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. Author manuscript; available in PMC: 2025 Feb 1.
Published in final edited form as: Mol Ecol Resour. 2023 Nov 27;24(2):e13901. doi: 10.1111/1755-0998.13901

A framework for identifying fertility gene targets for mammalian pest control

Anna C Clark 1,2,*, Rey Edison 3, Kevin Esvelt 3, Sebastian Kamau 3, Ludovic Dutoit 4, Jackson Champer 5, Samuel E Champer 2, Philipp W Messer 2, Alana Alexander 1,#, Neil J Gemmell 1,#
PMCID: PMC10860713  NIHMSID: NIHMS1944311  PMID: 38009398

Abstract

Fertility-targeted gene drives have been proposed as an ethical genetic approach for managing wild populations of vertebrate pests for public health and conservation benefit. This manuscript introduces a framework to identify and evaluate target gene suitability based on biological gene function, gene expression, and results from mouse knockout models. This framework identified 16 genes essential for male fertility and 12 genes important for female fertility that may be feasible targets for mammalian gene drives and other non-drive genetic pest control technology. Further, a comparative genomics analysis demonstrates the conservation of the identified genes across several globally significant invasive mammals. In addition to providing important considerations for identifying candidate genes, our framework and the genes identified in this study may have utility in developing additional pest control tools such as wildlife contraceptives.

1. INTRODUCTION

Invasive vertebrates pose a major global threat to natural ecosystems (Norbury et al., 2013), economies (Latham et al., 2020), and human health (Tait et al., 2017). Management of these pests can either be achieved through lethal measures or birth control. Traditional physical and chemical pest control approaches typically rely on lethality, often in ways that raise animal welfare concerns (Littin et al., 2004; Kirk et al., 2020). Lethal control approaches also raise concerns about off-target impacts (Eason et al., 2002, 2011), and efficacy (e.g., resistance to control measures; Anderson et al., 2016; Campbell et al., 2015). Thus, new pest control technology targeting the fertility of a population may gain a higher degree of social acceptability (Alexandre et al., 2021), particularly when combined with genetic engineering techniques (Kirk et al., 2020; Wilkinson & Fitzgerald 2006). This has motivated the development of novel molecular biocontrol approaches, such as gene drives. Gene drives increase the inheritance rate of particular alleles, which may permit such “selfish” alleles to reach fixation, or frequencies close to fixation, in a target population (Burt, 2003). There are several types of gene drives, including those that exist in nature (e.g. t-haplotype sex-ratio distorter in mice; Hermann et al., 1999) and others that may be engineered (Bunting et al., 2022), such as homing (Kyrou et al., 2018), toxin-antidote (Champer et al., 2020a), and modified t-haplotype (Campbell et al., 2019; Gierus et al., 2022) gene drive systems.

To achieve population suppression or eradication, a gene drive can be used to disrupt genes critical to survival or reproduction. For example, suppression of laboratory populations of Anopheles gambiae mosquitoes has been achieved using a drive system that disrupted doublesex, a gene controlling sexual differentiation in insects (Kyrou et al., 2018). However, engineered gene drives have been more challenging in mammals. It is evident that the successful conversion of wildtype alleles to drive alleles in the germline of the mammalian model, Mus musculus (house mouse) requires extremely tight regulation of the early meiotic expression of the drive construct (Grunwald et al., 2019). A recent experimental success in mice circumvents this challenge by using the naturally occurring murine t-haplotype to transmit an engineered CRISPR-Cas gene drive construct that disrupts the haplo-sufficient female fertility gene Prolactin, without the need for germline allele conversion (Gierus et al., 2022). Although these results are a significant milestone for the study of gene drives in mammalian pest species, there are concerns regarding the spread and the long term persistence of the t-haplotype considering that they are found at relatively low frequencies in wild populations (13–55%; Lenington et al., 1988), despite high t-haplotype transmission rates from heterozygous males (>95%; Lenington et al., 1988).

However, regardless of the type of suppression drive system (i.e., homing, toxin-antidote, t-haplotype), there is a research gap in the formal identification of biologically and ethically suitable candidate genes that could be used to manage invasive mammal populations. Identifying suitable target fertility genes is also a critical step in investigating and understanding the complex molecular challenges that can arise during gene drive development (e.g., genomic resistance; Champer et al., 2018), off-target mutagenesis (Langmüller et al., 2022), and predicting successful in vivo application (Rode et al., 2019)), as well as parameterisation in predictive gene drive models. An ideal target consists of a haplosufficient gene essential for fertility in one sex, such that the other sex can transmit the drive further. Targeting a haplosufficient gene essential for fertility in both sexes is not desirable, as such a drive is more likely to prematurely self-extinguish in a large population that is distributed across a heterogenous landscape, particularly if the species dispersal rate is low (Champer et al., 2021a). To mitigate this risk such a drive would instead need to have a reduced genetic load (e.g., could target a gene that only partially reduces fertility). However, such drives would require higher transmission efficiency to have sufficient power to eradicate the target population (e.g., a higher dispersal propensity of drive carriers, and high rates of perfect homology-directed repair).

In the context of selecting genes for mouse gene drives, both whole genome (Oh et al., 2021) and candidate-driven (e.g., coat-colour modifier tyrosinase (Tyr) (Grunwald et al., 2019) or haplosufficient female-fertility gene Prolactin (Prl) (Gierus et al., 2022)) approaches have been used. The coat-colour modifier used by Grunwald et al (2019) was used for simple observation of drive conversion during a proof of concept meiotic drive experiment, but is not expected to have an impact on fertility. The selection of Prl by Gierus et al (2022) was based on its well characterised role in female fertility. However, Prl is also a highly pleiotropic hormone with over 300 different functions in vertebrates (reviewed by Bole-Feysot et al., 1998, and Grattan & Kokay, 2008). In mice, Prolactin receptor activity is reported in 38 different organs without significant sexual dimorphic differences (Aoki et al., 2019). Drives that disrupt such a pleiotropic gene will likely reduce the fitness of drive carriers (particularly drive homozygous males), and affect the long term persistence of the drive in the population (in addition to raising animal welfare concerns).

The whole genome approach used by Oh et al (2021) was specifically designed to screen for fertility variants in target mouse populations that might satisfy the conditions of the Locally Fixed Alleles (LFA) approach, which has been proposed for gene drive population containment (Sudweeks et al., 2019). However, the LFA approach is limited by stringent conditions that are required for variants in evolutionarily conserved sequences to fix in one population, while remaining at low frequencies in non-target or “source” populations. As there may be more feasible methods for drive population confinement (e.g., underdominance (Champer et al., 2021b; Edgington & Alphey, 2018) and threshold dependent drives (Backus & Delbourne, 2019)), a less restrictive approach is needed for identifying well characterised loci that may later be interrogated for utility with approaches like LFA.

Here, we utilise the large body of reproductive research generated on the laboratory mouse to identify targets for the development of gene drives. We focussed on mice, not only because they are globally important invasive species, but the majority of our understanding of the molecular basis of mammalian reproduction comes from research conducted on house mice. We established rigorous criteria involving biological gene function, gene expression, and results from mouse gene knockout (KO) models to identify potential fertility target genes that could be biologically suitable (i.e., those with minimal known off-target effects) for mouse population management using gene drives. These criteria were subsequently used to identify 16 genes indispensable for male fertility and 12 for female fertility that are strong targets for a population suppression drive in mice. We further demonstrated that several of these genes have sequence conservation, which may permit these genes to be investigated and targeted for the development of gene drive systems in other invasive mammalian species. The complexity of gene drive modelling and the expense of laboratory experimentation means that the use of this framework for rigorous pre-screening of potential fertility targets using existing data will represent an efficiency multiplier for researchers seeking to develop gene drive technology for the management of invasive mammals.

2. MATERIALS AND METHODS

2.1. Gene identification and evaluation

2.1.1. Initial gene identification

We first developed and implemented a gene evaluation framework to systematically identify candidate fertility genes for gene drives in mice. Using the Online Mendelian Inheritance of Man (OMIM) database, we tested and refined keyword searches for genes relating to mammalian gametes or zygote-specific fertility. We used OMIM for initial gene identification as it is a comprehensive resource containing published literature on disease states and loss-of-function mutation causes of mammalian infertility (Hamosh et al., 2005). To verify that our searches yielded relevant results, Izumo1, a gene previously identified as specifically essential for male fertility, was utilised as a positive control. The final optimised search yielded a list of 172 genes and allelic variants with the following keyword string:

(infertil* OR sterility OR sterile OR inviable OR inviability) AND (sperm OR ovum OR egg OR gamete OR zygote) AND (genetic OR gene OR allele) AND (human OR mammal OR mouse OR rat)

We also added a list of 19 genes previously identified as indispensable for fertility in the mammalian research model, house mouse, by the Reproductive Genomics Program at the Jackson Laboratory for Mammalian Genetics (hereafter referred to as the Schimenti and Handel gene list; Schimenti and Handel 2018). Gene names, OMIM access number, phenotype, key reference and target sex were recorded. For uniformity, reported gene names and symbols are consistent with nomenclature approved by the HUGO Gene Nomenclature Committee for the gene ortholog in the respective species (Yates et al., 2017). Where there were doubts about the suitability of a gene (e.g., evidence suggestive of functional redundancy or conflicting phenotyping results), genes were excluded.

2.1.2. Inclusion and exclusion criteria

Following the initial gene identification process, we subjected the gene list to a systematic evaluation against three predefined criteria, to identify suitable gene targets for mammalian gene drives. For genes that meet our inclusion criteria, we reviewed and updated their functional descriptions, and associated key references between 7th November 2018 and 2nd December 2022.

1. Sex-specific fertility phenotype:

Candidate genes must affect gamete and pre-implantation phenotypes to limit instances of individuals carrying moribund embryos and ethical issues surrounding terminating embryonic development and the production of organisms with significant health defects. Evidence of a complete sex-biased fertility phenotype is required to permit the opposite sex to transmit the drive. Summaries of published literature provided by OMIM and additional peer-reviewed literature were reviewed to exclude genes producing sub-fertility or minor fertility knockout phenotypes in the non-target sex.

It is important to note that there are biological similarities between infertility and cancer pathologies, where components of both conditions result from failure to maintain control of somatic and germ cell proliferation (Nagirnaja et al., 2018; Tarín et al., 2015). In cancer, these associated genes are significantly overexpressed, so drives removing function and eliminating gene activity are unlikely to be associated with increased cancer susceptibility. Nevertheless, we excluded genes with evidence of tumour complications in knock-out mice, as this additional source of mortality could have implications on drive performance and ethical acceptability.

2. Tissue-specific expression pattern:

Gene expression (assessed via RNAseq data) had to be restricted or highly overexpressed in specific reproductive tissue (testis or ovary), although minor expression elsewhere was permitted. This criterion reduces the potential for unanticipated phenotypic effects in off-target tissues, which could reduce the fitness of drive-propagating individuals and consequently limit the persistence and spread of the gene drive and may raise ethical concerns. Transcriptome data from the Mouse ENCODE Project via the National Centre for Biotechnology Information (NCBI) online database and Gene Expression Atlas from 23rd September 2018 to 10th October 2018 was used to assess gene expression patterns in different adult tissues in relation to functional pathways, cell type, and developmental stage (Geer et al., 2010; Yue et al., 2014). To construct a quantitative threshold for expression outside the central reproductive tissue, we compared the gene expression results from Dmrt1 and Amh, two genes with strong effects on sexual phenotype (SI Appendix 1; Petryszak et al., 2016; Soumillon et al., 2013). Based on this, genes with non-target tissue expression levels exceeding 5% of the expression levels in target reproductive tissue were excluded to reduce complications of pleiotropic genes.

3. Knock-out mouse model:

Raw data from in vivo mouse knock-out experiments for each gene of interest were compiled to validate gene expression profiles and support gene function predictions, particularly where it was difficult to isolate and quantify if gene activity in a specific tissue corresponded to an observable phenotype. In vitro data were also collected to support the phenotypic observations from in vivo experiments. To detect potential sub-fertility phenotypes and intermediate fecundity phenotypes segregating with heterozygotes for each gene, t-tests were employed to interrogate differences in litter sizes between genotypes (SI Appendix 2). We investigated this because drive-propagating individuals carrying a single null gene copy need to be viable with no overt somatic developmental phenotypes to ensure drive transmission to the subsequent generation. The histological impact of the gene knock-outs in specific reproductive tissue (e.g., reduced testis size) was noted. Still, only genes that failed to produce healthy knock-out (KO) offspring were excluded. The absence of potential somatic phenotypes, overt development, abnormal behaviour, and viability between genotypes was confirmed via the literature or through direct correspondence with the original studies’ authors.

2.1.3. Female fertility genes

A further 23 female candidate genes were identified by our MIT authors through a manual, non-systematic search of Mouse Genome Informatics (MGI) between August 2017 and April 2019. As this research group was specifically interested in identifying female fertility genes for mouse experiments, the search terms “folliculogenesis”, “granulosa cell development”, and “subcortical maternal complex” were used to identify all female fertility genes except for Zp3 and Izumo1r, which were previously identified. Following initial identification, these genes were put through a series of filters, optimising for genes where homozygous mutant female mice displayed near or complete sterility, while heterozygous females and carrier males displayed mostly wild-type fertility. Other exclusion criteria included parturition defects (death of pups during childbirth) and abnormal somatic phenotypes resulting in a significantly reduced fitness of that genotype. When compared to the list of genes excluded from our systematic search, many of these female fertility genes had been omitted due to broad somatic expression profiles (i.e., failing our ‘tissue-specific expression pattern’ criteria). To examine the impact of ‘relaxing’ this specific criteria, we instead evaluated these 23 female fertility genes based on female-specific, non-syndromic sterility and the availability of a mouse KO model, ultimately resulting in a list of female target loci (hereafter referred to as the female candidate gene list).

2.2. Evolutionary conservation

Invasive vertebrates exist in many terrestrial ecosystems. However, the majority are non-model organisms for which a reference genome has only recently been assembled (stoat (Mustela erminea), and brushtail possum (Trichosurus vulpecula); Bond et al., 2023) or are yet to be sequenced. Where reference sequences are available, conservation within coding regions in well-studied mammalian fertility genes may indicate functional conservation that can be further interrogated once population datasets are available (i.e., identification of variation related to species divergence, and investigation of sites that are near fixed in the population of interest, which may enable population-specific gRNA design for multiple species (previously evaluated in silico; Sudweeks et al., 2019)). Identifying conserved genes would also provide a starting point for empirical experiments, and subsequent tailoring of drive type to the requirements of a particular species. Targeting the same, well characterised gene in multiple species will also be useful to reduce overall resource expenditure for drive development across a number of vertebrate pests. As an island nation with an abundance of mammalian pest species, we use Aotearoa New Zealand (NZ) as an example system to explore the utility of our framework for potentially assisting with gene drive development in the future.

2.2.1. Sequence extraction and processing

Genome assemblies and corresponding gene feature files (GFF) of nine important invasive species in NZ: house mouse (Mus musculus), Norway rat (Rattus norvegicus), ship rat (Rattus rattus), rabbit (Oryctolagus cuniculus), western European hedgehog (Erinaceus europaeus), domestic cat (Felis catus), domestic ferret (Mustela putorius furo), stoat (Mustela erminea), and common brushtail possum (Trichosurus vulpecula), were retrieved from NCBI on 5th November 2020 (SI Appendix 3). Genomic coordinates for each Gnomon-predicted coding sequence (CDS) region within our identified candidate genes were extracted from the GFFs for each species (SI Appendix 4; Souvorov et al., 2010). The extracted coordinates were converted to zero-based BED formatted coordinates using BEDTools (v2.29.2; Quinlan 2014) and used to extract the corresponding nucleotide sequence. To enable gRNA multiplexing, CDS sequences less than 80 nt were omitted, to allow a minimum of 3 gRNAs of 20 nt each spaced 10 nt apart (CRISPR HDR-based gene drives exhibit optimum drive conversion efficiency with 2–4 gRNAs, and there must be a sufficient number of gRNAs to avoid functional resistance alleles; Champer et al., 2020b). Where genes were not present in their respective GFF, a manual search of NCBI for synonymous gene names was conducted to verify the absence of the gene ortholog from the respective target species or to determine the species-specific gene name. Subsequently, the gene feature coordinates for the Zpbp ortholog in the brushtail possum were manually added to our dataset.

2.2.2. Multiple sequence alignment

We used LASTZ (1.04.03; Harris 2007) to perform pairwise alignments of coding regions within our candidate genes of interest (SI Appendix 5). Gap opening was allowed to capture insertions and deletions that may result from species divergence, and the default seed pattern was used. Nucleotide variation was limited to five mismatches per 20 nt during the gapped extension of seed hits to high-scoring pairs (HSP). Back-end filtering of the resulting pairwise alignment blocks set a minimum sequence identity of 75%, consistent with the tolerance of CRISPR-Cas9 gRNAs to no more than five nucleotide mismatches per 20 nt gRNA target sequence (Zheng et al., 2017).

The subsequent alignments were then processed by MULTIZ (v10.6; sourced from https://github.com/multiz/multiz.git) to generate a multiple species local alignment of sequence blocks of more than 80 nt (SI Appendix 6). Each conserved sequence block was visualised using tidyverse (v1.3.0; SI Appendix 7; Wickham et al., 2019). Gap openings, including the ends of the alignments, were processed as mismatch penalties, due to the impact of a “missing sequence” on the efficacy of gRNAs designed for these sequence blocks.

3. RESULTS

3.1. Gene identification

3.1.1. Evaluation and characterisation

We developed and implemented a gene evaluation framework to identify candidate fertility genes for mouse gene drives (Figure 1). An initial OMIM gene search generated a list of 172 genes and allelic variants important for mammalian fertility (SI Appendix 8), to which we added 19 genes from the Schimenti and Handel list to broaden the scope of our investigation (SI Appendix 9; Schimenti & Handel 2018). From the total list of 191 genes and allelic variants, eight were found to be duplicates (e.g., alternative allelic variants). These duplicates were grouped by unique gene name, generating a list of 183 unique genes. Application of our filtering criteria identified that 103 genes demonstrated incomplete evidence of a sex-specific phenotype; these genes were subsequently excluded. Sixteen of the remaining 80 genes were specifically critical for male fertility in mice (none of the female fertility genes assessed met our stringent criteria), contributing to several important pre-fertilisation processes, including gametogenesis, gamete recognition, and chromatin remodelling (Table 1; see SI Appendix 9 and 10a for excluded genes). Gene names are consistent with the mouse orthologs reported by HGNC. However, variation in nomenclature usage across bodies of literature and databases is evident.

Figure 1.

Figure 1

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Framework used to identify target loci for mammalian fertility-based gene drives. Created with Biorender.com.

Table 1.

The 16 genes essential for male fertility in mice identified via our framework. Gene name, OMIM access number, a brief functional description, chromosome number in relation to house mouse (Mus musculus; GRCm39: RefSeq assembly accession = GCF_000001635.27) and key functional reference are shown.

Gene OMIM Phenotype Chr Ref
Capza3 608722 Testis-specific actin-capping protein that controls actin polymerisation during spermiogenesis. Expression restricted to spermatids (Shima et al., 2004). 6 1
Catsper1 606389 Two of four primary subunits in a sperm-specific Ca2+ ion channel located in the flagellar, and are required for the hyperactivation of sperm. Expression is restricted to testes, specifically in developing spermatids, (expression is not detected in mature sperm (Carlson et al., 2005)). 19 2
Catsper2 607249 “  “  “  “  “  “ 2 2
Dpy19l2 613893 Spermatid structure between acrosomal and nuclear membranes. Expression is restricted to spermatids. 9 3
Gapdhs 610712 Enzyme localising to the principal piece of the flagellar and involved in energy supply for sperm propulsion. Expression restricted to spermatogenesis. 7 4
Izumo1 609278 Gamete recognition protein on the sperm cell surface that interacts with the oocyte receptor Izumo1r. 7 5
Meig1 614174 Involved in protein recruitment for sperm flagellar formation. 2 6
Oaz3 605138 Mammalian antizyme in developing spermatids that inhibits polyamine uptake and ornithine decarboxylase activity. Expressed in haploid germ cells. Deficiency results in separated heads and tails of sperm. 3 7
Ppp3r2 613821 Protein phosphatase regulatory subunit that is expressed throughout sperm development. Deficiency results in inflexible sperm midpiece, sperm motility and morphological defects. 4 8
Slc26a8 608480 Chloride/sulfate exchange channel in germ cells and required for sperm motility. Proteins localize in spermatocyte membranes and naïve spermatids. 17 9
Spaca1 612739 Transmembrane protein localising in the equatorial segment of mature sperm. Expression associated with the inner and outer acrosomal membranes. 4 10
Spag4 603038 Works with KASH proteins to couple sperm nuclear structures to the cytoskeleton. Expressed in haploid male germ cells. 2 11
Spem1 615116 Plays a role in detaching cytoplasm from the spermatid nucleus and flagellum neck region, required for straightening and stretching of the sperm head and neck. Expressed late in spermatid development. 11 12
Sun5 613942 Located in the inner membrane of the sperm nuclear envelope and is the most basal protein for flagellar anchoring. First expressed during spermiogenesis (haploid spermatids; Shang et al., 2017). 2 13
Tssk6 610712 Serine/threonine protein kinase that is required for post-meiotic chromatin remodelling. Expression in spermatids, not spermatocytes (Jha et al., 2017). Another member of this family, Tssk3, has been demonstrated to produce a more dramatic phenotype in KO mice (Nayyab et al., 2021). 8 14
Zpbp 608498 Secondary binding between acrosome-reacted sperm and the zona pellucida. Expressed in spermiogenesis (haploid stages of sperm development). Heterozygous males have an intermediate phenotype (increased levels of abnormal sperm, however, fecundity is comparable to WT sires). 11 15
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3.1.2. Evaluation of the female fertility genes

The list of 23 additional female fertility genes identified by our MIT authors were evaluated using mouse knockouts for evidence of sex-biased sterility – no tissue-specific expression criteria were applied (see Methods, section 2.1.3). Eight genes were found to produce either a sub-fertile or potentially lethal phenotype and were subsequently removed from further analysis (SI Appendix 10b). While genes with sex-specific lethal phenotypes may have utility in suppression gene drives, we urge caution with using lethal genes due to the extensive ethical considerations in addition to the development of gene editing technology. The genes associated with sub-fertility phenotypes could be potential targets in gene drives with reduced suppressive power, however, they may be dependent on genetic background and will likely require additional phenotyping of individuals from the target population. Many of the remaining 12 genes (Table 2) have functional roles in the maternal effect process, gamete recognition, cell signalling, and gene regulatory processes at various stages before embryonic implantation. Two of these genes, Afp and Pgr, emerged as strong candidates for HDR-based gene drives, despite expression patterns outside the germline (Table 2).

Table 2.

The 12 genes essential for female fertility in mice identified through our evaluation. Asterisks label two genes with somatic expression, which are therefore strong candidates for HDR-based gene drives controlled by a germline promoter. Gene name, OMIM accession number, a brief functional description, chromosome number with relation to house mouse (Mus musculus; GRCm39: RefSeq assembly accession = GCF_000001635.27) and key functional reference are shown.

Gene OMIM Phenotype Chr Ref
Afp* 104150 Not required for embryonic development, but KO female mice demonstrate anovulation due to the collapse of the hypothalamic/pituitary system (De Mees et al., 2006).
Heterozygote intercrosses produce viable offspring with normal sex ratios at expected Mendelian ratios. Appears to produce defeminised maternal behaviour in Afp null mice (not concerning as female null mice are sterile; Bakker et al., 2006; Keller et al., 2010). It is also a growth promoter through suppressing apoptosis (biomarker for hepatocellular carcinoma; Chen et al., 2020), but the physiological/functional impact of this in KO mice is not well understood.		 5 1
Pgr* 607311 Ovulation failure due to lack of response to progesterone. Defeminisation behaviour in KO females. KO males have enhanced sexual behaviour, and pharmacological inhibition of Pgr produces results consistent with gene KO (Schneider et al., 2005). Suitable for drive spread but not appropriate for baiting as interference may increase male reproductive behaviour. Preliminary work shows that Pgr KO mice have deficits in spatial memory. Yet to be determined how significant this would be to the lifetime reproductive success of drive homozygous males (could potentially have a reduced tendency to disperse and defend home territory; Joshi et al., 2023). 9 2
Dlgap5 617859 Defective proliferation of endometrial stroma tissue ultimately leads to implantation failure (Tsai et al., 2008). Additional roles in oocyte maturation. Female KO has defective oocyte divisions due to microtubule bipolarity establishment and maintenance (Breuer et al., 2010). 14 3
Figla 608697 Female KOs lack primordial follicle development, and demonstrate a reduction in oocyte mass and ovaries at birth due to failed oocyte-development gene activation and suppression of sperm-development genes.
Expression is detected through follicular development and in mature oocytes, ceasing in preimplantation embryos (Hu et al., 2010).		 6 4
Gdf9 601918 Failure of follicle maturation and differentiation past the primary one-layer stage. Expressed in oocytes across follicle development (except primordial follicles). 11 5
Izumo1r 615737 Gamete binding receptor that pairs with the sperm cell-surface protein Izumo1. Failed fertilisation due to loss of gamete recognition. 9 6
Nlrp5 609658 Embryos fail to proceed beyond the 2-cell stage due to defects in the subcortical maternal complex (SCMC) - essential for zygotic development. 7 7
Nobox 610934 Atrophic ovaries, characterised by failed maturation of follicles and the reduction in ovarian mass. Also important for zygote genome activation and preimplantation embryo development (Madissoon et al., 2016). 6 8
Padi6 610363 Abnormal cytoplasmic lattice development and failed genome activation following fertilisation, consequent of failed post-translational modification during oocyte maturation (highly expressed in oocytes). 4 9
Ooep 611689 Asymmetrical zygotic cell division, ultimately terminating development at the 2-cell stage, resulting from the loss of structural stability of the SCMC (plays important roles in RNA metabolism and zygote genome activation). 9 10
Tle6 612399 “  “  “  “  “  “ 10 10
Zp3 182889 Defective zona pellucida (ZP) development in oocytes, resulting in failure to trigger acrosomal reaction in sperm before fertilisation. Heterozygotes only have a ZP half as thick as the ZP observed from WT oocytes. Expression appears to be from multiple structures, including oocyte (strong expression in the nucleus during prophase; Gao et al., 2017). Needs further RNA seq work: may be useful for homing drives if expression ceases before recombination. 5 11
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3.2. Evolutionary conservation

3.2.1. Overall patterns of sequence conservation

Our multiple species alignments suggest sequence conservation across the nine evaluated mammalian species, including a marsupial representative (brushtail possum; Bond et al., 2023). Sequences of more than 80 nt and at least 75% identity were identified across 10 of 16 evaluated male target genes (Catsper1, Catsper2, Gapdhs, Meig1, Oaz3, Slc26a8, Spaca1, Spag4, Tssk6 and Zpbp; SI Appendix 11), and 4 of 12 evaluated female candidates (Dlgap5, Pgr, Nobox, and Zp3; SI Appendix 12). However, not every species was necessarily represented in each of these conserved blocks (Appendix 13 and 14).

4. DISCUSSION

Our systematic gene assessment and subsequent evaluation identified 16 male fertility genes that are critical for mouse fertility and are strong candidates for genetic population suppression approaches. Using a modified version of our initial evaluation approach (in which we allow minor expression beyond reproductive tissue), we additionally identified 12 female fertility genes. These genes are involved in various reproductive pathways, including structural and gamete recognition, which are processes preceding fertilisation, making these targets a more ethical choice than those with post-fertilisation functions. Many of these genes have previously been suggested as targets for non-hormonal human contraceptives (e.g., Ppp3r2; (Castaneda & Matzuk 2015)), and their significance to mammalian fertility has additionally been identified in reviews of the monogenic causes of mammalian fertility (Jiao et al., 2021; Oud et al., 2019). In the process of identifying these genes, we have established a systematic gene identification framework with broad utility, including identifying biological targets for developing species-specific toxins across various taxa.

Male fertility bias and the shortfall of using expression data

During our systematic search and filtering, we observed a complete bias towards genes that are specifically indispensable for male fertility, which highlights that most female fertility factors appear to have phenotypic effects on males, with the reverse being less common due to the extensive and unique set of spermatogenic genes (Matzuk & Lamb 2002). This is consistent with biases presented by the Reproductive Genomics Program (reported a 15:2 ratio of male-specific to female-specific infertility phenotypes; Matzuk & Lamb 2008, Schimenti & Handel 2018) and Mouse Genome Informatics (reported a 59:21 ratio of male-specific to female-specific infertility phenotypes; Schimenti & Handel 2018). Through our systematic evaluation process, we discovered that most genes specifically indispensable for female fertility demonstrated gene activity in non-target tissue exceeding our tissue-specific expression threshold of 5% (i.e., the activity of genes essential to female fertility is broad; Dean & Mank 2016). Such genes were subsequently omitted such as to prevent the selection of any genes that may have multiple biological roles and generate several pathological phenotypes in allelic-null individuals.

Excluding genes with high expression in somatic tissues also filtered out genes that are somatically expressed but are deposited in the germ cells where they play a significant role in gamete development, maturation and the zygotic activation processes (e.g., maternal and paternal effect genes). Future searches should consider refining the keyword string to probe for genes with isolated expression in granulosa and Sertoli cells as initial drive spread would likely benefit from heterozygotes of both sexes being capable of transmitting the drive to offspring (Deredec et al., 2008). Alternatively, expression of target fertility genes must precede drive initiated disruption (i.e., meiosis I; Weitzel et al., 2021) so that heterozygous drive carriers remain fertile (Deredec et al., 2008). Fine-scale spatial characterisation of these expression windows during gamete development remains a major challenge and is an emerging field of new research as technological capabilities advance (e.g., single cell sequencing).

The substantial male target bias identified through our systematic search is also problematic for suppression gene drive development and application because homing gene drives are most effective at inducing population suppression when targeting female fertility (Burt 2003; Deredec et al., 2008; Prowse et al., 2017). This is because female reproductive success is the major limiting factor in population growth and viability in most species (Galizi et al., 2016; Robertson et al., 2006; Schliekelman et al., 2005; Wedekind 2002). To alleviate the challenges of using gene expression profiles to characterise function for female fertility genes, we utilised fertility and viability data from established mouse knock-out models to verify that gene function predictions are germline-specific. The production of healthy, viable knock-out mice affirms the absence of developmental consequences to the homozygous mutant mice, which implies the absence of any additional phenotypic effects if a disrupted copy of the gene is coupled to a gene drive (assuming the gene drive construct itself does not have a fitness cost). While generating knockout mice is more resource intensive than employing transcriptomics, validating the fitness and phenotypes of the disrupted candidate gene in both sexes is essential for informing laboratory and computational experiments.

Functional characterisation

The variations in methods, sample sizes, and the way in which results are reported across the literature was challenging to manage when verifying the knockout mouse phenotypes evaluated in this project. Where data were available, we found insignificant differences in the litter size between heterozygotes and wildtype mice of the target sex, validating the absence of a significant intermediate fitness cost in heterozygotes arising from the disruption of one copy of the target gene. However, it should be noted that these results are limited by the experimental environment and genetic background of the mice that were tested. Additionally, both female fertility genes identified here as HDR-based gene drive candidates (Afp and Pgr) appear to play a role in sexual differentiation of the brain early in development and thus have the potential to produce behavioural differences. Further work is necessary in order to characterise how significant this is to the fitness of homozygous null males and confirm the absence of dosage effects in heterozygous gene drive females to understand behavioural limitations of both disrupting such target genes, and carrying the transgenic gene drive construct. This has been a recent area of interest for the naturally occurring murine t-haplotype drive, where t-haplotype carrying male mice demonstrate high dispersal rates to avoid the fitness cost of the t-haplotype during sperm competition in densely population areas (Runge & Lindholm, 2021). The t-haplotype also raises intriguing questions on how species-specific, or even population-specific, behaviour may affect gene drive dynamics at the population level (Manser et al., 2020). While additional variations in genetic background and behavioural phenotypes may appear modest, their effects may be amplified by the dynamics of a gene drive system in large heterogeneous wild populations. Future work should also consider using individuals from target populations of interest in representative microcosm environments to control for local genetic and behavioural variation.

Evolutionary conservation

Several identified fertility genes demonstrate sequence conservation across multiple mammalian lineages, often spanning millions of years of evolutionary divergence. These findings separate the genes into distinct sets – those that are species or lineage-specific, and those that are more broadly conserved and thus may have general utility across a range of target species with species-specific gRNAs (Appendix 11 and 12). Where resources are limited, targeting research into general utility genes could substantially reduce the overall research and development expenditure across a cohort of invasive species. Further work should investigate the level of conservation of potential gRNA recognition sites within the target population, to inform expected rates of pre-existing drive resistance.

The functional role of the candidate male fertility genes is heavily biased towards sperm structural development and motility before fertilisation, except Tssk6, which regulates the expression of other spermatogenic genes through managing post-meiotic chromatin remodelling in maturing spermatids (Spiridonov et al., 2005). This is likely the result of stronger sexual selection and therefore faster evolutionary rates in sperm-associated genes, which is highlighted in our finding that evolutionarily conserved regulatory genes functioning in fertility are more abundant in the female candidate gene list. Targeting these key regulatory proteins that function in gametogenesis presents a method to create gene knockout redundancy for the gene drive system.

Previously, Oh et al (2019) identified 3 fertility genes that could be targeted using a multiplexed drive that might be localised using the Locally Fixed Alleles (LFA) approach: hexokinase 1 (Hk1), desmoglein 3 (Dsg3), and zygote arrest 1 (Zar1). From an animal ethics standpoint, Hk1 is not a suitable drive target as KO mice have several somatic abnormalities (Peters et al., 2001), which would also significantly impair drive spread and persistence in a target population. Dsg3 KO females are able to initially produce pups, but experience a rapid age-related reduction in fertility (Kountikov et al., 2015). This initial fertility, given the short life cycle of mice, may extend the timeline of population suppression, which would be highly undesirable from a practical standpoint. Dsg3 KO mice also exhibit additional developmental abnormalities that would be disadvantageous for wild animals (Kountikov et al., 2015). Zar1 is a gene that we excluded from our search due to its tumour suppressor function in other vertebrates (reviewed by (Deutschmeyer & Richter, 2020)). In the context of LFA, the frequency of Hk1, Dsg3 and Zar1 variants (or drive susceptible alleles) in the non-target “source” populations are still found at intermediate frequencies (>0.15 and <0.50), which would result in some level of drive effect on off-target populations in cases of drive migration. Due to the stringent conditions required for the feasibility of the LFA approach, including sufficient population isolation time, it is likely that other methods of drive containment will also be required.

Additional considerations when screening potential candidate genes include testing gRNA cutting efficiency at such conserved target sites. Further, whole genome assessment is necessary to mitigate the risk of off-target mutagenesis where conserved sequences have been evolutionarily duplicated, giving rise to gene families (a set of genes with a homologous sequence origin but divergent molecular function). Variant assessments of target populations will also enable high-resolution characterisation of target sequences and further facilitate assessments of inter- and intra-species conservation, important for considering drive containment (e.g., LFA approach; Sudweeks et al., 2019) and the evolution of resistance.

Beyond the application of gene drives for pest population management, the fertility genes identified in this study may have utility as targets for wildlife contraceptives. These could be designed to target species-specific variation to limit effects on non-target species. Species-specificity would be particularly desirable in contexts where the invasive/pest species occupies the same, or adjacent, niche as closely related native/non-problematic species, as exemplified by invasive and native marsupial mammals in Australia. Further development of these contraceptives may explore the use of viral agents and RNA interference to knockdown the activity of these fertility genes to induce infertility in long-lived pest species, which have protracted suppression timelines when using vertically transmitted technology like gene drives.

5. CONCLUSION

This stringent systematic analysis has identified a list of sex-specific fertility genes as potential candidate genes for population suppression gene drives in multiple mammal species. The identified candidates have some level of evolutionary conservation and may also have value in further applications, including the development of human and wildlife contraceptives. Additionally, we establish a systematic gene identification framework, including key considerations for candidate gene selection, which may also have utility in other applications, including identifying biological targets for developing species-specific toxins, and wildlife contraceptives. Further work, including tissue expression profiling, gene knockouts, and population analysis for each target species, will be essential to confirm functional specificity and conservation across mammalian reproductive systems.

ACKNOWLEDGEMENTS

We thank members of the Gemmell and Messer Labs for helpful discussion, and extend our gratitude to the corresponding authors of the original knock-out mouse experiments for additional clarification of experiment methodology and results. The authors wish to acknowledge the use of New Zealand eScience Infrastructure (NeSI) high performance computing facilities, consulting support and/or training services as part of this research. New Zealand’s national facilities are provided by NeSI and funded jointly by NeSI’s collaborator institutions and through the Ministry of Business, Innovation & Employment’s Research Infrastructure programme. URL https://www.nesi.org.nz. ACC and AA are supported by funding from Predator Free 2050 Ltd. ACC is also supported by funding from Fulbright New Zealand, Ministry for Primary Industries, and Ministry for Pacific Peoples (Toloa Tertiary Scholarship). NJG was supported by funding from Predator Free 2050, Genomics Aotearoa, and the University of Otago. PWM is supported by the National Institutes of Health award R01GM127418. This manuscript was greatly improved by the comments of three anonymous reviewers and Professor Lisette Waits.

BENEFIT SHARING STATEMENT

This study brings together researchers from several different countries, including scientists based in Aotearoa New Zealand (NZ) - one of the main locations discussed for the application of mammalian gene drives. Local socio-political knowledge informed decisions during the study, to ensure that our results are relevant to both the NZ and international contexts. All collaborators are included as co-authors, with the appropriate contributing parties recognised in the Acknowledgements. Our framework and results are accessible to the broader public via Figshare, as described above.

Footnotes

CONFLICT OF INTEREST

The authors have no conflicts of interest to declare.

DATA ACCESSIBILITY

Supplementary information and additional data available from the Figshare Digital Repository. Please note that this document must be downloaded for active links to additional information. Alternatively, you can copy and paste the links from the Figshare viewing window: https://doi.org/10.6084/m9.figshare.21905646.v5

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