 40 37 65 59 46 68 51 35 23 54 38 47 37 25 17 43 36 54 45 47 31 Immotile (%) p < 0.01 52 38 ± 6 ± 5 17 8 7 20 9 23 14 3 13 12 3 18 10 17 3 12 9 13 15 14 26 14 11 11 11 11 Non- progressive spermatozoa (%) NS 12 13 ± 13 ± 11 49 35 11 48 23 52 37 49 32 28 42 29 31 55 60 43 50 44 50 60 69 31 50 35 44 42 58 Progressive Progressive spermatozoa (%) p < 0.01 36 ± 128 ± 114 184 144 9.35 130 23.4 226.8 58.94 201.96 95.2 5.12 50.4 12.1 34.3 396.62 73.92 196.02 480 99.9 304.5 227.5 282 50.6 14 224.4 204 57 182.6 Total Total of spermatozoa (mill) NS 107 ± 33 ± 44 55 96 1.7 52 13 54 42.1 91.8 14 1.6 10.5 5.5 34.3 141.65 17.6 36.3 120 33.3 87 91 47 23 10 66 68 38 83 Concentration Concentration (mill/mL) NS 43 ± 1 ± 2 3 1.5 5.5 2.5 1.8 4.2 1.4 2.2 6.8 3.2 4.8 2.2 1 2.8 4.2 5.4 4 3 3.5 2.5 6 2.2 1.4 3.4 3 1.5 2.2 Volume Volume (mL) Paternal sperm analysis Paternal NS 3 Control Control Control Control Control Control Control Control Control Control Control Control Control Case Case Case Case Case Case Case Case Case Case Case Case Case Ofspring autism case/ control signifcant signifcant (NS) > 0.05 Ofspring autism case 9/22/17 9/15/17 9/15/17 9/6/17 9/6/17 5/23/17 3/22/17 11/3/16 10/21/16 10/20/16 10/17/16 10/11/16 10/11/16 3/7/16 9/5/17 12/21/15 9/28/15 9/9/15 8/14/15 8/12/15 8/4/15 7/30/15 7/29/15 7/29/15 7/28/15 7/27/15 Collection Collection date of sample (Case vs. Control), not Control), vs. (Case Ofspring control ± 5 ± 3 34 33 34 27 36 29 32 41 34 31 33 35 36 36 35 24 39 31 35 32 31 40 33 34 38 43 31 Father age Father (years) at pregnancy NS 34 ± 4 ± 5 42 43 38 54 43 37 42 41 36 38 44 46 41 40 46 37 45 39 45 42 41 49 37 38 41 45 42 Father age Father (years) upon collection NS 42 ± 3 ± 5 41 43 38 54 43 37 42 41 36 38 44 46 41 40 46 35 – – 45 42 41 39 37 38 41 44 42 Age Age (years) NS 42 Sperm samples and clinical semen analysis. ± SD ± SD Mean IVI 26 IVI 25 IVI 24 IVI 23 IVI 22 IVI 21 IVI 20 IVI 19 IVI 18 IVI 17 IVI 16 IVI 15 IVI 14 IVI 13 IVI 12 IVI 11 IVI 10 IVI 9 IVI 8 IVI 7 IVI 6 IVI 5 IVI 4 IVI 3 IVI 2 IVI 1 Statistical comparison Mean (a) Sperm samples and clinical analysis 1 Table sample Study Sample Garrido et al. Clin Epigenet (2021) 13:6 Page 4 of 13 Actual control Actual control Actual control Actual control Actual control Actual control Actual case Actual case* Actual case Actual control Actual Case* Actual case Actual control Actual case Actual control Actual case Actual case Actual case Identifed control Identifed control Identifed control Identifed control Identifed control Identifed control Identifed case Identifed case Identifed case Identifed case Identifed control Identifed control Identifed case Identifed case Identifed control Identifed control Identifed control Identifed case test was used to assess any statistical diferences between case and control groups. (b) Blinded test sample set #1 analysis. The analysis was was analysis The sample set #1 analysis. Blinded test (b) groups. case and control between diferences statistical assess any used to was t test A Students samples is provided. case and control SD for ± (continued) BS 18 BS 17 BS 8 BS 16 BS 7 BS 15 BS 6 BS 14* BS 5 BS 13 BS 4* BS 12 BS 3 BS 11 BS 2 BS 10 BS 1 BS 9 performed to identify the case or control samples and then confrmed once completed after analysis unblinded. (c) Blinded test sample set #2 analysis. IVI-RMA provided an additional ten blinded samples, and following and following blinded samples, an additional ten provided IVI-RMA sample set #2 analysis. (c) Blinded test unblinded. after analysis completed once samples and then confrmed performed the case or control identify to with an (*) on the labels indicated false-negative are The identifcations assess accuracy unblinded to (Actual). was (Identifed) analysis 1 Table and motility analysis, progression motility and formal and analysis, sperm concentration collected and performed Spain, that of semen volume, the clinical analysis Valencia, IVI-RMA, by provided samples were The (a) The analysis. mean (b) Blinded test sample set 1 analysis Blinded test (b) sample set 2 analysis (c) IVI blinded test Garrido et al. Clin Epigenet (2021) 13:6 Page 5 of 13
of the patient sperm collection, age of the father upon males with autistic children have a sperm DMR signa- collection and age of the father at conception of child ture that is distinct from males without autistic children are all not statistically diferent and provided in Table 1. (control). Although age can impact sperm DNA methylation, the Te genomic features of the ofspring autism sus- mean age upon sperm collection, which required a 3-year ceptibility DMRs were investigated. Te chromosomal collection period, for the case and control was not statis- locations of the DMRs at p < 1e−05 within the human tically diferent, as given in Table 1. In addition, no sta- genome are presented in Fig. 1b. Te red arrowheads tistical diference was observed in the age of the fathers indicate the individual DMRs, and the black boxes rep- at conception of child, as given in Table 1. All the autis- resent clusters of DMRs. Te DMRs are present on all tic children were males. Since the focus was on paternal chromosomes. Te CpG density of the DMRs is generally sperm, and due to IRB restrictions, the ofspring ASD less than 10 CpG per 100 bp with 1–3 CpG predominant spectrum severity was not considered. Te human sub- for the paternal ofspring autism susceptibility DMRs, jects approval and informed consent were obtained from as shown in Fig. 1c. Te size of the DMRs was predomi- all participants prior to the initiation of the study and nantly 1–3 kb for the sperm DMRs, as shown in Fig. 1d. approved by the Ethics Committee of Valencian Infertility Additional genomic features are presented in Additional Institute—Reproductive Medicine Associates (IVIRMA) fle 4: Table S1. Te log-fold change (LFC) in DMA Valencia, Spain, with code, #1311-VLC-136-FC. methylation in Additional fle 3: Table S1 demonstrated Individual patient sperm samples from the collection for the 805 DMR in the autism group that 303 have an upon enrollment were prepared for sperm analysis, and increase in DNA methylation and 502 have a decrease an aliquot was taken and fash frozen with liquid nitro- in DNA methylation. Te autism DMRs involved a 38% gen and stored at − 20 °C until shipment on dry ice for increase or 62% decrease in DNA methylation. Terefore, the epigenetic analysis. Te samples were thawed, and the majority of the sperm DMRs had low CpG density, prior to DNA isolation, the sperm were sonicated to termed a CpG desert, and were 1 kb in length with either destroy and remove any contaminating somatic cells, an increase or decrease in DNA methylation. as previously described [16]. Due to the sperm nuclei Te paternal ofspring autism susceptibility sperm being resistant to sonication, any contaminating somatic DMR-associated genes and corresponding gene func- cells are removed following sonication. Te DNA was tional categories were determined, as presented in Addi- extracted from the sperm and then fragmented for a tional fle 4: Table S1. Te total autism 805 DMRs had methylated DNA immunoprecipitation (MeDIP) pro- 193 with no DMR gene associations (24%), and the DMRs cedure to obtain methylated DNA for analysis to iden- were intergenic and not associated with genes. From the tify diferential DNA methylated regions (DMRs). Te 612 DMR with gene associations (76%), there were 493 MeDIP is a genome-wide analysis examining 95% of the DMR that overlapped with annotated genes. Tere were genome, which is comprised of low-density CpG regions 17 DMR in the 1–1000 bp and 62 DMR in the 1–5 kb in comparison with the less than 5% of the genome of proximal promoter regions. Tere were 40 DMRs in the high-density CpG regions such as CpG islands. Te 5–10 kb distal promoter region. Terefore, approximately MeDIP DNA libraries were prepared for next-generation 20% of the DMR are in the proximal and distal promoter DNA sequencing by creating individual patient sequenc- region and 80% overlapping the gene annotation regions. ing libraries. Samples were then sequenced for bioinfor- Tere were 193 DMRs that were intergenic. Tese DMRs matic analysis, as described in Additional fle 1 section. A are intergenic and not proximal to genes, but can infu- comparison of the sequences between the control (non- ence gene expression events for megabase distances autism children) and case (autism children) participant through ncRNA and chromatin structure alterations, as sperm samples identifed DMRs, as shown in Fig. 1a. At a previously described [19, 20]. Genes within 10 kb of a p value of p < 1e−05 there were 805 DMRs identifed with DMR were identifed, which has previously been shown the majority being a single 1-kb window with fewer (i.e., to be optimal for both proximal and distal promoter six) having multiple adjacent 1-kb windows involved. Te regions and epigenetic associations [21]. Te functional DMRs at a number of p values are presented for p < 001 categories corresponding to each DMR-associated gene to p < 1e−07, Fig. 1a. Te DMRs at EdgeR p < 1e−05 all are summarized in Fig. 2a. Te signaling, transcription had false discovery rate (FDR) of p < 0.05 and were used and metabolism functional categories are predominant. for subsequent data analysis. Te p < 1e−05 was used to Tis refects that these gene functional categories have optimize DMR numbers and statistical considerations. the highest number of genes within them. A compari- A list of these DMRs with various genomic features (e.g., son of previously identifed genes associated with neu- CpG density and chromosomal location) are presented rodegeneration, neurodevelopment and autism with the in Additional fle 3: Table S1. Observations suggest that DMR-associated genes of this study is summarized in Garrido et al. Clin Epigenet (2021) 13:6 Page 6 of 13
a b
c d
Fig. 1 a DMR identifcations. Autism case versus control sperm DMR analysis. The number of DMRs found using diferent p value cutof thresholds. The all window column shows all DMRs. The multiple window column shows the number of DMRs containing at least two adjacent signifcant windows and the number of DMRs with each specifc number of signifcant windows at a p value threshold of 1e 05. b Autism case versus control − patient DMR analysis. The DMR locations on the individual chromosomes are identifed. All DMRs at a p value threshold of p < 1e 05 are shown with − the red arrowheads and clusters of DMRs with the black boxes. c DMR CpG density in the autism case versus control patient DMRs. The number of DMRs at diferent CpG densities is indicated. All DMRs at a p value threshold of p < 1e 05. d Autism case versus control patient DMR lengths in − kilobases. All DMRs at a p value threshold of 1e 05 are shown −
Fig. 2b. Tese autism-associated genes have previously Additional fle 5: Table S2. Te DMR-associated genes been shown to be regulated or involve genetic muta- were also used in a gene pathway or gene set analysis to tions within autism patients, and the gene symbols, identify associated pathways. Interestingly, the top path- descriptions and associated references are presented in way or gene set identifed was autism, and the majority Garrido et al. Clin Epigenet (2021) 13:6 Page 7 of 13
(See fgure on next page.) Fig. 2 a DMR-associated gene categories. DMRs at a p value threshold p < 1e 05 are shown. b Autism case versus control DMR PCA. PCA for DMRs − at p < 1e 05. The frst two principal components used and samples color code index indicated. The underlying data are the RPKM read depth for − all DMRs. c DMR-associated genes and autism. The paternal ofspring autism-susceptible DMRs previously shown to correlate with autism and associated neurodegenerative disease are presented. DMR-associated genes from the current study were compared to genes associated with autism in the published literature using Pathway Studio software (Elsevier, Inc.). Those that were in common are depicted of the subsequent pathways with greater than three in Table 1b. All were accurately identifed, except one genes were all neurodevelopmental- or neurobiology- false negative which was identifed as a control, but actu- associated pathways, as given in Table 2. All those gene ally was a case sample. A second set of ten blinded test sets were found to be signifcant, and a list of the specifc samples (BS9–BS18) was provided by IVI-RMA clinical DMR-associated genes is provided, as given in Table 2. collaborators and was also identifed in an independent As shown with all the DMR-associated genes, Additional analysis as case or control, as given in Table 1c. All were fle 5: Table S2, the associated genes in Table 2 also had accurately identifed, except one false negative which was approximately a 50% mixture of genes with an increase or identifed as a control, but was actually a case sample. decrease in DNA methylation. Terefore, the DMR-asso- Terefore, the blinded test set analysis indicated all but ciated genes did correlate well with previously identifed one in each test set were accurately identifed for approx- autism- and neurodevelopment-associated genes. Since imately a 90% accuracy in the analysis. Since multiple the sperm DMRs will impact the embryo epigenomes analysis was used for this blinded test set analysis, ran- and transcriptomes of all subsequent somatic cells, this dom batch efect outlier DMRs identifed were removed dynamic cascade of developmental epigenetics needs to to optimize the analysis. Although signifcantly more be considered in potential links in sperm epigenetics and validation with larger clinical test sets is needed, the cur- potential neurological impacts on autism. rent study provides the proof of concept that epigenetic Te fnal analysis examined the statistical signifcance biomarkers potentially exist and may be used to diagnose and validation of the DMRs identifed. A permuta- that a father may potentially have a child with a suscepti- tion analysis was performed on the DMRs to determine bility for autism. whether the DMRs were due to background variation in the data or randomly generated. Te permutation Discussion analysis shows the number of DMRs generated from the Te frequency of autism in the population has dramati- control versus autism case comparison was signifcantly cally increased over tenfold the past several decades. greater than the DMRs generated from random sub- Tis increase appears to be due in part to increased diag- set analysis, Additional fle 3: Figure S2. Te red line to nosis efciency from 1975 to the early 2000s, as well as the right indicates the comparison DMRs versus the low greater public awareness of the disease [3]. Te more numbers from the random subset comparison. Another recent increase in the last couple of decades suggests analysis involved a cross-validation of the DMRs and environmental factors, and exposures also have a criti- demonstrated approximately 80% accuracy in the con- cal role in autism prevalence. Although many suggestions frmation of the DMRs to assess autism susceptibil- have been made on specifc toxicants and factors being ity [22]. A principal component analysis (PCA) of the involved, more extensive analysis and better understand- control male sperm without an autistic child versus the ing of autism etiology are needed to understand this male sperm with an autistic child is presented in Fig. 2c. increase in autism frequency [8]. An example is the sug- A clear separation of the DMR principal components is gestion-assisted reproduction and in vitro fertilization is seen between the groups. Tis demonstrates a distinction involved, but follow-up studies demonstrated no risk of between the DMR principal components. ASD in children born after assisted reproduction [23, 24]. Te fnal validation involved using blinded test sets of One factor that has been correlated with autism is pater- samples for analysis to identify and assess the accuracy nal age [4, 9, 15] and sperm DNA methylation alterations to determine actual case or control samples. Tree dif- [11, 25]. Previous studies have shown a hypermethylation ferent molecular analyses of the original 13 cases and 13 of sperm DNA is associated with male infertility, abnor- controls were performed and combined for this test set mal sperm parameters and increasing age [13, 26, 27]. analysis. Te test set analysis involved four independent Terefore, the majority of DMR involve an increase in analyses that were combined for the fnal analysis. Te DNA methylation when associated with infertility or age. frst test set involved eight blinded samples selected from Te current study demonstrated 60% of the DMRs have the main analysis samples and reanalyzed (BS1–BS8) a decrease in DNA methylation, and 40% of DMRs an and identifed as actual case or control sample, as given increase in DNA methylation, Additional fle 4: Table S1. Garrido et al. Clin Epigenet (2021) 13:6 Page 8 of 13
a b
c Garrido et al. Clin Epigenet (2021) 13:6 Page 9 of 13 3.39E − 03 1.80E − 03 6.90E − 04 5.95E − 04 5.12E − 04 4.70E − 04 3.42E − 04 9.02E − 05 p value TMEM173, AP2B1, OPRM1 SLC7A5, STIM1 SLC7A5, DLGAP1, CCDC141, OPRM1, PDLIM5, GABBR1, CCDC141, DLGAP1, DNMT1, TCF4, ARHGEF10, SORCS2, CREBBP, ADRA2C, GPHN, USP46, RELN, DPYSL2, RBFOX1, IMMP2L, NRXN3, ALK ANKS1B, ELP4, ANKRD11, RELN, DPYSL2, ST3GAL5, ELP4, ANKRD11, RELN, DPYSL2, ANKS1B, TCF4 MCPH1, CREBBP, AGAP1, ST3GAL5, BCKDK, CDK19, DLGAP2, NARS2, PHF21A, ST3GAL5, BCKDK, CDK19, DLGAP2, ARHGEF10, MCPH1, CREBBP, CAMTA1, KDM2B, SORL1, DPYSL2, LIMK1, STIM1, RELN, PRMT7, TCF4, CDH18, EXOC4 LRMDA, RELN, ARHGAP32, OPRM1, DLGAP2, DIAPH3, RELN, ARHGAP32, OPRM1, DLGAP2, NRXN3, SLC25A12, TSHZ3, KCNMA1, CACNA1A, RORA SEMA3F, PAX2, AIPL1, MERTK PAX2, GRIN2B, GRIN1, ERBB4, PARK7, STX1A, RORA, DNMT1, GRIN1, ERBB4, PARK7, GRIN2B, KDM6B, PPARG, ERBB4, PIK3CD, NDRG1, CREBBP, NDRG1, CREBBP, ERBB4, PIK3CD, PPARG, KDM6B, GRIN2B, GRIN1, DMD, RELN GRIN1, DMD, GRIN2B, GRIN2B, PDE2A, ERBB4, AHI1, CCKBR, ANKS1B, ANKS1B, PDE2A, ERBB4, AHI1, CCKBR, GRIN2B, RBFOX1, SRGAP3, GRIN1, LIMK1, AHI1, DOCK3, RBFOX1, SRGAP3, ERBB4, DMD, TG, AHI1, ANKRD11, CHL1, TG, SRGAP3, ERBB4, DMD, RBFOX1, CD5, CCR5, GRIN1, GPHN, SLC7A5, SNTG2, SNTG2, GRIN1, GPHN, SLC7A5, CD5, CCR5, RBFOX1, Overlapping genes Overlapping 20 6 9 21 4 5 4 6 Percent Percent overlap 3 10 8 4 25 16 28 19 Overlap positive breast cancer breast - positive Severe visual impairment Severe Behavioral disorder Behavioral Estrogen receptor Estrogen Intellectual impairment Psychiatric disorder Psychiatric Neurodevelopmental disorder Neurodevelopmental Intellectual disability Autism Pathology gene set pathway Pathology 14 147 86 18 538 273 616 313 Total # Total of neighbors positive - positive DMR-associated gene pathways or gene sets. gene pathways DMR-associated breast cancer breast Protein regulators of severe visual impairment of severe regulators Protein Protein regulators of behavioral disorder of behavioral regulators Protein Protein regulators of estrogen receptor of estrogen regulators Protein Protein regulators of intellectual impairment regulators Protein Protein regulators of psychiatric disorder of psychiatric regulators Protein Protein regulators of neurodevelopmental disorder of neurodevelopmental regulators Protein Protein regulators of intellectual disability regulators Protein Protein regulators of autism regulators Protein 2 Table or gene set name Pathway presented are p value gene symbols and statistical overlapping overlap, percent genes, overlapping gene set pathway, pathology genes, number of neighbors for total pathway The Garrido et al. Clin Epigenet (2021) 13:6 Page 10 of 13
Terefore, a mixture of an increase and decrease in meth- autistic children [33]. Tat study used a targeted array- ylation is observed, which is distinct from the sperm based approach that focused on high-density CpG islands hypermethylation observed in male infertility and aging that constitutes approximately 1% of the genome, but [13, 26, 27]. Since all the paternal subjects were similar in does demonstrate such an analysis is feasible. Te current age and fertile (Table 1), the current study observations study was designed to use a genome-wide approach to appear to be distinct from infertility and aging-related identify altered DNA methylation for paternal sperm and DNA hypermethylation. Although some participants ofspring autism susceptibility. from both control and case populations were involved Although genetics will be involved in autism etiology, in in vitro fertilization upon sperm collection, male fac- genome-wide association studies (GWAS) have demon- tor infertility was not involved. No diferences in demo- strated generally less than 1% of the patients with a spe- graphics or clinical variables were observed. Te age cifc disease, such as neurodegenerative disease that has upon sperm collection between the case and control was a correlated genetic mutation [34]. ASD is similar to only not statistically diferent, as given in Table 1. In addi- a few percent correlation with associated genetic muta- tion, the age at conception of child was also not statis- tions [35]. An additional molecular mechanism to con- tically diferent between case and control participants. sider for ASD disease etiology involves epigenetics. Te Te comparison was biased on age of sperm collection current study uses a more epigenome-wide association to control for age diferences to minimize DNA methyla- study approach to investigate sperm DNA methylation in tion variation. However, similar observations were also fathers with or without autistic children. A procedure to obtained considering age of conception of child. Tere- assess DNA methylation alterations in low-density CpG fore, the current study was designed to identify sperm regions, that constitute over 95% of the human genome, epigenetic alterations (i.e., biomarkers) to assess a father’s was used in comparison with the high-density CpG pro- potential ability to transmit autism susceptibility to his cedures previously used. A signifcant signature of difer- ofspring. ential DNA methylation regions (DMRs) was identifed Altered germline epigenetics has been shown to impact comparing the sperm from fathers with or without autis- ofspring health later in life, and if permanently pro- tic children. Te genomic features of the DMRs were grammed, to promote the epigenetic transgenerational identifed and demonstrated generally 1-kb lengths and inheritance of disease and pathology to subsequent gen- low-density CpG regions. Te DMR-associated genes erations [15, 16]. Since sperm or egg epigenetics can were identifed, and a number of previously identifed impact the zygote epigenetics and transcription following autism-linked genes were present (Fig. 2b, Additional fertilization, as well as the subsequent stem cell popula- fle 5: Table S2 and Table 2). In regard to the autism tion in the early embryo epigenome and transcriptome, sperm DMR biomarkers, a separation in a principal com- all subsequently derived somatic cells also have the ponent analysis (PCA) was observed. In addition to this potential to have an altered cell-type specifc epigenomes validation, the permutation and cross-validation analy- and transcriptomes later in development [15, 28]. Tis ses help demonstrate the robustness and sensitivity of molecular alteration has been shown to be associated the analysis. Te validation studies with blinded sample with adult somatic cell epigenetics, transcriptomes and sets accurately identifed the majority of case and control associated diseases [29–31]. Te ability of an ancestral samples, but potential false-negative identifcation of case or early-life exposure to impact the germline epigenet- samples was observed. Te observations demonstrate the ics to subsequently impact the ofspring epigenetics and paternal sperm epigenetic analysis is potentially efective susceptibility to develop pathology and disease has been at identifying ofspring susceptibility for autism, but the established [15–17, 28–31] and is anticipated to be a current analysis needs to be improved with expanded component of autism etiology as well. Te current study clinical trials. observations support the concept that similar events may Although an epigenetic signature was identifed for contribute to autism etiology. paternal transmission of susceptibility of autism chil- Te application of a sperm molecular diagnostic is dren, which was identifed and statistically signifcant, optimally used in an assisted reproduction setting. Rou- a limitation of the current study is the low number of tine semen analysis and genetic testing are used in most samples used for the analysis. Although epigenetic in vitro fertilization clinical settings. Although epigenetic alterations occur at a signifcantly higher frequency analysis is not as routine, the proposal for such analysis than genetics, expanded clinical trials are required with has been made [18]. Te analysis of male infertility using increased numbers, greater ethnic diversity, and more sperm DNA methylation alterations has been developed thorough assessment of the impacts of paternal age. [32]. Epigenetic alterations (DNA methylation) in sperm Te impacts of these variables need to be elucidated have been shown to associate in fathers of families with to improve and expand the accuracy of the analysis. Garrido et al. Clin Epigenet (2021) 13:6 Page 11 of 13
Te expanded clinical trial with greater numbers and Epigenetic analysis, statistics and bioinformatics diverse subpopulations is essential to develop a useful Somatic cell contamination was removed by sonica- diagnostic. However, the current study does provide the tion, and the sperm DNA was isolated as previously proof of concept; such a diagnostic can be developed. described [16]. Methylated DNA immunoprecipita- Applications of the paternal ofspring autism suscep- tion (MeDIP), followed by next-generation sequencing tibility biomarker/diagnostic will potentially improve (MeDIP-Seq), was performed. MeDIP-Seq, sequencing the health care for ASD patients. Tis would allow IVF libraries, next-generation sequencing, and bioinfor- patients to assess risk and determine management pro- matics analysis were performed as described [16] and cedures. Importantly, this would allow clinicians to are found in Additional fle 1. Te statistical analysis plan the ofspring’s clinical management options more and validation protocols were performed as previously efciently. Potential preventative treatments could be described [16] and are found in Additional fle 1. All considered to reduce the severity of the autism spec- molecular data has been deposited into the public data- trum disorder. Te availability of the assay could also base at NCBI (GEO # GSE157417), and R code com- be used in a research setting to facilitate the identif- putational tools are available at GitHub (https://githu cation of environmental factors potentially involved in b.com/skinnerlab/MeDIP-seq) and www.skinner.wsu. the ASD etiology. Terefore, potential therapeutic and edu. preventative options not previously considered could be taken. Supplementary Information Te current study identifed a genome-wide signature The online version contains supplementary material available at https://doi. org/10.1186/s13148-020-00995-2. of DNA methylation sites that are associated with the paternal transmission of ofspring autism susceptibil- ity. Although a large clinical trial is needed to further Additional fle 1: Supplemental methods. validate the biomarkers and potential diagnostic, the Additional fle 2: Figure S1. Clinical group statistic comparison. The various sperm/semen characteristics in the Study case and control group current study provides the proof of concept for the assay were compared with the Blind group. The n-value, mean, standard devia‑ and biomarkers. Terefore, the identifcation of ofspring tion, and standard error mean are presented. The blind groups are within the mean SD of the case and control study. susceptibility can be assessed, allowing better clinical ± management of ASD. Te potential for therapy options Additional fle 3: Figure S2. Permutation analysis. The number of DMR for autism case versus control patient comparison for all permutation can be expanded to improve health care for ASD. Such analyses. The vertical red line shows the number of DMR found in the epigenetic biomarkers are anticipated to exist for many original analysis. All DMRs are defned using an edgeR p value threshold of p < 1e 05. disease and pathology conditions, which will facilitate the − future preventative medicine strategies for health care. Additional fle 4: Table S1. DMR lists at p < 1e 05 with presentation of name, chromosomal location, DMR start and stop− nucleotide number for In addition, the current study suggests epigenetic inher- chromosome, length (bp), number of 1 kb signifcant windows, minimum itance may play a role in ASD etiology and explain the p value, CpG number and density, DMR maximum, log-fold change (max‑ paternal transmission prevalence of the disease. LFC) ( increase DNA methylation and decrease DNA methylation), and gene +association within 10 kb and gene− functional categories. Additional fle 5: Table S2. Gene and protein regulators of autism. The DMR-associated autism-related genes with symbol, description, and Methods summary relevant references PubMed ID (PMID) numbers. Clinical sample collection
A single-center (IVIRMA Valencia, Spain) prospective Abbreviations and open clinical study was performed. Te participant ASD: Autism spectrum disorder; IVF: In vitro fertilization; DMRs: Diferential approval and informed consent were obtained from DNA methylation regions; GWAS: Genome-wide association studies; MeDIP: Methylated DNA immunoprecipitation; MeDIP-Seq: Methylated DNA immu‑ all participants prior to the clinical sample collection. noprecipitation followed by next-generation sequencing; FDR: False discovery Te study protocols were approved by the Institutional rate; LFC: Log-fold change; PCA: Principal component analysis; BS: Blinded Review Board Ethics Committee of Valencian Infertility samples. Institute—Reproductive Medicine Associates (IVIRMA) Acknowledgements Valencia, Spain, with code, #1311-VLC-136-FC. All We acknowledge Ms. Jayleana Barton and for technical assistance and all research was performed in accordance with relevant technicians from IVIRMA Andrology Laboratory for assistance in the clinical sample collection. We acknowledge Ms. Amanda Quilty for editing and Ms. guidelines/regulations. Te study was not designed for, Heather Johnson for assistance in preparation of the manuscript. We thank the nor did the IRB involve, the ability to correlate autism Genomics Core laboratory at WSU Spokane for sequencing data. We acknowl‑ child clinical information to be correlated. Te semen edge Epigenesys Inc., Pullman, WA, where the initial molecular analysis was performed. This study was supported by Epigenesys Inc., Pullman, WA, USA, was analyzed as described in Additional fle 1. Samples and John Templeton Foundation (50183 and 61174) (https://templeton.org/) were immersed in liquid nitrogen and then stored at grants to MKS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. −20 °C prior to analysis. Garrido et al. Clin Epigenet (2021) 13:6 Page 12 of 13
Authors’ contributions 8. Herbert MR. Contributions of the environment and environmentally NG performed clinical and sample collection oversight, data analysis and vulnerable physiology to autism spectrum disorders. Curr Opin Neurol. editing manuscript. FC was involved in patient’s recruitment and editing 2010;23(2):103–10. manuscript. RRE contributed to patient’s recruitment, clinical and sample col‑ 9. Yassin W, Kojima M, Owada K, Kuwabara H, Gonoi W, Aoki Y, et al. Paternal lection oversight, data analysis and editing manuscript. CS conceived, initiated age contribution to brain white matter aberrations in autism spectrum sample collection and edited the manuscript. ISR was involved in molecular disorder. Psychiatry Clin Neurosci. 2019;73(10):649–59. analysis, data analysis and editing manuscript. DB performed bioinformat‑ 10. Frans EM, Sandin S, Reichenberg A, Langstrom N, Lichtenstein P, ics, data analysis and editing manuscript. EN done sample processing, data McGrath JJ, et al. Autism risk across generations: a population-based analysis and editing manuscript. MBM was involved in sample processing, study of advancing grandpaternal and paternal age. JAMA Psych. molecular analysis, data analysis and editing manuscript. MKS conceived, 2013;70(5):516–21. analyzed the data, performed funding acquisition and wrote the manuscript. 11. Atsem S, Reichenbach J, Potabattula R, Dittrich M, Nava C, Depienne All authors read and approved the fnal manuscript. C, et al. Paternal age efects on sperm FOXK1 and KCNA7 meth‑ ylation and transmission into the next generation. Hum Mol Genet. Funding 2016;25(22):4996–5005. This study was supported by Epigenesys Inc., Pullman, WA, USA, and John 12. Milekic MH, Xin Y, O’Donnell A, Kumar KK, Bradley-Moore M, Malaspina Templeton Foundation (50183and61174) (https://templeton.org/) grants to D, et al. Age-related sperm DNA methylation changes are transmitted to MKS. The funders had no role in study design, data collection and analysis, ofspring and associated with abnormal behavior and dysregulated gene decision to publish, or preparation of the manuscript. expression. Mol Psych. 2015;20(8):995–1001. 13. Jenkins TG, Aston KI, Pfueger C, Cairns BR, Carrell DT. Age-associated Availability of data and materials sperm DNA methylation alterations: possible implications in ofspring All molecular data have been deposited into the public database at NCBI (GEO disease susceptibility. PLoS Genet. 2014;10(7):e1004458. # GSE157417), and R code computational tools are available at GitHub (https 14. Rumbold AR, Sevoyan A, Oswald TK, Fernandez RC, Davies MJ, Moore VM. ://github.com/skinnerlab/MeDIP-seq) and www.skinner.wsu.edu. Impact of male factor infertility on ofspring health and development. Fertil Steril. 2019;111(6):1047–53. Ethics approval and consent to participate 15. Nilsson E, Sadler-Riggleman I, Skinner MK. Environmentally induced The participant approval and informed consent were obtained from all par‑ epigenetic transgenerational inheritance of disease. Environ Epigenet. ticipants prior to the clinical sample collection. The IRB study was approved by 2018;4(2):1–13. the Ethics Committee of Valencian Infertility Institute—Reproductive Medicine 16. King SE, McBirney M, Beck D, Sadler-Riggleman I, Nilsson E, Skinner MK. Associates (IVIRMA) Valencia, Spain, with code, #1311-VLC-136-FC. All research Sperm epimutation biomarkers of obesity and pathologies following was performed in accordance with relevant guidelines/regulations. DDT induced epigenetic transgenerational inheritance of disease. Envi‑ ron Epigenet. 2019;5(2):1–15. Consent for publication 17. Anway MD, Cupp AS, Uzumcu M, Skinner MK. Epigenetic transgen‑ Not applicable. erational actions of endocrine disruptors and male fertility. Science. 2005;308(5727):1466–9. Competing interests 18. Simon L, Emery BR, Carrell DT. Review: diagnosis and impact of sperm The authors report no confict of interest. DNA alterations in assisted reproduction. Best Pract Res Clin Obstet Gynaecol. 2017;44:38–56. Author details 19. Haque MM, Nilsson EE, Holder LB, Skinner MK. Genomic Clustering of 1 IVI-RMA València, and IVI Foundation, Health Research Institute La Fe, diferential DNA methylated regions (epimutations) associated with the València, Spain. 2 Dept Ob/Gyn, València University/Instituto de Investigacion epigenetic transgenerational inheritance of disease and phenotypic vari‑ Clinica, Hospital Clinico de Valencia (INCLIVA), and Igenomix Foundation, ation. BMC Genom. 2016;17(418):1–13. València, Spain. 3 Beth Israel Deaconess Medical Center, Harvard University, 20. Skinner MK, Manikkam M, Haque MM, Zhang B, Savenkova M. Epigenetic Boston, USA. 4 Center for Reproductive Biology, School of Biological Sciences, transgenerational inheritance of somatic transcriptomes and epigenetic Washington State University, Pullman, WA 99164‑4236, USA. control regions. Genome Biol. 2012;13(10):R91. 21. Lehman DA, Patterson B, Johnston LA, Balzer T, Britton JS, Saint R, et al. Received: 1 July 2020 Accepted: 17 December 2020 Cis-regulatory elements of the mitotic regulator, string/Cdc25. Develop‑ ment. 1999;126(9):1793–803. 22. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: Springer; 2009. 23. Hvidtjorn D, Grove J, Schendel D, Schieve LA, Svaerke C, Ernst E, et al. Risk References of autism spectrum disorders in children born after assisted concep‑ 1. King BH, Navot N, Bernier R, Webb SJ. Update on diagnostic classifcation tion: a population-based follow-up study. J Epidemiol Commun Health. in autism. 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Autism Spectrum Disorders Working Group of The Psychiatric Genom‑ 27. Alkhaled Y, Laqqan M, Tierling S, Lo Porto C, Hammadeh ME. DNA meth‑ ics Consortium. Meta-analysis of GWAS of over 16,000 individuals with ylation level of spermatozoa from subfertile and proven fertile and its autism spectrum disorder highlights a novel locus at 10q24.32 and a relation to standard sperm parameters. Andrologia. 2018;50(6):e13011. signifcant overlap with schizophrenia. Mol Autism. 2017;8:21. 28. Jirtle RL, Skinner MK. Environmental epigenomics and disease suscepti‑ 6. Breuss MW, Antaki D, George RD, Kleiber M, James KN, Ball LL, et al. bility. Nat Rev Genet. 2007;8(4):253–62. Autism risk in ofspring can be assessed through quantifcation of male 29. Guerrero-Bosagna C, Savenkova M, Haque MM, Nilsson E, Skinner MK. sperm mosaicism. Nat Med. 2020;26(1):143–50. Environmentally induced epigenetic transgenerational inheritance of 7. Massrali A, Brunel H, Hannon E, Wong C, Baron-Cohen S, et al. 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